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Wisse JJ, Flinsenberg MJW, Jonkman AH, Goos TG, Gommers D. Respiratory rate monitoring in ICU patients and healthy volunteers using electrical impedance tomography: a validation study. Physiol Meas 2024; 45:055026. [PMID: 38588677 DOI: 10.1088/1361-6579/ad3c0e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 04/08/2024] [Indexed: 04/10/2024]
Abstract
Objective. The respiratory rate (RR) is considered one of the most informative vital signals. A well-validated standard for RR measurement in mechanically ventilated patient is capnography; a noninvasive technique for expiratory CO2measurements. Reliable RR measurements in spontaneously breathing patients remains a challenge as continuous mainstream capnography measurements are not available. This study aimed to assess the accuracy of RR measurement using electrical impedance tomography (EIT) in healthy volunteers and intensive care unit (ICU) patients on mechanical ventilation and spontaneously breathing post-extubation. Comparator methods included RR derived from both capnography and bioimpedance electrocardiogram (ECG) measurements.Approach. Twenty healthy volunteers wore an EIT belt and ECG electrodes while breathing through a capnometer within a 10-40 breaths per minute (BPM) range. Nineteen ICU patients underwent similar measurements during pressure support ventilation and spontaneously breathing after extubation from mechanical ventilation. Stable periods with regular breathing and no artefacts were selected, and agreement between measurement methods was assessed using Bland-Altman analysis for repeated measurements.Main result. Bland-Altman analysis revealed a bias less than 0.2 BPM, with tight limits of agreement (LOA) ±1.5 BPM in healthy volunteers and ventilated ICU patients when comparing EIT to capnography. Spontaneously breathing ICU patients had wider LOA (±2.5 BPM) when comparing EIT to ECG bioimpedance, but gold standard comparison was unavailable. RR measurements were stable for 91% of the time for capnography, 68% for EIT, and 64% of the ECG bioimpedance signals. After extubation, the percentage of stable periods decreased to 48% for EIT signals and to 55% for ECG bioimpedance.Significance. In periods of stable breathing, EIT demonstrated excellent RR measurement accuracy in healthy volunteers and ICU patients. However, stability of both EIT and ECG bioimpedance RR measurements declined in spontaneously breathing patients to approximately 50% of the time.
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Affiliation(s)
- J J Wisse
- Department of Adult Intensive Care, Erasmus Medical Center, Rotterdam, The Netherlands
- Department of Neonatal and Pediatric Intensive Care, Erasmus Medical Center-Sophia Children's Hospital, Rotterdam, The Netherlands
| | - M J W Flinsenberg
- Department of Adult Intensive Care, Erasmus Medical Center, Rotterdam, The Netherlands
- Department of Mechanical, Maritime and Materials Engineering, Delft University of Technology, Delft, The Netherlands
| | - A H Jonkman
- Department of Adult Intensive Care, Erasmus Medical Center, Rotterdam, The Netherlands
| | - T G Goos
- Department of Neonatal and Pediatric Intensive Care, Erasmus Medical Center-Sophia Children's Hospital, Rotterdam, The Netherlands
- Department of Mechanical, Maritime and Materials Engineering, Delft University of Technology, Delft, The Netherlands
| | - D Gommers
- Department of Adult Intensive Care, Erasmus Medical Center, Rotterdam, The Netherlands
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Goda MÁ, Charlton PH, Behar JA. pyPPG: a Python toolbox for comprehensive photoplethysmography signal analysis. Physiol Meas 2024; 45:045001. [PMID: 38478997 PMCID: PMC11003363 DOI: 10.1088/1361-6579/ad33a2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 02/21/2024] [Accepted: 03/13/2024] [Indexed: 04/09/2024]
Abstract
Objective.Photoplethysmography is a non-invasive optical technique that measures changes in blood volume within tissues. It is commonly and being increasingly used for a variety of research and clinical applications to assess vascular dynamics and physiological parameters. Yet, contrary to heart rate variability measures, a field which has seen the development of stable standards and advanced toolboxes and software, no such standards and limited open tools exist for continuous photoplethysmogram (PPG) analysis. Consequently, the primary objective of this research was to identify, standardize, implement and validate key digital PPG biomarkers.Approach.This work describes the creation of a standard Python toolbox, denotedpyPPG, for long-term continuous PPG time-series analysis and demonstrates the detection and computation of a high number of fiducial points and digital biomarkers using a standard fingerbased transmission pulse oximeter.Main results.The improved PPG peak detector had an F1-score of 88.19% for the state-of-the-art benchmark when evaluated on 2054 adult polysomnography recordings totaling over 91 million reference beats. The algorithm outperformed the open-source original Matlab implementation by ∼5% when benchmarked on a subset of 100 randomly selected MESA recordings. More than 3000 fiducial points were manually annotated by two annotators in order to validate the fiducial points detector. The detector consistently demonstrated high performance, with a mean absolute error of less than 10 ms for all fiducial points.Significance.Based on these fiducial points,pyPPGengineered a set of 74 PPG biomarkers. Studying PPG time-series variability usingpyPPGcan enhance our understanding of the manifestations and etiology of diseases. This toolbox can also be used for biomarker engineering in training data-driven models.pyPPGis available onhttps://physiozoo.com/.
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Affiliation(s)
- Márton Á Goda
- Faculty of Biomedical Engineering, Technion Institute of Technology, Technion-IIT, Haifa, 32000, Israel
- Pázmány Péter Catholic University Faculty of Information Technology and Bionics, Budapest, Práter u. 50/A, 1083, Hungary
| | - Peter H Charlton
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, United Kingdom
| | - Joachim A Behar
- Faculty of Biomedical Engineering, Technion Institute of Technology, Technion-IIT, Haifa, 32000, Israel
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Bloomfield LSP, Fudolig MI, Kim J, Llorin J, Lovato JL, McGinnis EW, McGinnis RS, Price M, Ricketts TH, Dodds PS, Stanton K, Danforth CM. Predicting stress in first-year college students using sleep data from wearable devices. PLOS DIGITAL HEALTH 2024; 3:e0000473. [PMID: 38602898 PMCID: PMC11008774 DOI: 10.1371/journal.pdig.0000473] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Accepted: 02/16/2024] [Indexed: 04/13/2024]
Abstract
Consumer wearables have been successful at measuring sleep and may be useful in predicting changes in mental health measures such as stress. A key challenge remains in quantifying the relationship between sleep measures associated with physiologic stress and a user's experience of stress. Students from a public university enrolled in the Lived Experiences Measured Using Rings Study (LEMURS) provided continuous biometric data and answered weekly surveys during their first semester of college between October-December 2022. We analyzed weekly associations between estimated sleep measures and perceived stress for participants (N = 525). Through mixed-effects regression models, we identified consistent associations between perceived stress scores and average nightly total sleep time (TST), resting heart rate (RHR), heart rate variability (HRV), and respiratory rate (ARR). These effects persisted after controlling for gender and week of the semester. Specifically, for every additional hour of TST, the odds of experiencing moderate-to-high stress decreased by 0.617 or by 38.3% (p<0.01). For each 1 beat per minute increase in RHR, the odds of experiencing moderate-to-high stress increased by 1.036 or by 3.6% (p<0.01). For each 1 millisecond increase in HRV, the odds of experiencing moderate-to-high stress decreased by 0.988 or by 1.2% (p<0.05). For each additional breath per minute increase in ARR, the odds of experiencing moderate-to-high stress increased by 1.230 or by 23.0% (p<0.01). Consistent with previous research, participants who did not identify as male (i.e., female, nonbinary, and transgender participants) had significantly higher self-reported stress throughout the study. The week of the semester was also a significant predictor of stress. Sleep data from wearable devices may help us understand and to better predict stress, a strong signal of the ongoing mental health epidemic among college students.
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Affiliation(s)
- Laura S. P. Bloomfield
- Gund Institute for Environment, University of Vermont, Burlington, Vermont, United States of America
- Department of Mathematics & Statistics, University of Vermont, Burlington, Vermont, United States of America
- Vermont Complex Systems Center, University of Vermont, Burlington, Vermont, United States of America
| | - Mikaela I. Fudolig
- Department of Mathematics & Statistics, University of Vermont, Burlington, Vermont, United States of America
- Vermont Complex Systems Center, University of Vermont, Burlington, Vermont, United States of America
| | - Julia Kim
- Vermont Complex Systems Center, University of Vermont, Burlington, Vermont, United States of America
| | - Jordan Llorin
- Vermont Complex Systems Center, University of Vermont, Burlington, Vermont, United States of America
| | - Juniper L. Lovato
- Vermont Complex Systems Center, University of Vermont, Burlington, Vermont, United States of America
| | - Ellen W. McGinnis
- Department of Social Science and Health Policy, Wake Forest University School of Medicine, Winston-Salem, North Carolina, United States of America
- Center for Remote Patient and Participant Monitoring, Wake Forest University School of Medicine, Winston-Salem, North Carolina, United States of America
| | - Ryan S. McGinnis
- Center for Remote Patient and Participant Monitoring, Wake Forest University School of Medicine, Winston-Salem, North Carolina, United States of America
- Department of Biomedical Engineering, Wake Forest University School of Medicine, Winston-Salem, North Carolina, United States of America
| | - Matt Price
- Vermont Complex Systems Center, University of Vermont, Burlington, Vermont, United States of America
- Department of Psychological Science, University of Vermont, Burlington, Vermont, United States of America
| | - Taylor H. Ricketts
- Gund Institute for Environment, University of Vermont, Burlington, Vermont, United States of America
- Rubenstein School of Environment and Natural Resources, University of Vermont, Burlington, Vermont, United States of America
| | - Peter Sheridan Dodds
- Vermont Complex Systems Center, University of Vermont, Burlington, Vermont, United States of America
- Department of Computer Science, University of Vermont, Burlington, Vermont, United States of America
| | - Kathryn Stanton
- Vermont Complex Systems Center, University of Vermont, Burlington, Vermont, United States of America
| | - Christopher M. Danforth
- Gund Institute for Environment, University of Vermont, Burlington, Vermont, United States of America
- Department of Mathematics & Statistics, University of Vermont, Burlington, Vermont, United States of America
- Vermont Complex Systems Center, University of Vermont, Burlington, Vermont, United States of America
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Chin WJ, Kwan BH, Lim WY, Tee YK, Darmaraju S, Liu H, Goh CH. A Novel Respiratory Rate Estimation Algorithm from Photoplethysmogram Using Deep Learning Model. Diagnostics (Basel) 2024; 14:284. [PMID: 38337800 PMCID: PMC10855057 DOI: 10.3390/diagnostics14030284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 12/29/2023] [Accepted: 01/26/2024] [Indexed: 02/12/2024] Open
Abstract
Respiratory rate (RR) is a critical vital sign that can provide valuable insights into various medical conditions, including pneumonia. Unfortunately, manual RR counting is often unreliable and discontinuous. Current RR estimation algorithms either lack the necessary accuracy or demand extensive window sizes. In response to these challenges, this study introduces a novel method for continuously estimating RR from photoplethysmogram (PPG) with a reduced window size and lower processing requirements. To evaluate and compare classical and deep learning algorithms, this study leverages the BIDMC and CapnoBase datasets, employing the Respiratory Rate Estimation (RRest) toolbox. The optimal classical techniques combination on the BIDMC datasets achieves a mean absolute error (MAE) of 1.9 breaths/min. Additionally, the developed neural network model utilises convolutional and long short-term memory layers to estimate RR effectively. The best-performing model, with a 50% train-test split and a window size of 7 s, achieves an MAE of 2 breaths/min. Furthermore, compared to other deep learning algorithms with window sizes of 16, 32, and 64 s, this study's model demonstrates superior performance with a smaller window size. The study suggests that further research into more precise signal processing techniques may enhance RR estimation from PPG signals.
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Affiliation(s)
- Wee Jian Chin
- Department of Mechatronics and Biomedical Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Kajang 43000, Selangor, Malaysia; (W.J.C.); (B.-H.K.); (Y.K.T.); (S.D.)
- Centre for Healthcare Science and Technology, Universiti Tunku Abdul Rahman, Kajang 43000, Selangor, Malaysia
| | - Ban-Hoe Kwan
- Department of Mechatronics and Biomedical Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Kajang 43000, Selangor, Malaysia; (W.J.C.); (B.-H.K.); (Y.K.T.); (S.D.)
- Centre for Healthcare Science and Technology, Universiti Tunku Abdul Rahman, Kajang 43000, Selangor, Malaysia
| | - Wei Yin Lim
- Electrical and Computer Systems Engineering, School of Engineering and Advanced Engineering Platform, Monash University Malaysia, Bandar Sunway 47500, Selangor, Malaysia;
| | - Yee Kai Tee
- Department of Mechatronics and Biomedical Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Kajang 43000, Selangor, Malaysia; (W.J.C.); (B.-H.K.); (Y.K.T.); (S.D.)
- Centre for Healthcare Science and Technology, Universiti Tunku Abdul Rahman, Kajang 43000, Selangor, Malaysia
| | - Shalini Darmaraju
- Department of Mechatronics and Biomedical Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Kajang 43000, Selangor, Malaysia; (W.J.C.); (B.-H.K.); (Y.K.T.); (S.D.)
| | - Haipeng Liu
- Centre for Intelligent Healthcare, Coventry University, Coventry CV1 5RW, UK;
| | - Choon-Hian Goh
- Department of Mechatronics and Biomedical Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Kajang 43000, Selangor, Malaysia; (W.J.C.); (B.-H.K.); (Y.K.T.); (S.D.)
- Centre for Healthcare Science and Technology, Universiti Tunku Abdul Rahman, Kajang 43000, Selangor, Malaysia
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Lu L, Zhu T, Morelli D, Creagh A, Liu Z, Yang J, Liu F, Zhang YT, Clifton DA. Uncertainties in the Analysis of Heart Rate Variability: A Systematic Review. IEEE Rev Biomed Eng 2024; 17:180-196. [PMID: 37186539 DOI: 10.1109/rbme.2023.3271595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
Heart rate variability (HRV) is an important metric with a variety of applications in clinical situations such as cardiovascular diseases, diabetes mellitus, and mental health. HRV data can be potentially obtained from electrocardiography and photoplethysmography signals, then computational techniques such as signal filtering and data segmentation are used to process the sampled data for calculating HRV measures. However, uncertainties arising from data acquisition, computational models, and physiological factors can lead to degraded signal quality and affect HRV analysis. Therefore, it is crucial to address these uncertainties and develop advanced models for HRV analysis. Although several reviews of HRV analysis exist, they primarily focus on clinical applications, trends in HRV methods, or specific aspects of uncertainties such as measurement noise. This paper provides a comprehensive review of uncertainties in HRV analysis, quantifies their impacts, and outlines potential solutions. To the best of our knowledge, this is the first study that presents a holistic review of uncertainties in HRV methods and quantifies their impacts on HRV measures from an engineer's perspective. This review is essential for developing robust and reliable models, and could serve as a valuable future reference in the field, particularly for dealing with uncertainties in HRV analysis.
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6
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Syversen A, Dosis A, Jayne D, Zhang Z. Wearable Sensors as a Preoperative Assessment Tool: A Review. SENSORS (BASEL, SWITZERLAND) 2024; 24:482. [PMID: 38257579 PMCID: PMC10820534 DOI: 10.3390/s24020482] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 01/06/2024] [Accepted: 01/09/2024] [Indexed: 01/24/2024]
Abstract
Surgery is a common first-line treatment for many types of disease, including cancer. Mortality rates after general elective surgery have seen significant decreases whilst postoperative complications remain a frequent occurrence. Preoperative assessment tools are used to support patient risk stratification but do not always provide a precise and accessible assessment. Wearable sensors (WS) provide an accessible alternative that offers continuous monitoring in a non-clinical setting. They have shown consistent uptake across the perioperative period but there has been no review of WS as a preoperative assessment tool. This paper reviews the developments in WS research that have application to the preoperative period. Accelerometers were consistently employed as sensors in research and were frequently combined with photoplethysmography or electrocardiography sensors. Pre-processing methods were discussed and missing data was a common theme; this was dealt with in several ways, commonly by employing an extraction threshold or using imputation techniques. Research rarely processed raw data; commercial devices that employ internal proprietary algorithms with pre-calculated heart rate and step count were most commonly employed limiting further feature extraction. A range of machine learning models were used to predict outcomes including support vector machines, random forests and regression models. No individual model clearly outperformed others. Deep learning proved successful for predicting exercise testing outcomes but only within large sample-size studies. This review outlines the challenges of WS and provides recommendations for future research to develop WS as a viable preoperative assessment tool.
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Affiliation(s)
- Aron Syversen
- School of Computing, University of Leeds, Leeds LS2 9JT, UK
| | - Alexios Dosis
- School of Medicine, University of Leeds, Leeds LS2 9JT, UK; (A.D.); (D.J.)
| | - David Jayne
- School of Medicine, University of Leeds, Leeds LS2 9JT, UK; (A.D.); (D.J.)
| | - Zhiqiang Zhang
- School of Electrical Engineering, University of Leeds, Leeds LS2 9JT, UK;
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Peláez-Coca MD, Hernando A, Lozano MT, Bolea J, Izquierdo D, Sánchez C. Heart Rate Variability to Automatically Identify Hyperbaric States Considering Respiratory Component. SENSORS (BASEL, SWITZERLAND) 2024; 24:447. [PMID: 38257541 PMCID: PMC11154234 DOI: 10.3390/s24020447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 01/04/2024] [Accepted: 01/08/2024] [Indexed: 01/24/2024]
Abstract
This study's primary objective was to identify individuals whose physiological responses deviated from the rest of the study population by automatically monitoring atmospheric pressure levels to which they are exposed and using parameters derived from their heart rate variability (HRV). To achieve this, 28 volunteers were placed in a dry hyperbaric chamber, where they experienced varying pressures from 1 to 5 atmospheres, with five sequential stops lasting five minutes each at different atmospheric pressures. The HRV was dissected into two components: the respiratory component, which is linked to respiration; and the residual component, which is influenced by factors beyond respiration. Nine parameters were assessed, including the respiratory rate, four classic HRV temporal parameters, and four frequency parameters. A k-nearest neighbors classifier based on cosine distance successfully identified the atmospheric pressures to which the subjects were exposed to. The classifier achieved an 88.5% accuracy rate in distinguishing between the 5 atm and 3 atm stages using only four features: respiratory rate, heart rate, and two frequency parameters associated with the subjects' sympathetic responses. Furthermore, the study identified 6 out of 28 subjects as having atypical responses across all pressure levels when compared to the majority. Interestingly, two of these subjects stood out in terms of gender and having less prior diving experience, but they still exhibited normal responses to immersion. This suggests the potential for establishing distinct safety protocols for divers based on their previous experience and gender.
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Affiliation(s)
- María Dolores Peláez-Coca
- Centro Universitario de la Defensa de Zaragoza, 50090 Zaragoza, Spain; (M.T.L.); (J.B.)
- BSICoS Group, I3A Institute, University of Zaragoza, IIS Aragón, 50009 Zaragoza, Spain; (A.H.); (C.S.)
| | - Alberto Hernando
- BSICoS Group, I3A Institute, University of Zaragoza, IIS Aragón, 50009 Zaragoza, Spain; (A.H.); (C.S.)
| | - María Teresa Lozano
- Centro Universitario de la Defensa de Zaragoza, 50090 Zaragoza, Spain; (M.T.L.); (J.B.)
- BSICoS Group, I3A Institute, University of Zaragoza, IIS Aragón, 50009 Zaragoza, Spain; (A.H.); (C.S.)
| | - Juan Bolea
- Centro Universitario de la Defensa de Zaragoza, 50090 Zaragoza, Spain; (M.T.L.); (J.B.)
| | - David Izquierdo
- GTF Group, I3A Institute, University of Zaragoza, 50009 Zaragoza, Spain;
| | - Carlos Sánchez
- BSICoS Group, I3A Institute, University of Zaragoza, IIS Aragón, 50009 Zaragoza, Spain; (A.H.); (C.S.)
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Sautillet B, Bourdillon N, Millet GP, Lemaître F, Cozette M, Delanaud S, Ahmaïdi S, Costalat G. Hot water immersion: Maintaining core body temperature above 38.5°C mitigates muscle fatigue. Scand J Med Sci Sports 2024; 34:e14503. [PMID: 37747708 DOI: 10.1111/sms.14503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 09/06/2023] [Accepted: 09/15/2023] [Indexed: 09/26/2023]
Abstract
PURPOSE Hot water immersion (HWI) has gained popularity to promote muscle recovery, despite limited data on the optimal heat dose. The purpose of this study was to compare the responses of two exogenous heat strains on core body temperature, hemodynamic adjustments, and key functional markers of muscle recovery following exercise-induced muscle damage (EIMD). METHODS Twenty-eight physically active males completed an individually tailored EIMD protocol immediately followed by one of the following recovery interventions: HWI (40°C, HWI40 ), HWI (41°C, HWI41 ) or warm water immersion (36°C, CON36 ). Gastrointestinal temperature (Tgi ), hemodynamic adjustments (cardiac output [CO], mean arterial pressure [MAP], and systemic vascular resistance [SVR]), pre-frontal cortex deoxyhemoglobin (HHb), ECG-derived respiratory frequency, and subjective perceptual measures were tracked throughout immersion. In addition, functional markers of muscle fatigue (maximal concentric peak torque [Tpeak ]) and muscle damage (late-phase rate of force development [RFD100-200 ]) were measured prior to EIMD (pre-), 24 h (post-24 h), and 48 h (post-48 h) post-EIMD. RESULTS By the end of immersion, HWI41 led to significantly higher Tgi values than HWI40 (38.8 ± 0.1 vs. 38.0°C ± 0.6°C, p < 0.001). While MAP was well maintained throughout immersion, only HWI41 led to increased (HHb) (+4.2 ± 1.47 μM; p = 0.005) and respiratory frequency (+4.0 ± 1.21 breath.min-1 ; p = 0.032). Only HWI41 mitigated the decline in RFD100-200 at post-24 h (-7.1 ± 31.8%; p = 0.63) and Tpeak at post-48 h (-3.1 ± 4.3%, p = 1). CONCLUSION In physically active males, maintaining a core body temperature of ~25 min within the range of 38.5°C-39°C has been found to be effective in improving muscle recovery, while minimizing the risk of excessive physiological heat strain.
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Affiliation(s)
- Benoît Sautillet
- Faculty of Sport Sciences, APERE Laboratory, UR 3300, University of Picardie Jules Verne, Amiens, France
| | - Nicolas Bourdillon
- Institute of Sport Sciences, University of Lausanne, Lausanne, Switzerland
| | - Grégoire P Millet
- Institute of Sport Sciences, University of Lausanne, Lausanne, Switzerland
| | - Fréderic Lemaître
- Faculty of Sport Sciences, CETAPS Laboratory, UR 3832, Normandy University, Rouen, France
| | - Maryne Cozette
- Faculty of Sport Sciences, APERE Laboratory, UR 3300, University of Picardie Jules Verne, Amiens, France
| | - Stéphane Delanaud
- PériTox UMR_I 01 laboratory, CURS-UPJV, F-80054, University of Picardie Jules Verne, Amiens, France
| | - Saïd Ahmaïdi
- Faculty of Sport Sciences, APERE Laboratory, UR 3300, University of Picardie Jules Verne, Amiens, France
| | - Guillaume Costalat
- Faculty of Sport Sciences, APERE Laboratory, UR 3300, University of Picardie Jules Verne, Amiens, France
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Shamantseva ND, Klishkovskaia TA, Ananyev SS, Aksenov AY, Moshonkina TR. Efficacy of Marker-Based Motion Capture for Respiratory Cycle Measurement: A Comparison with Spirometry. SENSORS (BASEL, SWITZERLAND) 2023; 23:9736. [PMID: 38139582 PMCID: PMC10748239 DOI: 10.3390/s23249736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 12/06/2023] [Accepted: 12/07/2023] [Indexed: 12/24/2023]
Abstract
Respiratory rate monitoring is fundamental in clinical settings, and the accuracy of measurement methods is critical. This study aimed to develop and validate methods for assessing respiratory rate and the duration leof respiratory cycle phases in different body positions using optoelectronic plethysmography (OEP) based on a motion capture video system. Two analysis methods, the summation method and the triangle method were developed. The study focused on determining the optimal number of markers while achieving accuracy in respiratory parameter measurements. The results showed that most analysis methods showed a difference of ≤0.5 breaths per minute, with R2 ≥ 0.94 (p < 0.001) compared to spirometry. The best OEP methods for respiratory rate were the abdominal triangles and the sum of abdominal markers in all body positions. The study explored inspiratory and expiratory durations. The research found that 5-9 markers were sufficient to accurately determine respiratory time components in all body positions, reducing the marker requirements compared to previous studies. This interchangeability of OEP methods with standard spirometry demonstrates the potential of non-invasive methods for the simultaneous assessment of body segment movements, center of pressure dynamics, and respiratory movements. Future research is required to improve the clinical applicability of these methods.
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Affiliation(s)
- Natalia D. Shamantseva
- Pavlov Institute of Physiology, Russian Academy of Sciences, 6 Makarova Emb., Saint Petersburg 199034, Russia; (S.S.A.); (T.R.M.)
| | - Tatiana A. Klishkovskaia
- Faculty of Information Measurement and Biotechnical Systems, Saint Petersburg Electrotechnical University “LETI”, 5 Professora Popova Str., Saint Petersburg 197022, Russia; (T.A.K.); (A.Y.A.)
| | - Sergey S. Ananyev
- Pavlov Institute of Physiology, Russian Academy of Sciences, 6 Makarova Emb., Saint Petersburg 199034, Russia; (S.S.A.); (T.R.M.)
| | - Andrey Y. Aksenov
- Faculty of Information Measurement and Biotechnical Systems, Saint Petersburg Electrotechnical University “LETI”, 5 Professora Popova Str., Saint Petersburg 197022, Russia; (T.A.K.); (A.Y.A.)
| | - Tatiana R. Moshonkina
- Pavlov Institute of Physiology, Russian Academy of Sciences, 6 Makarova Emb., Saint Petersburg 199034, Russia; (S.S.A.); (T.R.M.)
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10
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Sun S, Song W, Tong Y, Li X, Zhao M, Deng Q, Liu G, Liu Z, Liu C. A novel methodology for evaluation of S 2 wide split via estimated parameters. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 242:107777. [PMID: 37714021 DOI: 10.1016/j.cmpb.2023.107777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/11/2021] [Revised: 07/25/2023] [Accepted: 08/22/2023] [Indexed: 09/17/2023]
Abstract
BACKGROUND AND OBJECTIVE Aimed at the shortcomings of using time interval ( [Formula: see text] ) between the sounds produced by the aortic valve closure (A2) and the pulmonary valve closure (P2) to detect the wide splitting of the second heart sound (S2), which are the [Formula: see text] easily influenced by the heartbeat and not easily distinguished from the fixed splitting of S2 without considering the entire respiratory phase, and from the third heart sound (S3), this study proposes a novel methodology to detect the wide splitting of S2 using an estimated split coefficient of S2 ( [Formula: see text] ) combined with an adaptive number (NAda) of S2. METHODOLOGY The methodology is orderly summarized as follows: Stage 1 describes the segmentation-based S2 automatic location and extraction. A Gaussian mixture model (GMM)-based regression model for S2 is proposed to estimate the positions of A2 and P2, then an overlapping rate (OLR)-based [Formula: see text] and the [Formula: see text] are estimated, and finally, a NAda-S2 is automatically determined to calculate the statistics of [Formula: see text] and [Formula: see text] . In stage 3, based on the combination of estimated features, the detection of wide splitting of S2 is determined. RESULTS The performance is evaluated using a total of 3350-period heart sounds from 72 patients, with an overall accuracy of 100%, F1=1 and a Cohen's kappa value (κ) of 1. DISCUSSION The significant contributions are highlighted: A novel GMM-based efficient methodology is proposed for estimating the characteristics of A2 and P2. A novel OLR-based [Formula: see text] is defined to replace the current state-of-the-art criterion for evaluating the split degree of S2. Considering respiration phases combined with CR are proposed for the high-precision diagnosis of S2 wide split.
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Affiliation(s)
- Shuping Sun
- School of Information Science and Engineering, Hunan Institute of Science and Technology, Yueyang 414006, Hunan, China.
| | - Wei Song
- School of Information Science and Engineering, Hunan Institute of Science and Technology, Yueyang 414006, Hunan, China
| | - Yaonan Tong
- School of Information Science and Engineering, Hunan Institute of Science and Technology, Yueyang 414006, Hunan, China.
| | - Xu Li
- School of Economic, Bohai University, Liaoning Jinzhou 121013, China
| | - Man Zhao
- School of Economic, Bohai University, Liaoning Jinzhou 121013, China
| | - Qi Deng
- School of Information Science and Engineering, Hunan Institute of Science and Technology, Yueyang 414006, Hunan, China
| | - Guangyu Liu
- School of Information Science and Engineering, Hunan Institute of Science and Technology, Yueyang 414006, Hunan, China
| | - Zhi Liu
- School of Information Science and Engineering, Hunan Institute of Science and Technology, Yueyang 414006, Hunan, China
| | - Chao Liu
- School of Information Science and Engineering, Hunan Institute of Science and Technology, Yueyang 414006, Hunan, China
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11
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Charlton PH, Allen J, Bailón R, Baker S, Behar JA, Chen F, Clifford GD, Clifton DA, Davies HJ, Ding C, Ding X, Dunn J, Elgendi M, Ferdoushi M, Franklin D, Gil E, Hassan MF, Hernesniemi J, Hu X, Ji N, Khan Y, Kontaxis S, Korhonen I, Kyriacou PA, Laguna P, Lázaro J, Lee C, Levy J, Li Y, Liu C, Liu J, Lu L, Mandic DP, Marozas V, Mejía-Mejía E, Mukkamala R, Nitzan M, Pereira T, Poon CCY, Ramella-Roman JC, Saarinen H, Shandhi MMH, Shin H, Stansby G, Tamura T, Vehkaoja A, Wang WK, Zhang YT, Zhao N, Zheng D, Zhu T. The 2023 wearable photoplethysmography roadmap. Physiol Meas 2023; 44:111001. [PMID: 37494945 PMCID: PMC10686289 DOI: 10.1088/1361-6579/acead2] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 04/04/2023] [Accepted: 07/26/2023] [Indexed: 07/28/2023]
Abstract
Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology.
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Affiliation(s)
- Peter H Charlton
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, United Kingdom
- Research Centre for Biomedical Engineering, City, University of London, London, EC1V 0HB, United Kingdom
| | - John Allen
- Research Centre for Intelligent Healthcare, Coventry University, Coventry, CV1 5RW, United Kingdom
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, NE2 4HH, United Kingdom
| | - Raquel Bailón
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) Group, Aragon Institute of Engineering Research (I3A), IIS Aragon, University of Zaragoza, E-50018 Zaragoza, Spain
- CIBER-BBN, Instituto de Salud Carlos III, C/Monforte de Lemos 3-5, E-28029 Madrid, Spain
| | - Stephanie Baker
- College of Science and Engineering, James Cook University, Cairns, 4878 Queensland, Australia
| | - Joachim A Behar
- Faculty of Biomedical Engineering, Technion Israel Institute of Technology, Haifa, 3200003, Israel
| | - Fei Chen
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, 518055 Guandong, People’s Republic of China
| | - Gari D Clifford
- Department of Biomedical Informatics, Emory University, Atlanta, GA 30322, United States of America
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, United States of America
| | - David A Clifton
- Department of Engineering Science, University of Oxford, Oxford, OX3 7DQ, United Kingdom
| | - Harry J Davies
- Department of Electrical and Electronic Engineering, Imperial College London, London, SW7 2AZ, United Kingdom
| | - Cheng Ding
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, United States of America
- Department of Biomedical Engineering, Emory University, Atlanta, GA 30322, United States of America
| | - Xiaorong Ding
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, People’s Republic of China
| | - Jessilyn Dunn
- Department of Biomedical Engineering, Duke University, Durham, NC 27708-0187, United States of America
- Department of Biostatistics & Bioinformatics, Duke University, Durham, NC 27708-0187, United States of America
- Duke Clinical Research Institute, Durham, NC 27705-3976, United States of America
| | - Mohamed Elgendi
- Biomedical and Mobile Health Technology Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, 8008, Switzerland
| | - Munia Ferdoushi
- Department of Electrical and Computer Engineering, University of Southern California, 90089, Los Angeles, California, United States of America
- The Institute for Technology and Medical Systems (ITEMS), Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, United States of America
| | - Daniel Franklin
- Institute of Biomedical Engineering, Translational Biology & Engineering Program, Ted Rogers Centre for Heart Research, University of Toronto, Toronto, M5G 1M1, Canada
| | - Eduardo Gil
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) Group, Aragon Institute of Engineering Research (I3A), IIS Aragon, University of Zaragoza, E-50018 Zaragoza, Spain
- CIBER-BBN, Instituto de Salud Carlos III, C/Monforte de Lemos 3-5, E-28029 Madrid, Spain
| | - Md Farhad Hassan
- Department of Electrical and Computer Engineering, University of Southern California, 90089, Los Angeles, California, United States of America
- The Institute for Technology and Medical Systems (ITEMS), Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, United States of America
| | - Jussi Hernesniemi
- Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, 33720, Finland
- Tampere Heart Hospital, Wellbeing Services County of Pirkanmaa, Tampere, 33520, Finland
| | - Xiao Hu
- Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, 30322, Georgia, United States of America
- Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, 30322, Georgia, United States of America
- Department of Computer Sciences, College of Arts and Sciences, Emory University, Atlanta, GA 30322, United States of America
| | - Nan Ji
- Hong Kong Center for Cerebrocardiovascular Health Engineering (COCHE), Hong Kong Science and Technology Park, Hong Kong, 999077, People’s Republic of China
| | - Yasser Khan
- Department of Electrical and Computer Engineering, University of Southern California, 90089, Los Angeles, California, United States of America
- The Institute for Technology and Medical Systems (ITEMS), Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, United States of America
| | - Spyridon Kontaxis
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) Group, Aragon Institute of Engineering Research (I3A), IIS Aragon, University of Zaragoza, E-50018 Zaragoza, Spain
- CIBER-BBN, Instituto de Salud Carlos III, C/Monforte de Lemos 3-5, E-28029 Madrid, Spain
| | - Ilkka Korhonen
- Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, 33720, Finland
| | - Panicos A Kyriacou
- Research Centre for Biomedical Engineering, City, University of London, London, EC1V 0HB, United Kingdom
| | - Pablo Laguna
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) Group, Aragon Institute of Engineering Research (I3A), IIS Aragon, University of Zaragoza, E-50018 Zaragoza, Spain
- CIBER-BBN, Instituto de Salud Carlos III, C/Monforte de Lemos 3-5, E-28029 Madrid, Spain
| | - Jesús Lázaro
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) Group, Aragon Institute of Engineering Research (I3A), IIS Aragon, University of Zaragoza, E-50018 Zaragoza, Spain
- CIBER-BBN, Instituto de Salud Carlos III, C/Monforte de Lemos 3-5, E-28029 Madrid, Spain
| | - Chungkeun Lee
- Digital Health Devices Division, Medical Device Evaluation Department, National Institute of Food and Drug Safety Evaluation, Ministry of Food and Drug Safety, Cheongju, 28159, Republic of Korea
| | - Jeremy Levy
- Faculty of Biomedical Engineering, Technion Israel Institute of Technology, Haifa, 3200003, Israel
- Faculty of Electrical and Computer Engineering, Technion Institute of Technology, Haifa, 3200003, Israel
| | - Yumin Li
- State Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, People’s Republic of China
| | - Chengyu Liu
- State Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, People’s Republic of China
| | - Jing Liu
- Analog Devices Inc, San Jose, CA 95124, United States of America
| | - Lei Lu
- Department of Engineering Science, University of Oxford, Oxford, OX3 7DQ, United Kingdom
| | - Danilo P Mandic
- Department of Electrical and Electronic Engineering, Imperial College London, London, SW7 2AZ, United Kingdom
| | - Vaidotas Marozas
- Department of Electronics Engineering, Kaunas University of Technology, 44249 Kaunas, Lithuania
- Biomedical Engineering Institute, Kaunas University of Technology, 44249 Kaunas, Lithuania
| | - Elisa Mejía-Mejía
- Research Centre for Biomedical Engineering, City, University of London, London, EC1V 0HB, United Kingdom
| | - Ramakrishna Mukkamala
- Department of Bioengineering and Department of Anesthesiology and Perioperative Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Meir Nitzan
- Department of Physics/Electro-Optic Engineering, Lev Academic Center, 91160 Jerusalem, Israel
| | - Tania Pereira
- INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, Porto, 4200-465, Portugal
- Faculty of Engineering, University of Porto, Porto, 4200-465, Portugal
| | | | - Jessica C Ramella-Roman
- Department of Biomedical Engineering and Herbert Wertheim College of Medicine, Florida International University, Miami, FL 33174, United States of America
| | - Harri Saarinen
- Tampere Heart Hospital, Wellbeing Services County of Pirkanmaa, Tampere, 33520, Finland
| | - Md Mobashir Hasan Shandhi
- Department of Biomedical Engineering, Duke University, Durham, NC 27708-0187, United States of America
| | - Hangsik Shin
- Department of Digital Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, Republic of Korea
| | - Gerard Stansby
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, NE2 4HH, United Kingdom
- Northern Vascular Centre, Freeman Hospital, Newcastle upon Tyne, NE7 7DN, United Kingdom
| | - Toshiyo Tamura
- Future Robotics Organization, Waseda University, Tokyo, 1698050, Japan
| | - Antti Vehkaoja
- Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, 33720, Finland
- PulseOn Ltd, Espoo, 02150, Finland
| | - Will Ke Wang
- Department of Biomedical Engineering, Duke University, Durham, NC 27708-0187, United States of America
| | - Yuan-Ting Zhang
- Hong Kong Center for Cerebrocardiovascular Health Engineering (COCHE), Hong Kong Science and Technology Park, Hong Kong, 999077, People’s Republic of China
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, 999077, People’s Republic of China
| | - Ni Zhao
- Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong
| | - Dingchang Zheng
- Research Centre for Intelligent Healthcare, Coventry University, Coventry, CV1 5RW, United Kingdom
| | - Tingting Zhu
- Department of Engineering Science, University of Oxford, Oxford, OX3 7DQ, United Kingdom
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12
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Mehmood A, Sarouji A, Rahman MMU, Al-Naffouri TY. Your smartphone could act as a pulse-oximeter and as a single-lead ECG. Sci Rep 2023; 13:19277. [PMID: 37935806 PMCID: PMC10630323 DOI: 10.1038/s41598-023-45933-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 10/25/2023] [Indexed: 11/09/2023] Open
Abstract
In the post-covid19 era, every new wave of the pandemic causes an increased concern/interest among the masses to learn more about their state of well-being. Therefore, it is the need of the hour to come up with ubiquitous, low-cost, non-invasive tools for rapid and continuous monitoring of body vitals that reflect the status of one's overall health. In this backdrop, this work proposes a deep learning approach to turn a smartphone-the popular hand-held personal gadget-into a diagnostic tool to measure/monitor the three most important body vitals, i.e., pulse rate (PR), blood oxygen saturation level (aka SpO2), and respiratory rate (RR). Furthermore, we propose another method that could extract a single-lead electrocardiograph (ECG) of the subject. The proposed methods include the following core steps: subject records a small video of his/her fingertip by placing his/her finger on the rear camera of the smartphone, and the recorded video is pre-processed to extract the filtered and/or detrended video-photoplethysmography (vPPG) signal, which is then fed to custom-built convolutional neural networks (CNN), which eventually spit-out the vitals (PR, SpO2, and RR) as well as a single-lead ECG of the subject. To be precise, the contribution of this paper is twofold: (1) estimation of the three body vitals (PR, SpO2, RR) from the vPPG data using custom-built CNNs, vision transformer, and most importantly by CLIP model (a popular image-caption-generator model); (2) a novel discrete cosine transform+feedforward neural network-based method that translates the recorded video-PPG signal to a single-lead ECG signal. The significance of this work is twofold: (i) it allows rapid self-testing of body vitals (e.g., self-monitoring for covid19 symptoms), (ii) it enables rapid self-acquisition of a single-lead ECG, and thus allows early detection of atrial fibrillation (abormal heart beat or arrhythmia), which in turn could enable early intervention in response to a range of cardiovascular diseases, and could help save many precious lives. Our work could help reduce the burden on healthcare facilities and could lead to reduction in health insurance costs.
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Affiliation(s)
- Ahsan Mehmood
- Department of Electrical Engineering, KAUST, Thuwal, Kingdom of Saudi Arabia
| | - Asma Sarouji
- Department of Electrical Engineering, KAUST, Thuwal, Kingdom of Saudi Arabia
| | - M Mahboob Ur Rahman
- Department of Electrical Engineering, KAUST, Thuwal, Kingdom of Saudi Arabia.
| | - Tareq Y Al-Naffouri
- Department of Electrical Engineering, KAUST, Thuwal, Kingdom of Saudi Arabia
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13
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Hwang CS, Kim YH, Hyun JK, Kim JH, Lee SR, Kim CM, Nam JW, Kim EY. Evaluation of the Photoplethysmogram-Based Deep Learning Model for Continuous Respiratory Rate Estimation in Surgical Intensive Care Unit. Bioengineering (Basel) 2023; 10:1222. [PMID: 37892952 PMCID: PMC10604201 DOI: 10.3390/bioengineering10101222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 10/16/2023] [Accepted: 10/17/2023] [Indexed: 10/29/2023] Open
Abstract
The respiratory rate (RR) is a significant indicator to evaluate a patient's prognosis and status; however, it requires specific instrumentation or estimates from other monitored signals. A photoplethysmogram (PPG) is extensively used in clinical environments as well as in intensive care units (ICUs) to primarily monitor peripheral circulation while capturing indirect information about intrathoracic pressure changes. This study aims to apply and evaluate several deep learning models using a PPG for the continuous and accurate estimation of the RRs of patients. The dataset was collected twice for 2 min each in 100 patients aged 18 years and older from the surgical intensive care unit of a tertiary referral hospital. The BIDMC and CapnoBase public datasets were also analyzed. The collected dataset was preprocessed and split according to the 5-fold cross-validation. We used seven deep learning models, including our own Dilated Residual Neural Network, to check how accurately the RR estimates match the ground truth using the mean absolute error (MAE). As a result, when validated using the collected dataset, our model showed the best results with a 1.2628 ± 0.2697 MAE on BIDMC and RespNet and with a 3.1268 ± 0.6363 MAE on our dataset, respectively. In conclusion, RR estimation using PPG-derived models is still challenging and has many limitations. However, if there is an equal amount of data from various breathing groups to train, we expect that various models, including our Dilated ResNet model, which showed good results, can achieve better results than the current ones.
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Affiliation(s)
- Chi Shin Hwang
- Spass Inc., 905Ho, RnD Tower, 396, Worldcup Buk-ro, Mapo-gu, Seoul 03925, Republic of Korea; (C.S.H.); (J.W.N.)
| | - Yong Hwan Kim
- Spass Inc., 905Ho, RnD Tower, 396, Worldcup Buk-ro, Mapo-gu, Seoul 03925, Republic of Korea; (C.S.H.); (J.W.N.)
| | - Jung Kyun Hyun
- Spass Inc., 905Ho, RnD Tower, 396, Worldcup Buk-ro, Mapo-gu, Seoul 03925, Republic of Korea; (C.S.H.); (J.W.N.)
| | - Joon Hwang Kim
- Spass Inc., 905Ho, RnD Tower, 396, Worldcup Buk-ro, Mapo-gu, Seoul 03925, Republic of Korea; (C.S.H.); (J.W.N.)
| | - Seo Rak Lee
- Spass Inc., 905Ho, RnD Tower, 396, Worldcup Buk-ro, Mapo-gu, Seoul 03925, Republic of Korea; (C.S.H.); (J.W.N.)
| | - Choong Min Kim
- Spass Inc., 905Ho, RnD Tower, 396, Worldcup Buk-ro, Mapo-gu, Seoul 03925, Republic of Korea; (C.S.H.); (J.W.N.)
| | - Jung Woo Nam
- Spass Inc., 905Ho, RnD Tower, 396, Worldcup Buk-ro, Mapo-gu, Seoul 03925, Republic of Korea; (C.S.H.); (J.W.N.)
| | - Eun Young Kim
- Division of Trauma and Surgical Critical Care, Department of Surgery, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Banpo-daero 222, Seocho-gu, Seoul 06591, Republic of Korea
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14
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Kazijevs M, Samad MD. Deep imputation of missing values in time series health data: A review with benchmarking. J Biomed Inform 2023; 144:104440. [PMID: 37429511 PMCID: PMC10529422 DOI: 10.1016/j.jbi.2023.104440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 06/26/2023] [Accepted: 07/05/2023] [Indexed: 07/12/2023]
Abstract
The imputation of missing values in multivariate time series (MTS) data is critical in ensuring data quality and producing reliable data-driven predictive models. Apart from many statistical approaches, a few recent studies have proposed state-of-the-art deep learning methods to impute missing values in MTS data. However, the evaluation of these deep methods is limited to one or two data sets, low missing rates, and completely random missing value types. This survey performs six data-centric experiments to benchmark state-of-the-art deep imputation methods on five time series health data sets. Our extensive analysis reveals that no single imputation method outperforms the others on all five data sets. The imputation performance depends on data types, individual variable statistics, missing value rates, and types. Deep learning methods that jointly perform cross-sectional (across variables) and longitudinal (across time) imputations of missing values in time series data yield statistically better data quality than traditional imputation methods. Although computationally expensive, deep learning methods are practical given the current availability of high-performance computing resources, especially when data quality and sample size are of paramount importance in healthcare informatics. Our findings highlight the importance of data-centric selection of imputation methods to optimize data-driven predictive models.
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Affiliation(s)
- Maksims Kazijevs
- Department of Computer Science, Tennessee State University, Nashville, TN 37209, United States
| | - Manar D Samad
- Department of Computer Science, Tennessee State University, Nashville, TN 37209, United States.
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15
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Huang X, Zheng J, Ma Y, Hou M, Wang X. Analysis of emerging trends and hot spots in respiratory biomechanics from 2003 to 2022 based on CiteSpace. Front Physiol 2023; 14:1190155. [PMID: 37546534 PMCID: PMC10397404 DOI: 10.3389/fphys.2023.1190155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 07/12/2023] [Indexed: 08/08/2023] Open
Abstract
Introduction: With the global prevalence of coronavirus disease 2019 (COVID-19), an increasing number of people are experiencing respiratory discomfort. Respiratory biomechanics can monitor breathing patterns and respiratory movements and it is easier to prevent, diagnose, treat or rehabilitate. However, there is still a lack of global knowledge structure in the field of respiratory biomechanics. With the help of CiteSpace software, we aim to help researchers identify potential collaborators and collaborating institutions, hotspots and research frontiers in respiratory biomechanics. Methods: Articles on respiratory biomechanics from 2003 to 2022 were retrieved from the Web of Science Core Collection by using a specific strategy, resulting a total of 2,850 publications. We used CiteSpace 6.1.R6 to analyze the year of publication, journal/journals cited, country, institution, author/authors cited, references, keywords and research trends. Co-citation maps were created to visually observe research hot spots and knowledge structures. Results and discussion: The number of annual publications gradually increased over the past 20 years. Medical Physics published the most articles and had the most citations in this study. The United States was the most influential country, with the highest number and centrality of publications. The most productive and influential institution was Harvard University in the United States. Keall PJ was the most productive author and MCCLELLAND JR was the most cited authors The article by Keall PJ (2006) article (cocitation counts: 55) and the article by McClelland JR (2013) were the most representative and symbolic references, with the highest cocitation number and centrality, respectively. The top keywords were "radiotherapy", "volume", and "ventilation". The top Frontier keywords were "organ motion," "deep inspiration," and "deep learning". The keywords were clustered to form seven labels. Currently, the main area of research in respiratory biomechanics is respiratory motion related to imaging techniques. Future research may focus on respiratory assistance techniques and respiratory detection techniques. At the same time, in the future, we will pay attention to personalized medicine and precision medicine, so that people can monitor their health status anytime and anywhere.
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Affiliation(s)
- Xiaofei Huang
- College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, China
- Key Laboratory of Orthopedics & Traumatology of Traditional Chinese Medicine and Rehabilitation Ministry of Education, Fujian University of Traditional Chinese Medicine, Fuzhou, China
| | - Jiaqi Zheng
- College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, China
- Key Laboratory of Orthopedics & Traumatology of Traditional Chinese Medicine and Rehabilitation Ministry of Education, Fujian University of Traditional Chinese Medicine, Fuzhou, China
| | - Ye Ma
- Key Laboratory of Orthopedics & Traumatology of Traditional Chinese Medicine and Rehabilitation Ministry of Education, Fujian University of Traditional Chinese Medicine, Fuzhou, China
- Research Academy of Grand Health, Faculty of Sports Sciences, Ningbo University, Ningbo, China
- National-Local Joint Engineering Research Center of Rehabilitation Medicine Technology, Fuzhou, China
| | - Meijin Hou
- Key Laboratory of Orthopedics & Traumatology of Traditional Chinese Medicine and Rehabilitation Ministry of Education, Fujian University of Traditional Chinese Medicine, Fuzhou, China
- National-Local Joint Engineering Research Center of Rehabilitation Medicine Technology, Fuzhou, China
| | - Xiangbin Wang
- College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, China
- Key Laboratory of Orthopedics & Traumatology of Traditional Chinese Medicine and Rehabilitation Ministry of Education, Fujian University of Traditional Chinese Medicine, Fuzhou, China
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16
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Petek BJ, Al-Alusi MA, Moulson N, Grant AJ, Besson C, Guseh JS, Wasfy MM, Gremeaux V, Churchill TW, Baggish AL. Consumer Wearable Health and Fitness Technology in Cardiovascular Medicine: JACC State-of-the-Art Review. J Am Coll Cardiol 2023; 82:245-264. [PMID: 37438010 PMCID: PMC10662962 DOI: 10.1016/j.jacc.2023.04.054] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 04/26/2023] [Accepted: 04/28/2023] [Indexed: 07/14/2023]
Abstract
The use of consumer wearable devices (CWDs) to track health and fitness has rapidly expanded over recent years because of advances in technology. The general population now has the capability to continuously track vital signs, exercise output, and advanced health metrics. Although understanding of basic health metrics may be intuitive (eg, peak heart rate), more complex metrics are derived from proprietary algorithms, differ among device manufacturers, and may not historically be common in clinical practice (eg, peak V˙O2, exercise recovery scores). With the massive expansion of data collected at an individual patient level, careful interpretation is imperative. In this review, we critically analyze common health metrics provided by CWDs, describe common pitfalls in CWD interpretation, provide recommendations for the interpretation of abnormal results, present the utility of CWDs in exercise prescription, examine health disparities and inequities in CWD use and development, and present future directions for research and development.
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Affiliation(s)
- Bradley J Petek
- Division of Cardiology, Massachusetts General Hospital, Boston, Massachusetts, USA; Cardiovascular Performance Program, Massachusetts General Hospital, Boston, Massachusetts, USA; Knight Cardiovascular Institute, Oregon Health and Science University, Portland, Oregon, USA
| | - Mostafa A Al-Alusi
- Division of Cardiology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Nathaniel Moulson
- Division of Cardiology and Sports Cardiology BC, University of British Columbia, Vancouver, British Columbia, Canada
| | - Aubrey J Grant
- Division of Cardiology, Massachusetts General Hospital, Boston, Massachusetts, USA; Cardiovascular Performance Program, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Cyril Besson
- Swiss Olympic Medical Center, Lausanne University Hospital (CHUV), Lausanne, Switzerland; Institute for Sport Science, University of Lausanne (ISSUL), Lausanne, Switzerland
| | - J Sawalla Guseh
- Division of Cardiology, Massachusetts General Hospital, Boston, Massachusetts, USA; Cardiovascular Performance Program, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Meagan M Wasfy
- Division of Cardiology, Massachusetts General Hospital, Boston, Massachusetts, USA; Cardiovascular Performance Program, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Vincent Gremeaux
- Swiss Olympic Medical Center, Lausanne University Hospital (CHUV), Lausanne, Switzerland; Institute for Sport Science, University of Lausanne (ISSUL), Lausanne, Switzerland
| | - Timothy W Churchill
- Division of Cardiology, Massachusetts General Hospital, Boston, Massachusetts, USA; Cardiovascular Performance Program, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Aaron L Baggish
- Division of Cardiology, Massachusetts General Hospital, Boston, Massachusetts, USA; Cardiovascular Performance Program, Massachusetts General Hospital, Boston, Massachusetts, USA; Swiss Olympic Medical Center, Lausanne University Hospital (CHUV), Lausanne, Switzerland; Institute for Sport Science, University of Lausanne (ISSUL), Lausanne, Switzerland.
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17
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Shahbakhti M, Hakimi N, Horschig JM, Floor-Westerdijk M, Claassen J, Colier WNJM. Estimation of Respiratory Rate during Biking with a Single Sensor Functional Near-Infrared Spectroscopy (fNIRS) System. SENSORS (BASEL, SWITZERLAND) 2023; 23:3632. [PMID: 37050692 PMCID: PMC10099192 DOI: 10.3390/s23073632] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 03/28/2023] [Accepted: 03/29/2023] [Indexed: 06/19/2023]
Abstract
OBJECTIVE The employment of wearable systems for continuous monitoring of vital signs is increasing. However, due to substantial susceptibility of conventional bio-signals recorded by wearable systems to motion artifacts, estimation of the respiratory rate (RR) during physical activities is a challenging task. Alternatively, functional Near-Infrared Spectroscopy (fNIRS) can be used, which has been proven less vulnerable to the subject's movements. This paper proposes a fusion-based method for estimating RR during bicycling from fNIRS signals recorded by a wearable system. METHODS Firstly, five respiratory modulations are extracted, based on amplitude, frequency, and intensity of the oxygenated hemoglobin concentration (O2Hb) signal. Secondly, the dominant frequency of each modulation is computed using the fast Fourier transform. Finally, dominant frequencies of all modulations are fused, based on averaging, to estimate RR. The performance of the proposed method was validated on 22 young healthy subjects, whose respiratory and fNIRS signals were simultaneously recorded during a bicycling task, and compared against a zero delay Fourier domain band-pass filter. RESULTS The comparison between results obtained by the proposed method and band-pass filtering indicated the superiority of the former, with a lower mean absolute error (3.66 vs. 11.06 breaths per minute, p<0.05). The proposed fusion strategy also outperformed RR estimations based on the analysis of individual modulation. SIGNIFICANCE This study orients towards the practical limitations of traditional bio-signals for RR estimation during physical activities.
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Affiliation(s)
- Mohammad Shahbakhti
- Artinis Medical Systems, B.V., Einsteinweg 17, 6662 PW Elst, The Netherlands
- Biomedical Engineering Institute, Kaunas University of Technology, K. Barsausko 59, LT-51423 Kaunas, Lithuania
| | - Naser Hakimi
- Artinis Medical Systems, B.V., Einsteinweg 17, 6662 PW Elst, The Netherlands
- Department of Neonatology, Wilhelmina Children’s Hospital, University Medical Center Utrecht, Lundlaan 6, 3584 EA Utrecht, The Netherlands
| | - Jörn M. Horschig
- Artinis Medical Systems, B.V., Einsteinweg 17, 6662 PW Elst, The Netherlands
| | | | - Jurgen Claassen
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Houtlaan 4, 6525 XZ Nijmegen, The Netherlands
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18
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McKinstry-Wu AR, Wasilczuk AZ, Dailey WP, Eckenhoff RG, Kelz MB. In Vivo Photoadduction of Anesthetic Ligands in Mouse Brain Markedly Extends Sedation and Hypnosis. J Neurosci 2023; 43:2338-2348. [PMID: 36849414 PMCID: PMC10072292 DOI: 10.1523/jneurosci.1884-22.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 02/16/2023] [Accepted: 02/17/2023] [Indexed: 03/01/2023] Open
Abstract
Photoaffinity ligands are best known as tools used to identify the specific binding sites of drugs to their molecular targets. However, photoaffinity ligands have the potential to further define critical neuroanatomic targets of drug action. In the brains of WT male mice, we demonstrate the feasibility of using photoaffinity ligands in vivo to prolong anesthesia via targeted yet spatially restricted photoadduction of azi-m-propofol (aziPm), a photoreactive analog of the general anesthetic propofol. Systemic administration of aziPm with bilateral near-ultraviolet photoadduction in the rostral pons, at the border of the parabrachial nucleus and locus coeruleus, produced a 20-fold increase in the duration of sedative and hypnotic effects compared with control mice without UV illumination. Photoadduction that missed the parabrachial-coerulean complex also failed to extend the sedative or hypnotic actions of aziPm and was indistinguishable from nonadducted controls. Paralleling the prolonged behavioral and EEG consequences of on target in vivo photoadduction, we conducted electrophysiologic recordings in rostral pontine brain slices. Using neurons within the locus coeruleus to further highlight the cellular consequences of irreversible aziPm binding, we demonstrate transient slowing of spontaneous action potentials with a brief bath application of aziPm that becomes irreversible on photoadduction. Together, these findings suggest that photochemistry-based strategies are a viable new approach for probing CNS physiology and pathophysiology.SIGNIFICANCE STATEMENT Photoaffinity ligands are drugs capable of light-induced irreversible binding, which have unexploited potential to identify the neuroanatomic sites of drug action. We systemically administer a centrally acting anesthetic photoaffinity ligand in mice, conduct localized photoillumination within the brain to covalently adduct the drug at its in vivo sites of action, and successfully enrich irreversible drug binding within a restricted 250 µm radius. When photoadduction encompassed the pontine parabrachial-coerulean complex, anesthetic sedation and hypnosis was prolonged 20-fold, thus illustrating the power of in vivo photochemistry to help unravel neuronal mechanisms of drug action.
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Affiliation(s)
- Andrew R McKinstry-Wu
- Department of Anesthesiology and Critical Care, University of Pennsylvania Perelman School of Medicine, Philadelphia, Philadelphia 19104
- Neuroscience of Unconsciousness and Reanimation Research Alliance, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Philadelphia 19104
| | - Andrzej Z Wasilczuk
- Department of Anesthesiology and Critical Care, University of Pennsylvania Perelman School of Medicine, Philadelphia, Philadelphia 19104
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Philadelphia 19104
- Neuroscience of Unconsciousness and Reanimation Research Alliance, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Philadelphia 19104
| | - William P Dailey
- Department of Chemistry, University of Pennsylvania School of Arts and Sciences, Philadelphia, Pennsylvania 19104
| | - Roderic G Eckenhoff
- Department of Anesthesiology and Critical Care, University of Pennsylvania Perelman School of Medicine, Philadelphia, Philadelphia 19104
| | - Max B Kelz
- Department of Anesthesiology and Critical Care, University of Pennsylvania Perelman School of Medicine, Philadelphia, Philadelphia 19104
- Mahoney Institute for Neurosciences, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Philadelphia 19104
- Neuroscience of Unconsciousness and Reanimation Research Alliance, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Philadelphia 19104
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19
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Dong X, Wang Z, Cao L, Chen Z, Liang Y. Whale Optimization Algorithm with a Hybrid Relation Vector Machine: A Highly Robust Respiratory Rate Prediction Model Using Photoplethysmography Signals. Diagnostics (Basel) 2023; 13:diagnostics13050913. [PMID: 36900057 PMCID: PMC10000566 DOI: 10.3390/diagnostics13050913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Revised: 02/18/2023] [Accepted: 02/25/2023] [Indexed: 03/06/2023] Open
Abstract
Due to the simplicity and convenience of PPG signal acquisition, the detection of the respiration rate based on the PPG signal is more suitable for dynamic monitoring than the impedance spirometry method, but it is challenging to achieve accurate predictions from low-signal-quality PPG signals, especially in intensive-care patients with weak PPG signals. The goal of this study was to construct a simple model for respiration rate estimation based on PPG signals using a machine-learning approach fusing signal quality metrics to improve the accuracy of estimation despite the low-signal-quality PPG signals. In this study, we propose a method based on the whale optimization algorithm (WOA) with a hybrid relation vector machine (HRVM) to construct a highly robust model considering signal quality factors to estimate RR from PPG signals in real time. To detect the performance of the proposed model, we simultaneously recorded PPG signals and impedance respiratory rates obtained from the BIDMC dataset. The results of the respiration rate prediction model proposed in this study showed that the MAE and RMSE were 0.71 and 0.99 breaths/min, respectively, in the training set, and 1.24 and 1.79 breaths/min, respectively, in the test set. Compared without taking signal quality factors into account, MAE and RMSE are reduced by 1.28 and 1.67 breaths/min, respectively, in the training set, and reduced by 0.62 and 0.65 breaths/min in the test set. Even in the nonnormal breathing range below 12 bpm and above 24 bpm, the MAE reached 2.68 and 4.28 breaths/min, respectively, and the RMSE reached 3.52 and 5.01 breaths/min, respectively. The results show that the model that considers the PPG signal quality and respiratory quality proposed in this study has obvious advantages and application potential in predicting the respiration rate to cope with the problem of low signal quality.
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Affiliation(s)
- Xuhao Dong
- School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, China
| | - Ziyi Wang
- School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, China
| | - Liangli Cao
- School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, China
| | - Zhencheng Chen
- School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, China
- Guangxi Colleges and Universities Key Laboratory of Biomedical Sensors and Intelligent Instruments, Guilin 541004, China
- Guangxi Engineering Technology Research Center of Human Physiological Information Noninvasive Detection, Guilin 541004, China
- Correspondence: (Z.C.); (Y.L.)
| | - Yongbo Liang
- School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, China
- Guangxi Colleges and Universities Key Laboratory of Biomedical Sensors and Intelligent Instruments, Guilin 541004, China
- Guangxi Engineering Technology Research Center of Human Physiological Information Noninvasive Detection, Guilin 541004, China
- Correspondence: (Z.C.); (Y.L.)
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20
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Rathore KS, Vijayarangan S, Sp P, Sivaprakasam M. A Multifunctional Network with Uncertainty Estimation and Attention-Based Knowledge Distillation to Address Practical Challenges in Respiration Rate Estimation. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23031599. [PMID: 36772640 PMCID: PMC9920118 DOI: 10.3390/s23031599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 01/20/2023] [Accepted: 01/23/2023] [Indexed: 05/03/2023]
Abstract
Respiration rate is a vital parameter to indicate good health, wellbeing, and performance. As the estimation through classical measurement modes are limited only to rest or during slow movements, respiration rate is commonly estimated through physiological signals such as electrocardiogram and photoplethysmography due to the unobtrusive nature of wearable devices. Deep learning methodologies have gained much traction in the recent past to enhance accuracy during activities involving a lot of movement. However, these methods pose challenges, including model interpretability, uncertainty estimation in the context of respiration rate estimation, and model compactness in terms of deployment in wearable platforms. In this direction, we propose a multifunctional framework, which includes the combination of an attention mechanism, an uncertainty estimation functionality, and a knowledge distillation framework. We evaluated the performance of our framework on two datasets containing ambulatory movement. The attention mechanism visually and quantitatively improved instantaneous respiration rate estimation. Using Monte Carlo dropouts to embed the network with inferential uncertainty estimation resulted in the rejection of 3.7% of windows with high uncertainty, which consequently resulted in an overall reduction of 7.99% in the mean absolute error. The attention-aware knowledge distillation mechanism reduced the model's parameter count and inference time by 49.5% and 38.09%, respectively, without any increase in error rates. Through experimentation, ablation, and visualization, we demonstrated the efficacy of the proposed framework in addressing practical challenges, thus taking a step towards deployment in wearable edge devices.
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Affiliation(s)
- Kapil Singh Rathore
- Indian Institute of Technology Madras, Chennai 6000001, India
- Healthcare Technology Innovation Center, Chennai 6000001, India
| | - Sricharan Vijayarangan
- Indian Institute of Technology Madras, Chennai 6000001, India
- Healthcare Technology Innovation Center, Chennai 6000001, India
| | - Preejith Sp
- Healthcare Technology Innovation Center, Chennai 6000001, India
| | - Mohanasankar Sivaprakasam
- Indian Institute of Technology Madras, Chennai 6000001, India
- Healthcare Technology Innovation Center, Chennai 6000001, India
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21
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Shuzan MNI, Chowdhury MH, Chowdhury MEH, Murugappan M, Hoque Bhuiyan E, Arslane Ayari M, Khandakar A. Machine Learning-Based Respiration Rate and Blood Oxygen Saturation Estimation Using Photoplethysmogram Signals. Bioengineering (Basel) 2023; 10:bioengineering10020167. [PMID: 36829661 PMCID: PMC9952751 DOI: 10.3390/bioengineering10020167] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 01/22/2023] [Accepted: 01/23/2023] [Indexed: 02/03/2023] Open
Abstract
The continuous monitoring of respiratory rate (RR) and oxygen saturation (SpO2) is crucial for patients with cardiac, pulmonary, and surgical conditions. RR and SpO2 are used to assess the effectiveness of lung medications and ventilator support. In recent studies, the use of a photoplethysmogram (PPG) has been recommended for evaluating RR and SpO2. This research presents a novel method of estimating RR and SpO2 using machine learning models that incorporate PPG signal features. A number of established methods are used to extract meaningful features from PPG. A feature selection approach was used to reduce the computational complexity and the possibility of overfitting. There were 19 models trained for both RR and SpO2 separately, from which the most appropriate regression model was selected. The Gaussian process regression model outperformed all the other models for both RR and SpO2 estimation. The mean absolute error (MAE) for RR was 0.89, while the root-mean-squared error (RMSE) was 1.41. For SpO2, the model had an RMSE of 0.98 and an MAE of 0.57. The proposed system is a state-of-the-art approach for estimating RR and SpO2 reliably from PPG. If RR and SpO2 can be consistently and effectively derived from the PPG signal, patients can monitor their RR and SpO2 at a cheaper cost and with less hassle.
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Affiliation(s)
- Md Nazmul Islam Shuzan
- Department of Electrical, Electronic and System Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
| | - Moajjem Hossain Chowdhury
- Department of Electrical, Electronic and System Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
| | - Muhammad E. H. Chowdhury
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
- Correspondence: (M.E.H.C.); or (M.M.)
| | - Murugappan Murugappan
- Intelligent Signal Processing (ISP) Research Lab, Department of Electronics and Communication Engineering, Kuwait College of Science and Technology, Block 4, Doha 13133, Kuwait
- Department of Electronics and Communication Engineering, School of Engineering, Vels Institute of Sciences, Technology and Advanced Studies, Chennai 600117, Tamil Nadu, India
- Center for Excellence for Unmanned Aerial Systems, Universiti Malaysia Perlis, Perlis 02600, Malaysia
- Correspondence: (M.E.H.C.); or (M.M.)
| | - Enamul Hoque Bhuiyan
- BioMedical Engineering and Imaging Institute (BMEII), Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Mohamed Arslane Ayari
- Department of Civil and Architectural Engineering, Qatar University, Doha 2713, Qatar
| | - Amith Khandakar
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
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22
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Features from the photoplethysmogram and the electrocardiogram for estimating changes in blood pressure. Sci Rep 2023; 13:986. [PMID: 36653426 PMCID: PMC9849280 DOI: 10.1038/s41598-022-27170-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 12/27/2022] [Indexed: 01/19/2023] Open
Abstract
There is a growing emphasis being placed on the potential for cuffless blood pressure (BP) estimation through modelling of morphological features from the photoplethysmogram (PPG) and electrocardiogram (ECG). However, the appropriate features and models to use remain unclear. We investigated the best features available from the PPG and ECG for BP estimation using both linear and non-linear machine learning models. We conducted a clinical study in which changes in BP ([Formula: see text]BP) were induced by an infusion of phenylephrine in 30 healthy volunteers (53.8% female, 28.0 (9.0) years old). We extracted a large and diverse set of features from both the PPG and the ECG and assessed their individual importance for estimating [Formula: see text]BP through Shapley additive explanation values and a ranking coefficient. We trained, tuned, and evaluated linear (ordinary least squares, OLS) and non-linear (random forest, RF) machine learning models to estimate [Formula: see text]BP in a nested leave-one-subject-out cross-validation framework. We reported the results as correlation coefficient ([Formula: see text]), root mean squared error (RMSE), and mean absolute error (MAE). The non-linear RF model significantly ([Formula: see text]) outperformed the linear OLS model using both the PPG and the ECG signals across all performance metrics. Estimating [Formula: see text]SBP using the PPG alone ([Formula: see text] = 0.86 (0.23), RMSE = 5.66 (4.76) mmHg, MAE = 4.86 (4.29) mmHg) performed significantly better than using the ECG alone ([Formula: see text] = 0.69 (0.45), RMSE = 6.79 (4.76) mmHg, MAE = 5.28 (4.57) mmHg), all [Formula: see text]. The highest ranking features from the PPG largely modelled increasing reflected wave interference driven by changes in arterial stiffness. This finding was supported by changes observed in the PPG waveform in response to the phenylephrine infusion. However, a large number of features were required for accurate BP estimation, highlighting the high complexity of the problem. We conclude that the PPG alone may be further explored as a potential single source, cuffless, blood pressure estimator. The use of the ECG alone is not justified. Non-linear models may perform better as they are able to incorporate interactions between feature values and demographics. However, demographics may not adequately account for the unique and individualised relationship between the extracted features and BP.
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23
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Eisenkraft A, Goldstein N, Ben Ishay A, Fons M, Tabi M, Sherman AD, Merin R, Nachman D. Clinical validation of a wearable respiratory rate device: A brief report. Chron Respir Dis 2023; 20:14799731231198865. [PMID: 37612250 PMCID: PMC10461800 DOI: 10.1177/14799731231198865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/25/2023] Open
Abstract
BACKGROUND Respiratory rate (RR) is used for the diagnosis and management of medical conditions and can predict clinical changes. Heavy workload, understaffing, and errors related to poor recording make it underutilized. Wearable devices may facilitate its use. METHODS RR measurements using a wearable photoplethysmography-based monitor were compared with medical grade devices in complementary clinical scenarios: Study one included a comparison to a capnograph in 35 healthy volunteers; Study two included a comparison to a ventilator monitor in 18 ventilated patients; and Study three included a comparison to capnograph in 92 COVID-19 patients with active pulmonary disease. Pearson's correlations and Bland-Altman analysis were used to assess the accuracy and agreement between the measurement techniques, including stratification for Body Mass Index (BMI) and skin tone. Statistical significance was set at p ≤ 0.05. RESULTS High correlation was found in all studies (r = 0.991, 0.884, and 0.888, respectively, p < 0.001 for all). 95% LOA of ±2.3, 1.7-(-1.6), and ±3.9 with a bias of < 0.1 breaths per minute was found in Bland-Altman analysis in studies 1,2, and 3, respectively. In all, high accordance was found in all sub-groups. CONCLUSIONS RR measurements using the wearable monitor were highly-correlated with medical-grade devices in various clinical settings. TRIAL REGISTRATION ClinicalTrials.gov, https://clinicaltrials.gov/ct2/show/NCT03603860.
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Affiliation(s)
- Arik Eisenkraft
- Biobeat Technologies Ltd, Petah Tikva, Israel
- Institute for Research in Military Medicine, Faculty of Medicine, The Hebrew University of Jerusalem and the Israel Defense Force Medical Corps, Jerusalem, Israel
| | | | | | - Meir Fons
- Biobeat Technologies Ltd, Petah Tikva, Israel
| | | | | | - Roei Merin
- Biobeat Technologies Ltd, Petah Tikva, Israel
- Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Dean Nachman
- Institute for Research in Military Medicine, Faculty of Medicine, The Hebrew University of Jerusalem and the Israel Defense Force Medical Corps, Jerusalem, Israel
- Heart Institute, Hadassah Medical Center, The Hebrew University of Jerusalem, Jerusalem, Israel
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24
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Hakimi N, Shahbakhti M, Sappia S, Horschig JM, Bronkhorst M, Floor-Westerdijk M, Valenza G, Dudink J, Colier WNJM. Estimation of Respiratory Rate from Functional Near-Infrared Spectroscopy (fNIRS): A New Perspective on Respiratory Interference. BIOSENSORS 2022; 12:bios12121170. [PMID: 36551137 PMCID: PMC9775029 DOI: 10.3390/bios12121170] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 12/05/2022] [Accepted: 12/08/2022] [Indexed: 05/27/2023]
Abstract
OBJECTIVE Respiration is recognized as a systematic physiological interference in functional near-infrared spectroscopy (fNIRS). However, it remains unanswered as to whether it is possible to estimate the respiratory rate (RR) from such interference. Undoubtedly, RR estimation from fNIRS can provide complementary information that can be used alongside the cerebral activity analysis, e.g., sport studies. Thus, the objective of this paper is to propose a method for RR estimation from fNIRS. Our primary presumption is that changes in the baseline wander of oxygenated hemoglobin concentration (O2Hb) signal are related to RR. METHODS fNIRS and respiratory signals were concurrently collected from subjects during controlled breathing tasks at a constant rate from 0.1 Hz to 0.4 Hz. Firstly, the signal quality index algorithm is employed to select the best O2Hb signal, and then a band-pass filter with cut-off frequencies from 0.05 to 2 Hz is used to remove very low- and high-frequency artifacts. Secondly, troughs of the filtered O2Hb signal are localized for synthesizing the baseline wander (S1) using cubic spline interpolation. Finally, the fast Fourier transform of the S1 signal is computed, and its dominant frequency is considered as RR. In this paper, two different datasets were employed, where the first one was used for the parameter adjustment of the proposed method, and the second one was solely used for testing. RESULTS The low mean absolute error between the reference and estimated RRs for the first and second datasets (2.6 and 1.3 breaths per minute, respectively) indicates the feasibility of the proposed method for RR estimation from fNIRS. SIGNIFICANCE This paper provides a novel view on the respiration interference as a source of complementary information in fNIRS.
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Affiliation(s)
- Naser Hakimi
- Artinis Medical Systems, B.V., Einsteinweg 17, 6662 PW Elst, The Netherlands
- Department of Neonatology, Wilhelmina Children’s Hospital, University Medical Center Utrecht, Lundlaan 6, 3584 EA Utrecht, The Netherlands
| | - Mohammad Shahbakhti
- Artinis Medical Systems, B.V., Einsteinweg 17, 6662 PW Elst, The Netherlands
- Biomedical Engineering Institute, Kaunas University of Technology, K. Barsausko 59, LT-51423 Kaunas, Lithuania
| | - Sofia Sappia
- Artinis Medical Systems, B.V., Einsteinweg 17, 6662 PW Elst, The Netherlands
| | - Jörn M. Horschig
- Artinis Medical Systems, B.V., Einsteinweg 17, 6662 PW Elst, The Netherlands
| | - Mathijs Bronkhorst
- Artinis Medical Systems, B.V., Einsteinweg 17, 6662 PW Elst, The Netherlands
| | | | - Gaetano Valenza
- Bioengineering and Robotics Research Center E. Piaggio and the Department of Information Engineering, School of Engineering, University of Pisa, Via G. Caruso 16, 56122 Pisa, Italy
| | - Jeroen Dudink
- Department of Neonatology, Wilhelmina Children’s Hospital, University Medical Center Utrecht, Lundlaan 6, 3584 EA Utrecht, The Netherlands
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25
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Budig M, Stoohs R, Keiner M. Validity of Two Consumer Multisport Activity Tracker and One Accelerometer against Polysomnography for Measuring Sleep Parameters and Vital Data in a Laboratory Setting in Sleep Patients. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22239540. [PMID: 36502241 PMCID: PMC9741062 DOI: 10.3390/s22239540] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 11/25/2022] [Accepted: 12/01/2022] [Indexed: 05/16/2023]
Abstract
Two commercial multisport activity trackers (Garmin Forerunner 945 and Polar Ignite) and the accelerometer ActiGraph GT9X were evaluated in measuring vital data, sleep stages and sleep/wake patterns against polysomnography (PSG). Forty-nine adult patients with suspected sleep disorders (30 males/19 females) completed a one-night PSG sleep examination followed by a multiple sleep latency test (MSLT). Sleep parameters, time in bed (TIB), total sleep time (TST), wake after sleep onset (WASO), sleep onset latency (SOL), awake time (WASO + SOL), sleep stages (light, deep, REM sleep) and the number of sleep cycles were compared. Both commercial trackers showed high accuracy in measuring vital data (HR, HRV, SpO2, respiratory rate), r > 0.92. For TIB and TST, all three trackers showed medium to high correlation, r > 0.42. Garmin had significant overestimation of TST, with MAE of 84.63 min and MAPE of 25.32%. Polar also had an overestimation of TST, with MAE of 45.08 min and MAPE of 13.80%. ActiGraph GT9X results were inconspicuous. The trackers significantly underestimated awake times (WASO + SOL) with weak correlation, r = 0.11−0.57. The highest MAE was 50.35 min and the highest MAPE was 83.02% for WASO for Garmin and ActiGraph GT9X; Polar had the highest MAE of 21.17 min and the highest MAPE of 141.61% for SOL. Garmin showed significant deviations for sleep stages (p < 0.045), while Polar only showed significant deviations for sleep cycle (p = 0.000), r < 0.50. Garmin and Polar overestimated light sleep and underestimated deep sleep, Garmin significantly, with MAE up to 64.94 min and MAPE up to 116.50%. Both commercial trackers Garmin and Polar did not detect any daytime sleep at all during the MSLT test. The use of the multisport activity trackers for sleep analysis can only be recommended for general daily use and for research purposes. If precise data on sleep stages and parameters are required, their use is limited. The accuracy of the vital data measurement was adequate. Further studies are needed to evaluate their use for medical purposes, inside and outside of the sleep laboratory. The accelerometer ActiGraph GT9X showed overall suitable accuracy in detecting sleep/wake patterns.
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Affiliation(s)
- Mario Budig
- Department of Sports Science, German University of Health & Sport, 85737 Ismaning, Germany
| | | | - Michael Keiner
- Department of Sports Science, German University of Health & Sport, 85737 Ismaning, Germany
- Correspondence:
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26
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Stubbe L, Houel N, Cottin F. Accuracy and reliability of the optoelectronic plethysmography and the heart rate systems for measuring breathing rates compared with the spirometer. Sci Rep 2022; 12:19255. [PMID: 36357452 PMCID: PMC9648890 DOI: 10.1038/s41598-022-23915-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Accepted: 11/07/2022] [Indexed: 11/12/2022] Open
Abstract
Measuring breathing rates without a mouthpiece is of interest in clinical settings. Electrocardiogram devices and, more recently, optoelectronic plethysmography (OEP) methods can estimate breathing rates with only a few electrodes or motion-capture markers placed on the patient. This study estimated and compared the accuracy and reliability of three non-invasive devices: an OEP system with 12 markers, an electrocardiogram device and the conventional spirometer. Using the three devices simultaneously, we recorded 72 six-minute epochs on supine subjects. Our results show that the OEP system has a very low limit of agreement and a bias lower than 0.4% compared with the spirometer, indicating that these devices can be used interchangeably. We observed comparable results for electrocardiogram devices. The OEP system facilitates breathing rate measurements and offers a more complete chest-lung volume analysis that can be easily associated with heart rate analysis without any synchronisation process, for useful features for clinical applications and intensive care.
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Affiliation(s)
- Laurent Stubbe
- grid.460789.40000 0004 4910 6535Université Paris-Saclay, CIAMS EA 4532, 91405 Orsay, France ,grid.112485.b0000 0001 0217 6921Université d’Orléans, CIAMS EA 4532, 45067 Orléans, France ,ESO-Paris Recherche, Ecole Supérieure d’Ostéopathie – Paris, 77420 Champs Sur Marne, France
| | - Nicolas Houel
- grid.11667.370000 0004 1937 0618Université de Reims Champagne-Ardenne, PSMS, Reims, France
| | - François Cottin
- grid.460789.40000 0004 4910 6535Université Paris-Saclay, CIAMS EA 4532, 91405 Orsay, France ,grid.112485.b0000 0001 0217 6921Université d’Orléans, CIAMS EA 4532, 45067 Orléans, France
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El Gharbi M, Fernández-García R, Gil I. Wireless Communication Platform Based on an Embroidered Antenna-Sensor for Real-Time Breathing Detection. SENSORS (BASEL, SWITZERLAND) 2022; 22:8667. [PMID: 36433264 PMCID: PMC9699000 DOI: 10.3390/s22228667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 10/27/2022] [Accepted: 11/07/2022] [Indexed: 06/16/2023]
Abstract
Wearable technology has been getting more attention for monitoring vital signs in various medical fields, particularly in breathing monitoring. To monitor respiratory patterns, there is a current set of challenges related to the lack of user comfort, reliability, and rigidity of the systems, as well as challenges related to processing data. Therefore, the need to develop user-friendly and reliable wireless approaches to address these problems is required. In this paper, a novel, full, compact textile breathing sensor is investigated. Specifically, an embroidered meander dipole antenna sensor integrated into an e-textile T-shirt with a Bluetooth transmitter for real-time breathing monitoring was developed and tested. The proposed antenna-based sensor is designed to transmit data over wireless communication networks at 2.4 GHz and is made of a silver-coated nylon thread. The sensing mechanism of the proposed system is based on the detection of a received signal strength indicator (RSSI) transmitted wirelessly by the antenna-based sensor, which is found to be sensitive to stretch. The respiratory system is placed on the middle of the human chest; the area of the proposed system is 4.5 × 0.48 cm2, with 2.36 × 3.17 cm2 covered by the transmitter module. The respiratory signal is extracted from the variation of the RSSI signal emitted at 2.4 GHz from the detuned embroidered antenna-based sensor embedded into a commercial T-shirt and detected using a laptop. The experimental results demonstrated that breathing signals can be acquired wirelessly by the RSSI via Bluetooth. The RSSI range change was from -80 dBm to -72 dBm, -88 dBm to -79 dBm and -85 dBm to -80 dBm during inspiration and expiration for normal breathing, speaking and movement, respectively. We tested the feasibility assessment for breathing monitoring and we demonstrated experimentally that the standard wireless networks, which measure the RSSI signal via standard Bluetooth protocol, can be used to detect human respiratory status and patterns in real time.
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Lee S, Moon H, Al-antari MA, Lee G. Dual-Sensor Signals Based Exact Gaussian Process-Assisted Hybrid Feature Extraction and Weighted Feature Fusion for Respiratory Rate and Uncertainty Estimations. SENSORS (BASEL, SWITZERLAND) 2022; 22:8386. [PMID: 36366083 PMCID: PMC9654728 DOI: 10.3390/s22218386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 10/22/2022] [Accepted: 10/23/2022] [Indexed: 06/16/2023]
Abstract
Accurately estimating respiratory rate (RR) has become essential for patients and the elderly. Hence, we propose a novel method that uses exact Gaussian process regression (EGPR)-assisted hybrid feature extraction and feature fusion based on photoplethysmography and electrocardiogram signals to improve the reliability of accurate RR and uncertainty estimations. First, we obtain the power spectral features and use the multi-phase feature model to compensate for insufficient input data. Then, we combine four different feature sets and choose features with high weights using a robust neighbor component analysis. The proposed EGPR algorithm provides a confidence interval representing the uncertainty. Therefore, the proposed EGPR algorithm, including hybrid feature extraction and weighted feature fusion, is an excellent model with improved reliability for accurate RR estimation. Furthermore, the proposed EGPR methodology is likely the only one currently available that provides highly stable variation and confidence intervals. The proposed EGPR-MF, 0.993 breath per minute (bpm), and EGPR-feature fusion, 1.064 (bpm), show the lowest mean absolute error compared to the other models.
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Affiliation(s)
- Soojeong Lee
- Department of Computer Engineering, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, Korea
| | - Hyeonjoon Moon
- Department of Computer Engineering, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, Korea
| | - Mugahed A. Al-antari
- Department of Artificial intelligence, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, Korea
| | - Gangseong Lee
- Ingenium College, Kwangwoon University, 20 Kwangwoon-ro, Nowon-gu, Seoul 01897, Korea
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29
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Chowdhury MH, Shuzan MNI, Chowdhury MEH, Reaz MBI, Mahmud S, Al Emadi N, Ayari MA, Ali SHM, Bakar AAA, Rahman SM, Khandakar A. Lightweight End-to-End Deep Learning Solution for Estimating the Respiration Rate from Photoplethysmogram Signal. BIOENGINEERING (BASEL, SWITZERLAND) 2022; 9:bioengineering9100558. [PMID: 36290527 PMCID: PMC9598342 DOI: 10.3390/bioengineering9100558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Revised: 09/28/2022] [Accepted: 09/29/2022] [Indexed: 11/05/2022]
Abstract
Respiratory ailments are a very serious health issue and can be life-threatening, especially for patients with COVID. Respiration rate (RR) is a very important vital health indicator for patients. Any abnormality in this metric indicates a deterioration in health. Hence, continuous monitoring of RR can act as an early indicator. Despite that, RR monitoring equipment is generally provided only to intensive care unit (ICU) patients. Recent studies have established the feasibility of using photoplethysmogram (PPG) signals to estimate RR. This paper proposes a deep-learning-based end-to-end solution for estimating RR directly from the PPG signal. The system was evaluated on two popular public datasets: VORTAL and BIDMC. A lightweight model, ConvMixer, outperformed all of the other deep neural networks. The model provided a root mean squared error (RMSE), mean absolute error (MAE), and correlation coefficient (R) of 1.75 breaths per minute (bpm), 1.27 bpm, and 0.92, respectively, for VORTAL, while these metrics were 1.20 bpm, 0.77 bpm, and 0.92, respectively, for BIDMC. The authors also showed how fine-tuning a small subset could increase the performance of the model in the case of an out-of-distribution dataset. In the fine-tuning experiments, the models produced an average R of 0.81. Hence, this lightweight model can be deployed to mobile devices for real-time monitoring of patients.
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Affiliation(s)
- Moajjem Hossain Chowdhury
- Department of Electrical, Electronic and System Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
| | - Md Nazmul Islam Shuzan
- Department of Electrical, Electronic and System Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
| | - Muhammad E. H. Chowdhury
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
- Correspondence: (M.E.H.C.); (M.B.I.R.); (M.A.A.)
| | - Mamun Bin Ibne Reaz
- Department of Electrical, Electronic and System Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
- Correspondence: (M.E.H.C.); (M.B.I.R.); (M.A.A.)
| | - Sakib Mahmud
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
| | - Nasser Al Emadi
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
| | - Mohamed Arselene Ayari
- Department of Civil and Architectural Engineering, Qatar University, Doha 2713, Qatar
- Technology Innovation and Engineering Education Unit (TIEE), Qatar University, Doha 2713, Qatar
- Correspondence: (M.E.H.C.); (M.B.I.R.); (M.A.A.)
| | - Sawal Hamid Md Ali
- Department of Electrical, Electronic and System Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
| | - Ahmad Ashrif A. Bakar
- Department of Electrical, Electronic and System Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
| | - Syed Mahfuzur Rahman
- Department of Biomedical Engineering, Military Institute of Science and Technology, Mirpur Cantonment, Dhaka 1216, Bangladesh
| | - Amith Khandakar
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
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IoT-Based Healthcare-Monitoring System towards Improving Quality of Life: A Review. Healthcare (Basel) 2022; 10:healthcare10101993. [PMID: 36292441 PMCID: PMC9601552 DOI: 10.3390/healthcare10101993] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 09/30/2022] [Accepted: 10/06/2022] [Indexed: 11/04/2022] Open
Abstract
The Internet of Things (IoT) is essential in innovative applications such as smart cities, smart homes, education, healthcare, transportation, and defense operations. IoT applications are particularly beneficial for providing healthcare because they enable secure and real-time remote patient monitoring to improve the quality of people’s lives. This review paper explores the latest trends in healthcare-monitoring systems by implementing the role of the IoT. The work discusses the benefits of IoT-based healthcare systems with regard to their significance, and the benefits of IoT healthcare. We provide a systematic review on recent studies of IoT-based healthcare-monitoring systems through literature review. The literature review compares various systems’ effectiveness, efficiency, data protection, privacy, security, and monitoring. The paper also explores wireless- and wearable-sensor-based IoT monitoring systems and provides a classification of healthcare-monitoring sensors. We also elaborate, in detail, on the challenges and open issues regarding healthcare security and privacy, and QoS. Finally, suggestions and recommendations for IoT healthcare applications are laid down at the end of the study along with future directions related to various recent technology trends.
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De Lazzari N, Wichum F, Götte M, David C, Seid K, Tewes M. Entwicklung einer KI-gestützten Bewegungstherapie bei
onkologischen Palliativpatienten. B&G BEWEGUNGSTHERAPIE UND GESUNDHEITSSPORT 2022. [DOI: 10.1055/a-1909-5766] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Zusammenfassung
HintergrundDie wechselnde Symptomlast ist eine große Hürde
in der Sporttherapie von onkologischen Palliativpatienten. Die täglich
variierende Symptomstärke erschwert die Einstellung einer optimalen
Trainingsbelastung und stellt neben der Motivation eine große Barriere
für die Teilnahme an bewegungstherapeutischen Interventionen dar. Ein
durch Künstliche Intelligenz (KI) gesteuertes Training könnte
helfen, die Trainingseinheiten individuell anzupassen und die Autonomie von
Palliativpatienten zu erhalten.
Methoden Fünf Patienten mit fortgeschrittener unheilbarer
Krebsdiagnose haben im Rahmen der Routineversorgung eine supervidierte
Bewegungstherapie absolviert. Dabei wurde ein Elektrokardiogramm über
einen Polar H10 Brustgurt aufgezeichnet und daraus kardiale und respiratorische
Vitalparameter extrahiert. Eine Klassifikation in drei Intensitätsstufen
über KI erfolgte anhand von neuronalen Netzen.
Ergebnisse Das KI-gesteuerte Training hat eine sehr hohe
Klassifikationsgüte (F1-Score: 0,95±0,05) durch die Vereinigung
von respiratorischen und kardialen Vitalparametern. Diese Kombination erzielt
genauere Klassifikationsergebnisse als die einzelnen Datensätze
für kardiale Parameter (0,93±0,06) und respiratorische Parameter
(0,72±0,06). Die Berücksichtigung einer Baselinemessung hat eine
positive Wirkung auf die Klassifikationsgenauigkeit.
Diskussion Diese Studie stellt die erste Untersuchung zum Einsatz von KI
zur Klassifizierung von trainingswissenschaftlichen Inhalten bei onkologischen
Palliativpatienten dar. Diese vulnerable Patientengruppe kann von einer
objektiven Erfassung des Belastungsniveaus anhand von Parametern des
kardiovaskulären Systems profitieren. Mit nur fünf Patienten
wird die Aussagekraft dieser explorativen Studie über Kreuzvalidierung
hergestellt. Zukünftig sollen weitere Parameter wie ein subjektives
Empfinden, Alter, Größe und Geschlecht die Klassifikation weiter
verbessern. In einem integrierten System ist eine individuelle
Trainingssteuerung in Echtzeit möglich.
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Affiliation(s)
- Nico De Lazzari
- Westdeutsches Tumorzentrum – Comprehensive Cancer Center,
Innere Klinik (Tumorforschung), Universitätsklinikum Essen, 45122 Essen,
Deutschland
| | - Felix Wichum
- Fraunhofer IMS, Universität Duisburg-Essen, 47057 Duisburg,
Deutschland
| | - Miriam Götte
- Westdeutsches Tumorzentrum – Comprehensive Cancer Center,
Klinik für Kinderheilkunde 3, Universitätsklinikum Essen, 45122
Essen, Deutschland
| | - Corinna David
- Fachhochschule Münster, Fachbereich
Physikingenieurwesen
| | - Karsten Seid
- Fraunhofer-Institut für Mikroelektronische Schaltungen und
Systeme (IMS), 47057 Duisburg und Fachgebiet Elektronische Bauelemente und
Schaltungen (EBS), Universität Duisburg-Essen
| | - Mitra Tewes
- Palliativmedizin der Universitätsmedizin Essen,
Universitätsklinikum Essen, 45122 Essen, Deutschland
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Rogers B, Schaffarczyk M, Gronwald T. Estimation of Respiratory Frequency in Women and Men by Kubios HRV Software Using the Polar H10 or Movesense Medical ECG Sensor during an Exercise Ramp. SENSORS (BASEL, SWITZERLAND) 2022; 22:7156. [PMID: 36236256 PMCID: PMC9573071 DOI: 10.3390/s22197156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/13/2022] [Revised: 09/06/2022] [Accepted: 09/19/2022] [Indexed: 06/16/2023]
Abstract
Monitoring of the physiologic metric, respiratory frequency (RF), has been shown to be of value in health, disease, and exercise science. Both heart rate (HR) and variability (HRV), as represented by variation in RR interval timing, as well as analysis of ECG waveform variability, have shown potential in its measurement. Validation of RF accuracy using newer consumer hardware and software applications have been sparse. The intent of this report is to assess the precision of the RF derived using Kubios HRV Premium software version 3.5 with the Movesense Medical sensor single-channel ECG (MS ECG) and the Polar H10 (H10) HR monitor. Gas exchange data (GE), RR intervals (H10), and continuous ECG (MS ECG) were recorded from 21 participants performing an incremental cycling ramp to failure. Results showed high correlations between the reference GE and both the H10 (r = 0.85, SEE = 4.2) and MS ECG (r = 0.95, SEE = 2.6). Although median values were statistically different via Wilcoxon testing, adjusted median differences were clinically small for the H10 (RF about 1 breaths/min) and trivial for the MS ECG (RF about 0.1 breaths/min). ECG based measurement with the MS ECG showed reduced bias, limits of agreement (maximal bias, -2.0 breaths/min, maximal LoA, 6.1 to -10.0 breaths/min) compared to the H10 (maximal bias, -3.9 breaths/min, maximal LoA, 8.2 to -16.0 breaths/min). In conclusion, RF derived from the combination of the MS ECG sensor with Kubios HRV Premium software, tracked closely to the reference device through an exercise ramp, illustrates the potential for this system to be of practical usage during endurance exercise.
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Affiliation(s)
- Bruce Rogers
- College of Medicine, University of Central Florida, 6850 Lake Nona Boulevard, Orlando, FL 32827-7408, USA
| | - Marcelle Schaffarczyk
- Interdisciplinary Institute of Exercise Science and Sports Medicine, MSH Medical School Hamburg, University of Applied Sciences and Medical University, Am Kaiserkai 1, 20457 Hamburg, Germany
| | - Thomas Gronwald
- Interdisciplinary Institute of Exercise Science and Sports Medicine, MSH Medical School Hamburg, University of Applied Sciences and Medical University, Am Kaiserkai 1, 20457 Hamburg, Germany
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A Smartphone-Based Biosensor for Non-Invasive Monitoring of Total Hemoglobin Concentration in Humans with High Accuracy. BIOSENSORS 2022; 12:bios12100781. [PMID: 36290919 PMCID: PMC9599156 DOI: 10.3390/bios12100781] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 09/16/2022] [Accepted: 09/18/2022] [Indexed: 11/16/2022]
Abstract
In this paper, we propose a smartphone-based biosensor for detecting human total hemoglobin concentration in vivo with high accuracy. Compared to the existing biosensors used to measure hemoglobin concentration, the smartphone-based sensor utilizes the camera, memory, and computing power of the phone. Thus, the cost is largely reduced. Compared to existing smartphone-based sensors, we developed a highly integrated multi-wavelength LED module and a specially designed phone fixture to reduce spatial errors and motion artifacts, respectively. In addition, we embedded a new algorithm into our smartphone-based sensor to improve the measurement accuracy; an L*a*b* color space transformation and the “a” parameter were used to perform the final quantification. We collected 24 blood samples from normal and anemic populations. The adjusted R2 of the prediction results obtained from the multiple linear regression method reached 0.880, and the RMSE reached 9.04, which met the accuracy requirements of non-invasive detection of hemoglobin concentration.
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34
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Polak AG, Klich B, Saganowski S, Prucnal MA, Kazienko P. Processing Photoplethysmograms Recorded by Smartwatches to Improve the Quality of Derived Pulse Rate Variability. SENSORS (BASEL, SWITZERLAND) 2022; 22:7047. [PMID: 36146394 PMCID: PMC9502353 DOI: 10.3390/s22187047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 09/09/2022] [Accepted: 09/14/2022] [Indexed: 06/16/2023]
Abstract
Cardiac monitoring based on wearable photoplethysmography (PPG) is widespread because of its usability and low cost. Unfortunately, PPG is negatively affected by various types of disruptions, which could introduce errors to the algorithm that extracts pulse rate variability (PRV). This study aims to identify the nature of such artifacts caused by various types of factors under the conditions of precisely planned experiments. We also propose methods for their reduction based solely on the PPG signal while preserving the frequency content of PRV. The accuracy of PRV derived from PPG was compared to heart rate variability (HRV) derived from the accompanying ECG. The results indicate that filtering PPG signals using the discrete wavelet transform and its inverse (DWT/IDWT) is suitable for removing slow components and high-frequency noise. Moreover, the main benefit of amplitude demodulation is better preparation of the PPG to determine the duration of pulse cycles and reduce the impact of some other artifacts. Post-processing applied to HRV and PRV indicates that the correction of outliers based on local statistical measures of signals and the autoregressive (AR) model is only important when the PPG is of low quality and has no effect under good signal quality. The main conclusion is that the DWT/IDWT, followed by amplitude demodulation, enables the proper preparation of the PPG signal for the subsequent use of PRV extraction algorithms, particularly at rest. However, post-processing in the proposed form should be applied more in the situations of observed strong artifacts than in motionless laboratory experiments.
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Affiliation(s)
- Adam G. Polak
- Department of Electronic and Photonic Metrology, Wrocław University of Science and Technology, 50-317 Wrocław, Poland
| | - Bartłomiej Klich
- Department of Artificial Intelligence, Wrocław University of Science and Technology, 50-370 Wrocław, Poland
| | - Stanisław Saganowski
- Department of Artificial Intelligence, Wrocław University of Science and Technology, 50-370 Wrocław, Poland
| | - Monika A. Prucnal
- Department of Electronic and Photonic Metrology, Wrocław University of Science and Technology, 50-317 Wrocław, Poland
| | - Przemysław Kazienko
- Department of Artificial Intelligence, Wrocław University of Science and Technology, 50-370 Wrocław, Poland
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35
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Soliman MM, Ganti VG, Inan OT. Towards Wearable Estimation of Tidal Volume via Electrocardiogram and Seismocardiogram Signals. IEEE SENSORS JOURNAL 2022; 22:18093-18103. [PMID: 37091042 PMCID: PMC10120872 DOI: 10.1109/jsen.2022.3196601] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
The current COVID-19 pandemic highlights the critical importance of ubiquitous respiratory health monitoring. The two fundamental elements of monitoring respiration are respiration rate (the frequency of breathing) and tidal volume (TV, the volume of air breathed by the lungs in each breath). Wearable sensing systems have been demonstrated to provide accurate measurement of respiration rate, but TV remains challenging to measure accurately with wearable and unobtrusive technology. In this work, we leveraged electrocardiogram (ECG) and seismocardiogram (SCG) measurements obtained with a custom wearable sensing patch to derive an estimate of TV from healthy human participants. Specifically, we fused both ECG-derived and SCG-derived respiratory signals (EDR and SDR) and trained a machine learning model with gas rebreathing as the ground truth to estimate TV. The respiration cycle modulates ECG and SCG signals in multiple different ways that are synergistic. Thus, here we extract EDRs and SDRs using a multitude of different demodulation techniques. The extracted features are used to train a subject independent machine learning model to accurately estimate TV. By fusing the extracted EDRs and SDRs, we were able to estimate the TV with a root-mean-square error (RMSE) of 181.45 mL and Pearson correlation coefficient (r) of 0.61, with a global subject-independent model. We further show that SDRs are better TV estimators than EDRs. Among SDRs, amplitude modulated (AM) SCG features are the most correlated to TV. We demonstrated that fusing EDRs and SDRs can result in moderately accurate estimation of TV using a subject-independent model. Additionally, we highlight the most informative features for estimating TV. This work presents a significant step towards achieving continuous, calibration free, and unobtrusive TV estimation, which could advance the state of the art in wearable respiratory monitoring.
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Affiliation(s)
- Moamen M Soliman
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332
| | - Venu G Ganti
- Bioengineering Graduate Program, Georgia Institute of Technology, Atlanta, GA 30332
| | - Omer T Inan
- School of Electrical and Computer Engineering and, by courtesy, the Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332
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36
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Cheong SHR, Ng YJX, Lau Y, Lau ST. Wearable technology for early detection of COVID-19: A systematic scoping review. Prev Med 2022; 162:107170. [PMID: 35878707 PMCID: PMC9304072 DOI: 10.1016/j.ypmed.2022.107170] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 06/29/2022] [Accepted: 07/17/2022] [Indexed: 11/23/2022]
Abstract
Wearable technology is an emerging method for the early detection of coronavirus disease 2019 (COVID-19) infection. This scoping review explored the types, mechanisms, and accuracy of wearable technology for the early detection of COVID-19. This review was conducted according to the five-step framework of Arksey and O'Malley. Studies published between December 31, 2019 and December 15, 2021 were obtained from 10 electronic databases, namely, PubMed, Embase, Cochrane, CINAHL, PsycINFO, ProQuest, Scopus, Web of Science, IEEE Xplore, and Taylor & Francis Online. Grey literature, reference lists, and key journals were also searched. All types of articles describing wearable technology for the detection of COVID-19 infection were included. Two reviewers independently screened the articles against the eligibility criteria and extracted the data using a data charting form. A total of 40 articles were included in this review. There are 22 different types of wearable technology used to detect COVID-19 infections early in the existing literature and are categorized as smartwatches or fitness trackers (67%), medical devices (27%), or others (6%). Based on deviations in physiological characteristics, anomaly detection models that can detect COVID-19 infection early were built using artificial intelligence or statistical analysis techniques. Reported area-under-the-curve values ranged from 75% to 94.4%, and sensitivity and specificity values ranged from 36.5% to 100% and 73% to 95.3%, respectively. Further research is necessary to validate the effectiveness and clinical dependability of wearable technology before healthcare policymakers can mandate its use for remote surveillance.
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Affiliation(s)
- Shing Hui Reina Cheong
- Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
| | - Yu Jie Xavia Ng
- Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
| | - Ying Lau
- Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
| | - Siew Tiang Lau
- Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
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37
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Fan J, Yang S, Liu J, Zhu Z, Xiao J, Chang L, Lin S, Zhou J. A High Accuracy & Ultra-Low Power ECG-Derived Respiration Estimation Processor for Wearable Respiration Monitoring Sensor. BIOSENSORS 2022; 12:665. [PMID: 36005061 PMCID: PMC9405792 DOI: 10.3390/bios12080665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 07/29/2022] [Accepted: 08/01/2022] [Indexed: 06/15/2023]
Abstract
The respiratory rate is widely used for evaluating a person's health condition. Compared to other invasive and expensive methods, the ECG-derived respiration estimation is a more comfortable and affordable method to obtain the respiration rate. However, the existing ECG-derived respiration estimation methods suffer from low accuracy or high computational complexity. In this work, a high accuracy and ultra-low power ECG-derived respiration estimation processor has been proposed. Several techniques have been proposed to improve the accuracy and reduce the computational complexity (and thus power consumption), including QRS detection using refractory period refreshing and adaptive threshold EDR estimation. Implemented and fabricated using a 55 nm processing technology, the proposed processor achieves a low EDR estimation error of 0.73 on CEBS database and 1.2 on MIT-BIH Polysomnographic Database while demonstrating a record-low power consumption (354 nW) for the respiration monitoring, outperforming the existing designs. The proposed processor can be integrated in a wearable sensor for ultra-low power and high accuracy respiration monitoring.
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Toften S, Kjellstadli JT, Thu OKF, Ellingsen OJ. Noncontact Longitudinal Respiratory Rate Measurements in Healthy Adults Using Radar-Based Sleep Monitor (Somnofy): Validation Study. JMIR BIOMEDICAL ENGINEERING 2022. [DOI: 10.2196/36618] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Background
Respiratory rate (RR) is arguably the most important vital sign to detect clinical deterioration. Change in RR can also, for example, be associated with the onset of different diseases, opioid overdoses, intense workouts, or mood. However, unlike for most other vital parameters, an easy and accurate measuring method is lacking.
Objective
This study aims to validate the radar-based sleep monitor, Somnofy, for measuring RRs and investigate whether events affecting RR can be detected from personalized baselines calculated from nightly averages.
Methods
First, RRs from Somnofy for 37 healthy adults during full nights of sleep were extensively validated against respiratory inductance plethysmography. Then, the night-to-night consistency of a proposed filtered average RR was analyzed for 6 healthy participants in a pilot study in which they used Somnofy at home for 3 months.
Results
Somnofy measured RR 84% of the time, with mean absolute error of 0.18 (SD 0.05) respirations per minute, and Bland-Altman 95% limits of agreement adjusted for repeated measurements ranged from –0.99 to 0.85. The accuracy and coverage were substantially higher in deep and light sleep than in rapid eye movement sleep and wake. The results were independent of age, sex, and BMI, but dependent on supine sleeping position for some radar orientations. For nightly filtered averages, the 95% limits of agreement ranged from −0.07 to −0.04 respirations per minute. In the longitudinal part of the study, the nightly average was consistent from night to night, and all substantial deviations coincided with self-reported illnesses.
Conclusions
RRs from Somnofy were more accurate than those from any other alternative method suitable for longitudinal measurements. Moreover, the nightly averages were consistent from night to night. Thus, several factors affecting RR should be detectable as anomalies from personalized baselines, enabling a range of applications. More studies are necessary to investigate its potential in children and older adults or in a clinical setting.
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Selvakumar K, Vinodh Kumar E, Sailesh M, Varun M, Allan A, Biswajit N, Namrata P, Upasana S. Realtime PPG based respiration rate estimation for remote health monitoring applications. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103746] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Goldstein Y, Schätz M, Avigal M. Chest area segmentation in 3D images of sleeping patients. Med Biol Eng Comput 2022; 60:2159-2172. [DOI: 10.1007/s11517-022-02577-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2021] [Accepted: 04/12/2022] [Indexed: 10/18/2022]
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Cotur Y, Olenik S, Asfour T, Bruyns-Haylett M, Kasimatis M, Tanriverdi U, Gonzalez-Macia L, Lee HS, Kozlov AS, Güder F. Bioinspired Stretchable Transducer for Wearable Continuous Monitoring of Respiratory Patterns in Humans and Animals. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2203310. [PMID: 35730340 DOI: 10.1002/adma.202203310] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 06/15/2022] [Indexed: 06/15/2023]
Abstract
A bio-inspired continuous wearable respiration sensor modeled after the lateral line system of fish is reported which is used for detecting mechanical disturbances in the water. Despite the clinical importance of monitoring respiratory activity in humans and animals, continuous measurements of breathing patterns and rates are rarely performed in or outside of clinics. This is largely because conventional sensors are too inconvenient or expensive for wearable sensing for most individuals and animals. The bio-inspired air-silicone composite transducer (ASiT) is placed on the chest and measures respiratory activity by continuously measuring the force applied to an air channel embedded inside a silicone-based elastomeric material. The force applied on the surface of the transducer during breathing changes the air pressure inside the channel, which is measured using a commercial pressure sensor and mixed-signal wireless electronics. The transducer produced in this work are extensively characterized and tested with humans, dogs, and laboratory rats. The bio-inspired ASiT may enable the early detection of a range of disorders that result in altered patterns of respiration. The technology reported can also be combined with artificial intelligence and cloud computing to algorithmically detect illness in humans and animals remotely, reducing unnecessary visits to clinics.
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Affiliation(s)
- Yasin Cotur
- Department of Bioengineering, Imperial College London, London, SW7 2AZ, UK
| | - Selin Olenik
- Department of Bioengineering, Imperial College London, London, SW7 2AZ, UK
| | - Tarek Asfour
- Department of Bioengineering, Imperial College London, London, SW7 2AZ, UK
| | | | - Michael Kasimatis
- Department of Bioengineering, Imperial College London, London, SW7 2AZ, UK
| | - Ugur Tanriverdi
- Department of Bioengineering, Imperial College London, London, SW7 2AZ, UK
| | | | - Hong Seok Lee
- Department of Bioengineering, Imperial College London, London, SW7 2AZ, UK
| | - Andrei S Kozlov
- Department of Bioengineering, Imperial College London, London, SW7 2AZ, UK
| | - Firat Güder
- Department of Bioengineering, Imperial College London, London, SW7 2AZ, UK
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Mhajna M, Sadeh B, Yagel S, Sohn C, Schwartz N, Warsof S, Zahar Y, Reches A. A Novel, Cardiac-Derived Algorithm for Uterine Activity Monitoring in a Wearable Remote Device. Front Bioeng Biotechnol 2022; 10:933612. [PMID: 35928952 PMCID: PMC9343786 DOI: 10.3389/fbioe.2022.933612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Accepted: 06/15/2022] [Indexed: 11/13/2022] Open
Abstract
Background: Uterine activity (UA) monitoring is an essential element of pregnancy management. The gold-standard intrauterine pressure catheter (IUPC) is invasive and requires ruptured membranes, while the standard-of-care, external tocodynamometry (TOCO)’s accuracy is hampered by obesity, maternal movements, and belt positioning. There is an urgent need to develop telehealth tools enabling patients to remotely access care. Here, we describe and demonstrate a novel algorithm enabling remote, non-invasive detection and monitoring of UA by analyzing the modulation of the maternal electrocardiographic and phonocardiographic signals. The algorithm was designed and implemented as part of a wireless, FDA-cleared device designed for remote pregnancy monitoring. Two separate prospective, comparative, open-label, multi-center studies were conducted to test this algorithm.Methods: In the intrapartum study, 41 laboring women were simultaneously monitored with IUPC and the remote pregnancy monitoring device. Ten patients were also monitored with TOCO. In the antepartum study, 147 pregnant women were simultaneously monitored with TOCO and the remote pregnancy monitoring device.Results: In the intrapartum study, the remote pregnancy monitoring device and TOCO had sensitivities of 89.8 and 38.5%, respectively, and false discovery rates (FDRs) of 8.6 and 1.9%, respectively. In the antepartum study, a direct comparison of the remote pregnancy monitoring device to TOCO yielded a sensitivity of 94% and FDR of 31.1%. This high FDR is likely related to the low sensitivity of TOCO.Conclusion: UA monitoring via the new algorithm embedded in the remote pregnancy monitoring device is accurate and reliable and more precise than TOCO standard of care. Together with the previously reported remote fetal heart rate monitoring capabilities, this novel method for UA detection expands the remote pregnancy monitoring device’s capabilities to include surveillance, such as non-stress tests, greatly benefiting women and providers seeking telehealth solutions for pregnancy care.
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Affiliation(s)
- Muhammad Mhajna
- Nuvo-Group, Ltd, Tel-Aviv, Israel
- *Correspondence: Muhammad Mhajna,
| | | | - Simcha Yagel
- Department of Obstetrics and Gynecology, Hadassah Medical Center, Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Christof Sohn
- Department of Obstetrics and Gynecology, University Hospital, Heidelberg, Germany
| | - Nadav Schwartz
- Maternal and Child Health Research Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Steven Warsof
- Ob-Gyn/MFM at Eastern Virginia Medical School, Norfolk, VA, United States
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Krizea M, Gialelis J, Protopsaltis G, Mountzouris C, Theodorou G. Empowering People with a User-Friendly Wearable Platform for Unobtrusive Monitoring of Vital Physiological Parameters. SENSORS (BASEL, SWITZERLAND) 2022; 22:5226. [PMID: 35890907 PMCID: PMC9317673 DOI: 10.3390/s22145226] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 07/02/2022] [Accepted: 07/08/2022] [Indexed: 06/15/2023]
Abstract
Elderly people feel vulnerable especially after they are dismissed from health care facilities and return home. The purpose of this work was to alleviate this sense of vulnerability and empower these people by giving them the opportunity to unobtrusively record their vital physiological parameters. Bearing in mind all the parameters involved, we developed a user-friendly wrist-wearable device combined with a web-based application, to adequately address this need. The proposed compilation obtains the photoplethysmogram (PPG) from the subject's wrist and simultaneously extracts, in real time, the physiological parameters of heart rate (HR), blood oxygen saturation (SpO2) and respiratory rate (RR), based on algorithms embedded on the wearable device. The described process is conducted solely within the device, favoring the optimal use of the available resources. The aggregated data are transmitted via Wi-Fi to a cloud environment and stored in a database. A corresponding web-based application serves as a visualization and analytics tool, allowing the individuals to catch a glimpse of their physiological parameters on a screen and share their digital information with health professionals who can perform further processing and obtain valuable health information.
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Affiliation(s)
- Maria Krizea
- Applied Electronics Laboratory, Department of Electrical and Computer Engineering, University of Patras, 26504 Patras, Greece; (M.K.); (G.P.); (C.M.); (G.T.)
- Industrial Systems Institute, ATHENA RC, 26504 Patras, Greece
| | - John Gialelis
- Applied Electronics Laboratory, Department of Electrical and Computer Engineering, University of Patras, 26504 Patras, Greece; (M.K.); (G.P.); (C.M.); (G.T.)
- Industrial Systems Institute, ATHENA RC, 26504 Patras, Greece
| | - Grigoris Protopsaltis
- Applied Electronics Laboratory, Department of Electrical and Computer Engineering, University of Patras, 26504 Patras, Greece; (M.K.); (G.P.); (C.M.); (G.T.)
| | - Christos Mountzouris
- Applied Electronics Laboratory, Department of Electrical and Computer Engineering, University of Patras, 26504 Patras, Greece; (M.K.); (G.P.); (C.M.); (G.T.)
| | - Gerasimos Theodorou
- Applied Electronics Laboratory, Department of Electrical and Computer Engineering, University of Patras, 26504 Patras, Greece; (M.K.); (G.P.); (C.M.); (G.T.)
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Lee H, Lee J, Kwon Y, Kwon J, Park S, Sohn R, Park C. Multitask Siamese Network for Remote Photoplethysmography and Respiration Estimation. SENSORS 2022; 22:s22145101. [PMID: 35890781 PMCID: PMC9321619 DOI: 10.3390/s22145101] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Revised: 06/11/2022] [Accepted: 06/17/2022] [Indexed: 02/06/2023]
Abstract
Heart and respiration rates represent important vital signs for the assessment of a person’s health condition. To estimate these vital signs accurately, we propose a multitask Siamese network model (MTS) that combines the advantages of the Siamese network and the multitask learning architecture. The MTS model was trained by the images of the cheek including nose and mouth and forehead areas while sharing the same parameters between the Siamese networks, in order to extract the features about the heart and respiratory information. The proposed model was constructed with a small number of parameters and was able to yield a high vital-sign-prediction accuracy, comparable to that obtained from the single-task learning model; furthermore, the proposed model outperformed the conventional multitask learning model. As a result, we can simultaneously predict the heart and respiratory signals with the MTS model, while the number of parameters was reduced by 16 times with the mean average errors of heart and respiration rates being 2.84 and 4.21. Owing to its light weight, it would be advantageous to implement the vital-sign-monitoring model in an edge device such as a mobile phone or small-sized portable devices.
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Affiliation(s)
- Heejin Lee
- Department of Computer Engineering, Kwangwoon University, Seoul 01897, Korea; (H.L.); (Y.K.); (J.K.)
| | - Junghwan Lee
- Department of Information Convergence, Kwangwoon University, Seoul 01897, Korea;
| | - Yujin Kwon
- Department of Computer Engineering, Kwangwoon University, Seoul 01897, Korea; (H.L.); (Y.K.); (J.K.)
| | - Jiyoon Kwon
- Department of Computer Engineering, Kwangwoon University, Seoul 01897, Korea; (H.L.); (Y.K.); (J.K.)
| | - Sungmin Park
- Department of Electrical Engineering, Pohang University of Science and Technology, Seoul 37673, Korea;
| | - Ryanghee Sohn
- Emma Healthcare, Seongnam-si 13503, Korea
- Correspondence: (R.S.); (C.P.)
| | - Cheolsoo Park
- Department of Computer Engineering, Kwangwoon University, Seoul 01897, Korea; (H.L.); (Y.K.); (J.K.)
- Correspondence: (R.S.); (C.P.)
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Xia A. Potential benefits of Qi Gong meditation in quantifiable physiology: A five-year longitudinal observation. JOURNAL OF TRADITIONAL CHINESE MEDICAL SCIENCES 2022. [DOI: 10.1016/j.jtcms.2022.06.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022] Open
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Gazi AH, Jung H, Kimball JP, Inan OT. Improving Respiratory Timing Estimation Using Quality Indexing and Electrocardiogram-Derived Respiration. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:3249-3252. [PMID: 36086511 DOI: 10.1109/embc48229.2022.9871873] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Numerous applications require accurate estimation of respiratory timings. Respiratory effort (RSP) measurement is a popular approach to accomplish this, especially when the tightness of the sensing belt around the chest can be ensured. In less controlled settings, however, belt looseness and artifacts from movement of the belt on the chest can corrupt the signal. This paper demonstrates that respiration quality indexing and outlier removal can help mitigate these issues, improving estimates of respiration rate (RR), inspiration time (Ti), and expiration time (Te)., In a sample of 15 healthy human participants undergoing a protocol of five controlled breathing exercises in four postures each, electrocardiogram (ECG) and RSP signals were collected. RSP signals were processed to extract breath-by-breath estimates of RR, Ti, and Te. These estimates were compared against ground truth spirometry-based estimates using Bland-Altman analysis. We find that incorporating quality indexing and outlier removal prior to feature extraction improves the 95% limits of agreement by 10-40%. We also find that by using ECG-derived respiration (EDR) during periods of RSP artifact, the data removal necessary for accurate respiratory timing estimation is significantly reduced ( for all postures). These findings encourage the use of quality assessment and EDR to enhance the robustness of RR, Ti, and Te estimation from RSP signals. Clinical Relevance- Detecting stimulus-induced or pathological changes in respiratory function can enhance our understanding and monitoring of respiratory health. Quality assessment and the use of EDR help accomplish this by enabling more accurate measurement of respiratory timings.
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Goergen CJ, Tweardy MJ, Steinhubl SR, Wegerich SW, Singh K, Mieloszyk RJ, Dunn J. Detection and Monitoring of Viral Infections via Wearable Devices and Biometric Data. Annu Rev Biomed Eng 2022; 24:1-27. [PMID: 34932906 PMCID: PMC9218991 DOI: 10.1146/annurev-bioeng-103020-040136] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Mounting clinical evidence suggests that viral infections can lead to detectable changes in an individual's normal physiologic and behavioral metrics, including heart and respiration rates, heart rate variability, temperature, activity, and sleep prior to symptom onset, potentially even in asymptomatic individuals. While the ability of wearable devices to detect viral infections in a real-world setting has yet to be proven, multiple recent studies have established that individual, continuous data from a range of biometric monitoring technologies can be easily acquired and that through the use of machine learning techniques, physiological signals and warning signs can be identified. In this review, we highlight the existing knowledge base supporting the potential for widespread implementation of biometric data to address existing gaps in the diagnosis and treatment of viral illnesses, with a particular focus on the many important lessons learned from the coronavirus disease 2019 pandemic.
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Affiliation(s)
- Craig J Goergen
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA;
| | | | - Steven R Steinhubl
- physIQ Inc., Chicago, Illinois, USA
- Scripps Research Translational Institute, La Jolla, California, USA
| | | | - Karnika Singh
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA
| | | | - Jessilyn Dunn
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA
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Chan M, Ganti VG, Inan OT. Respiratory Rate Estimation Using U-Net-Based Cascaded Framework From Electrocardiogram and Seismocardiogram Signals. IEEE J Biomed Health Inform 2022; 26:2481-2492. [PMID: 35077375 PMCID: PMC9248781 DOI: 10.1109/jbhi.2022.3144990] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/14/2023]
Abstract
OBJECTIVE At-home monitoring of respiration is of critical urgency especially in the era of the global pandemic due to COVID-19. Electrocardiogram (ECG) and seismocardiogram (SCG) signals-measured in less cumbersome contact form factors than the conventional sealed mask that measures respiratory air flow-are promising solutions for respiratory monitoring. In particular, respiratory rates (RR) can be estimated from ECG-derived respiratory (EDR) and SCG-derived respiratory (SDR) signals. Yet, non-respiratory artifacts might still be present in these surrogates of respiratory signals, hindering the accuracy of the RRs estimated. METHODS In this paper, we propose a novel U-Net-based cascaded framework to address this problem. The EDR and SDR signals were transformed to the spectro-temporal domain and subsequently denoised by a 2D U-Net to reduce the non-respiratory artifacts. MAJOR RESULTS We have shown that the U-Net that fused an EDR input and an SDR input achieved a low mean absolute error of 0.82 breaths per minute (bpm) and a coefficient of determination (R2) of 0.89 using data collected from our chest-worn wearable patch. We also qualitatively provided insights on the complementariness between EDR and SDR signals and demonstrated the generalizability of the proposed framework. CONCLUSION ECG and SCG collected from a chest-worn wearable patch can complement each other and yield reliable RR estimation using the proposed cascaded framework. SIGNIFICANCE We anticipate that convenient and comfortable ECG and SCG measurement systems can be augmented with this framework to facilitate pervasive and accurate RR measurement.
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Deep learning for predicting respiratory rate from biosignals. Comput Biol Med 2022; 144:105338. [DOI: 10.1016/j.compbiomed.2022.105338] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 01/27/2022] [Accepted: 02/10/2022] [Indexed: 12/23/2022]
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Henricson J, Glasin J, Rindebratt S, Wilhelms D. Respiratory rate monitoring in healthy volunteers by central photoplethysmography compared to capnography. JOURNAL OF BIOPHOTONICS 2022; 15:e202100270. [PMID: 34874126 DOI: 10.1002/jbio.202100270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 11/29/2021] [Accepted: 12/04/2021] [Indexed: 06/13/2023]
Abstract
Monitoring of respiration is a central task in clinical medicine, crucial to patient safety. Despite the uncontroversial role of altered respiratory frequency as an important sign of impending or manifest deterioration, reliable measurement methods are mostly lacking outside of intensive care units and operating theaters. Photoplethysmography targeting the central blood circulation in the sternum could offer accurate and inexpensive monitoring of respiration. Changes in blood flow related to the different parts of the respiratory cycle are used to identify the respiratory pattern. The aim of this observational study was to compare photoplethysmography at the sternum to standard capnography in healthy volunteers. Bland Altman analysis showed good agreement (bias -0.21, SD 1.6, 95% limits of agreement -3.4 to 2.9) in respiratory rate values. Photoplethysmography provided high-quality measurements of respiratory rate comparable to capnographic measurements. This suggests that photoplethysmography may become a precise, cost-effective alternative for respiratory monitoring.
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Affiliation(s)
- Joakim Henricson
- Division of Clinical Chemistry and Pharmacology, Department of Biomedical and Clinical Sciences, Faculty of Health Sciences, Linköping University, Linköping, Sweden
- Department of Emergency Medicine, Local Health Care Services in Central Östergötland, Linköping, Sweden
| | - Joakim Glasin
- Department of Emergency Medicine, Local Health Care Services in Central Östergötland, Linköping, Sweden
| | - Sandra Rindebratt
- Department of Emergency Medicine, Local Health Care Services in Central Östergötland, Linköping, Sweden
| | - Daniel Wilhelms
- Division of Clinical Chemistry and Pharmacology, Department of Biomedical and Clinical Sciences, Faculty of Health Sciences, Linköping University, Linköping, Sweden
- Department of Emergency Medicine, Local Health Care Services in Central Östergötland, Linköping, Sweden
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