1
|
Vraka A, Zangróniz R, Quesada A, Hornero F, Alcaraz R, Rieta JJ. A Novel Signal Restoration Method of Noisy Photoplethysmograms for Uninterrupted Health Monitoring. SENSORS (BASEL, SWITZERLAND) 2023; 24:141. [PMID: 38203003 PMCID: PMC10781253 DOI: 10.3390/s24010141] [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: 11/19/2023] [Revised: 12/17/2023] [Accepted: 12/18/2023] [Indexed: 01/12/2024]
Abstract
Health-tracking from photoplethysmography (PPG) signals is significantly hindered by motion artifacts (MAs). Although many algorithms exist to detect MAs, the corrupted signal often remains unexploited. This work introduces a novel method able to reconstruct noisy PPGs and facilitate uninterrupted health monitoring. The algorithm starts with spectral-based MA detection, followed by signal reconstruction by using the morphological and heart-rate variability information from the clean segments adjacent to noise. The algorithm was tested on (a) 30 noisy PPGs of a maximum 20 s noise duration and (b) 28 originally clean PPGs, after noise addition (2-120 s) (1) with and (2) without cancellation of the corresponding clean segment. Sampling frequency was 250 Hz after resampling. Noise detection was evaluated by means of accuracy, sensitivity, and specificity. For the evaluation of signal reconstruction, the heart-rate (HR) was compared via Pearson correlation (PC) and absolute error (a) between ECGs and reconstructed PPGs and (b) between original and reconstructed PPGs. Bland-Altman (BA) analysis for the differences in HR estimation on original and reconstructed segments of (b) was also performed. Noise detection accuracy was 90.91% for (a) and 99.38-100% for (b). For the PPG reconstruction, HR showed 99.31% correlation in (a) and >90% for all noise lengths in (b). Mean absolute error was 1.59 bpm for (a) and 1.26-1.82 bpm for (b). BA analysis indicated that, in most cases, 90% or more of the recordings fall within the confidence interval, regardless of the noise length. Optimal performance is achieved even for signals of noise up to 2 min, allowing for the utilization and further analysis of recordings that would otherwise be discarded. Thereby, the algorithm can be implemented in monitoring devices, assisting in uninterrupted health-tracking.
Collapse
Affiliation(s)
- Aikaterini Vraka
- Biosignals and Minimally Invasive Technologies (BioMIT.org), Electronic Engineering Department, Universitat Politecnica de Valencia, 46022 Valencia, Spain;
| | - Roberto Zangróniz
- Research Group in Electronic, Biomedical and Telecommunication Engineering, University of Castilla-La Mancha, 16071 Cuenca, Spain; (R.Z.); (R.A.)
| | - Aurelio Quesada
- Arrhythmia Unit, Cardiology Department, General University Hospital Consortium of Valencia, 46014 Valencia, Spain;
| | - Fernando Hornero
- Cardiovascular Surgery Department, Hospital Clínico Universitario de Valencia, 46010 Valencia, Spain;
| | - Raúl Alcaraz
- Research Group in Electronic, Biomedical and Telecommunication Engineering, University of Castilla-La Mancha, 16071 Cuenca, Spain; (R.Z.); (R.A.)
| | - José J. Rieta
- Biosignals and Minimally Invasive Technologies (BioMIT.org), Electronic Engineering Department, Universitat Politecnica de Valencia, 46022 Valencia, Spain;
| |
Collapse
|
2
|
Li K, Cardoso C, Moctezuma-Ramirez A, Elgalad A, Perin E. Heart Rate Variability Measurement through a Smart Wearable Device: Another Breakthrough for Personal Health Monitoring? INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:7146. [PMID: 38131698 PMCID: PMC10742885 DOI: 10.3390/ijerph20247146] [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: 08/03/2023] [Revised: 11/06/2023] [Accepted: 11/27/2023] [Indexed: 12/23/2023]
Abstract
Heart rate variability (HRV) is a measurement of the fluctuation of time between each heartbeat and reflects the function of the autonomic nervous system. HRV is an important indicator for both physical and mental status and for broad-scope diseases. In this review, we discuss how wearable devices can be used to monitor HRV, and we compare the HRV monitoring function among different devices. In addition, we have reviewed the recent progress in HRV tracking with wearable devices and its value in health monitoring and disease diagnosis. Although many challenges remain, we believe HRV tracking with wearable devices is a promising tool that can be used to improve personal health.
Collapse
Affiliation(s)
- Ke Li
- Center for Preclinical Cardiovascular Research, The Texas Heart Institute, Houston, TX 77030, USA
| | - Cristiano Cardoso
- Center for Preclinical Cardiovascular Research, The Texas Heart Institute, Houston, TX 77030, USA
| | - Angel Moctezuma-Ramirez
- Center for Preclinical Cardiovascular Research, The Texas Heart Institute, Houston, TX 77030, USA
| | - Abdelmotagaly Elgalad
- Center for Preclinical Cardiovascular Research, The Texas Heart Institute, Houston, TX 77030, USA
| | - Emerson Perin
- Center for Clinical Research, The Texas Heart Institute, Houston, TX 77030, USA
| |
Collapse
|
3
|
Lu P, Creagh AP, Lu HY, Hai HB, Thwaites L, Clifton DA. 2D-WinSpatt-Net: A Dual Spatial Self-Attention Vision Transformer Boosts Classification of Tetanus Severity for Patients Wearing ECG Sensors in Low- and Middle-Income Countries. SENSORS (BASEL, SWITZERLAND) 2023; 23:7705. [PMID: 37765761 PMCID: PMC10535235 DOI: 10.3390/s23187705] [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: 07/14/2023] [Revised: 08/31/2023] [Accepted: 09/02/2023] [Indexed: 09/29/2023]
Abstract
Tetanus is a life-threatening bacterial infection that is often prevalent in low- and middle-income countries (LMIC), Vietnam included. Tetanus affects the nervous system, leading to muscle stiffness and spasms. Moreover, severe tetanus is associated with autonomic nervous system (ANS) dysfunction. To ensure early detection and effective management of ANS dysfunction, patients require continuous monitoring of vital signs using bedside monitors. Wearable electrocardiogram (ECG) sensors offer a more cost-effective and user-friendly alternative to bedside monitors. Machine learning-based ECG analysis can be a valuable resource for classifying tetanus severity; however, using existing ECG signal analysis is excessively time-consuming. Due to the fixed-sized kernel filters used in traditional convolutional neural networks (CNNs), they are limited in their ability to capture global context information. In this work, we propose a 2D-WinSpatt-Net, which is a novel Vision Transformer that contains both local spatial window self-attention and global spatial self-attention mechanisms. The 2D-WinSpatt-Net boosts the classification of tetanus severity in intensive-care settings for LMIC using wearable ECG sensors. The time series imaging-continuous wavelet transforms-is transformed from a one-dimensional ECG signal and input to the proposed 2D-WinSpatt-Net. In the classification of tetanus severity levels, 2D-WinSpatt-Net surpasses state-of-the-art methods in terms of performance and accuracy. It achieves remarkable results with an F1 score of 0.88 ± 0.00, precision of 0.92 ± 0.02, recall of 0.85 ± 0.01, specificity of 0.96 ± 0.01, accuracy of 0.93 ± 0.02 and AUC of 0.90 ± 0.00.
Collapse
Affiliation(s)
- Ping Lu
- Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK
| | - Andrew P. Creagh
- Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK
| | - Huiqi Y. Lu
- Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK
| | - Ho Bich Hai
- Oxford University Clinical Research Unit, Ho Chi Minh City 700000, Vietnam
| | | | - Louise Thwaites
- Oxford University Clinical Research Unit, Ho Chi Minh City 700000, Vietnam
| | - David A. Clifton
- Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK
- Oxford Suzhou Centre for Advanced Research, Suzhou 215123, China
| |
Collapse
|
4
|
Chen M, Wu S, Chen T, Wang C, Liu G. Information-Based Similarity of Ordinal Pattern Sequences as a Novel Descriptor in Obstructive Sleep Apnea Screening Based on Wearable Photoplethysmography Bracelets. BIOSENSORS 2022; 12:1089. [PMID: 36551056 PMCID: PMC9775447 DOI: 10.3390/bios12121089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 11/11/2022] [Accepted: 11/24/2022] [Indexed: 06/17/2023]
Abstract
Obstructive sleep apnea (OSA) is a common respiratory disorder associated with autonomic nervous system (ANS) dysfunction, resulting in abnormal heart rate variability (HRV). Capable of acquiring heart rate (HR) information with more convenience, wearable photoplethysmography (PPG) bracelets are proven to be a potential surrogate for electrocardiogram (ECG)-based devices. Meanwhile, bracelet-type PPG has been heavily marketed and widely accepted. This study aims to investigate the algorithm that can identify OSA with wearable devices. The information-based similarity of ordinal pattern sequences (OP_IBS), which is a modified version of the information-based similarity (IBS), has been proposed as a novel index to detect OSA based on wearable PPG signals. A total of 92 PPG recordings (29 normal subjects, 39 mild-moderate OSA subjects and 24 severe OSA subjects) were included in this study. OP_IBS along with classical indices were calculated. For severe OSA detection, the accuracy of OP_IBS was 85.9%, much higher than that of the low-frequency power to high-frequency power ratio (70.7%). The combination of OP_IBS, IBS, CV and LF/HF can achieve 91.3% accuracy, 91.0% sensitivity and 91.5% specificity. The performance of OP_IBS is significantly improved compared with our previous study based on the same database with the IBS method. In the Physionet database, OP_IBS also performed exceptionally well with an accuracy of 91.7%. This research shows that the OP_IBS method can access the HR dynamics of OSA subjects and help diagnose OSA in clinical environments.
Collapse
Affiliation(s)
- Mingjing Chen
- School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen 518107, China
- Alfred E. Mann Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089-1112, USA
| | - Shan Wu
- School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen 518107, China
| | - Tian Chen
- School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen 518107, China
| | - Changhong Wang
- School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen 518107, China
| | - Guanzheng Liu
- School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen 518107, China
| |
Collapse
|
5
|
Paccione CE, Stubhaug A, Diep LM, Rosseland LA, Jacobsen HB. Meditative-based diaphragmatic breathing vs. vagus nerve stimulation in the treatment of fibromyalgia-A randomized controlled trial: Body vs. machine. Front Neurol 2022; 13:1030927. [PMID: 36438970 PMCID: PMC9687386 DOI: 10.3389/fneur.2022.1030927] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 10/13/2022] [Indexed: 07/25/2023] Open
Abstract
IMPORTANCE Vagus nerve innervation via electrical stimulation and meditative-based diaphragmatic breathing may be promising treatment avenues for fibromyalgia. OBJECTIVE Explore and compare the treatment effectiveness of active and sham transcutaneous vagus nerve stimulation (tVNS) and meditative-based diaphragmatic breathing (MDB) for fibromyalgia. DESIGN Participants enrolled from March 2019-October 2020 and randomly assigned to active tVNS (n = 28), sham tVNS (n = 29), active MDB (n = 29), or sham MDB (n = 30). Treatments were self-delivered at home for 15 min/morning and 15 min/evening for 14 days. Follow-up was at 2 weeks. SETTING Outpatient pain clinic in Oslo, Norway. PARTICIPANTS 116 adults aged 18-65 years with severe fibromyalgia were consecutively enrolled and randomized. 86 participants (74%) had an 80% treatment adherence and 107 (92%) completed the study at 2 weeks; 1 participant dropped out due to adverse effects from active tVNS. INTERVENTIONS Active tVNS is placed on the cymba conchae of the left ear; sham tVNS is placed on the left earlobe. Active MDB trains users in nondirective meditation with deep breathing; sham MDB trains users in open-awareness meditation with paced breathing. MAIN OUTCOMES AND MEASURES Primary outcome was change from baseline in ultra short-term photoplethysmography-measured cardiac-vagal heart rate variability at 2 weeks. Prior to trial launch, we hypothesized that (1) those randomized to active MDB or active tVNS would display greater increases in heart rate variability compared to those randomized to sham MDB or sham tVNS after 2-weeks; (2) a change in heart rate variability would be correlated with a change in self-reported average pain intensity; and (3) active treatments would outperform sham treatments on all pain-related secondary outcome measures. RESULTS No significant across-group changes in heart rate variability were found. Furthermore, no significant correlations were found between changes in heart rate variability and average pain intensity during treatment. Significant across group differences were found for overall FM severity yet were not found for average pain intensity. CONCLUSIONS AND RELEVANCE These findings suggest that changes in cardiac-vagal heart rate variability when recorded with ultra short-term photoplethysmography in those with fibromyalgia may not be associated with treatment-specific changes in pain intensity. Further research should be conducted to evaluate potential changes in long-term cardiac-vagal heart rate variability in response to noninvasive vagus nerve innervation in those with fibromyalgia. CLINICAL TRIAL REGISTRATION https://clinicaltrials.gov/ct2/show/NCT03180554, Identifier: NCT03180554.
Collapse
Affiliation(s)
- Charles Ethan Paccione
- Division of Emergencies and Critical Care, Department of Pain Management and Research, Oslo University Hospital, Oslo, Norway
- Faculty of Medicine, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Mind-Body Lab, Department of Psychology, University of Oslo, Oslo, Norway
| | - Audun Stubhaug
- Division of Emergencies and Critical Care, Department of Pain Management and Research, Oslo University Hospital, Oslo, Norway
- Faculty of Medicine, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Lien My Diep
- Oslo Center for Biostatistics and Epidemiology, Oslo University Hospital, Oslo, Norway
| | - Leiv Arne Rosseland
- Faculty of Medicine, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Emergencies and Critical Care, Department of Research and Development, Oslo University Hospital, Oslo, Norway
| | - Henrik Børsting Jacobsen
- Division of Emergencies and Critical Care, Department of Pain Management and Research, Oslo University Hospital, Oslo, Norway
- Mind-Body Lab, Department of Psychology, University of Oslo, Oslo, Norway
| |
Collapse
|
6
|
Garikapati K, Turnbull S, Bennett RG, Campbell TG, Kanawati J, Wong MS, Thomas SP, Chow CK, Kumar S. The Role of Contemporary Wearable and Handheld Devices in the Diagnosis and Management of Cardiac Arrhythmias. Heart Lung Circ 2022; 31:1432-1449. [PMID: 36109292 DOI: 10.1016/j.hlc.2022.08.001] [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: 04/13/2022] [Revised: 07/18/2022] [Accepted: 08/01/2022] [Indexed: 10/14/2022]
Abstract
Cardiac arrhythmias are associated with significant morbidity, mortality and economic burden on the health care system. Detection and surveillance of cardiac arrhythmias using medical grade non-invasive methods (electrocardiogram, Holter monitoring) is the accepted standard of care. Whilst their accuracy is excellent, significant limitations remain in terms of accessibility, ease of use, cost, and a suboptimal diagnostic yield (up to ∼50%) which is critically dependent on the duration of monitoring. Contemporary wearable and handheld devices that utilise photoplethysmography and the electrocardiogram present a novel opportunity for remote screening and diagnosis of arrhythmias. They have significant advantages in terms of accessibility and availability with the potential of enhancing the diagnostic yield of episodic arrhythmias. However, there is limited data on the accuracy and diagnostic utility of these devices and their role in therapeutic decision making in clinical practice remains unclear. Evidence is mounting that they may be useful in screening for atrial fibrillation, and anecdotally, for the diagnosis of other brady and tachyarrhythmias. Recently, there has been an explosion of patient uptake of such devices for self-monitoring of arrhythmias. Frequently, the clinician is presented such information for review and comment, which may influence clinical decisions about treatment. Further studies are needed before incorporation of such technologies in routine clinical practice, given the lack of systematic data on their accuracy and utility. Moreover, challenges with regulation of quality standards and privacy remain. This state-of-the-art review summarises the role of novel ambulatory, commercially available, heart rhythm monitors in the diagnosis and management of cardiac arrhythmias and their expanding role in the diagnostic and therapeutic paradigm in cardiology.
Collapse
Affiliation(s)
- Kartheek Garikapati
- Department of Cardiology, Westmead Hospital, Westmead Applied Research Centre, University of Sydney, Sydney, NSW Australia
| | - Samual Turnbull
- Department of Cardiology, Westmead Hospital, Westmead Applied Research Centre, University of Sydney, Sydney, NSW Australia
| | - Richard G Bennett
- Department of Cardiology, Westmead Hospital, Westmead Applied Research Centre, University of Sydney, Sydney, NSW Australia
| | - Timothy G Campbell
- Department of Cardiology, Westmead Hospital, Westmead Applied Research Centre, University of Sydney, Sydney, NSW Australia
| | - Juliana Kanawati
- Department of Cardiology, Westmead Hospital, Westmead Applied Research Centre, University of Sydney, Sydney, NSW Australia
| | - Mary S Wong
- Department of Cardiology, Westmead Hospital, Westmead Applied Research Centre, University of Sydney, Sydney, NSW Australia
| | - Stuart P Thomas
- Department of Cardiology, Westmead Hospital, Westmead Applied Research Centre, University of Sydney, Sydney, NSW Australia
| | - Clara K Chow
- Department of Cardiology, Westmead Hospital, Westmead Applied Research Centre, University of Sydney, Sydney, NSW Australia
| | - Saurabh Kumar
- Department of Cardiology, Westmead Hospital, Westmead Applied Research Centre, University of Sydney, Sydney, NSW Australia.
| |
Collapse
|
7
|
Lu P, Ghiasi S, Hagenah J, Hai HB, Hao NV, Khanh PNQ, Khoa LDV, Thwaites L, Clifton DA, Zhu T. Classification of Tetanus Severity in Intensive-Care Settings for Low-Income Countries Using Wearable Sensing. SENSORS (BASEL, SWITZERLAND) 2022; 22:6554. [PMID: 36081013 PMCID: PMC9460354 DOI: 10.3390/s22176554] [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: 07/25/2022] [Revised: 08/19/2022] [Accepted: 08/22/2022] [Indexed: 06/15/2023]
Abstract
Infectious diseases remain a common problem in low- and middle-income countries, including in Vietnam. Tetanus is a severe infectious disease characterized by muscle spasms and complicated by autonomic nervous system dysfunction in severe cases. Patients require careful monitoring using electrocardiograms (ECGs) to detect deterioration and the onset of autonomic nervous system dysfunction as early as possible. Machine learning analysis of ECG has been shown of extra value in predicting tetanus severity, however any additional ECG signal analysis places a high demand on time-limited hospital staff and requires specialist equipment. Therefore, we present a novel approach to tetanus monitoring from low-cost wearable sensors combined with a deep-learning-based automatic severity detection. This approach can automatically triage tetanus patients and reduce the burden on hospital staff. In this study, we propose a two-dimensional (2D) convolutional neural network with a channel-wise attention mechanism for the binary classification of ECG signals. According to the Ablett classification of tetanus severity, we define grades 1 and 2 as mild tetanus and grades 3 and 4 as severe tetanus. The one-dimensional ECG time series signals are transformed into 2D spectrograms. The 2D attention-based network is designed to extract the features from the input spectrograms. Experiments demonstrate a promising performance for the proposed method in tetanus classification with an F1 score of 0.79 ± 0.03, precision of 0.78 ± 0.08, recall of 0.82 ± 0.05, specificity of 0.85 ± 0.08, accuracy of 0.84 ± 0.04 and AUC of 0.84 ± 0.03.
Collapse
Affiliation(s)
- Ping Lu
- Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK
| | - Shadi Ghiasi
- Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK
| | - Jannis Hagenah
- Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK
| | - Ho Bich Hai
- Oxford University Clinical Research Unit, Ho Chi Minh City 700000, Vietnam
| | - Nguyen Van Hao
- Hospital of Tropical Diseases, Ho Chi Minh City 700000, Vietnam
| | | | - Le Dinh Van Khoa
- Oxford University Clinical Research Unit, Ho Chi Minh City 700000, Vietnam
| | | | - Louise Thwaites
- Oxford University Clinical Research Unit, Ho Chi Minh City 700000, Vietnam
| | - David A. Clifton
- Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK
- Hthe Oxford Suzhou Centre for Advanced Research, University of Oxford, Suzhou Dushu Lake Science and Education Innovation District, Suzhou 215123, China
| | - Tingting Zhu
- Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK
| |
Collapse
|
8
|
Rosol M, Gasior JS, Walecka I, Werner B, Cybulski G, Mlynczak M. Causality in cardiorespiratory signals in pediatric cardiac patients. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:355-358. [PMID: 36085711 DOI: 10.1109/embc48229.2022.9871750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Four different Granger causality-based methods - one linear and three nonlinear (Granger Causality, Kernel Granger Causality, large-scale Nonlinear Granger Causality, and Neural Network Granger Causality) were used for assessment and causal-based quantification of the respiratory sinus arrythmia (RSA) in the group of pediatric cardiac patients, based on the single-lead ECG and impedance pneumography signals (the latter as the tidal volume curve equivalent). Each method was able to detect the dependency (in terms of causal inference) between respiratory and cardiac signals. The correlations between quantified RSA and the demographic parameters were also studied, but the results differ for each method. Clinical relevance- The presented methods (among which NNGC seems to be the most valid) allow for quantification of RSA and study of dependency between tidal volume and RR intervals which may help to better understand association between respiratory and cardiovascular systems in different populations.
Collapse
|
9
|
Guo M, Nguyen L, Du H, Jin F. When Patients Recover From COVID-19: Data-Driven Insights From Wearable Technologies. Front Big Data 2022; 5:801998. [PMID: 35574570 PMCID: PMC9096352 DOI: 10.3389/fdata.2022.801998] [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: 10/26/2021] [Accepted: 03/28/2022] [Indexed: 11/17/2022] Open
Abstract
Coronavirus disease 2019 (COVID-19) is known as a contagious disease and caused an overwhelming of hospital resources worldwide. Therefore, deciding on hospitalizing COVID-19 patients or quarantining them at home becomes a crucial solution to manage an extremely big number of patients in a short time. This paper proposes a model which combines Long-short Term Memory (LSTM) and Deep Neural Network (DNN) to early and accurately classify disease stages of the patients to address the problem at a low cost. In this model, the LSTM component will exploit temporal features while the DNN component extracts attributed features to enhance the model's classification performance. Our experimental results demonstrate that the proposed model achieves substantially better prediction accuracy than existing state-of-art methods. Moreover, we explore the importance of different vital indicators to help patients and doctors identify the critical factors at different COVID-19 stages. Finally, we create case studies demonstrating the differences between severe and mild patients and show the signs of recovery from COVID-19 disease by extracting shape patterns based on temporal features of patients. In summary, by identifying the disease stages, this research will help patients understand their current disease situation. Furthermore, it will also help doctors to provide patients with an immediate treatment plan remotely that addresses their specific disease stages, thus optimizing their usage of limited medical resources.
Collapse
Affiliation(s)
- Muzhe Guo
- Department of Statistics, The George Washington University, Washington, DC, United States
| | - Long Nguyen
- Department of Computer Science and Data Science, School of Applied Computational Sciences, Meharry Medical College, Nashville, TN, United States
| | - Hongfei Du
- Department of Statistics, The George Washington University, Washington, DC, United States
| | - Fang Jin
- Department of Statistics, The George Washington University, Washington, DC, United States
- *Correspondence: Fang Jin
| |
Collapse
|
10
|
Guo J, Chen X, Zhao J, Zhang X, Chen X. An effective photoplethysmography heart rate estimation framework integrating two-level denoising method and heart rate tracking algorithm guided by finite state machine. IEEE J Biomed Health Inform 2022; 26:3731-3742. [PMID: 35380978 DOI: 10.1109/jbhi.2022.3165071] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
In order to achieve accurate heart rate (HR) estimation in complex scenes, this paper presents an effective photoplethysmography (PPG) HR estimation framework integrating two-level denoising method and HR tracking algorithm guided by finite state machine (FSM). Aiming at solving the problems of low signal-to-noise ratio and co-frequency (the noise frequency is close to the HR frequency) caused by motion artifacts, the two-level denoising method consisting of the cascaded adaptive filtering and the differential denoising guided by FSM are designed to remove motion-related noises in PPG signals. In order to solve the problem of HR tracking error caused by poor wrist contact, the HR tracking algorithm guided by FSM is proposed to obtain the global optimization capability. The results of HR estimation experiments conducted on the IEEE Signal Processing Cup database and the WeData database created by ourselves show that the proposed framework can effectively cope with the problems of low signal-to-noise ratio and co-frequency. Even if tracking errors occur due to poor wristband contact, the proposed HR tracking algorithm guided by FSM can correct them in time when the HR component appears again. The average absolute error of HR estimation on the two databases are 1.76 BPM (beats per minute) and 2.77 BPM, respectively, which is more accurate compared to other algorithms.
Collapse
|
11
|
Shintomi A, Izumi S, Yoshimoto M, Kawaguchi H. Effectiveness of the heartbeat interval error and compensation method on heart rate variability analysis. Healthc Technol Lett 2022; 9:9-15. [PMID: 35340403 PMCID: PMC8927864 DOI: 10.1049/htl2.12023] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 01/29/2022] [Accepted: 02/22/2022] [Indexed: 01/22/2023] Open
Abstract
The purpose of this study is to evaluate the effectiveness of heartbeat error and compensation methods on heart rate variability (HRV) with mobile and wearable sensor devices. The HRV analysis extracts multiple indices related to the heart and autonomic nervous system from beat‐to‐beat intervals. These HRV analysis indices are affected by the heartbeat interval mismatch, which is caused by sampling error from measurement hardware and inherent errors from the state of human body. Although the sampling rate reduction is a common method to reduce power consumption on wearable devices, it degrades the accuracy of the heartbeat interval. Furthermore, wearable devices often use photoplethysmography (PPG) instead of electrocardiogram (ECG) to measure heart rate. However, there are inherent errors between PPG and ECG, because the PPG is affected by blood pressure fluctuations, vascular stiffness, and body movements. This paper evaluates the impact of these errors on HRV analysis using dataset including both ECG and PPG from 28 subjects. The evaluation results showed that the error compensation method improved the accuracy of HRV analysis in time domain, frequency domain and non‐linear analysis. Furthermore, the error compensation by the algorithm was found to be effective for both PPG and ECG.
Collapse
Affiliation(s)
- Ayaka Shintomi
- Graduate School of System Informatics Kobe University 1‐1 Rokkodai‐cho Nada‐ku Kobe Hyogo Japan
| | - Shintaro Izumi
- Graduate School of System Informatics Kobe University 1‐1 Rokkodai‐cho Nada‐ku Kobe Hyogo Japan
| | - Masahiko Yoshimoto
- Graduate School of System Informatics Kobe University 1‐1 Rokkodai‐cho Nada‐ku Kobe Hyogo Japan
| | - Hiroshi Kawaguchi
- Graduate School of System Informatics Kobe University 1‐1 Rokkodai‐cho Nada‐ku Kobe Hyogo Japan
| |
Collapse
|
12
|
Dewi E, Hadiyoso S, Mengko TER, Zakaria H, Astami K. Cardiovascular system modeling using windkessel segmentation model based on photoplethysmography measurements of fingers and toes. JOURNAL OF MEDICAL SIGNALS & SENSORS 2022; 12:192-201. [PMID: 36120404 PMCID: PMC9480512 DOI: 10.4103/jmss.jmss_101_21] [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: 03/15/2021] [Revised: 02/13/2022] [Accepted: 02/22/2022] [Indexed: 11/18/2022]
Abstract
Background: Photoplethysmography (PPG) contains information about the health condition of the heart and blood vessels. Cardiovascular system modeling using PPG signal measurements can represent, analyze, and predict the cardiovascular system. Methods: This study aims to make a cardiovascular system model using a Windkessel model by dividing the blood vessels into seven segments. This process involves the PPG signal of the fingertips and toes for further analysis to obtain the condition of the elasticity of the blood vessels as the main parameter. The method is to find the Resistance, Inductance, and Capacitance (RLC) value of each segment of the body through the equivalent equation between the electronic unit and the cardiovascular unit. The modeling made is focused on PPG parameters in the form of stiffness index, the time delay (△t), and augmentation index. Results: The results of the model simulation using PSpice were then compared with the results of measuring the PPG signal to analyze changes in the behavior of the PPG signal taken from ten healthy people with an average age of 46 years, compared to ten cardiac patients with an average age of 48 years. It is found that decreasing 20% of capacitance value and the arterial stiffness parameter will close to cardiac patients' data. Compared with the measurement results, the correlation of the PPG signal in the simulation model is more than 0.9. Conclusions: The proposed model is expected to be used in the early detection of arterial stiffness. It can also be used to study the dynamics of the cardiovascular system, including changes in blood flow velocity and blood pressure.
Collapse
|
13
|
Alharbi EA, Jones JM, Alomainy A. Non-Invasive Solutions to Identify Distinctions Between Healthy and Mild Cognitive Impairments Participants. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2022; 10:2700206. [PMID: 35711336 PMCID: PMC9191685 DOI: 10.1109/jtehm.2022.3175361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 03/09/2022] [Accepted: 04/28/2022] [Indexed: 11/07/2022]
Affiliation(s)
- Eaman A. Alharbi
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, U.K
| | - Janelle M. Jones
- School of Biological and Behavioural Sciences, Queen Mary University of London, London, U.K
| | - Akram Alomainy
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, U.K
| |
Collapse
|
14
|
Raz S, Lahad M. Physiological indicators of emotional arousal related to ANS activity in response to associative cards for psychotherapeutic PTSD treatment. Front Psychiatry 2022; 13:933692. [PMID: 36419970 PMCID: PMC9676269 DOI: 10.3389/fpsyt.2022.933692] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Accepted: 10/17/2022] [Indexed: 11/09/2022] Open
Abstract
SEE FAR CBT is an integrative treatment protocol for PTSD and anxiety disorders which combines CBT, body-mind (somatic experience) and imagery-based (fantastic reality; FR) methods. FR is introduced using associative therapeutic cards (COPE cards) to represent both "a pleasant/safe place" and the re-narrating process of the traumatic story. Although some preliminary evidence exists regarding the impact of COPE cards integration in psychotherapy, further validation is needed as to whether these cards can induce distinct arousal-affective states in the observer. The aim of this study was to examine whether exposure to COPE cards evoke different emotional-psychophysiological states using objective physiological measures reflecting autonomic nervous system responses; hence, to further validate its use as a potentially effective tool within the context of SEE FAR CBT therapeutic process. Ninety-five healthy under-graduate participants were first exposed to high-arousal, negatively-valenced cards and asked to put themselves in a state of emotional/physical arousal. Afterwards, they were exposed to low-arousal, positively-valenced cards and were asked to try to calm and relax to the best of their ability. Heart rate, blood pressure and heart rate variability (HRV) were measured at baseline, at the arousal phase and finally at the relaxation phase. It was found that exposure to arousing negative cards resulted in significant increase in blood pressure and a decrease in HRV, while exposure to relaxing positive cards resulted in significant decrease in blood pressure and an increase in HRV. These findings support the efficacy and utility of associative COPE cards in affecting psychophysiological arousal.
Collapse
Affiliation(s)
- Sivan Raz
- Department of Psychology, Tel-Hai College, Upper Galilee, Israel.,Department of Behavioral Sciences, Max Stern Yezreel Valley College, Emek Yezreel, Israel
| | - Mooli Lahad
- Department of Psychology, Tel-Hai College, Upper Galilee, Israel
| |
Collapse
|
15
|
Antali F, Kulin D, Lucz KI, Szabó B, Szűcs L, Kulin S, Miklós Z. Multimodal Assessment of the Pulse Rate Variability Analysis Module of a Photoplethysmography-Based Telemedicine System. SENSORS 2021; 21:s21165544. [PMID: 34450986 PMCID: PMC8401087 DOI: 10.3390/s21165544] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 08/06/2021] [Accepted: 08/14/2021] [Indexed: 12/25/2022]
Abstract
Alterations of heart rate variability (HRV) are associated with various (patho)physiological conditions; therefore, HRV analysis has the potential to become a useful diagnostic module of wearable/telemedical devices to support remote cardiovascular/autonomic monitoring. Continuous pulse recordings obtained by photoplethysmography (PPG) can yield pulse rate variability (PRV) indices similar to HRV parameters; however, it is debated whether PRV/HRV parameters are interchangeable. In this study, we assessed the PRV analysis module of a digital arterial PPG-based telemedical system (SCN4ALL). We used Bland–Altman analysis to validate the SCN4ALL PRV algorithm to Kubios Premium software and to determine the agreements between PRV/HRV results calculated from 2-min long PPG and ECG captures recorded simultaneously in healthy individuals (n = 33) at rest and during the cold pressor test, and in diabetic patients (n = 12) at rest. We found an ideal agreement between SCN4ALL and Kubios outputs (bias < 2%). PRV and HRV parameters showed good agreements for interbeat intervals, SDNN, and RMSSD time-domain variables, for total spectral and low-frequency power (LF) frequency-domain variables, and for non-linear parameters in healthy subjects at rest and during cold pressor challenge. In diabetics, good agreements were observed for SDNN, LF, and SD2; and moderate agreement was observed for total power. In conclusion, the SCN4ALL PRV analysis module is a good alternative for HRV analysis for numerous conventional HRV parameters.
Collapse
Affiliation(s)
- Flóra Antali
- Institute of Translational Medicine, Semmelweis University, 1094 Budapest, Hungary;
- E-Med4All Europe Ltd., 1036 Budapest, Hungary; (K.I.L.); (B.S.); (L.S.); (S.K.)
- Correspondence: (F.A.); (Z.M.); Tel.: +36-70-323-7431 (F.A.); +36-20-585-8099 (Z.M.)
| | - Dániel Kulin
- Institute of Translational Medicine, Semmelweis University, 1094 Budapest, Hungary;
- E-Med4All Europe Ltd., 1036 Budapest, Hungary; (K.I.L.); (B.S.); (L.S.); (S.K.)
| | - Konrád István Lucz
- E-Med4All Europe Ltd., 1036 Budapest, Hungary; (K.I.L.); (B.S.); (L.S.); (S.K.)
| | - Balázs Szabó
- E-Med4All Europe Ltd., 1036 Budapest, Hungary; (K.I.L.); (B.S.); (L.S.); (S.K.)
| | - László Szűcs
- E-Med4All Europe Ltd., 1036 Budapest, Hungary; (K.I.L.); (B.S.); (L.S.); (S.K.)
- Antal Bejczy Center for Intelligent Robotics, Óbuda University, 1034 Budapest, Hungary
| | - Sándor Kulin
- E-Med4All Europe Ltd., 1036 Budapest, Hungary; (K.I.L.); (B.S.); (L.S.); (S.K.)
| | - Zsuzsanna Miklós
- Institute of Translational Medicine, Semmelweis University, 1094 Budapest, Hungary;
- Correspondence: (F.A.); (Z.M.); Tel.: +36-70-323-7431 (F.A.); +36-20-585-8099 (Z.M.)
| |
Collapse
|
16
|
Shih CH, Chou PC, Chou TL, Huang TW. Measurement of Cancer-Related Fatigue Based on Heart Rate Variability: Observational Study. J Med Internet Res 2021; 23:e25791. [PMID: 36260384 PMCID: PMC8406124 DOI: 10.2196/25791] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 03/20/2021] [Accepted: 05/04/2021] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Cancer-related fatigue is a serious side effect of cancer, and its treatment can disrupt the quality of life of patients. Clinically, the standard method for assessing cancer-related fatigue relies on subjective experience retrieved from patient self-reports, such as the Brief Fatigue Inventory (BFI). However, most patients do not self-report their fatigue levels. OBJECTIVE In this study, we aim to develop an objective cancer-related fatigue assessment method to track and monitor fatigue in patients with cancer. METHODS In total, 12 patients with lung cancer who were undergoing chemotherapy or targeted therapy were enrolled. We developed frequency-domain parameters of heart rate variability (HRV) and BFI based on a wearable-based HRV measurement system. All patients completed the BFI-Taiwan version questionnaire and wore the device for 7 consecutive days to record HRV parameters such as low frequency (LF), high frequency (HF), and LF-HF ratio (LF-HF). Statistical analysis was used to map the correlation between subjective fatigue and objective data. RESULTS A moderate positive correlation was observed between the average LF-HF ratio and BFI in the sleep phase (ρ=0.86). The mapped BFI score derived by the BFI mapping method could approximate the BFI from the patient self-report. The mean absolute error rate between the subjective and objective BFI scores was 3%. CONCLUSIONS LF-HF is highly correlated with the cancer-related fatigue experienced by patients with lung cancer undergoing chemotherapy or targeted therapy. Beyond revealing fatigue levels objectively, continuous HRV recordings through the photoplethysmography watch device and the defined parameters (LF-HF) can define the active phase and sleep phase in patients with lung cancer who undergo chemotherapy or targeted chemotherapy, allowing a deduction of their sleep patterns.
Collapse
Affiliation(s)
- Chi-Huang Shih
- Department of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung, Taiwan
| | - Pai-Chien Chou
- Division of Thoracic Medicine, Department of Internal Medicine, Taipei Medical University Hospital, Taipei, Taiwan
- Division of Thoracic Medicine, Department of Internal Medicine, School of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Ting-Ling Chou
- School of Nursing, College of Nursing, Taipei Medical University, Taipei City, Taiwan
| | - Tsai-Wei Huang
- School of Nursing, College of Nursing, Taipei Medical University, Taipei City, Taiwan
- Cochrane Taiwan, Taipei Medical University, Taipei, Taiwan
- Center for Nursing and Healthcare Research in Clinical Practice Application, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
| |
Collapse
|
17
|
Leon C, Cabon S, Patural H, Gascoin G, Flamant C, Roue JM, Favrais G, Beuchee A, Pladys P, Carrault G. Evaluation of maturation in preterm infants through an ensemble machine learning algorithm using physiological signals. IEEE J Biomed Health Inform 2021; 26:400-410. [PMID: 34185652 DOI: 10.1109/jbhi.2021.3093096] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
This study was designed to test if heart rate variability (HRV) data from preterm and full-term infants could be used to estimate their functional maturational age (FMA), using a machine learning model. We propose that the FMA, and its deviation from the postmenstrual age (PMA) of the infants could inform physicians about the progress of the maturation of the infants. The HRV data was acquired from 50 healthy infants, born between 25 and 41 weeks of gestational age, who did not present any signs of abnormal maturation relative to their age group during the period of observation. The HRV features were used as input for a machine learning model that uses filtering and genetic algorithms for feature selection, and an ensemble machine learning (EML) algorithm, which combines linear and random forest regressions, to produce as output a FMA. Using HRV data, the FMA had a mean absolute error of 0.93 weeks, 95% CI [0.78, 1.08], compared to the PMA. These results demonstrate that HRV features of newborn infants can be used by an EML model to estimate their FMA. This method was also generalized using respiration rate variability (RRV) and bradycardia data, obtaining similar results. The FMA, predicted either by HRV, RRV or bradycardia, and its deviation from the true PMA of the infants, could be used as a surrogate measure of the maturational age of the infants, which could potentially be monitored non-invasively and in real-time in the setting of neonatal intensive care units.
Collapse
|
18
|
Leon C, Carrault G, Pladys P, Beuchee A. Early Detection of Late Onset Sepsis in Premature Infants Using Visibility Graph Analysis of Heart Rate Variability. IEEE J Biomed Health Inform 2021; 25:1006-1017. [PMID: 32881699 DOI: 10.1109/jbhi.2020.3021662] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
OBJECTIVE This study was designed to test the diagnostic value of visibility graph features derived from the heart rate time series to predict late onset sepsis (LOS) in preterm infants using machine learning. METHODS The heart rate variability (HRV) data was acquired from 49 premature newborns hospitalized in neonatal intensive care units (NICU). The LOS group consisted of patients who received more than five days of antibiotics, at least 72 hours after birth. The control group consisted of infants who did not receive antibiotics. HRV features in the days prior to the start of antibiotics (LOS group) or in a randomly selected period (control group) were compared against a baseline value calculated during a calibration period. After automatic feature selection, four machine learning algorithms were trained. All the tests were done using two variants of the feature set: one only included traditional HRV features, and the other additionally included visibility graph features. Performance was studied using area under the receiver operating characteristics curve (AUROC). RESULTS The best performance for detecting LOS was obtained with logistic regression, using the feature set including visibility graph features, with AUROC of 87.7% during the six hours preceding the start of antibiotics, and with predictive potential (AUROC above 70%) as early as 42 h before start of antibiotics. CONCLUSION These results demonstrate the usefulness of introducing visibility graph indexes in HRV analysis for sepsis prediction in newborns. SIGNIFICANCE The method proposed the possibility of non-invasive, real-time monitoring of risk of LOS in a NICU setting.
Collapse
|
19
|
Hurley NC, Spatz ES, Krumholz HM, Jafari R, Mortazavi BJ. A Survey of Challenges and Opportunities in Sensing and Analytics for Risk Factors of Cardiovascular Disorders. ACM TRANSACTIONS ON COMPUTING FOR HEALTHCARE 2021; 2:9. [PMID: 34337602 PMCID: PMC8320445 DOI: 10.1145/3417958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Accepted: 08/01/2020] [Indexed: 10/22/2022]
Abstract
Cardiovascular disorders cause nearly one in three deaths in the United States. Short- and long-term care for these disorders is often determined in short-term settings. However, these decisions are made with minimal longitudinal and long-term data. To overcome this bias towards data from acute care settings, improved longitudinal monitoring for cardiovascular patients is needed. Longitudinal monitoring provides a more comprehensive picture of patient health, allowing for informed decision making. This work surveys sensing and machine learning in the field of remote health monitoring for cardiovascular disorders. We highlight three needs in the design of new smart health technologies: (1) need for sensing technologies that track longitudinal trends of the cardiovascular disorder despite infrequent, noisy, or missing data measurements; (2) need for new analytic techniques designed in a longitudinal, continual fashion to aid in the development of new risk prediction techniques and in tracking disease progression; and (3) need for personalized and interpretable machine learning techniques, allowing for advancements in clinical decision making. We highlight these needs based upon the current state of the art in smart health technologies and analytics. We then discuss opportunities in addressing these needs for development of smart health technologies for the field of cardiovascular disorders and care.
Collapse
|
20
|
Fedjajevs A, Groenendaal W, Agell C, Hermeling E. Platform for Analysis and Labeling of Medical Time Series. SENSORS (BASEL, SWITZERLAND) 2020; 20:E7302. [PMID: 33352643 PMCID: PMC7766988 DOI: 10.3390/s20247302] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 12/12/2020] [Accepted: 12/16/2020] [Indexed: 01/29/2023]
Abstract
Reliable and diverse labeled reference data are essential for the development of high-quality processing algorithms for medical signals, such as electrocardiogram (ECG) and photoplethysmogram (PPG). Here, we present the Platform for Analysis and Labeling of Medical time Series (PALMS) designed in Python. Its graphical user interface (GUI) facilitates three main types of manual annotations-(1) fiducials, e.g., R-peaks of ECG; (2) events with an adjustable duration, e.g., arrhythmic episodes; and (3) signal quality, e.g., data parts corrupted by motion artifacts. All annotations can be attributed to the same signal simultaneously in an ergonomic and user-friendly manner. Configuration for different data and annotation types is straightforward and flexible in order to use a wide range of data sources and to address many different use cases. Above all, configuration of PALMS allows plugging-in existing algorithms to display outcomes of automated processing, such as automatic R-peak detection, and to manually correct them where needed. This enables fast annotation and can be used to further improve algorithms. The GUI is currently complemented by ECG and PPG algorithms that detect characteristic points with high accuracy. The ECG algorithm reached 99% on the MIT/BIH arrhythmia database. The PPG algorithm was validated on two public databases with an F1-score above 98%. The GUI and optional algorithms result in an advanced software tool that allows the creation of diverse reference sets for existing datasets.
Collapse
Affiliation(s)
- Andrejs Fedjajevs
- Stichting Imec the Netherlands, 5656 AE Eindhoven, The Netherlands; (W.G.); (C.A.); (E.H.)
| | | | | | | |
Collapse
|
21
|
Lam E, Aratia S, Wang J, Tung J. Measuring Heart Rate Variability in Free-Living Conditions Using Consumer-Grade Photoplethysmography: Validation Study. JMIR BIOMEDICAL ENGINEERING 2020. [DOI: 10.2196/17355] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023] Open
Abstract
Background
Heart rate variability (HRV) is used to assess cardiac health and autonomic nervous system capabilities. With the growing popularity of commercially available wearable technologies, the opportunity to unobtrusively measure HRV via photoplethysmography (PPG) is an attractive alternative to electrocardiogram (ECG), which serves as the gold standard. PPG measures blood flow within the vasculature using color intensity. However, PPG does not directly measure HRV; it measures pulse rate variability (PRV). Previous studies comparing consumer-grade PRV with HRV have demonstrated mixed results in short durations of activity under controlled conditions. Further research is required to determine the efficacy of PRV to estimate HRV under free-living conditions.
Objective
This study aims to compare PRV estimates obtained from a consumer-grade PPG sensor with HRV measurements from a portable ECG during unsupervised free-living conditions, including sleep, and examine factors influencing estimation, including measurement conditions and simple editing methods to limit motion artifacts.
Methods
A total of 10 healthy adults were recruited. Data from a Microsoft Band 2 and a Shimmer3 ECG unit were recorded simultaneously using a smartphone. Participants wore the devices for >90 min during typical day-to-day activities and while sleeping. After filtering, ECG data were processed using a combination of discrete wavelet transforms and peak-finding methods to identify R-R intervals. P-P intervals were edited for deletion using methods based on outlier detection and by removing sections affected by motion artifacts. Common HRV metrics were compared, including mean N-N, SD of N-N intervals, percentage of subsequent differences >50 ms (pNN50), root mean square of successive differences, low-frequency power (LF), and high-frequency power. Validity was assessed using root mean square error (RMSE) and Pearson correlation coefficient (R2).
Results
Data sets for 10 days and 9 corresponding nights were acquired. The mean RMSE was 182 ms (SD 48) during the day and 158 ms (SD 67) at night. R2 ranged from 0.00 to 0.66, with 2 of 19 (2 nights) trials considered moderate, 7 of 19 (2 days, 5 nights) fair, and 10 of 19 (8 days, 2 nights) poor. Deleting sections thought to be affected by motion artifacts had a minimal impact on the accuracy of PRV measures. Significant HRV and PRV differences were found for LF during the day and R-R, SDNN, pNN50, and LF at night. For 8 of the 9 matched day and night data sets, R2 values were higher at night (P=.08). P-P intervals were less sensitive to rapid R-R interval changes.
Conclusions
Owing to overall poor concurrent validity and inconsistency among participant data, PRV was found to be a poor surrogate for HRV under free-living conditions. These findings suggest that free-living HRV measurements would benefit from examining alternate sensing methods, such as multiwavelength PPG and wearable ECG.
Collapse
|
22
|
Mejia-Mejia E, Budidha K, Abay TY, May JM, Kyriacou PA. Heart Rate Variability and Multi-Site Pulse Rate Variability for the Assessment of Autonomic Responses to Whole-Body Cold Exposure. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:2618-2621. [PMID: 33018543 DOI: 10.1109/embc44109.2020.9175856] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Heart rate variability (HRV) is a noninvasive marker of cardiac autonomic activity and has been used in different circumstances to assess the autonomic responses of the body. Pulse rate variability (PRV), a similar variable obtained from pulse waves, has been used in recent years as a valid surrogate of HRV. However, the effect that localized changes in autonomic activity have in the relationship between HRV and PRV has not been entirely understood. In this study, a whole-body cold exposure protocol was performed to generate localized changes in autonomic activity, and HRV and PRV from different body sites were obtained. PRV measured from the earlobe and the finger was shown to differ from HRV, and the correlation between these variables was affected by the cold. Also, it was found that PRV from the finger was more affected by cold exposure than PRV from the earlobe. In conclusion, PRV is affected differently to HRV when localized changes in autonomic activity occur. Hence, PRV should not be considered as a valid surrogate of HRV under certain circumstances.Clinical Relevance- This indicates that pulse rate variability is affected differently to heart rate variability when autonomic activity is modified and suggests that pulse rate variability is not always a valid surrogate of heart rate variability.
Collapse
|
23
|
Paccione CE, Diep LM, Stubhaug A, Jacobsen HB. Motivational nondirective resonance breathing versus transcutaneous vagus nerve stimulation in the treatment of fibromyalgia: study protocol for a randomized controlled trial. Trials 2020; 21:808. [PMID: 32967704 PMCID: PMC7510318 DOI: 10.1186/s13063-020-04703-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Accepted: 08/27/2020] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Chronic widespread pain (CWP), including fibromyalgia (FM), affects one in every ten adults and is one of the leading causes of sick leave and emotional distress. Due to an unclear etiology and a complex pathophysiology, FM is a condition with few, if any, effective and safe treatments. However, current research within the field of vagal nerve innervation suggests psychophysiological and electrical means by which FM may be treated. This study will investigate the efficacy of two different noninvasive vagal nerve stimulation techniques for the treatment of FM. METHODS The study will use a randomized, single-blind, sham-controlled design to investigate the treatment efficacy of motivational nondirective resonance breathing (MNRB™) and transcutaneous vagus nerve stimulation (Nemos® tVNS) on patients diagnosed with FM. Consenting FM patients (N = 112) who are referred to the Department of Pain Management and Research at Oslo University Hospital, in Oslo, Norway, will be randomized into one of four independent groups. Half of these participants (N = 56) will be randomized to either an experimental tVNS group or a sham tVNS group. The other half (N = 56) will be randomized to either an experimental MNRB group or a sham MNRB group. Both active and sham treatment interventions will be delivered twice per day at home, 15 min/morning and 15 min/evening, for a total duration of 2 weeks (14 days). Participants are invited to the clinic twice, once for pre- and once for post-intervention data collection. The primary outcome is changes in photoplethysmography-measured heart rate variability. Secondary outcomes include self-reported pain intensity on a numeric rating scale, changes in pain detection threshold, pain tolerance threshold, and pressure pain limit determined by computerized pressure cuff algometry, blood pressure, and health-related quality of life. DISCUSSION The described randomized controlled trial aims to compare the efficacy of two vagal nerve innervation interventions, MNRB and tVNS, on heart rate variability and pain intensity in patients suffering from FM. This project tests a new and potentially effective means of treating a major public and global health concern where prevalence is high, disability is severe, and treatment options are limited. TRIAL REGISTRATION ClinicalTrials.gov NCT03180554 . Registered on August 06, 2017.
Collapse
Affiliation(s)
- Charles Ethan Paccione
- Doctoral Fellow in Medicine and Health Sciences, Faculty of Medicine, University of Oslo, Klaus Torgårds 3, 0372 Oslo, Norway
- Department of Pain Management and Research, Oslo University Hospital, Ullevål, Kirkeveien 166, 0853 Oslo, Norway
| | - Lien My Diep
- Oslo Center for Biostatistics and Epidemiology, Sognsvannsveien 9, 0372 Oslo, Norway
| | - Audun Stubhaug
- Department of Pain Management and Research, Oslo University Hospital, Ullevål, Kirkeveien 166, 0853 Oslo, Norway
| | - Henrik Børsting Jacobsen
- Department of Pain Management and Research, Oslo University Hospital, Ullevål, Kirkeveien 166, 0853 Oslo, Norway
| |
Collapse
|
24
|
Scardulla F, D’Acquisto L, Colombarini R, Hu S, Pasta S, Bellavia D. A Study on the Effect of Contact Pressure during Physical Activity on Photoplethysmographic Heart Rate Measurements. SENSORS (BASEL, SWITZERLAND) 2020; 20:E5052. [PMID: 32899540 PMCID: PMC7570982 DOI: 10.3390/s20185052] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 08/26/2020] [Accepted: 09/03/2020] [Indexed: 12/16/2022]
Abstract
Heart rate (HR) as an important physiological indicator could properly describe global subject's physical status. Photoplethysmographic (PPG) sensors are catching on in field of wearable sensors, combining the advantages in costs, weight and size. Nevertheless, accuracy in HR readings is unreliable specifically during physical activity. Among several identified sources that affect PPG recording, contact pressure (CP) between the PPG sensor and skin greatly influences the signals. METHODS In this study, the accuracy of HR measurements of a PPG sensor at different CP was investigated when compared with a commercial ECG-based chest strap used as a test control, with the aim of determining the optimal CP to produce a reliable signal during physical activity. Seventeen subjects were enrolled for the study to perform a physical activity at three different rates repeated at three different contact pressures of the PPG-based wristband. RESULTS The results show that the CP of 54 mmHg provides the most accurate outcome with a Pearson correlation coefficient ranging from 0.81 to 0.95 and a mean average percentage error ranging from 3.8% to 2.4%, based on the physical activity rate. CONCLUSION Authors found that changes in the CP have greater effects on PPG-HR signal quality than those deriving from the intensity of the physical activity and specifically, the individual best CP for each subject provided reliable HR measurements even for a high intensity of physical exercise with a Bland-Altman plot within ±11 bpm. Although future studies on a larger cohort of subjects are still needed, this study could contribute a profitable indication to enhance accuracy of PPG-based wearable devices.
Collapse
Affiliation(s)
- Francesco Scardulla
- Department of Engineering, University of Palermo, 90128 Palermo, Italy; (L.D.); (R.C.); (S.P.)
| | - Leonardo D’Acquisto
- Department of Engineering, University of Palermo, 90128 Palermo, Italy; (L.D.); (R.C.); (S.P.)
| | - Raffaele Colombarini
- Department of Engineering, University of Palermo, 90128 Palermo, Italy; (L.D.); (R.C.); (S.P.)
| | - Sijung Hu
- Wolfson School of Mechanical, Electrical and Manufacturing Engineering, Loughborough University, Loughborough, Leicestershire LE11 3TU, UK;
| | - Salvatore Pasta
- Department of Engineering, University of Palermo, 90128 Palermo, Italy; (L.D.); (R.C.); (S.P.)
| | - Diego Bellavia
- Department for the Treatment and Study of Cardiothoracic Diseases and Cardiothoracic Transplantation, IRCCS-ISMETT, via Tricomi n.5, 90127 Palermo, Italy;
| |
Collapse
|
25
|
Mejía-Mejía E, May JM, Torres R, Kyriacou PA. Pulse rate variability in cardiovascular health: a review on its applications and relationship with heart rate variability. Physiol Meas 2020; 41:07TR01. [DOI: 10.1088/1361-6579/ab998c] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
|
26
|
Aygun A, Ghasemzadeh H, Jafari R. Robust Interbeat Interval and Heart Rate Variability Estimation Method From Various Morphological Features Using Wearable Sensors. IEEE J Biomed Health Inform 2020; 24:2238-2250. [PMID: 31899444 PMCID: PMC11036325 DOI: 10.1109/jbhi.2019.2962627] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
We introduce a novel approach for robust estimation of physiological parameters such as interbeat interval (IBI) and heart rate variability (HRV) from cardiac signals captured with wearable sensors in the presence of motion artifacts. Motion artifact due to physical exercise is known as a major source of noise that contributes to a significant decline in the performance of IBI and HRV estimation techniques for cardiac monitoring in free-living environments. Therefore, developing robust estimation algorithms is essential for utilization of wearable sensors in daily life situations. The proposed approach includes two algorithmic components. First, we propose a combinatorial technique to select characteristic points that define heartbeats in noisy signals in time domain. The heartbeat detection problem is defined as a shortest path search problem on a direct acyclic graph that leverages morphological features of the cardiac signals by taking advantage of the time-continuity of heartbeats - each heartbeat ends with the starting point of the next heartbeat. The graph is constructed with vertices and edges representing candidate morphological features and IBIs, respectively. Second, we propose a fusion technique to combine physiological parameters estimated from different morphological features using the shortest path algorithm to obtain more accurate IBI/HRV estimations. We evaluate our techniques on motion-corrupted photoplethysmogram and electrocardiogram signals. Our results indicate that the estimated IBIs are highly correlated with the ground truth (r = 0.89) and detected HRV parameters indicate high correlation with the true HRV parameters. Furthermore, our findings demonstrate that the developed fusion technique, which utilizes different morphological features, achieves a correlation coefficient that is at least 3% higher than that obtained using single physiological characteristic.
Collapse
|
27
|
Wilson N, Guragain B, Verma A, Archer L, Tavakolian K. Blending Human and Machine: Feasibility of Measuring Fatigue Through the Aviation Headset. HUMAN FACTORS 2020; 62:553-564. [PMID: 31180741 DOI: 10.1177/0018720819849783] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
OBJECTIVE To determine viability of drowsiness detection, researchers study the feasibility of photoplethysmogram (PPG) data collection from the geography of the aviation headset, correlating to electrocardiogram (ECG) reference. BACKGROUND Fatigue has been a probable cause, contributing factor, or a finding in 20% of transportation incidents and accidents studied between January 2001 and December 2012. This operational hazard is particularly troublesome within aviation and airline operations. METHOD PPG and ECG data were collected synchronously from Federal Aviation Administration (FAA) commercially rated pilots during flight simulation in the window of circadian low (WOCL). Valid PPG and ECG data from 14 participants were analyzed, which yielded approximately 2 hr of data per participant for fatigue-related analysis. RESULTS The results of the study demonstrate clear trends toward decreased heart rate for both ECG and PPG and suggest progression of drowsiness between four separate periods (T1, T2, T3, and T4) selected during the study; however, the mean heart rate change from T1 to T4 was statistically significant. CONCLUSION The results suggest that ECG and PPG data can be an important tool to observe conditions where drowsiness or fatigue may add risk to the operation. In addition, the data show high correlation between ECG and PPG data, further suggesting that a simpler PPG sensor, mounted within the geography of the aviation headset, may streamline the operationalization of important physiological data. APPLICATION Incorporation of PPG sensors and associated signal processing methods into facilitating equipment, such as the aviation headset, may add a layer to operational safety.
Collapse
Affiliation(s)
| | - Bijay Guragain
- 459712 3579 University of North Dakota, Grand Forks, USA
| | - Ajay Verma
- 459712 3579 University of North Dakota, Grand Forks, USA
| | - Lewis Archer
- 459712 3579 University of North Dakota, Grand Forks, USA
| | | |
Collapse
|
28
|
Can YS, Gokay D, Kılıç DR, Ekiz D, Chalabianloo N, Ersoy C. How Laboratory Experiments Can Be Exploited forMonitoring Stress in the Wild: A Bridge BetweenLaboratory and Daily Life. SENSORS (BASEL, SWITZERLAND) 2020; 20:E838. [PMID: 32033238 PMCID: PMC7038725 DOI: 10.3390/s20030838] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/03/2020] [Revised: 01/24/2020] [Accepted: 02/01/2020] [Indexed: 11/17/2022]
Abstract
Chronic stress leads to poor well-being, and it has effects on life quality and health. Societymay have significant benefits from an automatic daily life stress detection system using unobtrusivewearable devices using physiological signals. However, the performance of these systems is notsufficiently accurate when they are used in unrestricted daily life compared to the systems testedin controlled real-life and laboratory conditions. To test our stress level detection system thatpreprocesses noisy physiological signals, extracts features, and applies machine learning classificationtechniques, we used a laboratory experiment and ecological momentary assessment based datacollection with smartwatches in daily life. We investigated the effect of different labeling techniquesand different training and test environments. In the laboratory environments, we had more controlledsituations, and we could validate the perceived stress from self-reports. When machine learningmodels were trained in the laboratory instead of training them with the data coming from daily life,the accuracy of the system when tested in daily life improved significantly. The subjectivity effectcoming from the self-reports in daily life could be eliminated. Our system obtained higher stresslevel detection accuracy results compared to most of the previous daily life studies.
Collapse
Affiliation(s)
- Yekta Said Can
- Computer Engineering Department, Bogazici University, Bebek, 34342 Istanbul, Turkey; (D.G.); (D.R.K.); (D.E.); (N.C.); (C.E.)
| | | | | | | | | | | |
Collapse
|
29
|
Manjarres J, Narvaez P, Gasser K, Percybrooks W, Pardo M. Physical Workload Tracking Using Human Activity Recognition with Wearable Devices. SENSORS 2019; 20:s20010039. [PMID: 31861639 PMCID: PMC6982756 DOI: 10.3390/s20010039] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Revised: 12/11/2019] [Accepted: 12/13/2019] [Indexed: 01/28/2023]
Abstract
In this work, authors address workload computation combining human activity recognition and heart rate measurements to establish a scalable framework for health at work and fitness-related applications. The proposed architecture consists of two wearable sensors: one for motion, and another for heart rate. The system employs machine learning algorithms to determine the activity performed by a user, and takes a concept from ergonomics, the Frimat's score, to compute the corresponding physical workload from measured heart rate values providing in addition a qualitative description of the workload. A random forest activity classifier is trained and validated with data from nine subjects, achieving an accuracy of 97.5%. Then, tests with 20 subjects show the reliability of the activity classifier, which keeps an accuracy up to 92% during real-time testing. Additionally, a single-subject twenty-day physical workload tracking case study evinces the system capabilities to detect body adaptation to a custom exercise routine. The proposed system enables remote and multi-user workload monitoring, which facilitates the job for experts in ergonomics and workplace health.
Collapse
|
30
|
Different lasers reveal different skin microcirculatory flowmotion - data from the wavelet transform analysis of human hindlimb perfusion. Sci Rep 2019; 9:16951. [PMID: 31740748 PMCID: PMC6861459 DOI: 10.1038/s41598-019-53213-2] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2019] [Accepted: 10/29/2019] [Indexed: 12/17/2022] Open
Abstract
Laser Doppler flowmetry (LDF) and reflection photoplethysmography (PPG) are standard technologies to access microcirculatory function in vivo. However, different light frequencies mean different interaction with tissues, such that LDF and PPG flowmotion curves might have distinct meanings, particularly during adaptative (homeostatic) processes. Therefore, we analyzed LDF and PPG perfusion signals obtained in response to opposite challenges. Young healthy volunteers, both sexes, were assigned to Group 1 (n = 29), submitted to a normalized Swedish massage procedure in one lower limb, increasing perfusion, or Group 2 (n = 14), submitted to a hyperoxia challenge test, decreasing perfusion. LDF (Periflux 5000) and PPG (PLUX-Biosignals) green light sensors applied distally on both lower limbs recorded perfusion changes for each experimental protocol. Both techniques detected the perfusion increase with massage, and the perfusion decrease with hyperoxia, in both limbs. Further analysis with the wavelet transform (WT) revealed better depth-related discriminative ability for PPG (more superficial, less blood sampling) compared with LDF in both challenges. Spectral amplitude profiles consistently demonstrated better sensitivity for LDF, especially regarding the lowest frequency components. Strong correlations between components were not found. Therefore, LDF and PPG flowmotion curves are not equivalent, a relevant finding to better study microcirculatory physiology.
Collapse
|
31
|
Dur O, Rhoades C, Ng MS, Elsayed R, van Mourik R, Majmudar MD. Design Rationale and Performance Evaluation of the Wavelet Health Wristband: Benchtop Validation of a Wrist-Worn Physiological Signal Recorder. JMIR Mhealth Uhealth 2018; 6:e11040. [PMID: 30327288 PMCID: PMC6231731 DOI: 10.2196/11040] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2018] [Revised: 08/23/2018] [Accepted: 09/10/2018] [Indexed: 11/23/2022] Open
Abstract
Background Wearable and connected health devices along with the recent advances in mobile and cloud computing provide a continuous, convenient-to-patient, and scalable way to collect personal health data remotely. The Wavelet Health platform and the Wavelet wristband have been developed to capture multiple physiological signals and to derive biometrics from these signals, including resting heart rate (HR), heart rate variability (HRV), and respiration rate (RR). Objective This study aimed to evaluate the accuracy of the biometric estimates and signal quality of the wristband. Methods Measurements collected from 35 subjects using the Wavelet wristband were compared with simultaneously recorded electrocardiogram and spirometry measurements. Results The HR, HRV SD of normal-to-normal intervals, HRV root mean square of successive differences, and RR estimates matched within 0.7 beats per minute (SD 0.9), 7 milliseconds (SD 10), 11 milliseconds (SD 12), and 1 breaths per minute (SD 1) mean absolute deviation of the reference measurements, respectively. The quality of the raw plethysmography signal collected by the wristband, as determined by the harmonic-to-noise ratio, was comparable with that obtained from measurements from a finger-clip plethysmography device. Conclusions The accuracy of the biometric estimates and high signal quality indicate that the wristband photoplethysmography device is suitable for performing pulse wave analysis and measuring vital signs.
Collapse
Affiliation(s)
- Onur Dur
- Wavelet Health, Mountain View, CA, United States
| | | | - Man Suen Ng
- Wavelet Health, Mountain View, CA, United States
| | - Ragwa Elsayed
- Biomedical Engineering, San Jose State University, San Jose, CA, United States
| | | | - Maulik D Majmudar
- Healthcare Transformation Lab, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| |
Collapse
|
32
|
A novel online method for identifying motion artifact and photoplethysmography signal reconstruction using artificial neural networks and adaptive neuro-fuzzy inference system. Neural Comput Appl 2018. [DOI: 10.1007/s00521-018-3767-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
|
33
|
Shokri-Kojori E, Tomasi D, Volkow ND. An Autonomic Network: Synchrony Between Slow Rhythms of Pulse and Brain Resting State Is Associated with Personality and Emotions. Cereb Cortex 2018; 28:3356-3371. [PMID: 29955858 PMCID: PMC6095212 DOI: 10.1093/cercor/bhy144] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2018] [Revised: 05/15/2018] [Accepted: 05/18/2018] [Indexed: 12/14/2022] Open
Abstract
The sympathetic system's role in modulating vasculature and its influence on emotions and personality led us to test the hypothesis that interactions between brain resting-state networks (RSNs) and pulse amplitude (indexing sympathetic activity) would be associated with emotions and personality. In 203 participants, we characterized RSN spatiotemporal characteristics, and phase-amplitude associations of RSN fluctuations with pulse and respiratory recordings. We found that RSNs are spatially reproducible within participants and were temporally associated with low frequencies (LFs < 0.1 Hz) in physiological signals. LF fluctuations in pulse amplitude were not related to cardiac electrical activity and preceded LF fluctuations in RSNs, while LF respiratory amplitude fluctuations followed LF fluctuations in RSNs. LF phase dispersion (PD) (lack of synchrony) between RSNs and pulse (PDpulse) (not respiratory) correlated with the common variability in measures of personality and emotions, with more synchrony being associated with more positive temperamental characteristics. Voxel-level PDpulse mapping revealed an "autonomic brain network," including sensory cortices and dorsal attention stream, with significant interactions with peripheral signals. Here, we uncover associations between pulse signal amplitude (presumably of sympathetic origin) and brain resting state, suggesting that interactions between central and autonomic nervous systems are important for characterizing personality and emotions.
Collapse
Affiliation(s)
- Ehsan Shokri-Kojori
- Laboratory of Neuroimaging, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, USA
| | - Dardo Tomasi
- Laboratory of Neuroimaging, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, USA
| | - Nora D Volkow
- Laboratory of Neuroimaging, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, USA
- National Institute on Drug Abuse, National Institutes of Health, Bethesda, MD, USA
| |
Collapse
|
34
|
Castaneda D, Esparza A, Ghamari M, Soltanpur C, Nazeran H. A review on wearable photoplethysmography sensors and their potential future applications in health care. ACTA ACUST UNITED AC 2018; 4:195-202. [PMID: 30906922 PMCID: PMC6426305 DOI: 10.15406/ijbsbe.2018.04.00125] [Citation(s) in RCA: 176] [Impact Index Per Article: 29.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Photoplethysmography (PPG) is an uncomplicated and inexpensive optical measurement method that is often used for heart rate monitoring purposes. PPG is a non-invasive technology that uses a light source and a photodetector at the surface of skin to measure the volumetric variations of blood circulation. Recently, there has been much interest from numerous researchers around the globe to extract further valuable information from the PPG signal in addition to heart rate estimation and pulse oxymetry readings. PPG signal’s second derivative wave contains important health-related information. Thus, analysis of this waveform can help researchers and clinicians to evaluate various cardiovascular-related diseases such as atherosclerosis and arterial stiffness. Moreover, investigating the second derivative wave of PPG signal can also assist in early detection and diagnosis of various cardiovascular illnesses that may possibly appear later in life. For early recognition and analysis of such illnesses, continuous and real-time monitoring is an important approach that has been enabled by the latest technological advances in sensor technology and wireless communications. The aim of this article is to briefly consider some of the current developments and challenges of wearable PPG-based monitoring technologies and then to discuss some of the potential applications of this technology in clinical settings.
Collapse
Affiliation(s)
- Denisse Castaneda
- Department of Electrical and Computer Engineering, University of Texas at El Paso, USA
| | - Aibhlin Esparza
- Department of Electrical and Computer Engineering, University of Texas at El Paso, USA
| | - Mohammad Ghamari
- Department of Energy and Mineral Engineering, Pennsylvania State University, USA
| | - Cinna Soltanpur
- Department of Electrical and Computer Engineering, University of Oklahoma, USA
| | - Homer Nazeran
- Department of Electrical and Computer Engineering, University of Texas at El Paso, USA
| |
Collapse
|
35
|
Kwon O, Jeong J, Kim HB, Kwon IH, Park SY, Kim JE, Choi Y. Electrocardiogram Sampling Frequency Range Acceptable for Heart Rate Variability Analysis. Healthc Inform Res 2018; 24:198-206. [PMID: 30109153 PMCID: PMC6085204 DOI: 10.4258/hir.2018.24.3.198] [Citation(s) in RCA: 48] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2018] [Revised: 07/03/2018] [Accepted: 07/12/2018] [Indexed: 11/23/2022] Open
Abstract
Objectives Heart rate variability (HRV) has gained recognition as a noninvasive marker of autonomic activity. HRV is considered a promising tool in various clinical scenarios. The optimal electrocardiogram (ECG) sampling frequency required to ensure sufficient precision of R–R intervals for HRV analysis has not yet been determined. Here, we aimed to determine the acceptable ECG sampling frequency range by analyzing ECG signals from patients who visited an emergency department with the chief complaint of acute intoxication or overdose. Methods The study included 83 adult patients who visited an emergency department with the chief complaint of acute poisoning. The original 1,000-Hz ECG signals were down-sampled to 500-, 250-, 100-, and 50-Hz sampling frequencies with linear interpolation. R–R interval data were analyzed for time-domain, frequency-domain, and nonlinear HRV parameters. Parameters derived from the data on down-sampled frequencies were compared with those derived from the data on 1,000-Hz signals, and Lin's concordance correlation coefficients were calculated. Results Down-sampling to 500 or 250 Hz resulted in excellent concordance. Signals down-sampled to 100 Hz produced acceptable results for time-domain analysis and Poincaré plots, but not for frequency-domain analysis. Down-sampling to 50 Hz proved to be unacceptable for both time- and frequency-domain analyses. At 50 Hz, the root-mean-squared successive differences and the power of high frequency tended to have high values and random errors. Conclusions A 250-Hz sampling frequency would be acceptable for HRV analysis. When frequency-domain analysis is not required, a 100-Hz sampling frequency would also be acceptable.
Collapse
Affiliation(s)
- Ohhwan Kwon
- Department of Emergency Medicine, Dong-A University Medical Center, Busan, Korea
| | - Jinwoo Jeong
- Department of Emergency Medicine, Dong-A University Medical Center, Busan, Korea.,Department of Emergency Medicine, Dong-A University College of Medicine, Busan, Korea
| | - Hyung Bin Kim
- Department of Emergency Medicine, Pusan National University Hospital, Busan, Korea
| | - In Ho Kwon
- Department of Emergency Medicine, Dong-A University Medical Center, Busan, Korea.,Department of Emergency Medicine, Dong-A University College of Medicine, Busan, Korea.,Department of Emergency Medicine, Graduate School, Kangwon National University School of Medicine, Chuncheon, Korea
| | - Song Yi Park
- Department of Emergency Medicine, Dong-A University Medical Center, Busan, Korea.,Department of Emergency Medicine, Dong-A University College of Medicine, Busan, Korea
| | - Ji Eun Kim
- Department of Emergency Medicine, Dong-A University Medical Center, Busan, Korea
| | - Yuri Choi
- Department of Emergency Medicine, Dong-A University Medical Center, Busan, Korea
| |
Collapse
|
36
|
Vescio B, Salsone M, Gambardella A, Quattrone A. Comparison between Electrocardiographic and Earlobe Pulse Photoplethysmographic Detection for Evaluating Heart Rate Variability in Healthy Subjects in Short- and Long-Term Recordings. SENSORS 2018. [PMID: 29533990 PMCID: PMC5877367 DOI: 10.3390/s18030844] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Heart rate variability (HRV) is commonly used to assess autonomic functions and responses to environmental stimuli. It is usually derived from electrocardiographic signals; however, in the last few years, photoplethysmography has been successfully used to evaluate beat-to-beat time intervals and to assess changes in the human heart rate under several conditions. The present work describes a simple design of a photoplethysmograph, using a wearable earlobe sensor. Beat-to-beat time intervals were evaluated as the time between subsequent pulses, thus generating a signal representative of heart rate variability, which was compared to RR intervals from classic electrocardiography. Twenty-minute pulse photoplethysmography and ECG recordings were taken simultaneously from 10 healthy individuals. Ten additional subjects were recorded for 24 h. Comparisons were made of raw signals and on time-domain and frequency-domain HRV parameters. There were small differences between the inter-beat intervals evaluated with the two techniques. The current findings suggest that our wearable earlobe pulse photoplethysmograph may be suitable for short and long-term home measuring and monitoring of HRV parameters.
Collapse
Affiliation(s)
| | - Maria Salsone
- Neuroimaging Unit, Institute of Molecular Bioimaging and Physiology of the National Research Council (IBFM-CNR), 88100 Catanzaro, Italy.
| | - Antonio Gambardella
- Department of Medical and Surgical Sciences, Magna Græcia University, 88100 Catanzaro, Italy.
| | - Aldo Quattrone
- Neuroimaging Unit, Institute of Molecular Bioimaging and Physiology of the National Research Council (IBFM-CNR), 88100 Catanzaro, Italy.
| |
Collapse
|
37
|
Morelli D, Bartoloni L, Colombo M, Plans D, Clifton DA. Profiling the propagation of error from PPG to HRV features in a wearable physiological-monitoring device. Healthc Technol Lett 2018; 5:59-64. [PMID: 29750114 PMCID: PMC5933374 DOI: 10.1049/htl.2017.0039] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2017] [Revised: 07/18/2017] [Accepted: 07/19/2017] [Indexed: 12/23/2022] Open
Abstract
Wearable physiological monitors are becoming increasingly commonplace in the consumer domain, but in literature there exists no substantive studies of their performance when measuring the physiology of ambulatory patients. In this Letter, the authors investigate the reliability of the heart-rate (HR) sensor in an exemplar ‘wearable’ wrist-worn monitoring system (the Microsoft Band 2); their experiments quantify the propagation of error from (i) the photoplethysmogram (PPG) acquired by pulse oximetry, to (ii) estimation of HR, and (iii) subsequent calculation of HR variability (HRV) features. Their experiments confirm that motion artefacts account for the majority of this error, and show that the unreliable portions of HR data can be removed, using the accelerometer sensor from the wearable device. The experiments further show that acquired signals contain noise with substantial energy in the high-frequency band, and that this contributes to subsequent variability in standard HRV features often used in clinical practice. The authors finally show that the conventional use of long-duration windows of data is not needed to perform accurate estimation of time-domain HRV features.
Collapse
Affiliation(s)
- Davide Morelli
- BioBeats Group Ltd, London, UK.,Dipartimento di Informatica, Università di Pisa, Pisa, Italy.,Center for Digital Economy, University of Surrey, Guildford, UK
| | - Leonardo Bartoloni
- BioBeats Group Ltd, London, UK.,Dipartimento di Informatica, Università di Pisa, Pisa, Italy
| | - Michele Colombo
- BioBeats Group Ltd, London, UK.,Dipartimento di Informatica, Università di Pisa, Pisa, Italy
| | - David Plans
- BioBeats Group Ltd, London, UK.,Center for Digital Economy, University of Surrey, Guildford, UK
| | - David A Clifton
- Department of Engineering Science, University of Oxford, Oxford, UK
| |
Collapse
|
38
|
Pinheiro N, Couceiro R, Henriques J, Muehlsteff J, Quintal I, Goncalves L, Carvalho P. Can PPG be used for HRV analysis? ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2017; 2016:2945-2949. [PMID: 28268930 DOI: 10.1109/embc.2016.7591347] [Citation(s) in RCA: 43] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Heart rate variability (HRV) represents one of the most promising markers of the autonomic nervous system (ANS) regulation. However, it requires the acquisition of the ECG signal in order to reliably detect the RR intervals, which is not always easily and comfortably available in personal health applications. Additionally, due to progress in single spot optical sensors, photoplethysmography (PPG) is an interesting alternative for heartbeat interval measurements, since it is a more convenient and a less intrusive measurement technique. Driven by the technological advances in such sensors, wrist-worn devices are becoming a commodity, and the interest in the assessment of HRV indexes from the PPG analysis (pulse rate variability - PRV) is rising. In this study, we investigate the hypothesis of using PRV features as surrogates for HRV indexes, in three different contexts: healthy subjects at rest, healthy subjects after physical exercise and subjects with cardiovascular diseases (CVD). Additionally, we also evaluate which are the characteristic points better suited for PRV analysis in these contexts, i.e. the PPG waveform characteristic points leading to the PRV features that present the best estimates of HRV (correlation and error analysis). The achieved results suggest that the PRV can be often used as an alternative for HRV analysis in healthy subjects, with significant correlations above 82%, for both time and frequency features. Contrarily, in the post-exercise and CVD subjects, time and (most importantly) frequency domain features shall be used with caution (mean correlations ranging from 68% to 88%).
Collapse
|
39
|
Timimi AAK, Ali MAM, Chellappan K. A Novel AMARS Technique for Baseline Wander Removal Applied to Photoplethysmogram. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2017; 11:627-639. [PMID: 28489546 DOI: 10.1109/tbcas.2017.2649940] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
A new digital filter, AMARS (aligning minima of alternating random signal) has been derived using trigonometry to regulate signal pulsations inline. The pulses are randomly presented in continuous signals comprising frequency band lower than the signal's mean rate. Frequency selective filters are conventionally employed to reject frequencies undesired by specific applications. However, these conventional filters only reduce the effects of the rejected range producing a signal superimposed by some baseline wander (BW). In this work, filters of different ranges and techniques were independently configured to preprocess a photoplethysmogram, an optical biosignal of blood volume dynamics, producing wave shapes with several BWs. The AMARS application effectively removed the encountered BWs to assemble similarly aligned trends. The removal implementation was found repeatable in both ear and finger photoplethysmograms, emphasizing the importance of BW removal in biosignal processing in retaining its structural, functional and physiological properties. We also believe that AMARS may be relevant to other biological and continuous signals modulated by similar types of baseline volatility.
Collapse
|
40
|
Guede-Fernandez F, Ferrer-Mileo V, Ramos-Castro J, Fernandez-Chimeno M, Garcia-Gonzalez MA. Real time heart rate variability assessment from Android smartphone camera photoplethysmography: Postural and device influences. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2016; 2015:7332-5. [PMID: 26737985 DOI: 10.1109/embc.2015.7320085] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
The aim of this paper is to present a smartphone based system for real-time pulse-to-pulse (PP) interval time series acquisition by frame-to-frame camera image processing. The developed smartphone application acquires image frames from built-in rear-camera at the maximum available rate (30 Hz) and the smartphone GPU has been used by Renderscript API for high performance frame-by-frame image acquisition and computing in order to obtain PPG signal and PP interval time series. The relative error of mean heart rate is negligible. In addition, measurement posture and the employed smartphone model influences on the beat-to-beat error measurement of heart rate and HRV indices have been analyzed. Then, the standard deviation of the beat-to-beat error (SDE) was 7.81 ± 3.81 ms in the worst case. Furthermore, in supine measurement posture, significant device influence on the SDE has been found and the SDE is lower with Samsung S5 than Motorola X. This study can be applied to analyze the reliability of different smartphone models for HRV assessment from real-time Android camera frames processing.
Collapse
|
41
|
Holper L, Seifritz E, Scholkmann F. Short-term pulse rate variability is better characterized by functional near-infrared spectroscopy than by photoplethysmography. JOURNAL OF BIOMEDICAL OPTICS 2016; 21:091308. [PMID: 27185106 DOI: 10.1117/1.jbo.21.9.091308] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2016] [Accepted: 04/18/2016] [Indexed: 05/29/2023]
Abstract
Pulse rate variability (PRV) can be extracted from functional near-infrared spectroscopy (fNIRS) (PRV(NIRS)) and photoplethysmography (PPG) (PRV(PPG)) signals. The present study compared the accuracy of simultaneously acquired PRV(NIRS) and PRV(PPG), and evaluated their different characterizations of the sympathetic (SNS) and parasympathetic (PSNS) autonomous nervous system activity. Ten healthy subjects were recorded during resting-state (RS) and respiratory challenges in two temperature conditions, i.e., room temperature (23°C) and cold temperature (4°C). PRV(NIRS) was recorded based on fNIRS measurement on the head, whereas PRV(PPG) was determined based on PPG measured at the finger. Accuracy between PRV(NIRS) and PRV(PPG), as assessed by cross-covariance and cross-sample entropy, demonstrated a high degree of correlation (r > 0.9), which was significantly reduced by respiration and cold temperature. Characterization of SNS and PSNS using frequency-domain, time-domain, and nonlinear methods showed that PRV(NIRS) provided significantly better information on increasing PSNS activity in response to respiration and cold temperature than PRV(PPG). The findings show that PRV(NIRS) may outperform PRV(PPG) under conditions in which respiration and temperature changes are present, and may, therefore, be advantageous in research and clinical settings, especially if characterization of the autonomous nervous system is desired.
Collapse
Affiliation(s)
- Lisa Holper
- University of Zurich, Department of Psychiatry, Psychotherapy, and Psychosomatics, Hospital of Psychiatry, Lenggstrasse 31, 8032 Zurich, Switzerland
| | - Erich Seifritz
- University of Zurich, Department of Psychiatry, Psychotherapy, and Psychosomatics, Hospital of Psychiatry, Lenggstrasse 31, 8032 Zurich, Switzerland
| | - Felix Scholkmann
- University Hospital Zurich, University of Zurich, Biomedical Optics Research Laboratory, Department of Neonatology, Frauenklinikstrasse 10, 8091 Zurich, Switzerland
| |
Collapse
|
42
|
Jeyhani V, Mahdiani S, Peltokangas M, Vehkaoja A. Comparison of HRV parameters derived from photoplethysmography and electrocardiography signals. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2016; 2015:5952-5. [PMID: 26737647 DOI: 10.1109/embc.2015.7319747] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Heart rate variability (HRV) has become a useful tool in analysis of cardiovascular system in both research and clinical fields. HRV has been also used in other applications such as stress level estimation in wearable devices. HRV is normally obtained from ECG as the time interval of two successive R waves. Recently PPG has been proposed as an alternative for ECG in HRV analysis to overcome some difficulties in measurement of ECG. In addition, PPG-HRV is also used in some commercial devices such as modern optical wrist-worn heart rate monitors. However, some researches have shown that PPG is not a surrogate for heart rate variability analysis. In this work, HRV analysis was applied on beat-to-beat intervals obtained from ECG and PPG in 19 healthy male subjects. Some important HRV parameters were calculated from PPG-HRV and ECG-HRV. Maximum of PPG and its second derivative were considered as two methods for obtaining the beat-to-beat signals from PPG and the results were compared with those achieved from ECG-HRV. Our results show that the smallest error happens in SDNN and SD2 with relative error of 2.46% and 2%, respectively. The most affected parameter is pNN50 with relative error of 29.89%. In addition, in our trial, using the maximum of PPG gave better results than its second derivative.
Collapse
|
43
|
Laurin A, Khosrow-Khavar F, Blaber AP, Tavakolian K. Accurate and consistent automatic seismocardiogram annotation without concurrent ECG. Physiol Meas 2016; 37:1588-604. [PMID: 27510446 DOI: 10.1088/0967-3334/37/9/1588] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Seismocardiography (SCG) is the measurement of vibrations in the sternum caused by the beating of the heart. Precise cardiac mechanical timings that are easily obtained from SCG are critically dependent on accurate identification of fiducial points. So far, SCG annotation has relied on concurrent ECG measurements. An algorithm capable of annotating SCG without the use any other concurrent measurement was designed. We subjected 18 participants to graded lower body negative pressure. We collected ECG and SCG, obtained R peaks from the former, and annotated the latter by hand, using these identified peaks. We also annotated the SCG automatically. We compared the isovolumic moment timings obtained by hand to those obtained using our algorithm. Mean ± confidence interval of the percentage of accurately annotated cardiac cycles were [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] for levels of negative pressure 0, -20, -30, -40, and -50 mmHg. LF/HF ratios, the relative power of low-frequency variations to high-frequency variations in heart beat intervals, obtained from isovolumic moments were also compared to those obtained from R peaks. The mean differences ± confidence interval were [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] for increasing levels of negative pressure. The accuracy and consistency of the algorithm enables the use of SCG as a stand-alone heart monitoring tool in healthy individuals at rest, and could serve as a basis for an eventual application in pathological cases.
Collapse
Affiliation(s)
- A Laurin
- Department of Biomedical Physiology and Kinesiology, Simon Fraser University, University Dr, Burnaby, BC, V5A 1S6, Canada. Inria Saclay Ile-de-France, Rue Honoré d'Estienne d'Orves, Palaiseau, 91120, France
| | | | | | | |
Collapse
|
44
|
Miyazaki J, Kuge H, Mori H, Izumi E, Tanaka H, Watanabe M. A Moxa Stimulation on the Leg Affected the Function of Stomach via Autonomic Nerve System and Polymodal Receptors. Health (London) 2016. [DOI: 10.4236/health.2016.88078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
|
45
|
Training improves walking capacity and cardiovascular function in arteritis. JOURNAL OF VASCULAR NURSING 2015; 32:51-4. [PMID: 24944171 DOI: 10.1016/j.jvn.2013.12.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2013] [Revised: 12/04/2013] [Accepted: 12/04/2013] [Indexed: 10/25/2022]
Abstract
Patients with arteritis have a high risk of mortality from cardiovascular disorders. However, whether these patients benefit from an intervention involving exercise remains unclear. In this study, we assessed the effects of an unsupervised exercise program on walking capacity, quality of life, and cardiovascular parameters of a patient with arteritis. A 33-year-old man reporting symptoms of claudication during walking was studied. Imaging tests revealed severe atherosclerosis and arteritis was diagnosed. Five weekly sessions of walking for 16 weeks increased claudication distance and total walking distance, produced improvements in six out of the eight health-related quality-of-life domains, decreased systolic blood pressure, and changed cardiac autonomic modulation toward parasympathetic modulation. This case report showed that unsupervised exercise training improved walking capacity, quality of life, and cardiovascular parameters in a patient with arteritis.
Collapse
|
46
|
Koenig J, De Kooning M, Bernardi A, Williams DP, Nijs J, Thayer JF, Daenen L. Lower Resting State Heart Rate Variability Relates to High Pain Catastrophizing in Patients with Chronic Whiplash-Associated Disorders and Healthy Controls. Pain Pract 2015; 16:1048-1053. [PMID: 26614574 DOI: 10.1111/papr.12399] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2015] [Accepted: 07/20/2015] [Indexed: 11/28/2022]
Abstract
Vagally mediated heart rate variability (vmHRV) is widely respected as a psychophysiological measure of emotion regulation capacity and serves as a readily available index of executive brain areas that exert an inhibitory influence on subcortical structures. Pain catastrophizing (PC) is conceptualized as the tendency to misinterpret and exaggerate pain-related situations that may be threatening. Chronic pain patients show lower vmHRV and higher PC. Previously, no study has investigated the association of PC and vmHRV. We examined the association of PC and vmHRV in a sample of patients with chronic whiplash-associated disorders (WAD, n = 30) and healthy controls (n = 31). Patients with WAD showed lower vmHRV, indexed by high-frequency HRV (effect size, Cohen's d = 0.442), and greater PC (d = 0.815). Zero-order and partial correlations controlling for age and sex revealed that vmHRV and PC are inversely related. The results provide evidence for a psychophysiological mechanism underlying PC, in particular in chronic pain patients.
Collapse
Affiliation(s)
- Julian Koenig
- Department of Psychology, The Ohio State University, Columbus, Ohio, U.S.A
| | - Margot De Kooning
- Pain in Motion Research Group, Departments of Human Physiology and Physiotherapy, Faculty of Physical Education and Physiotherapy, Vrije Universiteit Brussel (VUB), Brussel, Belgium.,Born Bunge Institute, Faculty of Medicine and Health Sciences, University of Antwerp (UA), Antwerp, Belgium
| | - Anthony Bernardi
- Department of Psychology, The Ohio State University, Columbus, Ohio, U.S.A
| | - DeWayne P Williams
- Department of Psychology, The Ohio State University, Columbus, Ohio, U.S.A
| | - Jo Nijs
- Pain in Motion Research Group, Departments of Human Physiology and Physiotherapy, Faculty of Physical Education and Physiotherapy, Vrije Universiteit Brussel (VUB), Brussel, Belgium
| | - Julian F Thayer
- Department of Psychology, The Ohio State University, Columbus, Ohio, U.S.A
| | - Liesbeth Daenen
- Pain in Motion Research Group, Departments of Human Physiology and Physiotherapy, Faculty of Physical Education and Physiotherapy, Vrije Universiteit Brussel (VUB), Brussel, Belgium
| |
Collapse
|
47
|
Riganello F, Cortese MD, Dolce G, Lucca LF, Sannita WG. The Autonomic System Functional State Predicts Responsiveness in Disorder of Consciousness. J Neurotrauma 2015; 32:1071-7. [PMID: 25604680 DOI: 10.1089/neu.2014.3539] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
Diagnosis and early prognosis of the vegetative state/unresponsive wakefulness syndrome (VS/UWS) and its differentiation from the minimally-conscious state still rest on the clinical observation of responsiveness. The incidence of established clinical indicators of responsiveness also has proven variable in the single subject and is correlated to measures of heart rate variability (HRV) describing the sympathetic/parasympathetic balance. We tested responsiveness when the HRV descriptors nuLF and peakLF were or were not in the ranges with highest incidence of response based on findings from previous studies (10.0-70.0 and 0.05-0.11 Hz, respectively). Testing was blind by The Coma Recovery Scale-revised in the two conditions and in two experimental sessions with a one-week interval. The incidence of responses was not randomly distributed in the "response" and "no-response" conditions (McNemar test; p < 0.0001). The observed incidence in the "response" condition (visual: 55.1%; auditory: 51.5%) was higher than predicted statistically (32.1%) or described in previous clinical studies; responses were only occasional in the "no-response" condition (visual, 15.9%; auditory, 13.4%). Models validated the predictability with high accuracy. The current clinical criteria for diagnosis and prognosis based on neurological signs should be reconsidered, including variability over time and the autonomic system functional state, which could also qualify per se as an independent indicator for diagnosis and prognosis.
Collapse
Affiliation(s)
- Francesco Riganello
- 1 Institute S. Anna and RAN-Research in Advanced Rehabilitation , Crotone, Italy
| | - Maria D Cortese
- 1 Institute S. Anna and RAN-Research in Advanced Rehabilitation , Crotone, Italy
| | - Giuliano Dolce
- 1 Institute S. Anna and RAN-Research in Advanced Rehabilitation , Crotone, Italy
| | - Lucia F Lucca
- 1 Institute S. Anna and RAN-Research in Advanced Rehabilitation , Crotone, Italy
| | - Walter G Sannita
- 2 Department of Neuroscience, Ophthalmology, and Genetics, University of Genova , Genova, Italy .,3 Department of Psychiatry, State University of New York , Stony Brook, New York
| |
Collapse
|
48
|
Comparison of foot finding methods for deriving instantaneous pulse rates from photoplethysmographic signals. J Clin Monit Comput 2015; 30:157-68. [DOI: 10.1007/s10877-015-9695-6] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2015] [Accepted: 04/17/2015] [Indexed: 11/27/2022]
|
49
|
Analysis of heart rate variability during auditory stimulation periods in patients with schizophrenia. J Clin Monit Comput 2014; 29:153-62. [DOI: 10.1007/s10877-014-9580-8] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2013] [Accepted: 05/06/2014] [Indexed: 02/06/2023]
|
50
|
Paradkar N, Chowdhury SR. Fuzzy entropy based motion artifact detection and pulse rate estimation for fingertip photoplethysmography. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2014; 2014:58-61. [PMID: 25569896 DOI: 10.1109/embc.2014.6943528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
The paper presents a fingertip photoplethysmography (PPG) based technique to estimate the pulse rate of the subject. The PPG signal obtained from a pulse oximeter is used for the analysis. The input samples are corrupted with motion artifacts due to minor motion of the subjects. Entropy measure of the input samples is used to detect the motion artifacts and estimate the pulse rate. A three step methodology is adapted to identify and classify signal peaks as true systolic peaks or artifact. CapnoBase database and CSL Benchmark database are used to analyze the technique and pulse rate estimation was performed with positive predictive value and sensitivity figures of 99.84% and 99.32% respectively for CapnoBase and 98.83% and 98.84% for CSL database respectively.
Collapse
|