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Skov RAC, Lawaetz J, Strøm M, Van Herzeele I, Konge L, Resch TA, Eiberg JP. Machine learning enhances assessment of proficiency in endovascular aortic repair simulations. Curr Probl Surg 2024; 61:101576. [PMID: 39266132 DOI: 10.1016/j.cpsurg.2024.101576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 02/09/2024] [Accepted: 07/23/2024] [Indexed: 09/14/2024]
Affiliation(s)
- Rebecca Andrea Conradsen Skov
- Department of Vascular Surgery, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark; Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark; Copenhagen Academy for Medical Education and Simulation (CAMES), Denmark.
| | - Jonathan Lawaetz
- Department of Vascular Surgery, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark; Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark; Copenhagen Academy for Medical Education and Simulation (CAMES), Denmark
| | - Michael Strøm
- Department of Vascular Surgery, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark; Copenhagen Academy for Medical Education and Simulation (CAMES), Denmark
| | - Isabelle Van Herzeele
- Department of Thoracic and Vascular Surgery, Ghent University Hospital, Ghent, Belgium
| | - Lars Konge
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark; Copenhagen Academy for Medical Education and Simulation (CAMES), Denmark
| | - Timothy Andrew Resch
- Department of Vascular Surgery, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark; Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark
| | - Jonas Peter Eiberg
- Department of Vascular Surgery, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark; Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark; Copenhagen Academy for Medical Education and Simulation (CAMES), Denmark
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2
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Skov RAC, Lawaetz J, Konge L, Resch TA, Aasvang EK, Meyhoff CS, Westerlin L, Jensen MK, Eiberg JP. Role-reversal simulation training to enhance performance and reduce stress of endovascular scrub nurses in the operating room. Curr Probl Surg 2024; 61:101577. [PMID: 39266129 DOI: 10.1016/j.cpsurg.2024.101577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 04/05/2024] [Accepted: 07/23/2024] [Indexed: 09/14/2024]
Affiliation(s)
- Rebecca Andrea Conradsen Skov
- Department of Vascular Surgery, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark; Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark; Copenhagen Academy for Medical Education and Simulation (CAMES), Copenhagen, Denmark.
| | - Jonathan Lawaetz
- Department of Vascular Surgery, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark; Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark; Copenhagen Academy for Medical Education and Simulation (CAMES), Copenhagen, Denmark
| | - Lars Konge
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark; Copenhagen Academy for Medical Education and Simulation (CAMES), Copenhagen, Denmark
| | - Timothy Andrew Resch
- Department of Vascular Surgery, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark; Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Eske Kvanner Aasvang
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark; Department of Anaesthesiology, Center for Cancer and Organ Dysfunction, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
| | - Christian Sylvest Meyhoff
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark; Department of Anaesthesia and Intensive Care, Copenhagen University Hospital - Bispebjerg and Frederiksberg, Copenhagen, Denmark
| | - Lise Westerlin
- Department of Vascular Surgery, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
| | - Mogens Kærsgaard Jensen
- Department of Cardiothoracic and Vascular Surgery, Aarhus University Hospital - Skejby, Aarhus, Denmark
| | - Jonas Peter Eiberg
- Department of Vascular Surgery, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark; Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark; Copenhagen Academy for Medical Education and Simulation (CAMES), Copenhagen, Denmark
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Dankovich LJ, Joyner JS, He W, Sesay A, Vaughn-Cooke M. CogWatch: An open-source platform to monitor physiological indicators for cognitive workload and stress. HARDWAREX 2024; 19:e00538. [PMID: 38962730 PMCID: PMC11220525 DOI: 10.1016/j.ohx.2024.e00538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Revised: 04/28/2024] [Accepted: 05/14/2024] [Indexed: 07/05/2024]
Abstract
Cognitive workload is a measure of the mental resources a user is dedicating to a given task. Low cognitive workload produces boredom and decreased vigilance, which can lead to an increase in response time. Under high cognitive workload the information processing burden of the user increases significantly, thereby compromising the ability to effectively monitor their environment for unexpected stimuli or respond to emergencies. In cognitive workload and stress monitoring research, sensors are used to measure applicable physiological indicators to infer the state of user. For example, electrocardiography or photoplethysmography are often used to track both the rate at which the heart beats and variability between the individual heart beats. Photoplethysmography and chest straps are also used in studies to track fluctuations in breathing rate. The Galvanic Skin Response is a change in sweat rate (especially on the palms and wrists) and is typically measured by tracking how the resistance of two probes at a fixed distance on the subject's skin changes over time. Finally, fluctuations in Skin Temperature are typically tracked with thermocouples or infrared light (IR) measuring systems in these experiments. While consumer options such a smartwatches for health tracking often have the integrated ability to perform photoplethysmography, they typically perform significant processing on the data which is not transparent to the user and often have a granularity of data that is far too low to be useful for research purposes. It is possible to purchase sensor boards that can be added to Arduino systems, however, these systems generally are very large and obtrusive. Additionally, at the high end of the spectrum there are medical tools used to track these physiological signals, but they are often very expensive and require specific software to be licensed for communication. In this paper, an open-source solution to create a physiological tracker with a wristwatch form factor is presented and validated, using conventional off-the-shelf components. The proposed tool is intended to be applied as a cost-effective solution for research and educational settings.
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Affiliation(s)
- Louis J. Dankovich
- University of Maryland at College Park, James A. Clark School of Engineering, 8228 Paint Branch Dr, College Park, MD 20742, United States
| | - Janell S. Joyner
- University of Maryland at College Park, James A. Clark School of Engineering, 8228 Paint Branch Dr, College Park, MD 20742, United States
| | - William He
- University of Maryland at College Park, James A. Clark School of Engineering, 8228 Paint Branch Dr, College Park, MD 20742, United States
| | - Ahmad Sesay
- University of Maryland at College Park, James A. Clark School of Engineering, 8228 Paint Branch Dr, College Park, MD 20742, United States
| | - Monifa Vaughn-Cooke
- Virginia Tech, VT Carilion School of Medicine, 2 Riverside Circle, Roanoke, VA 24016, United States
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Schipper F, van Sloun RJG, Grassi A, Brouwer J, van Meulen F, Overeem S, Fonseca P. Maximum a posteriori detection of heartbeats from a chest-worn accelerometer. Physiol Meas 2024; 45:035009. [PMID: 38430565 DOI: 10.1088/1361-6579/ad2f5e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Accepted: 03/01/2024] [Indexed: 03/04/2024]
Abstract
Objective. Unobtrusive long-term monitoring of cardiac parameters is important in a wide variety of clinical applications, such as the assesment of acute illness severity and unobtrusive sleep monitoring. Here we determined the accuracy and robustness of heartbeat detection by an accelerometer worn on the chest.Approach. We performed overnight recordings in 147 individuals (69 female, 78 male) referred to two sleep centers. Two methods for heartbeat detection in the acceleration signal were compared: one previously described approach, based on local periodicity, and a novel extended method incorporating maximumaposterioriestimation and a Markov decision process to approach an optimal solution.Main results. The maximumaposterioriestimation significantly improved performance, with a mean absolute error for the estimation of inter-beat intervals of only 3.5 ms, and 95% limits of agreement of -1.7 to +1.0 beats per minute for heartrate measurement. Performance held during posture changes and was only weakly affected by the presence of sleep disorders and demographic factors.Significance. The new method may enable the use of a chest-worn accelerometer in a variety of applications such as ambulatory sleep staging and in-patient monitoring.
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Affiliation(s)
- Fons Schipper
- Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Philips Research, Philips, Eindhoven, The Netherlands
| | - Ruud J G van Sloun
- Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Philips Research, Philips, Eindhoven, The Netherlands
| | - Angela Grassi
- Philips Research, Philips, Eindhoven, The Netherlands
| | - Jan Brouwer
- Philips Research, Philips, Eindhoven, The Netherlands
| | - Fokke van Meulen
- Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Kempenhaeghe Center for Sleep Medicine, Heeze, The Netherlands
| | - Sebastiaan Overeem
- Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Kempenhaeghe Center for Sleep Medicine, Heeze, The Netherlands
| | - Pedro Fonseca
- Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Philips Research, Philips, Eindhoven, The Netherlands
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Syversen A, Dosis A, Jayne D, Zhang Z. Wearable Sensors as a Preoperative Assessment Tool: A Review. SENSORS (BASEL, SWITZERLAND) 2024; 24:482. [PMID: 38257579 PMCID: PMC10820534 DOI: 10.3390/s24020482] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 01/06/2024] [Accepted: 01/09/2024] [Indexed: 01/24/2024]
Abstract
Surgery is a common first-line treatment for many types of disease, including cancer. Mortality rates after general elective surgery have seen significant decreases whilst postoperative complications remain a frequent occurrence. Preoperative assessment tools are used to support patient risk stratification but do not always provide a precise and accessible assessment. Wearable sensors (WS) provide an accessible alternative that offers continuous monitoring in a non-clinical setting. They have shown consistent uptake across the perioperative period but there has been no review of WS as a preoperative assessment tool. This paper reviews the developments in WS research that have application to the preoperative period. Accelerometers were consistently employed as sensors in research and were frequently combined with photoplethysmography or electrocardiography sensors. Pre-processing methods were discussed and missing data was a common theme; this was dealt with in several ways, commonly by employing an extraction threshold or using imputation techniques. Research rarely processed raw data; commercial devices that employ internal proprietary algorithms with pre-calculated heart rate and step count were most commonly employed limiting further feature extraction. A range of machine learning models were used to predict outcomes including support vector machines, random forests and regression models. No individual model clearly outperformed others. Deep learning proved successful for predicting exercise testing outcomes but only within large sample-size studies. This review outlines the challenges of WS and provides recommendations for future research to develop WS as a viable preoperative assessment tool.
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Affiliation(s)
- Aron Syversen
- School of Computing, University of Leeds, Leeds LS2 9JT, UK
| | - Alexios Dosis
- School of Medicine, University of Leeds, Leeds LS2 9JT, UK; (A.D.); (D.J.)
| | - David Jayne
- School of Medicine, University of Leeds, Leeds LS2 9JT, UK; (A.D.); (D.J.)
| | - Zhiqiang Zhang
- School of Electrical Engineering, University of Leeds, Leeds LS2 9JT, UK;
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Zillner L, Andreas M, Mach M. Wearable heart rate variability and atrial fibrillation monitoring to improve clinically relevant endpoints in cardiac surgery-a systematic review. Mhealth 2023; 10:8. [PMID: 38323143 PMCID: PMC10839520 DOI: 10.21037/mhealth-23-19] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 10/24/2023] [Indexed: 02/08/2024] Open
Abstract
Background This systematic review aims to highlight the untapped potential of heart rate variability (HRV) and atrial fibrillation (AF) monitoring by wearable health monitoring devices as a critical diagnostic tool in cardiac surgery (CS) patients. We reviewed established predictive capabilities of HRV and AF monitoring in specific cardiosurgical scenarios and provide a perspective on additional predictive properties of wearable health monitoring devices that need to be investigated. Methods After screening most relevant databases, we included 33 publications in this review. Perusing these publications on HRV's prognostic value, we could identify HRV as a predictor for sudden cardiac death, mortality after acute myocardial infarction (AMI), and post operative atrial fibrillation (POAF). With regards to standard AF assessment, which typically includes extensive periods of unrecorded cardiac activity, we demonstrated that continuous monitoring via wearables recorded significant cardiac events that would otherwise have been missed. Results Photoplethysmography and single-lead electrocardiogram (ECG) were identified as the most useful and convenient technical assessment modalities, and their advantages and disadvantages were described in detail. As a call to further action, we observed that the scientific community has relatively extensively explored wearable AF screening, whereas HRV assessment to improve relevant clinical outcomes in CS is rarely studied; it still has great potential to be leveraged. Conclusions Therefore, risk assessment in CS would benefit greatly from earlier preoperative and postoperative AF detection, comprehensive and accurate assessment of cardiac health through HRV metrics, and continuous long-term monitoring. These should be achievable via commercially available wearables.
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Affiliation(s)
- Liliane Zillner
- Department of Cardiac Surgery, Medical University of Vienna, Vienna, Austria
| | - Martin Andreas
- Department of Cardiac Surgery, Medical University of Vienna, Vienna, Austria
| | - Markus Mach
- Department of Cardiac Surgery, Medical University of Vienna, Vienna, Austria
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Montanari A, Ferlini A, Balaji AN, Mascolo C, Kawsar F. EarSet: A Multi-Modal Dataset for Studying the Impact of Head and Facial Movements on In-Ear PPG Signals. Sci Data 2023; 10:850. [PMID: 38040725 PMCID: PMC10692189 DOI: 10.1038/s41597-023-02762-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 11/17/2023] [Indexed: 12/03/2023] Open
Abstract
Photoplethysmography (PPG) is a simple, yet powerful technique to study blood volume changes by measuring light intensity variations. However, PPG is severely affected by motion artifacts, which hinder its trustworthiness. This problem is pressing in earables since head movements and facial expressions cause skin and tissue displacements around and inside the ear. Understanding such artifacts is fundamental to the success of earables for accurate cardiovascular health monitoring. However, the lack of in-ear PPG datasets prevents the research community from tackling this challenge. In this work, we report on the design of an ear tip featuring a 3-channels PPG and a co-located 6-axis motion sensor. This, enables sensing PPG data at multiple wavelengths and the corresponding motion signature from both ears. Leveraging our device, we collected a multi-modal dataset from 30 participants while performing 16 natural motions, including both head/face and full body movements. This unique dataset will greatly support research towards making in-ear vital signs sensing more accurate and robust, thus unlocking the full potential of the next-generation PPG-equipped earables.
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Affiliation(s)
| | - Andrea Ferlini
- Nokia Bell Labs, Cambridge, UK.
- University of Cambridge, Cambridge, UK.
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Teruel-Hernández E, López-Pina JA, Souto-Camba S, Báez-Suárez A, Medina-Ramírez R, Gómez-Conesa A. Improving Sleep Quality, Daytime Sleepiness, and Cognitive Function in Patients with Dementia by Therapeutic Exercise and NESA Neuromodulation: A Multicenter Clinical Trial. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:7027. [PMID: 37947583 PMCID: PMC10650908 DOI: 10.3390/ijerph20217027] [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: 10/11/2023] [Revised: 10/28/2023] [Accepted: 11/04/2023] [Indexed: 11/12/2023]
Abstract
Dementia is a progressive decline in cognitive functions caused by an alteration in the pattern of neural network connections. There is an inability to create new neuronal connections, producing behavioral disorders. The most evident alteration in patients with neurodegenerative diseases is the alteration of sleep-wake behavior. The aim of this study was to test the effect of two non-pharmacological interventions, therapeutic exercise (TE) and non-invasive neuromodulation through the NESA device (NN) on sleep quality, daytime sleepiness, and cognitive function of 30 patients diagnosed with dementia (non-invasive neuromodulation experimental group (NNG): mean ± SD, age: 71.6 ± 7.43 years; therapeutic exercise experimental group (TEG) 75.2 ± 8.63 years; control group (CG) 80.9 ± 4.53 years). The variables were evaluated by means of the Pittsburg Index (PSQI), the Epworth Sleepiness Scale (ESS), and the Mini-Cognitive Exam Test at four different times during the study: at baseline, after 2 months (after completion of the NNG), after 5 months (after completion of the TEG), and after 7 months (after 2 months of follow-up). Participants in the NNG and TEG presented significant improvements with respect to the CG, and in addition, the NNG generated greater relevant changes in the three variables with respect to the TEG (sleep quality (p = 0.972), daytime sleepiness (p = 0.026), and cognitive function (p = 0.127)). In conclusion, with greater effects in the NNG, both treatments were effective to improve daytime sleepiness, sleep quality, and cognitive function in the dementia population.
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Affiliation(s)
| | | | - Sonia Souto-Camba
- Department of Physiotherapy, Medicine and Biomedical Sciences, University of A Coruña, 15006 A Coruña, Spain;
| | - Aníbal Báez-Suárez
- Health Science Faculty, University of Las Palmas de Gran Canaria, 35016 Las Palmas, Spain;
| | - Raquel Medina-Ramírez
- SocDig Research Group, University of Las Palmas de Gran Canaria, 35016 Las Palmas, Spain;
| | - Antonia Gómez-Conesa
- Research Methods and Evaluation in the Social Sciences Research Group, Mare Nostrum Campus of International Excellence, University of Murcia, 30100 Murcia, Spain;
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Parlato S, Centracchio J, Esposito D, Bifulco P, Andreozzi E. ECG-Free Heartbeat Detection in Seismocardiography and Gyrocardiography Signals Provides Acceptable Heart Rate Variability Indices in Healthy and Pathological Subjects. SENSORS (BASEL, SWITZERLAND) 2023; 23:8114. [PMID: 37836942 PMCID: PMC10575135 DOI: 10.3390/s23198114] [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: 08/22/2023] [Revised: 09/12/2023] [Accepted: 09/25/2023] [Indexed: 10/15/2023]
Abstract
Cardio-mechanical monitoring techniques, such as Seismocardiography (SCG) and Gyrocardiography (GCG), have received an ever-growing interest in recent years as potential alternatives to Electrocardiography (ECG) for heart rate monitoring. Wearable SCG and GCG devices based on lightweight accelerometers and gyroscopes are particularly appealing for continuous, long-term monitoring of heart rate and its variability (HRV). Heartbeat detection in cardio-mechanical signals is usually performed with the support of a concurrent ECG lead, which, however, limits their applicability in standalone cardio-mechanical monitoring applications. The complex and variable morphology of SCG and GCG signals makes the ECG-free heartbeat detection task quite challenging; therefore, only a few methods have been proposed. Very recently, a template matching method based on normalized cross-correlation (NCC) has been demonstrated to provide very accurate detection of heartbeats and estimation of inter-beat intervals in SCG and GCG signals of pathological subjects. In this study, the accuracy of HRV indices obtained with this template matching method is evaluated by comparison with ECG. Tests were performed on two public datasets of SCG and GCG signals from healthy and pathological subjects. Linear regression, correlation, and Bland-Altman analyses were carried out to evaluate the agreement of 24 HRV indices obtained from SCG and GCG signals with those obtained from ECG signals, simultaneously acquired from the same subjects. The results of this study show that the NCC-based template matching method allowed estimating HRV indices from SCG and GCG signals of healthy subjects with acceptable accuracy. On healthy subjects, the relative errors on time-domain indices ranged from 0.25% to 15%, on frequency-domain indices ranged from 10% to 20%, and on non-linear indices were within 8%. The estimates obtained on signals from pathological subjects were affected by larger errors. Overall, GCG provided slightly better performances as compared to SCG, both on healthy and pathological subjects. These findings provide, for the first time, clear evidence that monitoring HRV via SCG and GCG sensors without concurrent ECG is feasible with the NCC-based template matching method for heartbeat detection.
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Affiliation(s)
| | - Jessica Centracchio
- Department of Electrical Engineering and Information Technologies, University of Naples Federico II, Via Claudio 21, 80125 Naples, Italy; (S.P.); (D.E.); (P.B.)
| | | | | | - Emilio Andreozzi
- Department of Electrical Engineering and Information Technologies, University of Naples Federico II, Via Claudio 21, 80125 Naples, Italy; (S.P.); (D.E.); (P.B.)
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Chiang AA, Khosla S. Consumer Wearable Sleep Trackers: Are They Ready for Clinical Use? Sleep Med Clin 2023; 18:311-330. [PMID: 37532372 DOI: 10.1016/j.jsmc.2023.05.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/04/2023]
Abstract
As the importance of good sleep continues to gain public recognition, the market for sleep-monitoring devices continues to grow. Modern technology has shifted from simple sleep tracking to a more granular sleep health assessment. We examine the available functionalities of consumer wearable sleep trackers (CWSTs) and how they perform in healthy individuals and disease states. Additionally, the continuum of sleep technology from consumer-grade to medical-grade is detailed. As this trend invariably grows, we urge professional societies to develop guidelines encompassing the practical clinical use of CWSTs and how best to incorporate them into patient care plans.
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Affiliation(s)
- Ambrose A Chiang
- Division of Sleep Medicine, Louis Stokes Cleveland VA Medical Center, 10701 East Blvd, Suite 2B-129, Cleveland, OH 44106, USA; Division of Pulmonary, Critical Care, and Sleep Medicine, University Hospitals Cleveland Medical Center, Cleveland, OH, USA; Department of Medicine, Case Western Reserve University, Cleveland, OH, USA.
| | - Seema Khosla
- North Dakota Center for Sleep, 1531 32nd Avenue S Ste 103, Fargo, ND 58103, USA
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Rinkevičius M, Charlton PH, Bailón R, Marozas V. Influence of Photoplethysmogram Signal Quality on Pulse Arrival Time during Polysomnography. SENSORS (BASEL, SWITZERLAND) 2023; 23:2220. [PMID: 36850820 PMCID: PMC9967654 DOI: 10.3390/s23042220] [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: 12/30/2022] [Revised: 02/05/2023] [Accepted: 02/15/2023] [Indexed: 06/18/2023]
Abstract
Intervals of low-quality photoplethysmogram (PPG) signals might lead to significant inaccuracies in estimation of pulse arrival time (PAT) during polysomnography (PSG) studies. While PSG is considered to be a "gold standard" test for diagnosing obstructive sleep apnea (OSA), it also enables tracking apnea-related nocturnal blood pressure fluctuations correlated with PAT. Since the electrocardiogram (ECG) is recorded synchronously with the PPG during PSG, it makes sense to use the ECG signal for PPG signal-quality assessment. (1) Objective: to develop a PPG signal-quality assessment algorithm for robust PAT estimation, and investigate the influence of signal quality on PAT during various sleep stages and events such as OSA. (2) Approach: the proposed algorithm uses R and T waves from the ECG to determine approximate locations of PPG pulse onsets. The MESA database of 2055 PSG recordings was used for this study. (3) Results: the proportions of high-quality PPG were significantly lower in apnea-related oxygen desaturation (matched-pairs rc = 0.88 and rc = 0.97, compared to OSA and hypopnea, respectively, when p < 0.001) and arousal (rc = 0.93 and rc = 0.98, when p < 0.001) than in apnea events. The significantly large effect size of interquartile ranges of PAT distributions was between low- and high-quality PPG (p < 0.001, rc = 0.98), and regular and irregular pulse waves (p < 0.001, rc = 0.74), whereas a lower quality of the PPG signal was found to be associated with a higher interquartile range of PAT across all subjects. Suggested PPG signal quality-based PAT evaluation reduced deviations (e.g., rc = 0.97, rc = 0.97, rc = 0.99 in hypopnea, oxygen desaturation, and arousal stages, respectively, when p < 0.001) and allowed obtaining statistically larger differences between different sleep stages and events. (4) Significance: the implemented algorithm has the potential to increase the robustness of PAT estimation in PSG studies related to nocturnal blood pressure monitoring.
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Affiliation(s)
- Mantas Rinkevičius
- Biomedical Engineering Institute, Kaunas University of Technology, K. Baršausko Str. 59, LT-51423 Kaunas, Lithuania
| | - Peter H. Charlton
- Department of Public Health and Primary Care, University of Cambridge, Cambridge CB2 1TN, UK
- Research Centre for Biomedical Engineering, University of London, London WC1E 7HU, UK
| | - Raquel Bailón
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) Group, Aragon Institute of Engineering Research (I3A), IIS Aragon, University of Zaragoza, 50009 Zaragoza, Spain
- Biomedical Research Networking Center (CIBER), 50018 Zaragoza, Spain
| | - Vaidotas Marozas
- Biomedical Engineering Institute, Kaunas University of Technology, K. Baršausko Str. 59, LT-51423 Kaunas, Lithuania
- Faculty of Electrical and Electronics Engineering, Kaunas University of Technology, Studentų Str. 50, LT-51368 Kaunas, Lithuania
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Rinne JKA, Miri S, Oksala N, Vehkaoja A, Kössi J. Evaluation of a wrist-worn photoplethysmography monitor for heart rate variability estimation in patients recovering from laparoscopic colon resection. J Clin Monit Comput 2023; 37:45-53. [PMID: 35394583 PMCID: PMC9852147 DOI: 10.1007/s10877-022-00854-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Accepted: 03/25/2022] [Indexed: 01/24/2023]
Abstract
To evaluate the accuracy of heart rate variability (HRV) parameters obtained with a wrist-worn photoplethysmography (PPG) monitor in patients recovering from minimally invasive colon resection to investigate whether PPG has potential in postoperative patient monitoring. 31 patients were monitored for three days or until discharge or reoperation using a wrist-worn PPG monitor (PulseOn, Finland) with a Holter monitor (Faros 360, Bittium Biosignals, Finland) as a reference measurement device. Beat-to-beat intervals (BBI) and HRV information collected by PPG were compared with RR intervals (RRI) and HRV obtained from the ECG reference after removing artefacts and ectopic beats. The beat-to-beat mean error (ME) and mean absolute error (MAE) of good quality heartbeat intervals obtained by wrist PPG were estimated as - 1.34 ms and 10.4 ms respectively. A significant variation in the accuracy of the HRV parameters was found. In the time domain, SDNN (9.11%), TRI (11.4%) and TINN (11.1%) were estimated with low relative MAE, while RMSSD (34.3%), pNN50 (139%) and NN50 (188%) had higher errors. The logarithmic parameters in the frequency domain (VLF Log, LF Log and HF Log) exhibited the lowest relative error, and for non-linear parameters, SD2 (7.5%), DFA α1 (8.25%) and DFA α2 (4.71%) were calculated much more accurately than SD1 (34.3%). The wrist PPG shows some potential for use in a clinical setting. The accuracy of several HRV parameters analyzed post hoc was found sufficient to be used in further studies concerning postoperative recovery of patients undergoing laparoscopic colon resection, although there were large errors in many common HRV parameters such as RMSSD, pNN50 and NN50, rendering them unusable.ClinicalTrials.gov Identifier: NCT04996511, August 9, 2021, retrospectively registered.
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Affiliation(s)
- Juha K A Rinne
- Department of Surgery, Päijät-Häme Central Hospital, Tampere University, Lahti, Finland.
| | - Seyedsadra Miri
- Finnish Cardiovascular Research Center - Tampere, Tampere University, Arvo Ylpön Katu 34 (33520 Tampere), P.O. Box 100, FI-33014, Tampere, Finland
- Faculty of Medicine and Health Technology, Tampere University, Arvo Ylpön Katu 34 (33520 Tampere), P.O. Box 100, FI-33014, Tampere, Finland
| | - Niku Oksala
- Finnish Cardiovascular Research Center - Tampere, Tampere University, Arvo Ylpön Katu 34 (33520 Tampere), P.O. Box 100, FI-33014, Tampere, Finland
- Faculty of Medicine and Health Technology, Tampere University, Arvo Ylpön Katu 34 (33520 Tampere), P.O. Box 100, FI-33014, Tampere, Finland
- Vascular Centre, Tampere University Hospital, Elämänaukio 2 (33520 Tampere), P.O. Box 2000, 33521, Tampere, Finland
| | - Antti Vehkaoja
- Finnish Cardiovascular Research Center - Tampere, Tampere University, Arvo Ylpön Katu 34 (33520 Tampere), P.O. Box 100, FI-33014, Tampere, Finland
- Faculty of Medicine and Health Technology, Tampere University, Arvo Ylpön Katu 34 (33520 Tampere), P.O. Box 100, FI-33014, Tampere, Finland
- PulseOn Ltd, Tekniikantie 12, 02150, Espoo, Finland
| | - Jyrki Kössi
- Department of Surgery, Päijät-Häme Central Hospital, Tampere University, Lahti, Finland
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Martínez-González-Moro I, Albertus Cámara I, Paredes Ruiz MJ. Influences of Intense Physical Effort on the Activity of the Autonomous Nervous System and Stress, as Measured with Photoplethysmography. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:16066. [PMID: 36498140 PMCID: PMC9735638 DOI: 10.3390/ijerph192316066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 11/27/2022] [Accepted: 11/28/2022] [Indexed: 06/17/2023]
Abstract
Background: The autonomic nervous system, which is composed of the sympathetic and parasympathetic nervous system, is closely related to the cardiovascular system. The temporal variation between each of the intervals between the consecutive “R” waves of an electrocardiogram is known as heart rate variability. Depending on the type of activity, both systems can be activated, and also influence the interval between “R” waves. Currently, with advancements in technology and electronic devices, photoplethysmography is used. Photoplethysmography detects changes in the intensity of reflected light that allow differentiation between systole and diastole and, therefore, determines the heart rate, its frequency and its variations. In this way, changes in the autonomic nervous system can be detected by devices such as the Max Pulse®. Objective: To determine whether the information provided by Max Pulse® on autonomic balance and stress is modified after intense physical exercise, thereby determining whether there is a relationship with body composition, and also whether there are differences with respect to gender. Materials and Methods: Fifty-three runners (38.9% female) with a mean age of 31.3 ± 8.1 years participated in the study. Two measurements (before and after intense physical effort) were performed with the Max Pulse® device. The flotoplethysmography measurement lasted 3 min, and was performed in the supine position. The exercise test was performed on a treadmill. It was initiated at a speed of 6 and 7 km/h for women and men, respectively. Subjects indicated the end of the test by making a hand gesture when unable to continue the test. Results: Autonomic nervous system activity and mental stress values decreased significantly (p < 0.05) in men and women, while autonomic nervous system balance decreased only in women. Physical stress increased (p < 0.05) in both sexes. Conclusions: Intense exercise causes changes in variables that assess autonomic nervous system balance and stress, as measured by a device based on photoplethysmography. The changes are evident in both sexes, and are not related to body composition.
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14
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Wearables in Cardiovascular Disease. J Cardiovasc Transl Res 2022:10.1007/s12265-022-10314-0. [PMID: 36085432 DOI: 10.1007/s12265-022-10314-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 08/29/2022] [Indexed: 10/14/2022]
Abstract
Wearable devices stand to revolutionize the way healthcare is delivered. From consumer devices that provide general health information and screen for medical conditions to medical-grade devices that allow collection of larger datasets that include multiple modalities, wearables have a myriad of potential uses, especially in cardiovascular disorders. In this review, we summarize the underlying technologies employed in these devices and discuss the regulatory and economic aspects of such devices as well as the future implications of their use.
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Zaunseder S, Vehkaoja A, Fleischhauer V, Hoog Antink C. Signal-to-noise ratio is more important than sampling rate in beat-to-beat interval estimation from optical sensors. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103538] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
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16
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Cui H, Wang Z, Yu B, Jiang F, Geng N, Li Y, Xu L, Zheng D, Zhang B, Lu P, Greenwald SE. Statistical Analysis of the Consistency of HRV Analysis Using BCG or Pulse Wave Signals. SENSORS (BASEL, SWITZERLAND) 2022; 22:2423. [PMID: 35336592 PMCID: PMC8951337 DOI: 10.3390/s22062423] [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: 12/30/2021] [Revised: 02/22/2022] [Accepted: 02/28/2022] [Indexed: 05/06/2023]
Abstract
Ballistocardiography (BCG) is considered a good alternative to HRV analysis with its non-contact and unobtrusive acquisition characteristics. However, consensus about its validity has not yet been established. In this study, 50 healthy subjects (26.2 ± 5.5 years old, 22 females, 28 males) were invited. Comprehensive statistical analysis, including Coefficients of Variation (CV), Lin’s Concordance Correlation Coefficient (LCCC), and Bland-Altman analysis (BA ratio), were utilized to analyze the consistency of BCG and ECG signals in HRV analysis. If the methods gave different answers, the worst case was taken as the result. Measures of consistency such as Mean, SDNN, LF gave good agreement (the absolute value of CV difference < 2%, LCCC > 0.99, BA ratio < 0.1) between J-J (BCG) and R-R intervals (ECG). pNN50 showed moderate agreement (the absolute value of CV difference < 5%, LCCC > 0.95, BA ratio < 0.2), while RMSSD, HF, LF/HF indicated poor agreement (the absolute value of CV difference ≥ 5% or LCCC ≤ 0.95 or BA ratio ≥ 0.2). Additionally, the R-R intervals were compared with P-P intervals extracted from the pulse wave (PW). Except for pNN50, which exhibited poor agreement in this comparison, the performances of the HRV indices estimated from the PW and the BCG signals were similar.
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Affiliation(s)
- Huiying Cui
- College of Medicine and Biological and Information Engineering, Northeastern University, Shenyang 110167, China; (H.C.); (Z.W.); (F.J.)
| | - Zhongyi Wang
- College of Medicine and Biological and Information Engineering, Northeastern University, Shenyang 110167, China; (H.C.); (Z.W.); (F.J.)
| | - Bin Yu
- Philips Design, 5611 AZ Eindhoven, The Netherlands;
| | - Fangfang Jiang
- College of Medicine and Biological and Information Engineering, Northeastern University, Shenyang 110167, China; (H.C.); (Z.W.); (F.J.)
| | - Ning Geng
- Department of Cardiology, Shengjing Hospital of China Medical University, Shenyang 110819, China;
| | - Yongchun Li
- Shenyang Contain Electronic Technology Co., Ltd., Shenyang 110167, China;
| | - Lisheng Xu
- College of Medicine and Biological and Information Engineering, Northeastern University, Shenyang 110167, China; (H.C.); (Z.W.); (F.J.)
- Neusoft Research of Intelligent Healthcare Technology, Co., Ltd., Shenyang 110167, China
| | - Dingchang Zheng
- Research Centre for Intelligent Healthcare, Coventry University, Coventry CV1 5RW, UK;
| | - Biyong Zhang
- BOBO Technology, Hangzhou 310000, China;
- User System Interaction Group, Industrial Design, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
| | - Peilin Lu
- Neuroscience Center, Department of Neurology, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou 310000, China;
| | - Stephen E. Greenwald
- Blizard Institute, Barts & The London School of Medicine & Dentistry, Queen Mary University of London, London E1 4NS, UK
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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.
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Affiliation(s)
- Ayaka Shintomi
- Graduate School of System InformaticsKobe University1‐1 Rokkodai‐choNada‐kuKobeHyogoJapan
| | - Shintaro Izumi
- Graduate School of System InformaticsKobe University1‐1 Rokkodai‐choNada‐kuKobeHyogoJapan
| | - Masahiko Yoshimoto
- Graduate School of System InformaticsKobe University1‐1 Rokkodai‐choNada‐kuKobeHyogoJapan
| | - Hiroshi Kawaguchi
- Graduate School of System InformaticsKobe University1‐1 Rokkodai‐choNada‐kuKobeHyogoJapan
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