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Khalili M, Lingawi S, Hutton J, Fordyce CB, Christenson J, Shadgan B, Grunau B, Kuo C. Detecting cardiac states with wearable photoplethysmograms and implications for out-of-hospital cardiac arrest detection. Sci Rep 2024; 14:23185. [PMID: 39369015 PMCID: PMC11455951 DOI: 10.1038/s41598-024-74117-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2024] [Accepted: 09/24/2024] [Indexed: 10/07/2024] Open
Abstract
Out-of-hospital cardiac arrest (OHCA) is a global health problem affecting approximately 4.4 million individuals yearly. OHCA has a poor survival rate, specifically when unwitnessed (accounting for up to 75% of cases). Rapid recognition can significantly improve OHCA survival, and consumer wearables with continuous cardiopulmonary monitoring capabilities hold potential to "witness" cardiac arrest and activate emergency services. In this study, we used an arterial occlusion model to simulate cardiac arrest and investigated the ability of infrared photoplethysmogram (PPG) sensors, often utilized in consumer wearable devices, to differentiate normal cardiac pulsation, pulseless cardiac (i.e., resembling a cardiac arrest), and non-physiologic (i.e., off-body) states. Across the classification models trained and evaluated on three anatomical locations, higher classification performances were observed on the finger (macro average F1-score of 0.964 on the fingertip and 0.954 on the finger base) compared to the wrist (macro average F1-score of 0.837). The wrist-based classification model, which was trained and evaluated using all PPG measurements, including both high- and low-quality recordings, achieved a macro average precision and recall of 0.922 and 0.800, respectively. This wrist-based model, which represents the most common form factor in consumer wearables, could only capture about 43.8% of pulseless events. However, models trained and tested exclusively on high-quality recordings achieved higher classification outcomes (macro average F1-score of 0.975 on the fingertip, 0.973 on the finger base, and 0.934 on the wrist). The fingertip model had the highest performance to differentiate arterial occlusion pulselessness from normal cardiac pulsation and off-body measurements with macro average precision and recall of 0.978 and 0.972, respectively. This model was able to identify 93.7% of pulseless states (i.e., resembling a cardiac arrest event), with a 0.4% false positive rate. All classification models relied on a combination of time-, power spectral density (PSD)-, and frequency-domain features to differentiate normal cardiac pulsation, pulseless cardiac, and off-body PPG recordings. However, our best model represented an idealized detection condition, relying on ensuring high-quality PPG data for training and evaluation of machine learning algorithms. While 90.7% of our PPG recordings from the fingertip were considered of high quality, only 53.2% of the measurements from the wrist passed the quality criteria. Our findings have implications for adapting consumer wearables to provide OHCA detection, involving advancements in hardware and software to ensure high-quality measurements in real-world settings, as well as development of wearables with form factors that enable high-quality PPG data acquisition more consistently. Given these improvements, we demonstrate that OHCA detection can feasibly be made available to anyone using PPG-based consumer wearables.
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Affiliation(s)
- Mahsa Khalili
- Department of Emergency Medicine, University of British Columbia, 2775 Laurel Street, Vancouver, BC, V5Z 1M9, Canada.
- British Columbia Resuscitation Research Collaborative, Providence Research, 1190 Hornby Street, Vancouver, BC, V6Z 2K5, Canada.
- Centre for Advancing Health Outcomes, University of British Columbia, 1081 Burrard St, Vancouver, BC, V6Z 1Y6, Canada.
- School of Biomedical Engineering, University of British Columbia, 2222 Health Sciences Mall, Vancouver, BC, V6T 1Z3, Canada.
- Centre for Aging SMART, University of British Columbia, 2635 Laurel Street, Vancouver, BC, V5Z 1M9, Canada.
- International Collaboration on Repair Discoveries, 818 West 10th Avenue, Vancouver, BC, V5Z 1M9, Canada.
| | - Saud Lingawi
- British Columbia Resuscitation Research Collaborative, Providence Research, 1190 Hornby Street, Vancouver, BC, V6Z 2K5, Canada
- Centre for Advancing Health Outcomes, University of British Columbia, 1081 Burrard St, Vancouver, BC, V6Z 1Y6, Canada
- School of Biomedical Engineering, University of British Columbia, 2222 Health Sciences Mall, Vancouver, BC, V6T 1Z3, Canada
- Centre for Aging SMART, University of British Columbia, 2635 Laurel Street, Vancouver, BC, V5Z 1M9, Canada
| | - Jacob Hutton
- Department of Emergency Medicine, University of British Columbia, 2775 Laurel Street, Vancouver, BC, V5Z 1M9, Canada
- British Columbia Resuscitation Research Collaborative, Providence Research, 1190 Hornby Street, Vancouver, BC, V6Z 2K5, Canada
- Centre for Advancing Health Outcomes, University of British Columbia, 1081 Burrard St, Vancouver, BC, V6Z 1Y6, Canada
- British Columbia Emergency Health Services, 2955 Virtual Way, Vancouver, BC, V5M 4X6, Canada
| | - Christopher B Fordyce
- British Columbia Resuscitation Research Collaborative, Providence Research, 1190 Hornby Street, Vancouver, BC, V6Z 2K5, Canada
- Centre for Advancing Health Outcomes, University of British Columbia, 1081 Burrard St, Vancouver, BC, V6Z 1Y6, Canada
- Division of Cardiology and Centre for Cardiovascular Innovation, Vancouver General Hospital, University of British Columbia, 2775 Laurel St, Vancouver, BC, V5Z 1M9, Canada
| | - Jim Christenson
- Department of Emergency Medicine, University of British Columbia, 2775 Laurel Street, Vancouver, BC, V5Z 1M9, Canada
- British Columbia Resuscitation Research Collaborative, Providence Research, 1190 Hornby Street, Vancouver, BC, V6Z 2K5, Canada
- Centre for Advancing Health Outcomes, University of British Columbia, 1081 Burrard St, Vancouver, BC, V6Z 1Y6, Canada
| | - Babak Shadgan
- British Columbia Resuscitation Research Collaborative, Providence Research, 1190 Hornby Street, Vancouver, BC, V6Z 2K5, Canada
- School of Biomedical Engineering, University of British Columbia, 2222 Health Sciences Mall, Vancouver, BC, V6T 1Z3, Canada
- International Collaboration on Repair Discoveries, 818 West 10th Avenue, Vancouver, BC, V5Z 1M9, Canada
- Department of Orthopedic Surgery, University of British Columbia, 2775 Laurel Street, Vancouver, BC, V5Z 1M9, Canada
| | - Brian Grunau
- Department of Emergency Medicine, University of British Columbia, 2775 Laurel Street, Vancouver, BC, V5Z 1M9, Canada
- British Columbia Resuscitation Research Collaborative, Providence Research, 1190 Hornby Street, Vancouver, BC, V6Z 2K5, Canada
- Centre for Advancing Health Outcomes, University of British Columbia, 1081 Burrard St, Vancouver, BC, V6Z 1Y6, Canada
- British Columbia Emergency Health Services, 2955 Virtual Way, Vancouver, BC, V5M 4X6, Canada
| | - Calvin Kuo
- British Columbia Resuscitation Research Collaborative, Providence Research, 1190 Hornby Street, Vancouver, BC, V6Z 2K5, Canada
- School of Biomedical Engineering, University of British Columbia, 2222 Health Sciences Mall, Vancouver, BC, V6T 1Z3, Canada
- Centre for Aging SMART, University of British Columbia, 2635 Laurel Street, Vancouver, BC, V5Z 1M9, Canada
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Ding C, Xiao R, Wang W, Holdsworth E, Hu X. Photoplethysmography based atrial fibrillation detection: a continually growing field. Physiol Meas 2024; 45:04TR01. [PMID: 38530307 DOI: 10.1088/1361-6579/ad37ee] [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: 10/28/2023] [Accepted: 03/26/2024] [Indexed: 03/27/2024]
Abstract
Objective. Atrial fibrillation (AF) is a prevalent cardiac arrhythmia associated with significant health ramifications, including an elevated susceptibility to ischemic stroke, heart disease, and heightened mortality. Photoplethysmography (PPG) has emerged as a promising technology for continuous AF monitoring for its cost-effectiveness and widespread integration into wearable devices. Our team previously conducted an exhaustive review on PPG-based AF detection before June 2019. However, since then, more advanced technologies have emerged in this field.Approach. This paper offers a comprehensive review of the latest advancements in PPG-based AF detection, utilizing digital health and artificial intelligence (AI) solutions, within the timeframe spanning from July 2019 to December 2022. Through extensive exploration of scientific databases, we have identified 57 pertinent studies.Significance. Our comprehensive review encompasses an in-depth assessment of the statistical methodologies, traditional machine learning techniques, and deep learning approaches employed in these studies. In addition, we address the challenges encountered in the domain of PPG-based AF detection. Furthermore, we maintain a dedicated website to curate the latest research in this area, with regular updates on a regular basis.
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Affiliation(s)
- Cheng Ding
- Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, GA, United States of America
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, United States of America
| | - Ran Xiao
- Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, GA, United States of America
| | - Weijia Wang
- Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, GA, United States of America
| | - Elizabeth Holdsworth
- Georgia Tech Library, Georgia Institute of Technology, Atlanta, GA, United States of America
| | - Xiao Hu
- Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, GA, United States of America
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, United States of America
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, United States of America
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Wrist photoplethysmography-based assessment of ectopic burden in hemodialysis patients. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/19/2023]
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Bacevicius J, Taparauskaite N, Kundelis R, Sokas D, Butkuviene M, Stankeviciute G, Abramikas Z, Pilkiene A, Dvinelis E, Staigyte J, Marinskiene J, Audzijoniene D, Petrylaite M, Jukna E, Karuzas A, Juknevicius V, Jakaite R, Basyte-Bacevice V, Bileisiene N, Badaras I, Kiseliute M, Zarembaite G, Gudauskas M, Jasiunas E, Johnson L, Marozas V, Aidietis A. Six-lead electrocardiography compared to single-lead electrocardiography and photoplethysmography of a wrist-worn device for atrial fibrillation detection controlled by premature atrial or ventricular contractions: six is smarter than one. Front Cardiovasc Med 2023; 10:1160242. [PMID: 37363094 PMCID: PMC10288196 DOI: 10.3389/fcvm.2023.1160242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 05/26/2023] [Indexed: 06/28/2023] Open
Abstract
Background Smartwatches are commonly capable to record a lead-I-like electrocardiogram (ECG) and perform a photoplethysmography (PPG)-based atrial fibrillation (AF) detection. Wearable technologies repeatedly face the challenge of frequent premature beats, particularly in target populations for screening of AF. Objective To investigate the potential diagnostic benefit of six-lead ECG compared to single-lead ECG and PPG-based algorithm for AF detection of the wrist-worn device. Methods and results From the database of DoubleCheck-AF 249 adults were enrolled in AF group (n = 121) or control group of SR with frequent premature ventricular (PVCs) or atrial (PACs) contractions (n = 128). Cardiac rhythm was monitored using a wrist-worn device capable of recording continuous PPG and simultaneous intermittent six-lead standard-limb-like ECG. To display a single-lead ECG, the six-lead ECGs were trimmed to lead-I-like ECGs. Two diagnosis-blinded cardiologists evaluated reference, six-lead and single-lead ECGs as "AF", "SR", or "Cannot be concluded". AF detection based on six-lead ECG, single-lead ECG, and PPG yielded a sensitivity of 99.2%, 95.7%, and 94.2%, respectively. The higher number of premature beats per minute was associated with false positive outcomes of single-lead ECG (18.80 vs. 5.40 beats/min, P < 0.01), six-lead ECG (64.3 vs. 5.8 beats/min, P = 0.018), and PPG-based detector (13.20 vs. 5.60 beats/min, P = 0.05). Single-lead ECG required 3.4 times fewer extrasystoles than six-lead ECG to result in a false positive outcome. In a control subgroup of PACs, the specificity of six-lead ECG, single-lead ECG, and PPG dropped to 95%, 83.8%, and 90%, respectively. The diagnostic value of single-lead ECG (AUC 0.898) was inferior to six-lead ECG (AUC 0.971) and PPG-based detector (AUC 0.921). In a control subgroup of PVCs, the specificity of six-lead ECG, single-lead ECG, and PPG was 100%, 96.4%, and 96.6%, respectively. The diagnostic value of single-lead ECG (AUC 0.961) was inferior to six-lead ECG (AUC 0.996) and non-inferior to PPG-based detector (AUC 0.954). Conclusions A six-lead wearable-recorded ECG demonstrated the superior diagnostic value of AF detection compared to a single-lead ECG and PPG-based AF detection. The risk of type I error due to the widespread use of smartwatch-enabled single-lead ECGs in populations with frequent premature beats is significant.
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Affiliation(s)
- Justinas Bacevicius
- Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Vilnius, Lithuania
- Center of Cardiology and Angiology, Vilnius University Hospital Santaros Klinikos, Vilnius, Lithuania
| | - Neringa Taparauskaite
- Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Vilnius, Lithuania
- Center of Cardiology and Angiology, Vilnius University Hospital Santaros Klinikos, Vilnius, Lithuania
| | - Ricardas Kundelis
- Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Vilnius, Lithuania
- Center of Cardiology and Angiology, Vilnius University Hospital Santaros Klinikos, Vilnius, Lithuania
| | - Daivaras Sokas
- Biomedical Engineering Institute, Kaunas University of Technology, Kaunas, Lithuania
| | - Monika Butkuviene
- Biomedical Engineering Institute, Kaunas University of Technology, Kaunas, Lithuania
| | - Guoste Stankeviciute
- Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Vilnius, Lithuania
| | - Zygimantas Abramikas
- Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Vilnius, Lithuania
- Center of Cardiology and Angiology, Vilnius University Hospital Santaros Klinikos, Vilnius, Lithuania
| | - Aiste Pilkiene
- Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Vilnius, Lithuania
| | - Ernestas Dvinelis
- Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Vilnius, Lithuania
- Center of Cardiology and Angiology, Vilnius University Hospital Santaros Klinikos, Vilnius, Lithuania
| | - Justina Staigyte
- Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Vilnius, Lithuania
- Center of Cardiology and Angiology, Vilnius University Hospital Santaros Klinikos, Vilnius, Lithuania
| | - Julija Marinskiene
- Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Vilnius, Lithuania
- Center of Cardiology and Angiology, Vilnius University Hospital Santaros Klinikos, Vilnius, Lithuania
| | - Deimile Audzijoniene
- Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Vilnius, Lithuania
- Center of Cardiology and Angiology, Vilnius University Hospital Santaros Klinikos, Vilnius, Lithuania
| | - Marija Petrylaite
- Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Vilnius, Lithuania
- Center of Cardiology and Angiology, Vilnius University Hospital Santaros Klinikos, Vilnius, Lithuania
| | - Edvardas Jukna
- Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Vilnius, Lithuania
- Center of Cardiology and Angiology, Vilnius University Hospital Santaros Klinikos, Vilnius, Lithuania
| | - Albinas Karuzas
- Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Vilnius, Lithuania
- Center of Cardiology and Angiology, Vilnius University Hospital Santaros Klinikos, Vilnius, Lithuania
| | - Vytautas Juknevicius
- Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Vilnius, Lithuania
- Center of Cardiology and Angiology, Vilnius University Hospital Santaros Klinikos, Vilnius, Lithuania
| | - Rusne Jakaite
- Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Vilnius, Lithuania
- Center of Cardiology and Angiology, Vilnius University Hospital Santaros Klinikos, Vilnius, Lithuania
| | | | - Neringa Bileisiene
- Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Vilnius, Lithuania
- Center of Cardiology and Angiology, Vilnius University Hospital Santaros Klinikos, Vilnius, Lithuania
| | - Ignas Badaras
- Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Vilnius, Lithuania
- Center of Cardiology and Angiology, Vilnius University Hospital Santaros Klinikos, Vilnius, Lithuania
| | - Margarita Kiseliute
- Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Vilnius, Lithuania
| | - Gintare Zarembaite
- Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Vilnius, Lithuania
| | - Modestas Gudauskas
- Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Vilnius, Lithuania
- Center of Cardiology and Angiology, Vilnius University Hospital Santaros Klinikos, Vilnius, Lithuania
| | - Eugenijus Jasiunas
- Center of Informatics and Development, Vilnius University Hospital Santaros Klinikos, Vilnius, Lithuania
| | - Linda Johnson
- Department of Clinical Sciences, Malmö, Lund University, Lund, Sweden
| | - Vaidotas Marozas
- Biomedical Engineering Institute, Kaunas University of Technology, Kaunas, Lithuania
- Electronics Engineering Department, Kaunas University of Technology, Kaunas, Lithuania
| | - Audrius Aidietis
- Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Vilnius, Lithuania
- Center of Cardiology and Angiology, Vilnius University Hospital Santaros Klinikos, Vilnius, Lithuania
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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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Knight S, Lipoth J, Namvari M, Gu C, Hedayati Ch. M, Syed-Abdul S, Spiteri RJ. The Accuracy of Wearable Photoplethysmography Sensors for Telehealth Monitoring: A Scoping Review. Telemed J E Health 2022. [DOI: 10.1089/tmj.2022.0182] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Affiliation(s)
- Sheida Knight
- Department of Computer Science, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
| | - Jessica Lipoth
- Department of Computer Science, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
| | - Mina Namvari
- Department of Computer Science, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
| | - Carol Gu
- Center for Bio-Integrated Electronics at Northwestern University, Evanston, Illinois, USA
| | | | - Shabbir Syed-Abdul
- Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei, Taiwan
| | - Raymond J. Spiteri
- Department of Computer Science, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
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Sološenko A, Paliakaitė B, Marozas V, Sörnmo L. Training Convolutional Neural Networks on Simulated Photoplethysmography Data: Application to Bradycardia and Tachycardia Detection. Front Physiol 2022; 13:928098. [PMID: 35923223 PMCID: PMC9339964 DOI: 10.3389/fphys.2022.928098] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 06/15/2022] [Indexed: 11/23/2022] Open
Abstract
Objective: To develop a method for detection of bradycardia and ventricular tachycardia using the photoplethysmogram (PPG). Approach: The detector is based on a dual-branch convolutional neural network (CNN), whose input is the scalograms of the continuous wavelet transform computed in 5-s segments. Training and validation of the CNN is accomplished using simulated PPG signals generated from RR interval series extracted from public ECG databases. Manually annotated real PPG signals from the PhysioNet/CinC 2015 Challenge Database are used for performance evaluation. The performance is compared to that of a pulse-based reference detector. Results: The sensitivity/specificity were found to be 98.1%/97.9 and 76.6%/96.8% for the CNN-based detector, respectively, whereas the corresponding results for the pulse-based detector were 94.7%/99.8 and 67.1%/93.8%, respectively. Significance: The proposed detector may be useful for continuous, long-term monitoring of bradycardia and tachycardia using wearable devices, e.g., wrist-worn devices, especially in situations where sensitivity is favored over specificity. The study demonstrates that simulated PPG signals are suitable for training and validation of a CNN.
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Affiliation(s)
- Andrius Sološenko
- Biomedical Engineering Institute, Kaunas University of Technology, Kaunas, Lithuania
- *Correspondence: Andrius Sološenko ,
| | - Birutė Paliakaitė
- Biomedical Engineering Institute, Kaunas University of Technology, Kaunas, Lithuania
| | - Vaidotas Marozas
- Biomedical Engineering Institute, Kaunas University of Technology, Kaunas, Lithuania
- Department of Electronics Engineering, Kaunas University of Technology, Kaunas, Lithuania
| | - Leif Sörnmo
- Department of Biomedical Engineering, Lund University, Lund, Sweden
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Goergen CJ, Tweardy MJ, Steinhubl SR, Wegerich SW, Singh K, Mieloszyk RJ, Dunn J. Detection and Monitoring of Viral Infections via Wearable Devices and Biometric Data. Annu Rev Biomed Eng 2022; 24:1-27. [PMID: 34932906 PMCID: PMC9218991 DOI: 10.1146/annurev-bioeng-103020-040136] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Mounting clinical evidence suggests that viral infections can lead to detectable changes in an individual's normal physiologic and behavioral metrics, including heart and respiration rates, heart rate variability, temperature, activity, and sleep prior to symptom onset, potentially even in asymptomatic individuals. While the ability of wearable devices to detect viral infections in a real-world setting has yet to be proven, multiple recent studies have established that individual, continuous data from a range of biometric monitoring technologies can be easily acquired and that through the use of machine learning techniques, physiological signals and warning signs can be identified. In this review, we highlight the existing knowledge base supporting the potential for widespread implementation of biometric data to address existing gaps in the diagnosis and treatment of viral illnesses, with a particular focus on the many important lessons learned from the coronavirus disease 2019 pandemic.
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Affiliation(s)
- Craig J Goergen
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA;
| | | | - Steven R Steinhubl
- physIQ Inc., Chicago, Illinois, USA
- Scripps Research Translational Institute, La Jolla, California, USA
| | | | - Karnika Singh
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA
| | | | - Jessilyn Dunn
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA
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Galli A, Montree RJH, Que S, Peri E, Vullings R. An Overview of the Sensors for Heart Rate Monitoring Used in Extramural Applications. SENSORS (BASEL, SWITZERLAND) 2022; 22:4035. [PMID: 35684656 PMCID: PMC9185322 DOI: 10.3390/s22114035] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 05/23/2022] [Accepted: 05/24/2022] [Indexed: 06/02/2023]
Abstract
This work presents an overview of the main strategies that have been proposed for non-invasive monitoring of heart rate (HR) in extramural and home settings. We discuss three categories of sensing according to what physiological effect is used to measure the pulsatile activity of the heart, and we focus on an illustrative sensing modality for each of them. Therefore, electrocardiography, photoplethysmography, and mechanocardiography are presented as illustrative modalities to sense electrical activity, mechanical activity, and the peripheral effect of heart activity. In this paper, we describe the physical principles underlying the three categories and the characteristics of the different types of sensors that belong to each class, and we touch upon the most used software strategies that are currently adopted to effectively and reliably extract HR. In addition, we investigate the strengths and weaknesses of each category linked to the different applications in order to provide the reader with guidelines for selecting the most suitable solution according to the requirements and constraints of the application.
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Affiliation(s)
- Alessandra Galli
- Department of Information Engineering, University of Padova, I-35131 Padova, Italy;
| | - Roel J. H. Montree
- Department of Electrical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands; (R.J.H.M.); (S.Q.); (E.P.)
| | - Shuhao Que
- Department of Electrical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands; (R.J.H.M.); (S.Q.); (E.P.)
| | - Elisabetta Peri
- Department of Electrical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands; (R.J.H.M.); (S.Q.); (E.P.)
| | - Rik Vullings
- Department of Electrical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands; (R.J.H.M.); (S.Q.); (E.P.)
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10
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Prieto-Avalos G, Cruz-Ramos NA, Alor-Hernández G, Sánchez-Cervantes JL, Rodríguez-Mazahua L, Guarneros-Nolasco LR. Wearable Devices for Physical Monitoring of Heart: A Review. BIOSENSORS 2022; 12:292. [PMID: 35624593 PMCID: PMC9138373 DOI: 10.3390/bios12050292] [Citation(s) in RCA: 40] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 04/27/2022] [Accepted: 04/29/2022] [Indexed: 12/19/2022]
Abstract
Cardiovascular diseases (CVDs) are the leading cause of death globally. An effective strategy to mitigate the burden of CVDs has been to monitor patients' biomedical variables during daily activities with wearable technology. Nowadays, technological advance has contributed to wearables technology by reducing the size of the devices, improving the accuracy of sensing biomedical variables to be devices with relatively low energy consumption that can manage security and privacy of the patient's medical information, have adaptability to any data storage system, and have reasonable costs with regard to the traditional scheme where the patient must go to a hospital for an electrocardiogram, thus contributing a serious option in diagnosis and treatment of CVDs. In this work, we review commercial and noncommercial wearable devices used to monitor CVD biomedical variables. Our main findings revealed that commercial wearables usually include smart wristbands, patches, and smartwatches, and they generally monitor variables such as heart rate, blood oxygen saturation, and electrocardiogram data. Noncommercial wearables focus on monitoring electrocardiogram and photoplethysmography data, and they mostly include accelerometers and smartwatches for detecting atrial fibrillation and heart failure. However, using wearable devices without healthy personal habits will cause disappointing results in the patient's health.
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Affiliation(s)
- Guillermo Prieto-Avalos
- Tecnológico Nacional de México/I.T. Orizaba, Av. Oriente 9 No. 852 Col. Emiliano Zapata, Orizaba 94320, Veracruz, Mexico; (G.P.-A.); (N.A.C.-R.); (L.R.-M.); (L.R.G.-N.)
| | - Nancy Aracely Cruz-Ramos
- Tecnológico Nacional de México/I.T. Orizaba, Av. Oriente 9 No. 852 Col. Emiliano Zapata, Orizaba 94320, Veracruz, Mexico; (G.P.-A.); (N.A.C.-R.); (L.R.-M.); (L.R.G.-N.)
| | - Giner Alor-Hernández
- Tecnológico Nacional de México/I.T. Orizaba, Av. Oriente 9 No. 852 Col. Emiliano Zapata, Orizaba 94320, Veracruz, Mexico; (G.P.-A.); (N.A.C.-R.); (L.R.-M.); (L.R.G.-N.)
| | - José Luis Sánchez-Cervantes
- CONACYT-Tecnológico Nacional de México/I. T. Orizaba, Av. Oriente 9 No. 852 Col. Emiliano Zapata, Orizaba 94320, Veracruz, Mexico;
| | - Lisbeth Rodríguez-Mazahua
- Tecnológico Nacional de México/I.T. Orizaba, Av. Oriente 9 No. 852 Col. Emiliano Zapata, Orizaba 94320, Veracruz, Mexico; (G.P.-A.); (N.A.C.-R.); (L.R.-M.); (L.R.G.-N.)
| | - Luis Rolando Guarneros-Nolasco
- Tecnológico Nacional de México/I.T. Orizaba, Av. Oriente 9 No. 852 Col. Emiliano Zapata, Orizaba 94320, Veracruz, Mexico; (G.P.-A.); (N.A.C.-R.); (L.R.-M.); (L.R.G.-N.)
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11
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Bacevicius J, Abramikas Z, Dvinelis E, Audzijoniene D, Petrylaite M, Marinskiene J, Staigyte J, Karuzas A, Juknevicius V, Jakaite R, Basyte-Bacevice V, Bileisiene N, Solosenko A, Sokas D, Petrenas A, Butkuviene M, Paliakaite B, Daukantas S, Rapalis A, Marinskis G, Jasiunas E, Darma A, Marozas V, Aidietis A. High Specificity Wearable Device With Photoplethysmography and Six-Lead Electrocardiography for Atrial Fibrillation Detection Challenged by Frequent Premature Contractions: DoubleCheck-AF. Front Cardiovasc Med 2022; 9:869730. [PMID: 35463751 PMCID: PMC9019128 DOI: 10.3389/fcvm.2022.869730] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 03/08/2022] [Indexed: 01/25/2023] Open
Abstract
Background Consumer smartwatches have gained attention as mobile health (mHealth) tools able to detect atrial fibrillation (AF) using photoplethysmography (PPG) or a short strip of electrocardiogram (ECG). PPG has limited accuracy due to the movement artifacts, whereas ECG cannot be used continuously, is usually displayed as a single-lead signal and is limited in asymptomatic cases. Objective DoubleCheck-AF is a validation study of a wrist-worn device dedicated to providing both continuous PPG-based rhythm monitoring and instant 6-lead ECG with no wires. We evaluated its ability to differentiate between AF and sinus rhythm (SR) with particular emphasis on the challenge of frequent premature beats. Methods and Results We performed a prospective, non-randomized study of 344 participants including 121 patients in AF. To challenge the specificity of the device two control groups were selected: 95 patients in stable SR and 128 patients in SR with frequent premature ventricular or atrial contractions (PVCs/PACs). All ECG tracings were labeled by two independent diagnosis-blinded cardiologists as “AF,” “SR” or “Cannot be concluded.” In case of disagreement, a third cardiologist was consulted. A simultaneously recorded ECG of Holter monitor served as a reference. It revealed a high burden of ectopy in the corresponding control group: 6.2 PVCs/PACs per minute, bigeminy/trigeminy episodes in 24.2% (31/128) and runs of ≥3 beats in 9.4% (12/128) of patients. AF detection with PPG-based algorithm, ECG of the wearable and combination of both yielded sensitivity and specificity of 94.2 and 96.9%; 99.2 and 99.1%; 94.2 and 99.6%, respectively. All seven false-positive PPG-based cases were from the frequent PVCs/PACs group compared to none from the stable SR group (P < 0.001). In the majority of these cases (6/7) cardiologists were able to correct the diagnosis to SR with the help of the ECG of the device (P = 0.012). Conclusions This is the first wearable combining PPG-based AF detection algorithm for screening of AF together with an instant 6-lead ECG with no wires for manual rhythm confirmation. The system maintained high specificity despite a remarkable amount of frequent single or multiple premature contractions.
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Affiliation(s)
- Justinas Bacevicius
- Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Vilnius, Lithuania.,Center of Cardiology and Angiology, Vilnius University Hospital Santaros Klinikos, Vilnius, Lithuania
| | - Zygimantas Abramikas
- Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Vilnius, Lithuania.,Center of Cardiology and Angiology, Vilnius University Hospital Santaros Klinikos, Vilnius, Lithuania
| | - Ernestas Dvinelis
- Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Vilnius, Lithuania.,Center of Cardiology and Angiology, Vilnius University Hospital Santaros Klinikos, Vilnius, Lithuania
| | - Deimile Audzijoniene
- Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Vilnius, Lithuania.,Center of Cardiology and Angiology, Vilnius University Hospital Santaros Klinikos, Vilnius, Lithuania
| | - Marija Petrylaite
- Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Vilnius, Lithuania.,Center of Cardiology and Angiology, Vilnius University Hospital Santaros Klinikos, Vilnius, Lithuania
| | - Julija Marinskiene
- Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Vilnius, Lithuania.,Center of Cardiology and Angiology, Vilnius University Hospital Santaros Klinikos, Vilnius, Lithuania
| | - Justina Staigyte
- Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Vilnius, Lithuania.,Center of Cardiology and Angiology, Vilnius University Hospital Santaros Klinikos, Vilnius, Lithuania
| | - Albinas Karuzas
- Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Vilnius, Lithuania.,Center of Cardiology and Angiology, Vilnius University Hospital Santaros Klinikos, Vilnius, Lithuania
| | - Vytautas Juknevicius
- Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Vilnius, Lithuania.,Center of Cardiology and Angiology, Vilnius University Hospital Santaros Klinikos, Vilnius, Lithuania
| | - Rusne Jakaite
- Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Vilnius, Lithuania.,Center of Cardiology and Angiology, Vilnius University Hospital Santaros Klinikos, Vilnius, Lithuania
| | | | - Neringa Bileisiene
- Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Vilnius, Lithuania.,Center of Cardiology and Angiology, Vilnius University Hospital Santaros Klinikos, Vilnius, Lithuania
| | - Andrius Solosenko
- Biomedical Engineering Institute, Kaunas University of Technology, Kaunas, Lithuania
| | - Daivaras Sokas
- Biomedical Engineering Institute, Kaunas University of Technology, Kaunas, Lithuania
| | - Andrius Petrenas
- Biomedical Engineering Institute, Kaunas University of Technology, Kaunas, Lithuania
| | - Monika Butkuviene
- Biomedical Engineering Institute, Kaunas University of Technology, Kaunas, Lithuania
| | - Birute Paliakaite
- Biomedical Engineering Institute, Kaunas University of Technology, Kaunas, Lithuania
| | - Saulius Daukantas
- Biomedical Engineering Institute, Kaunas University of Technology, Kaunas, Lithuania
| | - Andrius Rapalis
- Biomedical Engineering Institute, Kaunas University of Technology, Kaunas, Lithuania
| | - Germanas Marinskis
- Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Vilnius, Lithuania.,Center of Cardiology and Angiology, Vilnius University Hospital Santaros Klinikos, Vilnius, Lithuania
| | - Eugenijus Jasiunas
- Center of Informatics and Development, Vilnius University Hospital Santaros Klinikos, Vilnius, Lithuania
| | - Angeliki Darma
- Heart Center Leipzig at University of Leipzig and Leipzig Heart Institute, Leipzig, Germany
| | - Vaidotas Marozas
- Biomedical Engineering Institute, Kaunas University of Technology, Kaunas, Lithuania.,Department of Electronics Engineering, Kaunas University of Technology, Kaunas, Lithuania
| | - Audrius Aidietis
- Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Vilnius, Lithuania.,Center of Cardiology and Angiology, Vilnius University Hospital Santaros Klinikos, Vilnius, Lithuania
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12
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Impact of recording length and other arrhythmias on atrial fibrillation detection from wrist photoplethysmogram using smartwatches. Sci Rep 2022; 12:5364. [PMID: 35354873 PMCID: PMC8967835 DOI: 10.1038/s41598-022-09181-1] [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: 07/15/2021] [Accepted: 03/10/2022] [Indexed: 11/08/2022] Open
Abstract
This study aimed to evaluate whether quantitative analysis of wrist photoplethysmography (PPG) could detect atrial fibrillation (AF). Continuous electrocardiograms recorded using an electrophysiology recording system and PPG obtained using a wrist-worn smartwatch were simultaneously collected from patients undergoing catheter ablation or electrical cardioversion. PPG features were extracted from 10, 25, 40, and 80 heartbeats of the split segments. Machine learning with a support vector machine and random forest approach were used to detect AF. A total of 116 patients were evaluated. We annotated > 117 h of PPG. A total of 6475 and 3957 segments of 25-beat pulse-to-pulse intervals (PPIs) were annotated as AF and sinus rhythm, respectively. The accuracy of the 25 PPIs yielded a test area under the receiver operating characteristic curve (AUC) of 0.9676, which was significantly better than the AUC for the 10 PPIs (0.9453; P < .001). PPGs obtained from another 38 patients with frequent premature ventricular/atrial complexes (PVCs/PACs) were used to evaluate the impact of other arrhythmias on diagnostic accuracy. The new AF detection algorithm achieved an AUC of 0.9680. The appropriate data length of PPG for optimizing the PPG analytics program was 25 heartbeats. Algorithm modification using a machine learning approach shows robustness to PVCs/PACs.
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13
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Xintarakou A, Sousonis V, Asvestas D, Vardas PE, Tzeis S. Remote Cardiac Rhythm Monitoring in the Era of Smart Wearables: Present Assets and Future Perspectives. Front Cardiovasc Med 2022; 9:853614. [PMID: 35299975 PMCID: PMC8921479 DOI: 10.3389/fcvm.2022.853614] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 02/08/2022] [Indexed: 12/14/2022] Open
Abstract
Remote monitoring and control of heart function are of primary importance for patient evaluation and management, especially in the modern era of precision medicine and personalized approach. Breaking technological developments have brought to the frontline a variety of smart wearable devices, such as smartwatches, chest patches/straps, or sensors integrated into clothing and footwear, which allow continuous and real-time recording of heart rate, facilitating the detection of cardiac arrhythmias. However, there is great diversity and significant differences in the type and quality of the information they provide, thus impairing their integration into daily clinical practice and the relevant familiarization of practicing physicians. This review will summarize the different types and dominant functions of cardiac smart wearables available in the market. Furthermore, we report the devices certified by official American and/or European authorities and the respective sources of evidence. Finally, we comment pertinent limitations and caveats as well as the potential answers that flow from the latest technological achievements and future perspectives.
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Affiliation(s)
| | | | | | - Panos E Vardas
- Heart Sector, Hygeia Hospitals Group, HHG, Athens, Greece.,European Heart Agency, European Society of Cardiology, Brussels, Belgium
| | - Stylianos Tzeis
- Department of Cardiology, Hygeia Group, Mitera Hospital, Athens, Greece
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14
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Charlton PH, Kyriacou PA, Mant J, Marozas V, Chowienczyk P, Alastruey J. Wearable Photoplethysmography for Cardiovascular Monitoring. PROCEEDINGS OF THE IEEE. INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS 2022; 110:355-381. [PMID: 35356509 PMCID: PMC7612541 DOI: 10.1109/jproc.2022.3149785] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Revised: 01/06/2022] [Accepted: 01/27/2022] [Indexed: 05/29/2023]
Abstract
Smart wearables provide an opportunity to monitor health in daily life and are emerging as potential tools for detecting cardiovascular disease (CVD). Wearables such as fitness bands and smartwatches routinely monitor the photoplethysmogram signal, an optical measure of the arterial pulse wave that is strongly influenced by the heart and blood vessels. In this survey, we summarize the fundamentals of wearable photoplethysmography and its analysis, identify its potential clinical applications, and outline pressing directions for future research in order to realize its full potential for tackling CVD.
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Affiliation(s)
- Peter H. Charlton
- Department of Biomedical EngineeringSchool of Biomedical Engineering and Imaging SciencesKing’s College London, King’s Health PartnersLondonSE1 7EUU.K.
- Research Centre for Biomedical Engineering, CityUniversity of LondonLondonEC1V 0HBU.K.
- Department of Public Health and Primary CareUniversity of CambridgeCambridgeCB1 8RNU.K.
| | - Panicos A. Kyriacou
- Research Centre for Biomedical Engineering, CityUniversity of LondonLondonEC1V 0HBU.K.
| | - Jonathan Mant
- Department of Public Health and Primary CareUniversity of CambridgeCambridgeCB1 8RNU.K.
| | - Vaidotas Marozas
- Department of Electronics Engineering and the Biomedical Engineering Institute, Kaunas University of Technology44249KaunasLithuania
| | - Phil Chowienczyk
- Department of Clinical PharmacologyKing’s College LondonLondonSE1 7EHU.K.
| | - Jordi Alastruey
- Department of Biomedical EngineeringSchool of Biomedical Engineering and Imaging SciencesKing’s College London, King’s Health PartnersLondonSE1 7EUU.K.
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15
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Väliaho ES, Lipponen JA, Kuoppa P, Martikainen TJ, Jäntti H, Rissanen TT, Castrén M, Halonen J, Tarvainen MP, Laitinen TM, Laitinen TP, Santala OE, Rantula O, Naukkarinen NS, Hartikainen JEK. Continuous 24-h Photoplethysmogram Monitoring Enables Detection of Atrial Fibrillation. Front Physiol 2022; 12:778775. [PMID: 35058796 PMCID: PMC8764282 DOI: 10.3389/fphys.2021.778775] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Accepted: 11/29/2021] [Indexed: 01/12/2023] Open
Abstract
Aim: Atrial fibrillation (AF) detection is challenging because it is often asymptomatic and paroxysmal. We evaluated continuous photoplethysmogram (PPG) for signal quality and detection of AF. Methods: PPGs were recorded using a wrist-band device in 173 patients (76 AF, 97 sinus rhythm, SR) for 24 h. Simultaneously recorded 3-lead ambulatory ECG served as control. The recordings were split into 10-, 20-, 30-, and 60-min time-frames. The sensitivity, specificity, and F1-score of AF detection were evaluated for each time-frame. AF alarms were generated to simulate continuous AF monitoring. Sensitivities, specificities, and positive predictive values (PPVs) of the alarms were evaluated. User experiences of PPG and ECG recordings were assessed. The study was registered in the Clinical Trials database (NCT03507335). Results: The quality of PPG signal was better during night-time than in daytime (67.3 ± 22.4% vs. 30.5 ± 19.4%, p < 0.001). The 30-min time-frame yielded the highest F1-score (0.9536), identifying AF correctly in 72/76 AF patients (sensitivity 94.7%), only 3/97 SR patients receiving a false AF diagnosis (specificity 96.9%). The sensitivity and PPV of the simulated AF alarms were 78.2 and 97.2% at night, and 49.3 and 97.0% during the daytime. 82% of patients were willing to use the device at home. Conclusion: PPG wrist-band provided reliable AF identification both during daytime and night-time. The PPG data’s quality was better at night. The positive user experience suggests that wearable PPG devices could be feasible for continuous rhythm monitoring.
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Affiliation(s)
- Eemu-Samuli Väliaho
- School of Medicine, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland.,Doctoral School, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland
| | - Jukka A Lipponen
- Department of Applied Physics, Faculty of Science and Forestry, University of Eastern Finland, Kuopio, Finland
| | - Pekka Kuoppa
- Department of Applied Physics, Faculty of Science and Forestry, University of Eastern Finland, Kuopio, Finland
| | - Tero J Martikainen
- Department of Emergency Care, Kuopio University Hospital, Kuopio, Finland
| | - Helena Jäntti
- Center for Prehospital Emergency Care, Kuopio University Hospital, Kuopio, Finland
| | | | - Maaret Castrén
- Department of Emergency Medicine, University of Helsinki, Helsinki, Finland.,Department of Emergency Medicine and Services, Helsinki University Hospital, Helsinki, Finland
| | - Jari Halonen
- School of Medicine, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland.,Heart Center, Kuopio University Hospital, Kuopio, Finland
| | - Mika P Tarvainen
- Department of Applied Physics, Faculty of Science and Forestry, University of Eastern Finland, Kuopio, Finland.,Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, Kuopio, Finland
| | | | - Tomi P Laitinen
- Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, Kuopio, Finland.,Imaging Center, Kuopio University Hospital, Kuopio, Finland
| | - Onni E Santala
- School of Medicine, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland.,Doctoral School, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland
| | - Olli Rantula
- School of Medicine, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland
| | - Noora S Naukkarinen
- School of Medicine, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland
| | - Juha E K Hartikainen
- School of Medicine, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland.,Heart Center, Kuopio University Hospital, Kuopio, Finland
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16
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Butkuviene M, Petrenas A, Solosenko A, Martin-Yebra A, Marozas V, Sornmo L. Considerations on Performance Evaluation of Atrial Fibrillation Detectors. IEEE Trans Biomed Eng 2021; 68:3250-3260. [PMID: 33750686 DOI: 10.1109/tbme.2021.3067698] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
OBJECTIVE A large number of atrial fibrillation (AF) detectors have been published in recent years, signifying that the comparison of detector performance plays a central role, though not always consistent. The aim of this study is to shed needed light on aspects crucial to the evaluation of detection performance. METHODS Three types of AF detector, using either information on rhythm, rhythm and morphology, or segments of ECG samples, are implemented and studied on both real and simulated ECG signals. The properties of different performance measures are investigated, for example, in relation to dataset imbalance. RESULTS The results show that performance can differ considerably depending on the way detector output is compared to database annotations, i.e., beat-to-beat, segment-to-segment, or episode-to-episode comparison. Moreover, depending on the type of detector, the results substantiate that physiological and technical factors, e.g., changes in ECG morphology, rate of atrial premature beats, and noise level, can have a considerable influence on performance. CONCLUSION The present study demonstrates overall strengths and weaknesses of different types of detector, highlights challenges in AF detection, and proposes five recommendations on how to handle data and characterize performance.
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17
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Tao X, Dang H, Xu X, Zhou X, Xiong D. Multi-feature Fusion of Deep Neural Network for Screening Atrial Fibrillation Using ECG Signals. J Imaging Sci Technol 2021. [DOI: 10.2352/j.imagingsci.technol.2021.65.3.030412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
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18
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Paliakaitė B, Petrėnas A, Sološenko A, Marozas V. Modeling of artifacts in the wrist photoplethysmogram: Application to the detection of life-threatening arrhythmias. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102421] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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19
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Leroux A, Rzasa-Lynn R, Crainiceanu C, Sharma T. Wearable Devices: Current Status and Opportunities in Pain Assessment and Management. Digit Biomark 2021; 5:89-102. [PMID: 34056519 PMCID: PMC8138140 DOI: 10.1159/000515576] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Accepted: 03/01/2021] [Indexed: 12/17/2022] Open
Abstract
INTRODUCTION We investigated the possibilities and opportunities for using wearable devices that measure physical activity and physiometric signals in conjunction with ecological momentary assessment (EMA) data to improve the assessment and treatment of pain. METHODS We considered studies with cross-sectional and longitudinal designs as well as interventional or observational studies correlating pain scores with measures derived from wearable devices. A search was also performed on studies that investigated physical activity and physiometric signals among patients with pain. RESULTS Few studies have assessed the possibility of incorporating wearable devices as objective tools for contextualizing pain and physical function in free-living environments. Of the studies that have been conducted, most focus solely on physical activity and functional outcomes as measured by a wearable accelerometer. Several studies report promising correlations between pain scores and signals derived from wearable devices, objectively measured physical activity, and physical function. In addition, there is a known association between physiologic signals that can be measured by wearable devices and pain, though studies using wearable devices to measure these signals and associate them with pain in free-living environments are limited. CONCLUSION There exists a great opportunity to study the complex interplay between physiometric signals, physical function, and pain in a real-time fashion in free-living environments. The literature supports the hypothesis that wearable devices can be used to develop reproducible biosignals that correlate with pain. The combination of wearable devices and EMA will likely lead to the development of clinically meaningful endpoints that will transform how we understand and treat pain patients.
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Affiliation(s)
- Andrew Leroux
- Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, Colorado, USA
| | - Rachael Rzasa-Lynn
- Department of Anesthesiology, University of Colorado, Aurora, Colorado, USA
| | - Ciprian Crainiceanu
- Department of Biostatistics, Johns Hopkins University, Baltimore, Maryland, USA
| | - Tushar Sharma
- Department of Anesthesiology, University of Colorado, Aurora, Colorado, USA
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20
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Han D, Bashar SK, Zieneddin F, Ding E, Whitcomb C, McManus DD, Chon KH. Digital Image Processing Features of Smartwatch Photoplethysmography for Cardiac Arrhythmia Detection. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:4071-4074. [PMID: 33018893 DOI: 10.1109/embc44109.2020.9176142] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The aim of our work is to design an algorithm to detect premature atrial contraction (PAC), premature ventricular contraction (PVC), and atrial fibrillation (AF) among normal sinus rhythm (NSR) using smartwatch photoplethysmographic (PPG) data. Novel image processing features and two machine learning methods are used to enhance the PAC/PVC detection results of the Poincaré plot method. Compared with support vector machine (SVM) methods, the Random Forests (RF) method performs better. It yields a 10-fold cross validation (CV) averaged sensitivity, specificity, positive predicted value (PPV), negative predicted value (NPV), and accuracy for PAC/PVC labels of 63%, 98%, 83%, 94%, and 93%, respectively, and a 10-fold CV averaged sensitivity, specificity, PPV, NPV, and accuracy for AF subjects of 92%, 96%, 85%, 98%, and 95%, respectively. This is one of the first studies to derive image processing features from Poincaré plots to further enhance the accuracy of PAC/PVC detection using PPG recordings from a smartwatch.
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21
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Han D, Bashar SK, Mohagheghian F, Ding E, Whitcomb C, McManus DD, Chon KH. Premature Atrial and Ventricular Contraction Detection using Photoplethysmographic Data from a Smartwatch. SENSORS (BASEL, SWITZERLAND) 2020; 20:E5683. [PMID: 33028000 PMCID: PMC7582300 DOI: 10.3390/s20195683] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Revised: 09/19/2020] [Accepted: 09/30/2020] [Indexed: 12/12/2022]
Abstract
We developed an algorithm to detect premature atrial contraction (PAC) and premature ventricular contraction (PVC) using photoplethysmographic (PPG) data acquired from a smartwatch. Our PAC/PVC detection algorithm is composed of a sequence of algorithms that are combined to discriminate various arrhythmias. A novel vector resemblance method is used to enhance the PAC/PVC detection results of the Poincaré plot method. The new PAC/PVC detection algorithm with our automated motion and noise artifact detection approach yielded a sensitivity of 86% for atrial fibrillation (AF) subjects while the overall sensitivity was 67% when normal sinus rhythm (NSR) subjects were also included. The specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy values for the combined data consisting of both NSR and AF subjects were 97%, 81%, 94% and 92%, respectively, for PAC/PVC detection combined with our automated motion and noise artifact detection approach. Moreover, when AF detection was compared with and without PAC/PVC, the sensitivity and specificity increased from 94.55% to 98.18% and from 95.75% to 97.90%, respectively. For additional independent testing data, we used two datasets: a smartwatch PPG dataset that was collected in our ongoing clinical study, and a pulse oximetry PPG dataset from the Medical Information Mart for Intensive Care III database. The PAC/PVC classification results of the independent testing on these two other datasets are all above 92% for sensitivity, specificity, PPV, NPV, and accuracy. The proposed combined approach to detect PAC and PVC can ultimately lead to better accuracy in AF detection. This is one of the first studies involving detection of PAC and PVC using PPG recordings from a smartwatch. The proposed method can potentially be of clinical importance as this enhanced capability can lead to fewer false positive detections of AF, especially for those NSR subjects with frequent episodes of PAC/PVC.
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Affiliation(s)
- Dong Han
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA; (D.H.); (S.K.B.); (F.M.)
| | - Syed Khairul Bashar
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA; (D.H.); (S.K.B.); (F.M.)
| | - Fahimeh Mohagheghian
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA; (D.H.); (S.K.B.); (F.M.)
| | - Eric Ding
- Division of Cardiology, University of Massachusetts Medical School, Worcester, MA 01655, USA; (E.D.); (C.W.); (D.D.M.)
| | - Cody Whitcomb
- Division of Cardiology, University of Massachusetts Medical School, Worcester, MA 01655, USA; (E.D.); (C.W.); (D.D.M.)
| | - David D. McManus
- Division of Cardiology, University of Massachusetts Medical School, Worcester, MA 01655, USA; (E.D.); (C.W.); (D.D.M.)
| | - Ki H. Chon
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA; (D.H.); (S.K.B.); (F.M.)
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22
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Henriksson M, Martin-Yebra A, Butkuviene M, Rasmussen JG, Marozas V, Petrenas A, Savelev A, Platonov PG, Sornmo L. Modeling and Estimation of Temporal Episode Patterns in Paroxysmal Atrial Fibrillation. IEEE Trans Biomed Eng 2020; 68:319-329. [PMID: 32746005 DOI: 10.1109/tbme.2020.2995563] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
OBJECTIVE The present study proposes a model-based, statistical approach to characterizing episode patterns in paroxysmal atrial fibrillation (AF). Thanks to the rapid advancement of noninvasive monitoring technology, the proposed approach should become increasingly relevant in clinical practice. METHODS History-dependent point process modeling is employed to characterize AF episode patterns, using a novel alternating, bivariate Hawkes self-exciting model. In addition, a modified version of a recently proposed statistical model to simulate AF progression throughout a lifetime is considered, involving non-Markovian rhythm switching and survival functions. For each model, the maximum likelihood estimator is derived and used to find the model parameters from observed data. RESULTS Using three databases with a total of 59 long-term ECG recordings, the goodness-of-fit analysis demonstrates that the proposed alternating, bivariate Hawkes model fits SR-to-AF transitions in 40 recordings and AF-to-SR transitions in 51; the corresponding numbers for the AF model with non-Markovian rhythm switching are 40 and 11, respectively. Moreover, the results indicate that the model parameters related to AF episode clustering, i.e., aggregation of temporal AF episodes, provide information complementary to the well-known clinical parameter AF burden. CONCLUSION Point process modeling provides a detailed characterization of the occurrence pattern of AF episodes that may improve the understanding of arrhythmia progression.
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Atrial fibrillation monitoring with wrist-worn photoplethysmography-based wearables: State-of-the-art review. CARDIOVASCULAR DIGITAL HEALTH JOURNAL 2020; 1:45-51. [PMID: 35265873 PMCID: PMC8890076 DOI: 10.1016/j.cvdhj.2020.03.001] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
Abstract
Early detection and diagnosis of atrial fibrillation (AF) is essential in order to prevent stroke and other severe health consequences. The challenges in diagnosing AF arise from its intermittent and asymptomatic nature. Wrist-worn devices that use monitoring based on photoplethysmography have been proposed recently as a possible solution because of their ability to monitor heart rate and rhythm for long periods of time at low cost. Long-term continuous monitoring with implantable devices has been shown to increase the percentage of detected AF episodes, but the additional value of wrist-worn devices has yet to be determined. In this review, we present the state of the art in AF detection with wrist-worn devices, discuss the potential of the technology and current knowledge gaps, and propose directions for future research. The state-of-the-art methods show excellent accuracy for AF detection. However, most of the studies were conducted in hospital settings, and more studies showing the accuracy of the technology for ambulatory long-term monitoring are needed. Objective comparison of results and methodologies among different studies currently is difficult due to the lack of adequate public datasets.
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24
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Chen E, Jiang J, Su R, Gao M, Zhu S, Zhou J, Huo Y. A new smart wristband equipped with an artificial intelligence algorithm to detect atrial fibrillation. Heart Rhythm 2020; 17:847-853. [DOI: 10.1016/j.hrthm.2020.01.034] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Accepted: 01/23/2020] [Indexed: 10/24/2022]
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25
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Harrison SL, Lane DA, Guo Y, Lip GYH. The potential for photoplethysmographic (PPG)-based smart devices in atrial fibrillation detection. Expert Rev Med Devices 2020; 17:253-255. [PMID: 32138559 DOI: 10.1080/17434440.2020.1740085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Affiliation(s)
- Stephanie L Harrison
- Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart & Chest Hospital, Liverpool, UK
| | - Deirdre A Lane
- Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart & Chest Hospital, Liverpool, UK.,Aalborg Thrombosis Research Unit, Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
| | - Yutao Guo
- Department of Cardiology, General Hospital of the People's Liberation Army, Beijing, China
| | - Gregory Y H Lip
- Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart & Chest Hospital, Liverpool, UK.,Aalborg Thrombosis Research Unit, Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
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26
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Eerikainen LM, Bonomi AG, Schipper F, Dekker LRC, de Morree HM, Vullings R, Aarts RM. Detecting Atrial Fibrillation and Atrial Flutter in Daily Life Using Photoplethysmography Data. IEEE J Biomed Health Inform 2019; 24:1610-1618. [PMID: 31689222 DOI: 10.1109/jbhi.2019.2950574] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
OBJECTIVE Photoplethysmography (PPG) enables unobtrusive heart rate monitoring, which can be used in wrist-worn applications. Its potential for detecting atrial fibrillation (AF) has been recently presented. Besides AF, another cardiac arrhythmia increasing stroke risk and requiring treatment is atrial flutter (AFL). Currently, the knowledge about AFL detection with PPG is limited. The objective of our study was to develop a model that classifies AF, AFL, and sinus rhythm with or without premature beats from PPG and acceleration data measured at the wrist in daily life. METHODS A dataset of 40 patients was collected by measuring PPG and accelerometer data, as well as electrocardiogram as a reference, during 24-hour monitoring. The dataset was split into 75%-25% for training and testing a Random Forest (RF) model, which combines features from PPG, inter-pulse intervals (IPI), and accelerometer data, to classify AF, AFL, and other rhythms. The performance was compared to an AF detection algorithm combining traditional IPI features for determining the robustness of the accuracy in presence of AFL. RESULTS The RF model classified AF/AFL/other with sensitivity and specificity of 97.6/84.5/98.1% and 98.2/99.7/92.8%, respectively. The results with the IPI-based AF classifier showed that the majority of false detections were caused by AFL. CONCLUSION The PPG signal contains information to classify AFL in the presence of AF, sinus rhythm, or sinus rhythm with premature contractions. SIGNIFICANCE PPG could indicate presence of AFL, not only AF.
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Isakadze N, Martin SS. How useful is the smartwatch ECG? Trends Cardiovasc Med 2019; 30:442-448. [PMID: 31706789 DOI: 10.1016/j.tcm.2019.10.010] [Citation(s) in RCA: 83] [Impact Index Per Article: 16.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/05/2019] [Revised: 10/08/2019] [Accepted: 10/24/2019] [Indexed: 10/25/2022]
Abstract
Apple launched a novel feature of the Apple Watch (Apple Inc.) series 4 that enables consumers to record a rhythm strip and assist with self-diagnosis of atrial fibrillation (AF). The watch is paired with an app that provides automatic classification of the rhythm. Ability of the algorithm to identify AF has received Food and Drug Administration clearance. Given increasing use of direct-to-consumer devices, important questions regarding the utilization of such devices and their features in clinical practice arise. It is unclear how the data obtained from these devices can be optimally incorporated in patient care and what it means for patients. Safety and security of using wearables are also of concern. Furthermore, whether data generated from the Electrocardiogram (ECG) feature will be beneficial to public health is to be determined. We discuss possible uses and challenges of Apple's (Apple Inc.) newly launched ECG feature and review an upcoming trial looking at clinical applications and outcomes using this technology. We also review the literature on the Kardia (AliveCor Inc.) mobile and smartwatch ECG technology and briefly discuss Apple Watch irregular heartbeat notifications along with the Apple Heart Study.
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Affiliation(s)
- Nino Isakadze
- Department of Medicine, Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Seth S Martin
- Department of Medicine, Division of Cardiology, Ciccarone Center for the Prevention of Cardiovascular Disease, Johns Hopkins University School of Medicine, 600N Wolfe St, Carnegie 568, Baltimore, MD 21287, United States.
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