1
|
Sohn J, Shin H, Lee J, Kim HC. Validation of Electrocardiogram Based Photoplethysmogram Generated Using U-Net Based Generative Adversarial Networks. J Healthc Inform Res 2024; 8:140-157. [PMID: 38273980 PMCID: PMC10805750 DOI: 10.1007/s41666-023-00156-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 10/24/2023] [Accepted: 11/13/2023] [Indexed: 01/27/2024]
Abstract
Photoplethysmogram (PPG) performs an important role in alarming atrial fibrillation (AF). While the importance of PPG is emphasized, there is insufficient amount of openly available atrial fibrillation PPG data. We propose a U-net-based generative adversarial network (GAN) which synthesize PPG from paired electrocardiogram (ECG). To measure the performance of the proposed GAN, we compared the generated PPG to reference PPG in terms of morphology similarity and also examined its influence on AF detection classifier performance. First, morphology was compared using two different metrics against the reference signal: percent root mean square difference (PRD) and Pearson correlation coefficient. The mean PRD and Pearson correlation coefficient were 27% and 0.94, respectively. Heart rate variability (HRV) of the reference AF ECG and the generated PPG were compared as well. The p-value of the paired t-test was 0.248, indicating that no significant difference was observed between the two HRV values. Second, to validate the generated AF PPG dataset, four different datasets were prepared combining the generated PPG and real AF PPG. Each dataset was used to optimize a classification model while maintaining the same architecture. A test dataset was prepared to test the performance of each optimized model. Subsequently, these datasets were used to test the hypothesis whether the generated data benefits the training of an AF classifier. Comparing the performance metrics of each optimized model, the training dataset consisting of generated and real AF PPG showed a test accuracy result of 0.962, which was close to that of the dataset consisting only of real AF PPG data at 0.961. Furthermore, both models yielded the same F1 score of 0.969. Lastly, using only the generated AF PPG dataset resulted in test accuracy of 0.945, indicating that the trained model was capable of generating valuable AF PPG. Therefore, it can be concluded that the generated AF PPG can be used to augment insufficient data. To summarize, this study proposes a GAN-based method to generate atrial fibrillation PPG that can be used for training atrial fibrillation PPG classification models.
Collapse
Affiliation(s)
- Jangjay Sohn
- Institute of Medical & Biological Engineering, Medical Research Center, Seoul National University College of Medicine, Seoul, Korea
- Department of Electronic Engineering, Hanyang University, Seoul, Korea
| | - Heean Shin
- Samsung SDS R&D Center, Seoul, Republic of Korea
| | - Joonnyong Lee
- Mellowing Factory Co., Ltd., 131 Sapeyong-daero 57-gil, Seocho-gu, Seoul, 06535 Republic of Korea
| | - Hee Chan Kim
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, Korea
- Department of Biomedical Engineering, Seoul National University College of Medicine, 103, Daehak-ro, Jongno-gu, Seoul, 03080 Republic of Korea
| |
Collapse
|
2
|
Bester M, Almario Escorcia MJ, Fonseca P, Mollura M, van Gilst MM, Barbieri R, Mischi M, van Laar JOEH, Vullings R, Joshi R. The impact of healthy pregnancy on features of heart rate variability and pulse wave morphology derived from wrist-worn photoplethysmography. Sci Rep 2023; 13:21100. [PMID: 38036597 PMCID: PMC10689737 DOI: 10.1038/s41598-023-47980-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 11/20/2023] [Indexed: 12/02/2023] Open
Abstract
Due to the association between dysfunctional maternal autonomic regulation and pregnancy complications, tracking non-invasive features of autonomic regulation derived from wrist-worn photoplethysmography (PPG) measurements may allow for the early detection of deteriorations in maternal health. However, even though a plethora of these features-specifically, features describing heart rate variability (HRV) and the morphology of the PPG waveform (morphological features)-exist in the literature, it is unclear which of these may be valuable for tracking maternal health. As an initial step towards clarity, we compute comprehensive sets of HRV and morphological features from nighttime PPG measurements. From these, using logistic regression and stepwise forward feature elimination, we identify the features that best differentiate healthy pregnant women from non-pregnant women, since these likely capture physiological adaptations necessary for sustaining healthy pregnancy. Overall, morphological features were more valuable for discriminating between pregnant and non-pregnant women than HRV features (area under the receiver operating characteristics curve of 0.825 and 0.74, respectively), with the systolic pulse wave deterioration being the most valuable single feature, followed by mean heart rate (HR). Additionally, we stratified the analysis by sleep stages and found that using features calculated only from periods of deep sleep enhanced the differences between the two groups. In conclusion, we postulate that in addition to HRV features, morphological features may also be useful in tracking maternal health and suggest specific features to be included in future research concerning maternal health.
Collapse
Affiliation(s)
- M Bester
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ, Eindhoven, The Netherlands.
- Patient Care and Monitoring, Philips Research, 5656 AE, Eindhoven, The Netherlands.
| | - M J Almario Escorcia
- Patient Care and Monitoring, Philips Research, 5656 AE, Eindhoven, The Netherlands
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133, Milan, MI, Italy
| | - P Fonseca
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ, Eindhoven, The Netherlands
- Patient Care and Monitoring, Philips Research, 5656 AE, Eindhoven, The Netherlands
| | - M Mollura
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133, Milan, MI, Italy
| | - M M van Gilst
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ, Eindhoven, The Netherlands
- Sleep Medicine Center Kempenhaeghe, 5591 VE, Heeze, The Netherlands
| | - R Barbieri
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133, Milan, MI, Italy
| | - M Mischi
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ, Eindhoven, The Netherlands
| | - J O E H van Laar
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ, Eindhoven, The Netherlands
- Department of Obstetrics and Gynecology, Máxima Medical Centrum, De Run 4600, 5504 DB, Veldhoven, The Netherlands
| | - R Vullings
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ, Eindhoven, The Netherlands
| | - R Joshi
- Patient Care and Monitoring, Philips Research, 5656 AE, Eindhoven, The Netherlands
| |
Collapse
|
3
|
Charlton PH, Allen J, Bailón R, Baker S, Behar JA, Chen F, Clifford GD, Clifton DA, Davies HJ, Ding C, Ding X, Dunn J, Elgendi M, Ferdoushi M, Franklin D, Gil E, Hassan MF, Hernesniemi J, Hu X, Ji N, Khan Y, Kontaxis S, Korhonen I, Kyriacou PA, Laguna P, Lázaro J, Lee C, Levy J, Li Y, Liu C, Liu J, Lu L, Mandic DP, Marozas V, Mejía-Mejía E, Mukkamala R, Nitzan M, Pereira T, Poon CCY, Ramella-Roman JC, Saarinen H, Shandhi MMH, Shin H, Stansby G, Tamura T, Vehkaoja A, Wang WK, Zhang YT, Zhao N, Zheng D, Zhu T. The 2023 wearable photoplethysmography roadmap. Physiol Meas 2023; 44:111001. [PMID: 37494945 PMCID: PMC10686289 DOI: 10.1088/1361-6579/acead2] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 04/04/2023] [Accepted: 07/26/2023] [Indexed: 07/28/2023]
Abstract
Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology.
Collapse
Affiliation(s)
- Peter H Charlton
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, United Kingdom
- Research Centre for Biomedical Engineering, City, University of London, London, EC1V 0HB, United Kingdom
| | - John Allen
- Research Centre for Intelligent Healthcare, Coventry University, Coventry, CV1 5RW, United Kingdom
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, NE2 4HH, United Kingdom
| | - Raquel Bailón
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) Group, Aragon Institute of Engineering Research (I3A), IIS Aragon, University of Zaragoza, E-50018 Zaragoza, Spain
- CIBER-BBN, Instituto de Salud Carlos III, C/Monforte de Lemos 3-5, E-28029 Madrid, Spain
| | - Stephanie Baker
- College of Science and Engineering, James Cook University, Cairns, 4878 Queensland, Australia
| | - Joachim A Behar
- Faculty of Biomedical Engineering, Technion Israel Institute of Technology, Haifa, 3200003, Israel
| | - Fei Chen
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, 518055 Guandong, People’s Republic of China
| | - Gari D Clifford
- Department of Biomedical Informatics, Emory University, Atlanta, GA 30322, United States of America
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, United States of America
| | - David A Clifton
- Department of Engineering Science, University of Oxford, Oxford, OX3 7DQ, United Kingdom
| | - Harry J Davies
- Department of Electrical and Electronic Engineering, Imperial College London, London, SW7 2AZ, United Kingdom
| | - Cheng Ding
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, United States of America
- Department of Biomedical Engineering, Emory University, Atlanta, GA 30322, United States of America
| | - Xiaorong Ding
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, People’s Republic of China
| | - Jessilyn Dunn
- Department of Biomedical Engineering, Duke University, Durham, NC 27708-0187, United States of America
- Department of Biostatistics & Bioinformatics, Duke University, Durham, NC 27708-0187, United States of America
- Duke Clinical Research Institute, Durham, NC 27705-3976, United States of America
| | - Mohamed Elgendi
- Biomedical and Mobile Health Technology Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, 8008, Switzerland
| | - Munia Ferdoushi
- Department of Electrical and Computer Engineering, University of Southern California, 90089, Los Angeles, California, United States of America
- The Institute for Technology and Medical Systems (ITEMS), Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, United States of America
| | - Daniel Franklin
- Institute of Biomedical Engineering, Translational Biology & Engineering Program, Ted Rogers Centre for Heart Research, University of Toronto, Toronto, M5G 1M1, Canada
| | - Eduardo Gil
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) Group, Aragon Institute of Engineering Research (I3A), IIS Aragon, University of Zaragoza, E-50018 Zaragoza, Spain
- CIBER-BBN, Instituto de Salud Carlos III, C/Monforte de Lemos 3-5, E-28029 Madrid, Spain
| | - Md Farhad Hassan
- Department of Electrical and Computer Engineering, University of Southern California, 90089, Los Angeles, California, United States of America
- The Institute for Technology and Medical Systems (ITEMS), Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, United States of America
| | - Jussi Hernesniemi
- Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, 33720, Finland
- Tampere Heart Hospital, Wellbeing Services County of Pirkanmaa, Tampere, 33520, Finland
| | - Xiao Hu
- Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, 30322, Georgia, United States of America
- Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, 30322, Georgia, United States of America
- Department of Computer Sciences, College of Arts and Sciences, Emory University, Atlanta, GA 30322, United States of America
| | - Nan Ji
- Hong Kong Center for Cerebrocardiovascular Health Engineering (COCHE), Hong Kong Science and Technology Park, Hong Kong, 999077, People’s Republic of China
| | - Yasser Khan
- Department of Electrical and Computer Engineering, University of Southern California, 90089, Los Angeles, California, United States of America
- The Institute for Technology and Medical Systems (ITEMS), Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, United States of America
| | - Spyridon Kontaxis
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) Group, Aragon Institute of Engineering Research (I3A), IIS Aragon, University of Zaragoza, E-50018 Zaragoza, Spain
- CIBER-BBN, Instituto de Salud Carlos III, C/Monforte de Lemos 3-5, E-28029 Madrid, Spain
| | - Ilkka Korhonen
- Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, 33720, Finland
| | - Panicos A Kyriacou
- Research Centre for Biomedical Engineering, City, University of London, London, EC1V 0HB, United Kingdom
| | - Pablo Laguna
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) Group, Aragon Institute of Engineering Research (I3A), IIS Aragon, University of Zaragoza, E-50018 Zaragoza, Spain
- CIBER-BBN, Instituto de Salud Carlos III, C/Monforte de Lemos 3-5, E-28029 Madrid, Spain
| | - Jesús Lázaro
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) Group, Aragon Institute of Engineering Research (I3A), IIS Aragon, University of Zaragoza, E-50018 Zaragoza, Spain
- CIBER-BBN, Instituto de Salud Carlos III, C/Monforte de Lemos 3-5, E-28029 Madrid, Spain
| | - Chungkeun Lee
- Digital Health Devices Division, Medical Device Evaluation Department, National Institute of Food and Drug Safety Evaluation, Ministry of Food and Drug Safety, Cheongju, 28159, Republic of Korea
| | - Jeremy Levy
- Faculty of Biomedical Engineering, Technion Israel Institute of Technology, Haifa, 3200003, Israel
- Faculty of Electrical and Computer Engineering, Technion Institute of Technology, Haifa, 3200003, Israel
| | - Yumin Li
- State Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, People’s Republic of China
| | - Chengyu Liu
- State Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, People’s Republic of China
| | - Jing Liu
- Analog Devices Inc, San Jose, CA 95124, United States of America
| | - Lei Lu
- Department of Engineering Science, University of Oxford, Oxford, OX3 7DQ, United Kingdom
| | - Danilo P Mandic
- Department of Electrical and Electronic Engineering, Imperial College London, London, SW7 2AZ, United Kingdom
| | - Vaidotas Marozas
- Department of Electronics Engineering, Kaunas University of Technology, 44249 Kaunas, Lithuania
- Biomedical Engineering Institute, Kaunas University of Technology, 44249 Kaunas, Lithuania
| | - Elisa Mejía-Mejía
- Research Centre for Biomedical Engineering, City, University of London, London, EC1V 0HB, United Kingdom
| | - Ramakrishna Mukkamala
- Department of Bioengineering and Department of Anesthesiology and Perioperative Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Meir Nitzan
- Department of Physics/Electro-Optic Engineering, Lev Academic Center, 91160 Jerusalem, Israel
| | - Tania Pereira
- INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, Porto, 4200-465, Portugal
- Faculty of Engineering, University of Porto, Porto, 4200-465, Portugal
| | | | - Jessica C Ramella-Roman
- Department of Biomedical Engineering and Herbert Wertheim College of Medicine, Florida International University, Miami, FL 33174, United States of America
| | - Harri Saarinen
- Tampere Heart Hospital, Wellbeing Services County of Pirkanmaa, Tampere, 33520, Finland
| | - Md Mobashir Hasan Shandhi
- Department of Biomedical Engineering, Duke University, Durham, NC 27708-0187, United States of America
| | - Hangsik Shin
- Department of Digital Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, Republic of Korea
| | - Gerard Stansby
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, NE2 4HH, United Kingdom
- Northern Vascular Centre, Freeman Hospital, Newcastle upon Tyne, NE7 7DN, United Kingdom
| | - Toshiyo Tamura
- Future Robotics Organization, Waseda University, Tokyo, 1698050, Japan
| | - Antti Vehkaoja
- Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, 33720, Finland
- PulseOn Ltd, Espoo, 02150, Finland
| | - Will Ke Wang
- Department of Biomedical Engineering, Duke University, Durham, NC 27708-0187, United States of America
| | - Yuan-Ting Zhang
- Hong Kong Center for Cerebrocardiovascular Health Engineering (COCHE), Hong Kong Science and Technology Park, Hong Kong, 999077, People’s Republic of China
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, 999077, People’s Republic of China
| | - Ni Zhao
- Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong
| | - Dingchang Zheng
- Research Centre for Intelligent Healthcare, Coventry University, Coventry, CV1 5RW, United Kingdom
| | - Tingting Zhu
- Department of Engineering Science, University of Oxford, Oxford, OX3 7DQ, United Kingdom
| |
Collapse
|
4
|
Poh M, Battisti AJ, Cheng L, Lin J, Patwardhan A, Venkataraman GS, Athill CA, Patel NS, Patel CP, Machado CE, Ellis JT, Crosson LA, Tamura Y, Plowman RS, Turakhia MP, Ghanbari H. Validation of a Deep Learning Algorithm for Continuous, Real-Time Detection of Atrial Fibrillation Using a Wrist-Worn Device in an Ambulatory Environment. J Am Heart Assoc 2023; 12:e030543. [PMID: 37750558 PMCID: PMC10727259 DOI: 10.1161/jaha.123.030543] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 08/04/2023] [Indexed: 09/27/2023]
Abstract
BACKGROUND Wearable devices may be useful for identification, quantification and characterization, and management of atrial fibrillation (AF). To date, consumer wrist-worn devices for AF detection using photoplethysmography-based algorithms perform only periodic checks when the user is stationary and are US Food and Drug Administration cleared for prediagnostic uses without intended use for clinical decision-making. There is an unmet need for medical-grade diagnostic wrist-worn devices that provide long-term, continuous AF monitoring. METHODS AND RESULTS We evaluated the performance of a wrist-worn device with lead-I ECG and continuous photoplethysmography (Verily Study Watch) and photoplethysmography-based convolutional neural network for AF detection and burden estimation in a prospective multicenter study that enrolled 117 patients with paroxysmal AF. A 14-day continuous ECG monitor (Zio XT) served as the reference device to evaluate algorithm sensitivity and specificity for detection of AF in 15-minute intervals. A total of 91 857 intervals were contributed by 111 subjects with evaluable reference and test data (18.3 h/d median watch wear time). The watch was 96.1% sensitive (95% CI, 92.7%-98.0%) and 98.1% specific (95% CI, 97.2%-99.1%) for interval-level AF detection. Photoplethysmography-derived AF burden estimation was highly correlated with the reference device burden (R2=0.986) with a mean difference of 0.8% (95% limits of agreement, -6.6% to 8.2%). CONCLUSIONS Continuous monitoring using a photoplethysmography-based convolutional neural network incorporated in a wrist-worn device has clinical-grade performance for AF detection and burden estimation. These findings suggest that monitoring can be performed with wrist-worn wearables for diagnosis and clinical management of AF. REGISTRATION INFORMATION URL: https://www.clinicaltrials.gov; Unique identifier: NCT04546763.
Collapse
Affiliation(s)
| | | | | | - Janice Lin
- Verily Life SciencesSouth San FranciscoCA
| | | | | | | | | | | | | | | | | | | | - R. Scooter Plowman
- Verily Life SciencesSouth San FranciscoCA
- Stanford University Medical CenterPalo AltoCA
| | | | - Hamid Ghanbari
- Verily Life SciencesSouth San FranciscoCA
- University of MichiganAnn ArborMI
| |
Collapse
|
5
|
Reissenberger P, Serfözö P, Piper D, Juchler N, Glanzmann S, Gram J, Hensler K, Tonidandel H, Börlin E, D’Souza M, Badertscher P, Eckstein J. Determine atrial fibrillation burden with a photoplethysmographic mobile sensor: the atrial fibrillation burden trial: detection and quantification of episodes of atrial fibrillation using a cloud analytics service connected to a wearable with photoplethysmographic sensor. Eur Heart J Digit Health 2023; 4:402-410. [PMID: 37794868 PMCID: PMC10545505 DOI: 10.1093/ehjdh/ztad039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Revised: 05/18/2023] [Indexed: 10/06/2023]
Abstract
Aims Recent studies suggest that atrial fibrillation (AF) burden (time AF is present) is an independent risk factor for stroke. The aim of this trial was to study the feasibility and accuracy to identify AF episodes and quantify AF burden in patients with a known history of paroxysmal AF with a photoplethysmography (PPG)-based wearable. Methods and results In this prospective, single-centre trial, the PPG-based estimation of AF burden was compared with measurements of a conventional 48 h Holter electrocardiogram (ECG), which served as the gold standard. An automated algorithm performed PPG analysis, while a cardiologist, blinded for the PPG data, analysed the ECG data. Detected episodes of AF measured by both methods were aligned timewise.Out of 100 patients recruited, 8 had to be excluded due to technical issues. Data from 92 patients were analysed [55.4% male; age 73.3 years (standard deviation, SD: 10.4)]. Twenty-five patients presented AF during the study period. The intraclass correlation coefficient of total AF burden minutes detected by the two measurement methods was 0.88. The percentage of correctly identified AF burden over all patients was 85.1% and the respective parameter for non-AF time was 99.9%. Conclusion Our results demonstrate that a PPG-based wearable in combination with an analytical algorithm appears to be suitable for a semiquantitative estimation of AF burden in patients with a known history of paroxysmal AF. Trial Registration number NCT04563572.
Collapse
Affiliation(s)
- Pamela Reissenberger
- Department of Internal Medicine, University Hospital Basel, Petersgraben 4, 4031 Basel, Switzerland
| | - Peter Serfözö
- Department of Internal Medicine, University Hospital Basel, Petersgraben 4, 4031 Basel, Switzerland
| | - Diana Piper
- Preventicus, Ernst-Abbe-Str. 15, 07743 Jena, Germany
| | - Norman Juchler
- Institute of Computational Life Sciences, Zurich University of Applied Sciences, Schloss 1, 8820 Wädenswil, Switzerland
| | - Sara Glanzmann
- Department of Internal Medicine, University Hospital Basel, Petersgraben 4, 4031 Basel, Switzerland
| | - Jasmin Gram
- Department of Internal Medicine, University Hospital Basel, Petersgraben 4, 4031 Basel, Switzerland
| | - Karina Hensler
- Department of Internal Medicine, University Hospital Basel, Petersgraben 4, 4031 Basel, Switzerland
| | - Hannah Tonidandel
- Department of Internal Medicine, University Hospital Basel, Petersgraben 4, 4031 Basel, Switzerland
| | - Elena Börlin
- Department Digitalization & ICT, University Hospital Basel, Spitalstrasse 26, 4031 Basel, Switzerland
| | - Marcus D’Souza
- Department Digitalization & ICT, University Hospital Basel, Spitalstrasse 26, 4031 Basel, Switzerland
- Department of Neurology, University Hospital Basel, Petersgraben 4, 4031 Basel, Switzerland
| | - Patrick Badertscher
- Department of Cardiology, University Hospital Basel, Petersgraben 4, 4031 Basel, Switzerland
| | - Jens Eckstein
- Department of Internal Medicine, University Hospital Basel, Petersgraben 4, 4031 Basel, Switzerland
- Department Digitalization & ICT, University Hospital Basel, Spitalstrasse 26, 4031 Basel, Switzerland
| |
Collapse
|
6
|
Williams GJ, Al-Baraikan A, Rademakers FE, Ciravegna F, van de Vosse FN, Lawrie A, Rothman A, Ashley EA, Wilkins MR, Lawford PV, Omholt SW, Wisløff U, Hose DR, Chico TJA, Gunn JP, Morris PD. Wearable technology and the cardiovascular system: the future of patient assessment. Lancet Digit Health 2023; 5:e467-e476. [PMID: 37391266 DOI: 10.1016/s2589-7500(23)00087-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Revised: 02/26/2023] [Accepted: 04/19/2023] [Indexed: 07/02/2023]
Abstract
The past decade has seen a dramatic rise in consumer technologies able to monitor a variety of cardiovascular parameters. Such devices initially recorded markers of exercise, but now include physiological and health-care focused measurements. The public are keen to adopt these devices in the belief that they are useful to identify and monitor cardiovascular disease. Clinicians are therefore often presented with health app data accompanied by a diverse range of concerns and queries. Herein, we assess whether these devices are accurate, their outputs validated, and whether they are suitable for professionals to make management decisions. We review underpinning methods and technologies and explore the evidence supporting the use of these devices as diagnostic and monitoring tools in hypertension, arrhythmia, heart failure, coronary artery disease, pulmonary hypertension, and valvular heart disease. Used correctly, they might improve health care and support research.
Collapse
Affiliation(s)
- Gareth J Williams
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Abdulaziz Al-Baraikan
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Frank E Rademakers
- Faculty of Medicine, Department of Cardiology, KU Leuven, Leuven, Belgium
| | - Fabio Ciravegna
- Dipartimento di Informatica, Universitàdi Torino, Turin, Italy
| | - Frans N van de Vosse
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Allan Lawrie
- National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, UK
| | - Alexander Rothman
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK; Academic Directorate of Cardiothoracic Services, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Euan A Ashley
- Department of Medicine, Stanford University, Stanford, CA, US
| | - Martin R Wilkins
- National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, UK
| | - Patricia V Lawford
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK; Insigneo Institute for In Silico Medicine, University of Sheffield, Sheffield, UK
| | - Stig W Omholt
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
| | - Ulrik Wisløff
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway; School of Human Movement & Nutrition Sciences, University of Queensland, QLD, Australia
| | - D Rodney Hose
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK; Insigneo Institute for In Silico Medicine, University of Sheffield, Sheffield, UK
| | - Timothy J A Chico
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK; Insigneo Institute for In Silico Medicine, University of Sheffield, Sheffield, UK; Academic Directorate of Cardiothoracic Services, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK; BHF Data Centre, Health Data Research UK, London, UK
| | - Julian P Gunn
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK; Insigneo Institute for In Silico Medicine, University of Sheffield, Sheffield, UK; Academic Directorate of Cardiothoracic Services, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Paul D Morris
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK; Insigneo Institute for In Silico Medicine, University of Sheffield, Sheffield, UK; Academic Directorate of Cardiothoracic Services, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK.
| |
Collapse
|
7
|
Selder JL, Te Kolste HJ, Twisk J, Schijven M, Gielen W, Allaart CP. Accuracy of a Standalone Atrial Fibrillation Detection Algorithm Added to a Popular Wristband and Smartwatch: Prospective Diagnostic Accuracy Study. J Med Internet Res 2023; 25:e44642. [PMID: 37234033 DOI: 10.2196/44642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 03/13/2023] [Accepted: 04/30/2023] [Indexed: 05/27/2023] Open
Abstract
BACKGROUND Silent paroxysmal atrial fibrillation (AF) may be difficult to diagnose, and AF burden is hard to establish. In contrast to conventional diagnostic devices, photoplethysmography (PPG)-driven smartwatches or wristbands allow for long-term continuous heart rhythm assessment. However, most smartwatches lack an integrated PPG-AF algorithm. Adding a standalone PPG-AF algorithm to these wrist devices might open new possibilities for AF screening and burden assessment. OBJECTIVE The aim of this study was to assess the accuracy of a well-known standalone PPG-AF detection algorithm added to a popular wristband and smartwatch, with regard to discriminating AF and sinus rhythm, in a group of patients with AF before and after cardioversion (CV). METHODS Consecutive consenting patients with AF admitted for CV in a large academic hospital in Amsterdam, the Netherlands, were asked to wear a Biostrap wristband or Fitbit Ionic smartwatch with Fibricheck algorithm add-on surrounding the procedure. A set of 1-min PPG measurements and 12-lead reference electrocardiograms was obtained before and after CV. Rhythm assessment by the PPG device-software combination was compared with the 12-lead electrocardiogram. RESULTS A total of 78 patients were included in the Biostrap-Fibricheck cohort (156 measurement sets) and 73 patients in the Fitbit-Fibricheck cohort (143 measurement sets). Of the measurement sets, 19/156 (12%) and 7/143 (5%), respectively, were not classifiable by the PPG algorithm due to bad quality. The diagnostic performance in terms of sensitivity, specificity, positive predictive value, negative predictive value, and accuracy was 98%, 96%, 96%, 99%, 97%, and 97%, 100%, 100%, 97%, and 99%, respectively, at an AF prevalence of ~50%. CONCLUSIONS This study demonstrates that the addition of a well-known standalone PPG-AF detection algorithm to a popular PPG smartwatch and wristband without integrated algorithm yields a high accuracy for the detection of AF, with an acceptable unclassifiable rate, in a semicontrolled environment.
Collapse
Affiliation(s)
- Jasper L Selder
- Department of Cardiology, Amsterdam University Medical Center, Amsterdam, Netherlands
| | - Henryk Jan Te Kolste
- Department of Cardiology, Amsterdam University Medical Center, Amsterdam, Netherlands
| | - Jos Twisk
- Department of Epidemiology and Data Science, Amsterdam University Medical Center, Amsterdam, Netherlands
| | - Marlies Schijven
- Department of Surgery, Amsterdam University Medical Center, Amsterdam, Netherlands
| | | | - Cornelis P Allaart
- Department of Cardiology, Amsterdam University Medical Center, Amsterdam, Netherlands
| |
Collapse
|
8
|
Cao YT, Zhao XX, Yang YT, Zhu SJ, Zheng LD, Ying T, Sha Z, Zhu R, Wu T. Potential of electronic devices for detection of health problems in older adults at home: A systematic review and meta-analysis. Geriatr Nurs 2023; 51:54-64. [PMID: 36893611 DOI: 10.1016/j.gerinurse.2023.02.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Revised: 02/11/2023] [Accepted: 02/13/2023] [Indexed: 03/09/2023]
Abstract
OBJECTIVE The aim of this review was to evaluate the overall diagnostic performance of e-devices for detection of health problems in older adults at home. METHODS A systematic review was conducted following the PRISMA-DTA guidelines. RESULTS 31 studies were included with 24 studies included in meta-analysis. The included studies were divided into four categories according to the signals detected: physical activity (PA), vital signs (VS), electrocardiography (ECG) and other. The meta-analysis showed the pooled estimates of sensitivity and specificity were 0.94 and 0.98 respectively in the 'VS' group. The pooled sensitivity and specificity were 0.97 and 0.98 respectively in the 'ECG' group. CONCLUSIONS All kinds of e-devices perform well in diagnosing the common health problems. While ECG-based health problems detection system is more reliable than VS-based ones. For sole signal detection system has limitation in diagnosing specific health problems, more researches should focus on developing new systems combined of multiple signals.
Collapse
Affiliation(s)
- Yu-Ting Cao
- Shanghai YangZhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), School of Medicine, Tongji University, Shanghai 200092, China; Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of the Ministry of Education, Tongji Hospital, School of Medicine, Tongji University, 389 Xincun Road, 200065 Shanghai, China
| | - Xin-Xin Zhao
- Shanghai YangZhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), School of Medicine, Tongji University, Shanghai 200092, China
| | - Yi-Ting Yang
- Shanghai YangZhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), School of Medicine, Tongji University, Shanghai 200092, China; Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of the Ministry of Education, Tongji Hospital, School of Medicine, Tongji University, 389 Xincun Road, 200065 Shanghai, China
| | - Shi-Jie Zhu
- Shanghai YangZhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), School of Medicine, Tongji University, Shanghai 200092, China; Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of the Ministry of Education, Tongji Hospital, School of Medicine, Tongji University, 389 Xincun Road, 200065 Shanghai, China
| | - Liang-Dong Zheng
- Shanghai YangZhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), School of Medicine, Tongji University, Shanghai 200092, China; Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of the Ministry of Education, Tongji Hospital, School of Medicine, Tongji University, 389 Xincun Road, 200065 Shanghai, China
| | - Ting Ying
- Shanghai YangZhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), School of Medicine, Tongji University, Shanghai 200092, China; Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of the Ministry of Education, Tongji Hospital, School of Medicine, Tongji University, 389 Xincun Road, 200065 Shanghai, China
| | - Zhou Sha
- Shanghai YangZhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), School of Medicine, Tongji University, Shanghai 200092, China; Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of the Ministry of Education, Tongji Hospital, School of Medicine, Tongji University, 389 Xincun Road, 200065 Shanghai, China
| | - Rui Zhu
- Shanghai YangZhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), School of Medicine, Tongji University, Shanghai 200092, China; Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of the Ministry of Education, Tongji Hospital, School of Medicine, Tongji University, 389 Xincun Road, 200065 Shanghai, China.
| | - Tao Wu
- Shanghai University of Medicine & Health Sciences, 201318 Shanghai, China
| |
Collapse
|
9
|
van der Stam JA, Mestrom EHJ, Scheerhoorn J, Jacobs FENB, Nienhuijs S, Boer AK, van Riel NAW, de Morree HM, Bonomi AG, Scharnhorst V, Bouwman RA. The Accuracy of Wrist-Worn Photoplethysmogram-Measured Heart and Respiratory Rates in Abdominal Surgery Patients: Observational Prospective Clinical Validation Study. JMIR Perioper Med 2023; 6:e40474. [PMID: 36804173 PMCID: PMC9989911 DOI: 10.2196/40474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 01/12/2023] [Accepted: 01/31/2023] [Indexed: 02/22/2023] Open
Abstract
BACKGROUND Postoperative deterioration is often preceded by abnormal vital parameters. Therefore, vital parameters of postoperative patients are routinely measured by nursing staff. Wrist-worn sensors could potentially provide an alternative tool for the measurement of vital parameters in low-acuity settings. These devices would allow more frequent or even continuous measurements of vital parameters without relying on time-consuming manual measurements, provided their accuracy in this clinical population is established. OBJECTIVE This study aimed to assess the accuracy of heart rate (HR) and respiratory rate (RR) measures obtained via a wearable photoplethysmography (PPG) wristband in a cohort of postoperative patients. METHODS The accuracy of the wrist-worn PPG sensor was assessed in 62 post-abdominal surgery patients (mean age 55, SD 15 years; median BMI 34, IQR 25-40 kg/m2). The wearable obtained HR and RR measurements were compared to those of the reference monitor in the postanesthesia or intensive care unit. Bland-Altman and Clarke error grid analyses were performed to determine agreement and clinical accuracy. RESULTS Data were collected for a median of 1.2 hours per patient. With a coverage of 94% for HR and 34% for RR, the device was able to provide accurate measurements for the large majority of the measurements as 98% and 93% of the measurements were within 5 bpm or 3 rpm of the reference signal. Additionally, 100% of the HR and 98% of the RR measurements were clinically acceptable on Clarke error grid analysis. CONCLUSIONS The wrist-worn PPG device is able to provide measurements of HR and RR that can be seen as sufficiently accurate for clinical applications. Considering the coverage, the device was able to continuously monitor HR and report RR when measurements of sufficient quality were obtained. TRIAL REGISTRATION ClinicalTrials.gov NCT03923127; https://www.clinicaltrials.gov/ct2/show/NCT03923127.
Collapse
Affiliation(s)
- Jonna A van der Stam
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands.,Clinical Laboratory, Catharina Hospital, Eindhoven, Netherlands.,Expert Center Clinical Chemistry Eindhoven, Eindhoven, Netherlands
| | - Eveline H J Mestrom
- Department of Anesthesiology, Intensive Care & Pain Medicine, Catharina Hospital, Eindhoven, Netherlands
| | - Jai Scheerhoorn
- Department of Surgery, Catharina Hospital, Eindhoven, Netherlands
| | - Fleur E N B Jacobs
- Department of Medical Physics, Catharina Hospital, Eindhoven, Netherlands
| | - Simon Nienhuijs
- Department of Surgery, Catharina Hospital, Eindhoven, Netherlands
| | - Arjen-Kars Boer
- Clinical Laboratory, Catharina Hospital, Eindhoven, Netherlands.,Expert Center Clinical Chemistry Eindhoven, Eindhoven, Netherlands
| | - Natal A W van Riel
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands.,Expert Center Clinical Chemistry Eindhoven, Eindhoven, Netherlands.,Department of Vascular Medicine, Amsterdam University Medical Centers, Amsterdam, Netherlands
| | - Helma M de Morree
- Patient Care & Monitoring Department, Philips Research, Eindhoven, Netherlands
| | - Alberto G Bonomi
- Patient Care & Monitoring Department, Philips Research, Eindhoven, Netherlands
| | - Volkher Scharnhorst
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands.,Clinical Laboratory, Catharina Hospital, Eindhoven, Netherlands.,Expert Center Clinical Chemistry Eindhoven, Eindhoven, Netherlands
| | - R Arthur Bouwman
- Department of Anesthesiology, Intensive Care & Pain Medicine, Catharina Hospital, Eindhoven, Netherlands.,Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| |
Collapse
|
10
|
Saarinen HJ, Joutsen A, Korpi K, Halkola T, Nurmi M, Hernesniemi J, Vehkaoja A. Wrist-worn device combining PPG and ECG can be reliably used for atrial fibrillation detection in an outpatient setting. Front Cardiovasc Med 2023; 10:1100127. [PMID: 36844740 PMCID: PMC9949528 DOI: 10.3389/fcvm.2023.1100127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 01/19/2023] [Indexed: 02/11/2023] Open
Abstract
Aims The aim was to validate the performance of a monitoring system consisting of a wrist-worn device and a data management cloud service intended to be used by medical professionals in detecting atrial fibrillation (AF). Methods Thirty adult patients diagnosed with AF alone or AF with concomitant flutter were recruited. Continuous photoplethysmogram (PPG) and intermittent 30 s Lead I electrocardiogram (ECG) recordings were collected over 48 h. The ECG was measured four times a day at prescheduled times, when notified due to irregular rhythm detected by PPG, and when self-initiated based on symptoms. Three-channel Holter ECG was used as the reference. Results The subjects recorded a total of 1,415 h of continuous PPG data and 3.8 h of intermittent ECG data over the study period. The PPG data were analyzed by the system's algorithm in 5-min segments. The segments containing adequate amounts, at least ~30 s, of adequate quality PPG data for rhythm assessment algorithm, were included. After rejecting 46% of the 5-min segments, the remaining data were compared with annotated Holter ECG yielding AF detection sensitivity and specificity of 95.6 and 99.2%, respectively. The ECG analysis algorithm labeled 10% of the 30-s ECG records as inadequate quality and these were excluded from the analysis. The ECG AF detection sensitivity and specificity were 97.7 and 89.8%, respectively. The usability of the system was found to be good by both the study subjects and the participating cardiologists. Conclusion The system comprising of a wrist device and a data management service was validated to be suitable for use in patient monitoring and in the detection of AF in an ambulatory setting.Clinical Trial Registration: ClinicalTrials.gov/, NCT05008601.
Collapse
Affiliation(s)
- Harri Juhani Saarinen
- Heart Hospital, Tampere University Hospital, Tampere, Finland,*Correspondence: Harri Juhani Saarinen, ✉
| | - Atte Joutsen
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland,Department of Medical Physics, Tampere University Hospital, Tampere, Finland,Finnish Cardiovascular Research Center, Tampere University, Tampere, Finland
| | - Kirsi Korpi
- Heart Hospital, Tampere University Hospital, Tampere, Finland,PulseOn Oy, Espoo, Finland
| | | | | | - Jussi Hernesniemi
- Heart Hospital, Tampere University Hospital, Tampere, Finland,Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland,Finnish Cardiovascular Research Center, Tampere University, Tampere, Finland
| | - Antti Vehkaoja
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland,Finnish Cardiovascular Research Center, Tampere University, Tampere, Finland,PulseOn Oy, Espoo, Finland
| |
Collapse
|
11
|
Armañac-Julián P, Kontaxis S, Rapalis A, Marozas V, Laguna P, Bailón R, Gil E, Lázaro J. Reliability of pulse photoplethysmography sensors: Coverage using different setups and body locations. Front Electron 2022. [DOI: 10.3389/felec.2022.906324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Pulse photoplethysmography (PPG) is a simple and economical technique for obtaining cardiovascular information. In fact, PPG has become a very popular technology among wearable devices. However, the PPG signal is well-known to be very vulnerable to artifacts, and a good quality signal cannot be expected for most of the time in daily life. The percentage of time that a given measurement can be estimated (e.g., pulse rate) is denoted coverage (C), and it is highly dependent on the subject activity and on the configuration of the sensor, location, and stability of contact. This work aims to quantify the coverage of PPG sensors, using the simultaneously recorded electrocardiogram as a reference, with the PPG recorded at different places in the body and under different stress conditions. While many previous works analyzed the feasibility of PPG as a surrogate for heart rate variability analysis, there exists no previous work studying coverage to derive other cardiovascular indices. We report the coverage not only for estimating pulse rate (PR) but also for estimating pulse arrival time (PAT) and pulse amplitude variability (PAV). Three different datasets are analyzed for this purpose, consisting of a tilt-table test, an acute emotional stress test, and a heat stress test. The datasets include 19, 120, and 51 subjects, respectively, with PPG at the finger and at the forehead for the first two datasets and at the earlobe, in addition, for the latter. C ranges from 70% to 90% for estimating PR. Regarding the estimation of PAT, C ranges from 50% to 90%, and this is very dependent on the PPG sensor location, PPG quality, and the fiducial point (FP) chosen for the delineation of PPG. In fact, the delineation of the FP is critical in time for estimating derived series such as PAT due to the small dynamic range of these series. For the estimation of PAV, the C rates are between 70% and 90%. In general, lower C rates have been obtained for the PPG at the forehead. No difference in C has been observed between using PPG at the finger or at the earlobe. Then, the benefits of using either will depend on the application. However, different C rates are obtained using the same PPG signal, depending on the FP chosen for delineation. Lower C is reported when using the apex point of the PPG instead of the maximum flow velocity or the basal point, with a difference from 1% to even 10%. For further studies, each setup should first be analyzed and validated, taking the results and guidelines presented in this work into account, to study the feasibility of its recording devices with respect to each specific application.
Collapse
|
12
|
Cheng T, Jiang F, Li Q, Zeng J, Zhang B. Quantitative Analysis Using Consecutive Time Window for Unobtrusive Atrial Fibrillation Detection Based on Ballistocardiogram Signal. Sensors (Basel) 2022; 22:5516. [PMID: 35898020 PMCID: PMC9331962 DOI: 10.3390/s22155516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Revised: 07/19/2022] [Accepted: 07/19/2022] [Indexed: 06/15/2023]
Abstract
Atrial fibrillation (AF) is the most common clinically significant arrhythmia; therefore, AF detection is crucial. Here, we propose a novel feature extraction method to improve AF detection performance using a ballistocardiogram (BCG), which is a weak vibration signal on the body surface transmitted by the cardiogenic force. In this paper, continuous time windows (CTWs) are added to each BCG segment and recurrence quantification analysis (RQA) features are extracted from each time window. Then, the number of CTWs is discussed and the combined features from multiple time windows are ranked, which finally constitute the CTW-RQA features. As validation, the CTW-RQA features are extracted from 4000 BCG segments of 59 subjects, which are compared with classical time and time-frequency features and up-to-date energy features. The accuracy of the proposed feature is superior, and three types of features are fused to obtain the highest accuracy of 95.63%. To evaluate the importance of the proposed feature, the fusion features are ranked using a chi-square test. CTW-RQA features account for 60% of the first 10 fusion features and 65% of the first 17 fusion features. It follows that the proposed CTW-RQA features effectively supplement the existing BCG features for AF detection.
Collapse
Affiliation(s)
- Tianqing Cheng
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110819, China; (T.C.); (Q.L.); (J.Z.)
| | - Fangfang Jiang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110819, China; (T.C.); (Q.L.); (J.Z.)
| | - Qing Li
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110819, China; (T.C.); (Q.L.); (J.Z.)
| | - Jitao Zeng
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110819, China; (T.C.); (Q.L.); (J.Z.)
| | - Biyong Zhang
- College of Medicine and Biological Information Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands;
- BOBO Technology, Hangzhou 310000, China
| |
Collapse
|
13
|
Sun Z, Junttila J, Tulppo M, Seppanen T, Li X. Non-Contact Atrial Fibrillation Detection From Face Videos by Learning Systolic Peaks. IEEE J Biomed Health Inform 2022; 26:4587-4598. [PMID: 35867368 DOI: 10.1109/jbhi.2022.3193117] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
OBJECTIVE We propose a non-contact approach for atrial fibrillation (AF) detection from face videos. METHODS Face videos, electrocardiography (ECG), and contact photoplethysmography (PPG) from 100 healthy subjects and 100 AF patients are recorded. Data recordings from healthy subjects are all labeled as healthy. Two cardiologists evaluated ECG recordings of patients and labeled each recording as AF, sinus rhythm (SR), or atrial flutter (AFL). We use the 3D convolutional neural network for remote PPG monitoring and propose a novel loss function (Wasserstein distance) to use the timing of systolic peaks from contact PPG as the label for our model training. Then a set of heart rate variability (HRV) features are calculated from the inter-beat intervals, and a support vector machine (SVM) classifier is trained with HRV features. RESULTS Our proposed method can accurately extract systolic peaks from face videos for AF detection. The proposed method is trained with subject-independent 10-fold cross-validation with 30s video clips and tested on two tasks. 1) Classification of healthy versus AF: the accuracy, sensitivity, and specificity are 96.00%, 95.36%, and 96.12%. 2) Classification of SR versus AF: the accuracy, sensitivity, and specificity are 95.23%, 98.53%, and 91.12%. In addition, we also demonstrate the feasibility of non-contact AFL detection. CONCLUSION We achieve good performance of non-contact AF detection by learning systolic peaks. SIGNIFICANCE non-contact AF detection can be used for self-screening of AF symptoms for suspectable populations at home or self-monitoring of AF recurrence after treatment for chronic patients.
Collapse
|
14
|
Přibil J, Přibilová A, Frollo I. Comparison of Three Prototypes of PPG Sensors for Continual Real-Time Measurement in Weak Magnetic Field. Sensors (Basel) 2022; 22:3769. [PMID: 35632179 DOI: 10.3390/s22103769] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 05/11/2022] [Accepted: 05/14/2022] [Indexed: 12/04/2022]
Abstract
This paper is focused on investigation of three developed prototypes of sensors based on the photoplethysmography (PPG) principle for continual measurement of the PPG signal in the magnetic field environment with the inherent radiofrequency and electromagnetic disturbance. The tested prototypes differ in the used optical part of the PPG sensor and their working mode, control unit, power supply, and applied Bluetooth (BT) communication methods. The main aim of the current work was motivated by finding suitable and universal parameter settings for PPG signal real-time recording in different working mode conditions. Comparative measurements in laboratory conditions by certified commercial pulse oximeter and blood pressure monitor (BPM) devices show good stability and proper accuracy of finally determined heart rate values. The supplementary investigation certifies the necessity of the placement of the pressure cuff of the BPM device on the opposite arm than the tested PPG sensor. Measurement experiments inside the scanning area of the running weak field magnetic resonance scanner verify proper function and practical usability of sensed PPG signals for further processing and analysis in all three prototype cases. Additional testing shows that the BT transmission in the scanning area has no visible influence on the quality of the finally obtained scanner images.
Collapse
|
15
|
Bonini N, Vitolo M, Imberti JF, Proietti M, Romiti GF, Boriani G, Paaske Johnsen S, Guo Y, Lip GYH. Mobile health technology in atrial fibrillation. Expert Rev Med Devices 2022; 19:327-340. [PMID: 35451347 DOI: 10.1080/17434440.2022.2070005] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
INTRODUCTION Mobile health (mHealth) solutions in atrial fibrillation (AF) are becoming widespread, thanks to everyday life devices such as smartphones. Their use is validated both in monitoring and in screening scenarios. In the published literature, the diagnostic accuracy of mHealth solutions wide differs, and their current clinical use is not well established in principal guidelines. AREAS COVERED mHealth solutions have progressively built an AF-detection chain to guide patients from the device's alert signal to the health care practitioners' (HCPs) attention. This review aims to critically evaluate the latest evidence regarding mHealth devices and the future possible patient's uses in everyday life. EXPERT OPINION The patients are the first to be informed of the rhythm anomaly, leading to the urgency of increasing the patients' AF self-management. Furthermore, HCPs need to update themselves about mHealth devices use in clinical practice. Nevertheless, these are promising instruments in specific populations, such as post-stroke patients, to promote an early arrhythmia diagnosis in the post-ablation/cardioversion period, allowing checks on the efficacy of the treatment or intervention.
Collapse
Affiliation(s)
- Niccolò Bonini
- Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart & Chest Hospital, Liverpool, United Kingdom.,Cardiology Division, Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Policlinico di Modena, Modena, Italy
| | - Marco Vitolo
- Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart & Chest Hospital, Liverpool, United Kingdom.,Cardiology Division, Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Policlinico di Modena, Modena, Italy.,Clinical and Experimental Medicine PhD Program, University of Modena and Reggio Emilia, Modena, Italy
| | - Jacopo Francesco Imberti
- Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart & Chest Hospital, Liverpool, United Kingdom.,Cardiology Division, Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Policlinico di Modena, Modena, Italy.,Clinical and Experimental Medicine PhD Program, University of Modena and Reggio Emilia, Modena, Italy
| | - Marco Proietti
- Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart & Chest Hospital, Liverpool, United Kingdom.,Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy.,Geriatric Unit, IRCCS Istituti Clinici Scientifici Maugeri, Milan, Italy
| | - Giulio Francesco Romiti
- Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart & Chest Hospital, Liverpool, United Kingdom.,Department of Translational and Precision Medicine, Sapienza-University of Rome, Rome, Italy
| | - Giuseppe Boriani
- Cardiology Division, Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Policlinico di Modena, Modena, Italy
| | - Søren Paaske Johnsen
- Danish Center for Clinical Health Services Research (DACS), Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
| | - Yutao Guo
- Department of Pulmonary Vessel and Thrombotic Disease, Sixth Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Gregory Y H Lip
- Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart & Chest Hospital, Liverpool, United Kingdom.,Danish Center for Clinical Health Services Research (DACS), Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
| |
Collapse
|
16
|
Liu Z, Zhou B, Jiang Z, Chen X, Li Y, Tang M, Miao F. Multiclass Arrhythmia Detection and Classification From Photoplethysmography Signals Using a Deep Convolutional Neural Network. J Am Heart Assoc 2022; 11:e023555. [PMID: 35322685 PMCID: PMC9075456 DOI: 10.1161/jaha.121.023555] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Background Studies have reported the use of photoplethysmography signals to detect atrial fibrillation; however, the use of photoplethysmography signals in classifying multiclass arrhythmias has rarely been reported. Our study investigated the feasibility of using photoplethysmography signals and a deep convolutional neural network to classify multiclass arrhythmia types. Methods and Results ECG and photoplethysmography signals were collected simultaneously from a group of patients who underwent radiofrequency ablation for arrhythmias. A deep convolutional neural network was developed to classify multiple rhythms based on 10‐second photoplethysmography waveforms. Classification performance was evaluated by calculating the area under the microaverage receiver operating characteristic curve, overall accuracy, sensitivity, specificity, and positive and negative predictive values against annotations on the rhythm of arrhythmias provided by 2 cardiologists consulting the ECG results. A total of 228 patients were included; 118 217 pairs of 10‐second photoplethysmography and ECG waveforms were used. When validated against an independent test data set (23 384 photoplethysmography waveforms from 45 patients), the DCNN achieved an overall accuracy of 85.0% for 6 rhythm types (sinus rhythm, premature ventricular contraction, premature atrial contraction, ventricular tachycardia, supraventricular tachycardia, and atrial fibrillation); the microaverage area under the microaverage receiver operating characteristic curve was 0.978; the average sensitivity, specificity, and positive and negative predictive values were 75.8%, 96.9%, 75.2%, and 97.0%, respectively. Conclusions This study demonstrated the feasibility of classifying multiclass arrhythmias from photoplethysmography signals using deep learning techniques. The approach is attractive for population‐based screening and may hold promise for the long‐term surveillance and management of arrhythmia. Registration URL: www.chictr.org.cn. Identifier: ChiCTR2000031170.
Collapse
Affiliation(s)
- Zengding Liu
- Key Laboratory for Health Informatics Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Shenzhen China.,University of Chinese Academy of Sciences Beijing China
| | - Bin Zhou
- Department of Cardiology Laboratory of Heart Center Zhujiang HospitalSouthern Medical University Guangzhou China.,Fuwai HospitalNational Center for Cardiovascular DiseaseState Key Lab of Cardiovascular DiseaseNational Clinical Research Center of Cardiovascular DiseasesChinese Academy of Medical Sciences and Peking Union Medical College Beijing China
| | - Zhiming Jiang
- Key Laboratory for Health Informatics Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Shenzhen China
| | - Xi Chen
- Key Laboratory for Health Informatics Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Shenzhen China
| | - Ye Li
- Key Laboratory for Health Informatics Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Shenzhen China.,Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Shenzhen China
| | - Min Tang
- Fuwai HospitalNational Center for Cardiovascular DiseaseState Key Lab of Cardiovascular DiseaseNational Clinical Research Center of Cardiovascular DiseasesChinese Academy of Medical Sciences and Peking Union Medical College Beijing China
| | - Fen Miao
- Key Laboratory for Health Informatics Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Shenzhen China
| |
Collapse
|
17
|
Charlton PH, Kyriacou PA, Mant J, Marozas V, Chowienczyk P, Alastruey J. Wearable Photoplethysmography for Cardiovascular Monitoring. Proc IEEE Inst Electr Electron Eng 2022; 110:355-381. [PMID: 35356509 PMCID: PMC7612541 DOI: 10.1109/jproc.2022.3149785] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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.
Collapse
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.
| |
Collapse
|
18
|
Sehrawat O, Kashou AH, Noseworthy PA. Artificial Intelligence and Atrial Fibrillation. J Cardiovasc Electrophysiol 2022; 33:1932-1943. [PMID: 35258136 PMCID: PMC9717694 DOI: 10.1111/jce.15440] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/25/2021] [Revised: 02/03/2022] [Accepted: 03/01/2022] [Indexed: 11/30/2022]
Abstract
In the context of atrial fibrillation (AF), traditional clinical practices have thus far fallen short in several domains such as identifying patients at risk of incident AF or patients with concomitant undetected paroxysmal AF. Novel approaches leveraging artificial intelligence have the potential to provide new tools to deal with some of these old problems. In this review we focus on the roles of artificial intelligence-enabled ECG pertaining to AF, potential roles of deep learning (DL) models in the context of current knowledge gaps, as well as limitations of these models. One key area where DL models can translate to better patient outcomes is through automated ECG interpretation. Further, we overview some of the challenges facing AF screening and the harms and benefits of screening. In this context, a unique model was developed to detect underlying hidden AF from sinus rhythm and is discussed in detail with its potential uses. Knowledge gaps also remain regarding the best ways to monitor patients with embolic stroke of undetermined source (ESUS) and who would benefit most from oral anticoagulation. The AI-enabled AF model is one potential way to tackle this complex problem as it could be used to identify a subset of high-risk ESUS patients likely to benefit from empirical oral anticoagulation. Role of DL models assessing AF burden from long duration ECG data is also discussed as a way of guiding management. There is a trend towards the use of consumer-grade wristbands and watches to detect AF from photoplethysmography data. However, ECG currently remains the gold standard to detect arrythmias including AF. Lastly, role of adequate external validation of the models and clinical trials to study true performance is discussed. This article is protected by copyright. All rights reserved.
Collapse
Affiliation(s)
- Ojasav Sehrawat
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Anthony H Kashou
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | | |
Collapse
|
19
|
Elbey MA, Young D, Kanuri SH, Akella K, Murtaza G, Garg J, Atkins D, Bommana S, Sharma S, Turagam M, Pillarisetti J, Park P, Tummala R, Shah A, Koerber S, Shivamurthy P, Vasamreddy C, Gopinathannair R, Lakkireddy D. Diagnostic Utility of Smartwatch Technology for Atrial Fibrillation Detection - A Systematic Analysis. J Atr Fibrillation 2021; 13:20200446. [PMID: 34950348 DOI: 10.4022/jafib.20200446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Revised: 05/26/2020] [Accepted: 07/01/2020] [Indexed: 11/10/2022]
Abstract
Background Smartphone technologies have been recently developed to assess heart rate and rhythm, but their role in accurately detecting atrial fibrillation (AF) remains unknown. Objective We sought to perform a meta-analysis using prospective studies comparing Smartwatch technology with current monitoring standards for AF detection (ECG, Holter, Patch Monitor, ILR). Methods We performed a comprehensive literature search for prospective studies comparing Smartwatch technology simultaneously with current monitoring standards (ECG, Holter, and Patch monitor) for AF detection since inception to November 25th, 2019. The outcome studied was the accuracy of AF detection. Accuracy was determined with concomitant usage of ECG monitoring, Holter monitoring, loop recorder, or patch monitoring. Results A total of 9 observational studies were included comparing smartwatch technology, 3 using single-lead ECG monitoring, and six studies using photoplethysmography with routine AF monitoring strategies. A total of 1559 patients were enrolled (mean age 63.5 years, 39.5% had an AF history). The mean monitoring time was 75.6 days. Smartwatch was non-inferior to composite ECG monitoring strategies (OR 1.06, 95% CI 0.93 - 1.21, p=0.37), composite 12 lead ECG/Holter monitoring (OR 0.90, 95% CI 0.62 - 1.30, p=0.57) and patch monitoring (OR 1.28, 95% CI 0.84 - 1.94, p=0.24) for AF detection. The sensitivity and specificity for AF detection using a smartwatch was 95% and 94%, respectively. Conclusions Smartwatch based single-lead ECG and photoplethysmography appear to be reasonable alternatives for AF monitoring.
Collapse
Affiliation(s)
- Mehmet Ali Elbey
- Arrhythmia Research Fellow, Kansas City Heart Rhythm Institute, Overland Park, Kansas
| | - Daisy Young
- Department of Internal Medicine, Stony Brook Southampton Hospital, Southampton, NY
| | - Sri Harsha Kanuri
- Arrhythmia Research Fellow, Kansas City Heart Rhythm Institute, Overland Park, Kansas
| | - Krishna Akella
- Arrhythmia Research Fellow, Kansas City Heart Rhythm Institute, Overland Park, Kansas
| | - Ghulam Murtaza
- Arrhythmia Research Fellow, Kansas City Heart Rhythm Institute, Overland Park, Kansas
| | - Jalaj Garg
- Division of Cardiology, Cardiac Arrhythmia Service, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Donita Atkins
- Division of Cardiac Electrophysiology, Kansas City Heart Rhythm Institute & Research Foundation; Overland Park Regional Medical Center, HCA Midwest Overland Park, Kansas
| | - Sudha Bommana
- Division of Cardiac Electrophysiology, Kansas City Heart Rhythm Institute & Research Foundation; Overland Park Regional Medical Center, HCA Midwest Overland Park, Kansas
| | - Sharan Sharma
- Division of Cardiac Electrophysiology, Kansas City Heart Rhythm Institute & Research Foundation; Overland Park Regional Medical Center, HCA Midwest Overland Park, Kansas
| | - Mohit Turagam
- Helmsley Electrophysiology Center, Icahn School of Medicine at Mount Sinai, New York, NY
| | | | - Peter Park
- Division of Cardiac Electrophysiology, Kansas City Heart Rhythm Institute & Research Foundation; Overland Park Regional Medical Center, HCA Midwest Overland Park, Kansas
| | - Rangarao Tummala
- Division of Cardiac Electrophysiology, Kansas City Heart Rhythm Institute & Research Foundation; Overland Park Regional Medical Center, HCA Midwest Overland Park, Kansas
| | - Alap Shah
- Division of Cardiac Electrophysiology, Kansas City Heart Rhythm Institute & Research Foundation; Overland Park Regional Medical Center, HCA Midwest Overland Park, Kansas
| | - Scott Koerber
- Division of Cardiac Electrophysiology, Kansas City Heart Rhythm Institute & Research Foundation; Overland Park Regional Medical Center, HCA Midwest Overland Park, Kansas
| | - Poojita Shivamurthy
- Division of Cardiac Electrophysiology, Kansas City Heart Rhythm Institute & Research Foundation; Overland Park Regional Medical Center, HCA Midwest Overland Park, Kansas
| | - Chandrasekhar Vasamreddy
- Division of Cardiac Electrophysiology, Kansas City Heart Rhythm Institute & Research Foundation; Overland Park Regional Medical Center, HCA Midwest Overland Park, Kansas
| | - Rakesh Gopinathannair
- Division of Cardiac Electrophysiology, Kansas City Heart Rhythm Institute & Research Foundation; Overland Park Regional Medical Center, HCA Midwest Overland Park, Kansas
| | - Dhanunjaya Lakkireddy
- Division of Cardiac Electrophysiology, Kansas City Heart Rhythm Institute & Research Foundation; Overland Park Regional Medical Center, HCA Midwest Overland Park, Kansas
| |
Collapse
|
20
|
Khundaqji H, Hing W, Furness J, Climstein M. Wearable technology to inform the prediction and diagnosis of cardiorespiratory events: a scoping review. PeerJ 2021; 9:e12598. [PMID: 35036129 PMCID: PMC8710054 DOI: 10.7717/peerj.12598] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Accepted: 11/15/2021] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND The need for health systems that allow for continuous monitoring and early adverse event detection in individuals outside of the acute care setting has been highlighted by the global rise in chronic cardiorespiratory diseases and the recent COVID-19 pandemic. Currently, it is unclear what type of evidence exists concerning the use of physiological data collected from commercially available wrist and textile wearables to assist in clinical decision making. The aim of this review was therefore to systematically map and summarize the scientific literature surrounding the use of these wearables in clinical decision making as well as identify knowledge gaps to inform further research. METHODOLOGY Six electronic bibliographic databases were systematically searched (Ovid MEDLINE, EMBASE, CINAHL, PubMed, Scopus, and SportsDiscus). Publications from database inception to May 6, 2020 were reviewed for inclusion. Non-indexed literature relevant to this review was also searched systematically. Results were then collated, summarized and reported. RESULTS A total of 107 citations were retrieved and assessed for eligibility with 31 citations included in the final analysis. A review of the 31 papers revealed three major study designs which included (1) observational studies (n = 19), (2) case control series and reports (n = 8), and (3) reviews (n = 2). All papers examined the use of wearable monitoring devices for clinical decisions in the cardiovascular domain, with cardiac arrhythmias being the most studied. When compared to electrocardiogram (ECG) the performance of the wearables in facilitating clinical decisions varied depending upon the type of wearable, user's activity levels and setting in which they were employed. Observational studies collecting data in the inpatient and outpatient settings were equally represented. Eight case control series and reports were identified which reported on the use of wrist wearables in patients presenting to an emergency department or clinic to aid in the clinical diagnosis of a cardiovascular event. Two narrative reviews were identified which examined the impact of wearable devices in monitoring cardiovascular disease as well as potential challenges they may pose in the future. CONCLUSIONS To date, studies employing wearables to facilitate clinical decisions have largely focused upon the cardiovascular domain. Despite the ability of some wearables to collect physiological data accurately, there remains a need for a specialist physician to retrospectively review the raw data to make a definitive diagnosis. Analysis of the results has also highlighted gaps in the literature such as the absence of studies employing wearables to facilitate clinical decisions in the respiratory domain. The disproportionate study of wearables in atrial fibrillation detection in comparison to other cardiac arrhythmias and conditions, as well as the lack of diversity in the sample populations used prevents the generalizability of results.
Collapse
Affiliation(s)
- Hamzeh Khundaqji
- Faculty of Health Sciences & Medicine, Bond University, Gold Coast, Queensland, Australia
| | - Wayne Hing
- Faculty of Health Sciences & Medicine, Bond University, Gold Coast, Queensland, Australia
| | - James Furness
- Faculty of Health Sciences & Medicine, Bond University, Gold Coast, Queensland, Australia
| | - Mike Climstein
- Faculty of Health, Southern Cross University, Bilinga, Queensland, Australia
- Physical Activity, Lifestyle, Ageing and Wellbeing Faculty Research Group, University of Sydney, Sydney, New South Wales, Australia
| |
Collapse
|
21
|
Mestrom E, Deneer R, Bonomi AG, Margarito J, Gelissen J, Haakma R, Korsten HHM, Scharnhorst V, Bouwman RA. Validation of Heart Rate Extracted From Wrist-Based Photoplethysmography in the Perioperative Setting: Prospective Observational Study. JMIR Cardio 2021; 5:e27765. [PMID: 34734834 PMCID: PMC8603171 DOI: 10.2196/27765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 06/08/2021] [Accepted: 07/27/2021] [Indexed: 11/29/2022] Open
Abstract
Background Measurement of heart rate (HR) through an unobtrusive, wrist-worn optical HR monitor (OHRM) could enable earlier recognition of patient deterioration in low acuity settings and enable timely intervention. Objective The goal of this study was to assess the agreement between the HR extracted from the OHRM and the gold standard 5-lead electrocardiogram (ECG) connected to a patient monitor during surgery and in the recovery period. Methods In patients undergoing surgery requiring anesthesia, the HR reported by the patient monitor’s ECG module was recorded and stored simultaneously with the photopletysmography (PPG) from the OHRM attached to the patient’s wrist. The agreement between the HR reported by the patient’s monitor and the HR extracted from the OHRM’s PPG signal was assessed using Bland-Altman analysis during the surgical and recovery phase. Results A total of 271.8 hours of data in 99 patients was recorded simultaneously by the OHRM and patient monitor. The median coverage was 86% (IQR 65%-95%) and did not differ significantly between surgery and recovery (Wilcoxon paired difference test P=.17). Agreement analysis showed the limits of agreement (LoA) of the difference between the OHRM and the ECG HR were within the range of 5 beats per minute (bpm). The mean bias was –0.14 bpm (LoA between –3.08 bpm and 2.79 bpm) and –0.19% (LoA between –5 bpm to 5 bpm) for the PPG- measured HR compared to the ECG-measured HR during surgery; during recovery, it was –0.11 bpm (LoA between –2.79 bpm and 2.59 bpm) and –0.15% (LoA between –3.92% and 3.64%). Conclusions This study shows that an OHRM equipped with a PPG sensor can measure HR within the ECG reference standard of –5 bpm to 5 bpm or –10% to 10% in the perioperative setting when the PPG signal is of sufficient quality. This implies that an OHRM can be considered clinically acceptable for HR monitoring in low acuity hospitalized patients.
Collapse
Affiliation(s)
- Eveline Mestrom
- Department of Anesthesiology, Catharina Hospital Eindhoven, Eindhoven, Netherlands
| | - Ruben Deneer
- Clinical Laboratory, Catharina Hospital Eindhoven, Eindhoven, Netherlands.,Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands.,Expert Center Clinical Chemistry Eindhoven, Eindhoven, Netherlands
| | - Alberto G Bonomi
- Department of Personal Health, Philips Research, Eindhoven, Netherlands
| | - Jenny Margarito
- Department of Personal Health, Philips Research, Eindhoven, Netherlands
| | - Jos Gelissen
- Department of Personal Health, Philips Research, Eindhoven, Netherlands
| | - Reinder Haakma
- Department of Personal Health, Philips Research, Eindhoven, Netherlands
| | - Hendrikus H M Korsten
- Department of Anesthesiology, Catharina Hospital Eindhoven, Eindhoven, Netherlands.,Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Volkher Scharnhorst
- Clinical Laboratory, Catharina Hospital Eindhoven, Eindhoven, Netherlands.,Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands.,Expert Center Clinical Chemistry Eindhoven, Eindhoven, Netherlands
| | - R Arthur Bouwman
- Department of Anesthesiology, Catharina Hospital Eindhoven, Eindhoven, Netherlands.,Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| |
Collapse
|
22
|
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: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [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.
Collapse
|
23
|
Lopez Perales CR, Van Spall HGC, Maeda S, Jimenez A, Laţcu DG, Milman A, Kirakoya-Samadoulougou F, Mamas MA, Muser D, Casado Arroyo R. Mobile health applications for the detection of atrial fibrillation: a systematic review. Europace 2021; 23:11-28. [PMID: 33043358 DOI: 10.1093/europace/euaa139] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Indexed: 12/21/2022] Open
Abstract
AIMS Atrial fibrillation (AF) is the most common sustained arrhythmia and an important risk factor for stroke and heart failure. We aimed to conduct a systematic review of the literature and summarize the performance of mobile health (mHealth) devices in diagnosing and screening for AF. METHODS AND RESULTS We conducted a systematic search of MEDLINE, Embase, and the Cochrane Central Register of Controlled Trials. Forty-three studies met the inclusion criteria and were divided into two groups: 28 studies aimed at validating smart devices for AF diagnosis, and 15 studies used smart devices to screen for AF. Evaluated technologies included smartphones, with photoplethysmographic (PPG) pulse waveform measurement or accelerometer sensors, smartbands, external electrodes that can provide a smartphone single-lead electrocardiogram (iECG), such as AliveCor, Zenicor and MyDiagnostick, and earlobe monitor. The accuracy of these devices depended on the technology and the population, AliveCor and smartphone PPG sensors being the most frequent systems analysed. The iECG provided by AliveCor demonstrated a sensitivity and specificity between 66.7% and 98.5% and 99.4% and 99.0%, respectively. The PPG sensors detected AF with a sensitivity of 85.0-100% and a specificity of 93.5-99.0%. The incidence of newly diagnosed arrhythmia ranged from 0.12% in a healthy population to 8% among hospitalized patients. CONCLUSION Although the evidence for clinical effectiveness is limited, these devices may be useful in detecting AF. While mHealth is growing in popularity, its clinical, economic, and policy implications merit further investigation. More head-to-head comparisons between mHealth and medical devices are needed to establish their comparative effectiveness.
Collapse
Affiliation(s)
- Carlos Ruben Lopez Perales
- Department of Cardiology, Hopital Erasme, Université Libre de Bruxelles, Route de Lennik 808, 1070 Brussels, Belgium.,Servicio de Cardiología, Hospital Universitario Miguel Servet, Isabel La Catolica 1-3, Zaragoza 50009, Spain
| | - Harriette G C Van Spall
- Division of Cardiology, Department of Medicine, Population Health Research Institute, McMaster University, 1280 Main Street West, Hamilton, Ontario, Canada, Canada
| | - Shingo Maeda
- Advanced Arrhythmia Research, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, 113-8519 Tokyo, Japan
| | - Alejandro Jimenez
- Division of Cardiology, University of Maryland Medical Center, 22 S. Greene Street, Baltimore, MD 21201, USA
| | - Decebal Gabriel Laţcu
- Department of Cardiology, Centre Hospitalier Princesse Grace, Avenue Pasteur, 98000, Monaco, Monaco (Principalty)
| | - Anat Milman
- Department of Cardiology, Leviev Heart Institute, The Chaim Sheba Medical Center, Tel Hashomer, and Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Fati Kirakoya-Samadoulougou
- Centre de Recherche en Epidémiologie, Biostatistiques et Recherche Clinique, Ecole de Santé Publique, Université librede Bruxelles, Avenue Franklin Roosevelt 50 - 1050, Brussels, Belgium
| | - Mamas A Mamas
- Keele Cardiovascular Research Group, Keele University, Stoke-on-Trent, Keele, Newcastle ST5 5BG, UK.,Royal Stoke University Hospital, Newcastle Rd, Stoke-on-Trent ST4 6QG, UK
| | - Daniele Muser
- Section of Cardiac Electrophysiology, Hospital of the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104, USA
| | - Ruben Casado Arroyo
- Department of Cardiology, Hopital Erasme, Université Libre de Bruxelles, Route de Lennik 808, 1070 Brussels, Belgium
| |
Collapse
|
24
|
Huang S, Zhao T, Liu C, Qin A, Dong S, Yuan B, Xing W, Guo Z, Huang X, Cha Y, Cao J. Portable Device Improves the Detection of Atrial Fibrillation After Ablation. Int Heart J 2021; 62:786-791. [PMID: 34276021 DOI: 10.1536/ihj.21-067] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Asymptomatic recurrences of atrial fibrillation (AF) have been found to be common after ablation.A randomized controlled trial of AF screening using a handheld single-lead ECG monitor (BigThumb®) or a traditional follow-up strategy was conducted in patients with non-valvular AF after catheter ablation. Consecutive patients were randomized to either BigThumb Group (BT Group) or Traditional Follow-up Group (TF Group). The ECGs collected via BigThumb were compared using the automated AF detection algorithm, artificial intelligence (AI) algorithm, and cardiologists' manual review. Subsequent changes in adherence to oral anticoagulation of patients were also recorded. In this study, we examined 218 patients (109 in each group). After a follow-up of 345.4 ± 60.2 days, AF-free survival rate was 64.2% in BT Group and 78.9% in TF Group (P = 0.0163), with more adherence to oral anticoagulation in BT Group (P = 0.0052). The participants in the BT Group recorded 26133 ECGs, among which 3299 (12.6%) were diagnosed as AF by cardiologists' manual review. The sensitivity and specificity of the AI algorithm were 94.4% and 98.5% respectively, which are significantly higher than the automated AF detection algorithm (90.7% and 96.2%).As per our findings, it was determined that follow-up after AF ablation using BigThumb leads to a more frequent detection of AF recurrence and more adherence to oral anticoagulation. AI algorithm improves the accuracy of ECG diagnosis and has the potential to reduce the manual review.
Collapse
Affiliation(s)
- Songqun Huang
- Department of Cardiovasology, Changhai Hospital, Second Military Medical University
| | - Teng Zhao
- Department of Cardiovasology, Changhai Hospital, Second Military Medical University
| | - Chao Liu
- Department of Cardiovasology, Changhai Hospital, Second Military Medical University
| | - Aihong Qin
- Department of Cardiovasology, Changhai Hospital, Second Military Medical University
| | - Shaohua Dong
- Department of Cardiovasology, Changhai Hospital, Second Military Medical University
| | - Binhang Yuan
- Department of Computer Science, William Marsh Rice University
| | | | - Zhifu Guo
- Department of Cardiovasology, Changhai Hospital, Second Military Medical University
| | - Xinmiao Huang
- Department of Cardiovasology, Changhai Hospital, Second Military Medical University
| | - Yongmei Cha
- Division of Cardiovascular Diseases, Mayo Clinic
| | - Jiang Cao
- Department of Cardiovasology, Changhai Hospital, Second Military Medical University
| |
Collapse
|
25
|
Abstract
Atrial fibrillation (AF) will become one of the biggest challenges in cardiovascular medicine in the near future. Attempting an improvement in future patient care calls explicitly for the screening of subclinical AF. Digital health solutions implementing communication technologies for the collection and analysis of digitally assessable data will most likely serve this need. Several new rapidly developing methods were introduced in the past decade. Although the vast majority still require scientific validation, the body of evidence is growing and several randomized controlled trials are planned. This review aims to give an overview of current technologies with a specific focus on mobile health (mHealth) and appraise their value with regard to the available scientific data.
Collapse
|
26
|
Jiang F, Zhou Y, Ling T, Zhang Y, Zhu Z. Recent Research for Unobtrusive Atrial Fibrillation Detection Methods Based on Cardiac Dynamics Signals: A Survey. Sensors (Basel) 2021; 21:3814. [PMID: 34072986 DOI: 10.3390/s21113814] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Revised: 05/20/2021] [Accepted: 05/26/2021] [Indexed: 11/16/2022]
Abstract
Atrial fibrillation (AF) is the most common cardiac arrhythmia. It tends to cause multiple cardiac conditions, such as cerebral artery blockage, stroke, and heart failure. The morbidity and mortality of AF have been progressively increasing over the past few decades, which has raised widespread concern about unobtrusive AF detection in routine life. The up-to-date non-invasive AF detection methods include electrocardiogram (ECG) signals and cardiac dynamics signals, such as the ballistocardiogram (BCG) signal, the seismocardiogram (SCG) signal and the photoplethysmogram (PPG) signal. Cardiac dynamics signals can be collected by cushions, mattresses, fabrics, or even cameras, which is more suitable for long-term monitoring. Therefore, methods for AF detection by cardiac dynamics signals bring about extensive attention for recent research. This paper reviews the current unobtrusive AF detection methods based on the three cardiac dynamics signals, summarized as data acquisition and preprocessing, feature extraction and selection, classification and diagnosis. In addition, the drawbacks and limitations of the existing methods are analyzed, and the challenges in future work are discussed.
Collapse
|
27
|
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] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
|
28
|
Wang T, Qin Y. A novel multi-scale convolutional network with attention-based bidirectional gated recurrent unit for atrial fibrillation discrimination. Biocybern Biomed Eng 2021. [DOI: 10.1016/j.bbe.2021.02.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
|
29
|
Lazaro J, Reljin N, Hossain MB, Noh Y, Laguna P, Chon KH. Wearable Armband Device for Daily Life Electrocardiogram Monitoring. IEEE Trans Biomed Eng 2020; 67:3464-3473. [DOI: 10.1109/tbme.2020.2987759] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
|
30
|
Selder JL, Proesmans T, Breukel L, Dur O, Gielen W, van Rossum AC, Allaart CP. Assessment of a standalone photoplethysmography (PPG) algorithm for detection of atrial fibrillation on wristband-derived data. Comput Methods Programs Biomed 2020; 197:105753. [PMID: 32998102 DOI: 10.1016/j.cmpb.2020.105753] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Accepted: 09/07/2020] [Indexed: 06/11/2023]
Abstract
INTRODUCTION Atrial fibrillation (AF) is the most common cardiac arrhythmia in the developed world. Using photoplethysmography (PPG) and software algorithms, AF can be detected with high accuracy using smartphone camera-derived data. However, reports of diagnostic accuracy of standalone algorithms using wristband-derived PPG data are sparse, while this provides a means to perform long-term AF screening and monitoring. This study evaluated the diagnostic accuracy of a well-known standalone algorithm using wristband-derived PPG data. MATERIALS AND METHODS Subjects recruited from a community senior care organization were instructed to wear the Wavelet PPG wristband on one arm and the Alivecor KardiaBand one-lead-ECG wristband on the other. Three consecutive measurements (duration per measurement: 60 s for PPG and 30 s for one-lead ECG) were performed with both devices, simultaneously. The PPG data were analyzed by the Fibricheck standalone algorithm and the ECG data by the Kardia algorithm. The results were compared to a reference standard (interpretation of the one-lead ECG by two independent cardiologists). RESULTS A total of 180 PPGs and one-lead ECGs were recorded in 60 subjects, with a mean age of 70±17. AF was identified in 6 (10%) of the users, two users (3%) were not classifiable by the PPG algorithm and 1 user (2%) was not classifiable by the one-lead ECG algorithm. The diagnostic performance (sensitivity/specificity/positive predictive value/negative predictive value/accuracy) on user level was 100/96/75/100/97% for the PPG wristband and 100/98/86/100/98% for the one-lead ECG wristband. CONCLUSIONS In a small real-world cohort of elderly people, the standalone Fibricheck AF algorithm can accurately detect AF using Wavelet wristband-derived PPG data. Results are comparable to the Alivecor Kardia one-lead ECG device, with an acceptable unclassifiable/bad quality rate. This opens the door for long-term AF screening and monitoring.
Collapse
Affiliation(s)
- J L Selder
- Amsterdam UMC, Vrije Universiteit, Department of Cardiology, Amsterdam, Netherlands.
| | - T Proesmans
- Mobile Health Unit, Hasselt University, Diepenbeek, Belgium
| | - L Breukel
- Onze Lieve Vrouwe Gasthuis, Department of Cardiology, Amsterdam, Netherlands
| | - O Dur
- Wavelet Health, Mountain View, CA, United States
| | - W Gielen
- Silkeborg Regional Hospital, Diagnostic Center, Silkeborg, Denmark
| | - A C van Rossum
- Amsterdam UMC, Vrije Universiteit, Department of Cardiology, Amsterdam, Netherlands
| | - C P Allaart
- Amsterdam UMC, Vrije Universiteit, Department of Cardiology, Amsterdam, Netherlands
| |
Collapse
|
31
|
Genzoni E, Braun F, Zaen JV, Renevey P, Lemay M, Pruvot E, Vesin JM. Challenging the Limitations of Atrial Fibrillation Detection in the Presence of Other Cardiac Arrythmias. Annu Int Conf IEEE Eng Med Biol Soc 2020; 2020:5000-5003. [PMID: 33019109 DOI: 10.1109/embc44109.2020.9176418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Atrial fibrillation (AF) affects millions of people worldwide and needs to be diagnosed in its early stage to provide proper treatment. However, the numerous wearable devices available today are not yet able to discriminate AF episodes from other cardiac arrhythmias and merely detect normal vs abnormal rhythms.In this study we investigated the performance of a traditional classifier - designed to distinguish AF and sinus rhythm (SR) using inter-beat intervals (IBI) - when confronted with other - non-AF - arrhythmias. This classifier was challenged with data of 37 patients wearing an optical heart rate monitor device during catheter ablation procedures. We first analyzed the classification performance of pure AF vs SR and then gradually introduced non-AF arrhythmias in the time windows used for classification.We obtained a high classification performance (accuracy, sensitivity and specificity of 0.979, 1.000 and 0.966) for purely AF and SR. In contrast, when increasing the maximal possible number of non-AF arrhythmias to 50%, the performance decreased to an accuracy, sensitivity and specificity of 0.886, 0.998 and 0.853. While sinus tachycardia led to false positives the classification was not impaired by the presence of extrasystoles, bigeminy, bradycardia, frequent ectopic beats or atrial flutter.Our study quantifies to what extent a traditional IBI-based classifier is not sufficient to distinguish AF from other arrhythmias. Future work should concentrate on acquiring datasets with a high diversity of arrhythmias and employing new classification features.
Collapse
|
32
|
Giebel GD. Use of mHealth Devices to Screen for Atrial Fibrillation: Cost-Effectiveness Analysis. JMIR Mhealth Uhealth 2020; 8:e20496. [PMID: 33021489 PMCID: PMC7576464 DOI: 10.2196/20496] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Revised: 09/03/2020] [Accepted: 09/03/2020] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND With an estimated prevalence of around 3% and an about 2.5-fold increased risk of stroke, atrial fibrillation (AF) is a serious threat for patients and a high economic burden for health care systems all over the world. Patients with AF could benefit from screening through mobile health (mHealth) devices. Thus, an early diagnosis is possible with mHealth devices, and the risk for stroke can be markedly reduced by using anticoagulation therapy. OBJECTIVE The aim of this work was to assess the cost-effectiveness of algorithm-based screening for AF with the aid of photoplethysmography wrist-worn mHealth devices. Even if prevented strokes and prevented deaths from stroke are the most relevant patient outcomes, direct costs were defined as the primary outcome. METHODS A Monte Carlo simulation was conducted based on a developed state-transition model; 30,000 patients for each CHA2DS2-VASc (Congestive heart failure, Hypertension, Age≥75 years, Diabetes mellitus, Stroke, Vascular disease, Age 65-74 years, Sex category [female]) score from 1 to 9 were simulated. The first simulation served to estimate the economic burden of AF without the use of mHealth devices. The second simulation served to simulate the economic burden of AF with the use of mHealth devices. Afterwards, the groups were compared in terms of costs, prevented strokes, and deaths from strokes. RESULTS The CHA2DS2-VASc score as well as the electrocardiography (ECG) confirmation rate had the biggest impact on costs as well as number of strokes. The higher the risk score, the lower were the costs per prevented stroke. Higher ECG confirmation rates intensified this effect. The effect was not seen in groups with lower risk scores. Over 10 years, the use of mHealth (assuming a 75% ECG confirmation rate) resulted in additional costs (€1=US $1.12) of €441, €567, €536, €520, €606, €625, €623, €692, and €847 per patient for a CHA2DS2-VASc score of 1 to 9, respectively. The number of prevented strokes tended to be higher in groups with high risk for stroke. Higher ECG confirmation rates led to higher numbers of prevented strokes. The use of mHealth (assuming a 75% ECG confirmation rate) resulted in 25 (7), -68 (-54), 98 (-5), 266 (182), 346 (271), 642 (440), 722 (599), 1111 (815), and 1116 (928) prevented strokes (fatal) for CHA2DS2-VASc score of 1 to 9, respectively. Higher device accuracy in terms of sensitivity led to even more prevented fatal strokes. CONCLUSIONS The use of mHealth devices to screen for AF leads to increased costs but also a reduction in the incidence of stroke. In particular, in patients with high CHA2DS2-VASc scores, the risk for stroke and death from stroke can be markedly reduced.
Collapse
Affiliation(s)
- Godwin D Giebel
- E-Government/E-Health, Department of Business Information Systems, Baden-Wuerttemberg Cooperative State University Mannheim, Mannheim, Germany
| |
Collapse
|
33
|
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) 2020; 20:E5683. [PMID: 33028000 PMCID: PMC7582300 DOI: 10.3390/s20195683] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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.
Collapse
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.)
| |
Collapse
|
34
|
蒋 明, 鲁 薏, 李 杨, 项 宜, 张 鞠, 王 志. [Research on electrocardiogram classification using deep residual network with pyramid convolution structure]. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi 2020; 37:692-698. [PMID: 32840087 PMCID: PMC10319544 DOI: 10.7507/1001-5515.201912048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 12/15/2019] [Indexed: 11/03/2022]
Abstract
Recently, deep neural networks (DNNs) have been widely used in the field of electrocardiogram (ECG) signal classification, but the previous models have limited ability to extract features from raw ECG data. In this paper, a deep residual network model based on pyramidal convolutional layers (PC-DRN) was proposed to implement ECG signal classification. The pyramidal convolutional (PC) layer could simultaneously extract multi-scale features from the original ECG data. And then, a deep residual network was designed to train the classification model for arrhythmia detection. The public dataset provided by the physionet computing in cardiology challenge 2017(CinC2017) was used to validate the classification experiment of 4 types of ECG data. In this paper, the harmonic mean F 1 of classification accuracy and recall was selected as the evaluation indexes. The experimental results showed that the average sequence level F 1 ( SeqF 1) of PC-DRN was improved from 0.857 to 0.920, and the average set level F 1 ( SetF 1) was improved from 0.876 to 0.925. Therefore, the PC-DRN model proposed in this paper provided a promising way for the feature extraction and classification of ECG signals, and provided an effective tool for arrhythmia classification.
Collapse
Affiliation(s)
- 明峰 蒋
- 浙江理工大学 信息学院(杭州 310018)School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, P.R.China
| | - 薏 鲁
- 浙江理工大学 信息学院(杭州 310018)School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, P.R.China
| | - 杨 李
- 浙江理工大学 信息学院(杭州 310018)School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, P.R.China
| | - 宜坤 项
- 浙江理工大学 信息学院(杭州 310018)School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, P.R.China
| | - 鞠成 张
- 浙江理工大学 信息学院(杭州 310018)School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, P.R.China
| | - 志康 王
- 浙江理工大学 信息学院(杭州 310018)School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, P.R.China
| |
Collapse
|
35
|
Eerikäinen LM, Bonomi AG, Dekker LRC, Vullings R, Aarts RM. Atrial fibrillation monitoring with wrist-worn photoplethysmography-based wearables: State-of-the-art review. Cardiovasc Digit Health J 2020; 1:45-51. [PMID: 35265873 DOI: 10.1016/j.cvdhj.2020.03.001] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [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.
Collapse
|
36
|
Abstract
Atrial fibrillation (AF) is a major cause of morbidity and mortality globally, and much of this is driven by challenges in its timely diagnosis and treatment. Existing and emerging mobile technologies have been used to successfully identify AF in a variety of clinical and community settings, and while these technologies offer great promise for revolutionizing AF detection and screening, several major barriers may impede their effectiveness. The unclear clinical significance of device-detected AF, potential challenges in integrating patient-generated data into existing healthcare systems and clinical workflows, harm resulting from potential false positives, and identifying the appropriate scope of population-based screening efforts are all potential concerns that warrant further investigation. It is crucial for stakeholders such as healthcare providers, researchers, funding agencies, insurers, and engineers to actively work together in fulfilling the tremendous potential of mobile technologies to improve AF identification and management on a population level.
Collapse
Affiliation(s)
- Eric Y Ding
- From the Department of Population and Quantitative Health Sciences and Division of Cardiology, Department of Medicine, University of Massachusetts Medical School (E.Y.D., D.D.M.)
| | - Gregory M Marcus
- Division of Cardiology, Department of Medicine, University of California, San Francisco (G.M.M.)
| | - David D McManus
- From the Department of Population and Quantitative Health Sciences and Division of Cardiology, Department of Medicine, University of Massachusetts Medical School (E.Y.D., D.D.M.)
| |
Collapse
|
37
|
Abstract
Machine learning applications in cardiology have rapidly evolved in the past decade. With the availability of machine learning tools coupled with vast data sources, the management of atrial fibrillation (AF), a common chronic disease with significant associated morbidity and socioeconomic impact, is undergoing a knowledge and practice transformation in the increasingly complex healthcare environment. Among other advances, deep-learning machine learning methods, including convolutional neural networks, have enabled the development of AF screening pathways using the ubiquitous 12-lead ECG to detect asymptomatic paroxysmal AF in at-risk populations (such as those with cryptogenic stroke), the refinement of AF and stroke prediction schemes through comprehensive digital phenotyping using structured and unstructured data abstraction from the electronic health record or wearable monitoring technologies, and the optimization of treatment strategies, ranging from stroke prophylaxis to monitoring of antiarrhythmic drug (AAD) therapy. Although the clinical and population-wide impact of these tools continues to be elucidated, such transformative progress does not come without challenges, such as the concerns about adopting black box technologies, assessing input data quality for training such models, and the risk of perpetuating rather than alleviating health disparities. This review critically appraises the advances of machine learning related to the care of AF thus far, their potential future directions, and its potential limitations and challenges.
Collapse
Affiliation(s)
| | - Xiaoxi Yao
- Robert D and Patricia E Kern Center for the Science of Health Care Delivery (X.Y.), Mayo Clinic, Rochester, MN
- Division of Health Care Policy and Research, Department of Health Sciences Research (X.Y.), Mayo Clinic, Rochester, MN
| | - James P. Pirruccello
- Broad Institute, Cambridge, MA (J.P.P., A.A.P.)
- Division of Cardiology, Massachusetts General Hospital, Boston (J.P.P.)
| | | | - Peter A. Noseworthy
- From the Department of Cardiovascular Medicine (K.C.S., P.A.N.), Mayo Clinic, Rochester, MN
| |
Collapse
|
38
|
Kwon S, Hong J, Choi EK, Lee B, Baik C, Lee E, Jeong ER, Koo BK, Oh S, Yi Y. Detection of Atrial Fibrillation Using a Ring-Type Wearable Device (CardioTracker) and Deep Learning Analysis of Photoplethysmography Signals: Prospective Observational Proof-of-Concept Study. J Med Internet Res 2020; 22:e16443. [PMID: 32348254 PMCID: PMC7273241 DOI: 10.2196/16443] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 02/24/2020] [Accepted: 03/21/2020] [Indexed: 12/23/2022] Open
Abstract
Background Continuous photoplethysmography (PPG) monitoring with a wearable device may aid the early detection of atrial fibrillation (AF). Objective We aimed to evaluate the diagnostic performance of a ring-type wearable device (CardioTracker, CART), which can detect AF using deep learning analysis of PPG signals. Methods Patients with persistent AF who underwent cardioversion were recruited prospectively. We recorded PPG signals at the finger with CART and a conventional pulse oximeter before and after cardioversion over a period of 15 min (each instrument). Cardiologists validated the PPG rhythms with simultaneous single-lead electrocardiography. The PPG data were transmitted to a smartphone wirelessly and analyzed with a deep learning algorithm. We also validated the deep learning algorithm in 20 healthy subjects with sinus rhythm (SR). Results In 100 study participants, CART generated a total of 13,038 30-s PPG samples (5850 for SR and 7188 for AF). Using the deep learning algorithm, the diagnostic accuracy, sensitivity, specificity, positive-predictive value, and negative-predictive value were 96.9%, 99.0%, 94.3%, 95.6%, and 98.7%, respectively. Although the diagnostic accuracy decreased with shorter sample lengths, the accuracy was maintained at 94.7% with 10-s measurements. For SR, the specificity decreased with higher variability of peak-to-peak intervals. However, for AF, CART maintained consistent sensitivity regardless of variability. Pulse rates had a lower impact on sensitivity than on specificity. The performance of CART was comparable to that of the conventional device when using a proper threshold. External validation showed that 94.99% (16,529/17,400) of the PPG samples from the control group were correctly identified with SR. Conclusions A ring-type wearable device with deep learning analysis of PPG signals could accurately diagnose AF without relying on electrocardiography. With this device, continuous monitoring for AF may be promising in high-risk populations. Trial Registration ClinicalTrials.gov NCT04023188; https://clinicaltrials.gov/ct2/show/NCT04023188
Collapse
Affiliation(s)
- Soonil Kwon
- Seoul National University Hospital, Seoul, Republic of Korea
| | - Joonki Hong
- Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Eue-Keun Choi
- Seoul National University Hospital, Seoul, Republic of Korea
| | | | | | - Euijae Lee
- Seoul National University Hospital, Seoul, Republic of Korea
| | - Eui-Rim Jeong
- Department of Information and Communication Engineering, Hanbat National University, Daejeon, Republic of Korea
| | - Bon-Kwon Koo
- Seoul National University Hospital, Seoul, Republic of Korea
| | - Seil Oh
- Seoul National University Hospital, Seoul, Republic of Korea
| | - Yung Yi
- Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| |
Collapse
|
39
|
Hochstadt A, Havakuk O, Chorin E, Schwartz AL, Merdler I, Laufer M, Lubman N, Ghantous E, Viskin S, Rosso R. Continuous heart rhythm monitoring using mobile photoplethysmography in ambulatory patients. J Electrocardiol 2020; 60:138-141. [PMID: 32361522 DOI: 10.1016/j.jelectrocard.2020.04.017] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2020] [Revised: 04/18/2020] [Accepted: 04/23/2020] [Indexed: 10/24/2022]
Abstract
BACKGROUND Wearable devices using photo-plethysmography (PPG) can accurately detect heart beats and may be useful for heart rate measurement and diagnosis of arrhythmias such as atrial fibrillation (AF). A previous study of a new portable PPG sensor (CardiacSense) showed high accuracy in heart rate measurement and AF detection in resting patients. We report a trial done to test the same device in active ambulatory patients with diverse characteristics. METHODS A cohort of 24 ambulatory volunteers, underwent simultaneous PPG recording and continuous electrocardiogram (ECG) recording under different environmental conditions and situations. Per study protocol, the subjects were diverse in age, BMI, hair density and skin tone. Four subjects had AF. Heart rate measurement using the PPG device was compared to measurements by ECG. RESULTS Of 163,527 recorded ECG-detected beats in the trial, 86,929 (53.2%) were also recorded by the PPG device. Most undetected heart beats were due to motion induced noise. Correlation between ECG and PPG was high (R = 0.94, p < 0.0001), yet in subjects with AF correlation was lower (R = 0.80, p < 0.0001). A Bland-Altman analysis showed the mean difference between measurements was -0.7 ms (95% limit of agreement -93.8 to 92.2). A total of 86,217 (99.9%) of all RR measurements were reliably measured (RR difference within 100 ms). Reliability was sustained (>99.8%) in subjects of all groups including subjects with AF. CONCLUSIONS This study showed that, in the absence of movement-related noise, the CardiacSense PPG device can reliably detect HR in a variety of situations and subjects' characteristics.
Collapse
Affiliation(s)
- Aviram Hochstadt
- Department of Cardiology, Tel Aviv Sourasky Medical Center and Sackler School of Medicine, Tel Aviv University, Israel.
| | - Ofer Havakuk
- Department of Cardiology, Tel Aviv Sourasky Medical Center and Sackler School of Medicine, Tel Aviv University, Israel
| | - Ehud Chorin
- Department of Cardiology, Tel Aviv Sourasky Medical Center and Sackler School of Medicine, Tel Aviv University, Israel
| | - Arie Lorin Schwartz
- Department of Cardiology, Tel Aviv Sourasky Medical Center and Sackler School of Medicine, Tel Aviv University, Israel
| | - Ilan Merdler
- Department of Cardiology, Tel Aviv Sourasky Medical Center and Sackler School of Medicine, Tel Aviv University, Israel
| | - Michal Laufer
- Department of Cardiology, Tel Aviv Sourasky Medical Center and Sackler School of Medicine, Tel Aviv University, Israel
| | | | - Eihab Ghantous
- Department of Cardiology, Tel Aviv Sourasky Medical Center and Sackler School of Medicine, Tel Aviv University, Israel
| | - Sami Viskin
- Department of Cardiology, Tel Aviv Sourasky Medical Center and Sackler School of Medicine, Tel Aviv University, Israel; CardiacSense LTD., Caesarea, Israel
| | - Raphael Rosso
- Department of Cardiology, Tel Aviv Sourasky Medical Center and Sackler School of Medicine, Tel Aviv University, Israel
| |
Collapse
|
40
|
Lown M, Brown M, Brown C, Yue AM, Shah BN, Corbett SJ, Lewith G, Stuart B, Moore M, Little P. Machine learning detection of Atrial Fibrillation using wearable technology. PLoS One 2020; 15:e0227401. [PMID: 31978173 PMCID: PMC6980577 DOI: 10.1371/journal.pone.0227401] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Accepted: 12/04/2019] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Atrial Fibrillation is the most common arrhythmia worldwide with a global age adjusted prevalence of 0.5% in 2010. Anticoagulation treatment using warfarin or direct oral anticoagulants is effective in reducing the risk of AF-related stroke by approximately two-thirds and can provide a 10% reduction in overall mortality. There has been increased interest in detecting AF due to its increased incidence and the possibility to prevent AF-related strokes. Inexpensive consumer devices which measure the ECG may have the potential to accurately detect AF but do not generally incorporate diagnostic algorithms. Machine learning algorithms have the potential to improve patient outcomes particularly where diagnoses are made from large volumes or complex patterns of data such as in AF. METHODS We designed a novel AF detection algorithm using a de-correlated Lorenz plot of 60 consecutive RR intervals. In order to reduce the volume of data, the resulting images were compressed using a wavelet transformation (JPEG200 algorithm) and the compressed images were used as input data to a Support Vector Machine (SVM) classifier. We used the Massachusetts Institute of Technology (MIT)-Beth Israel Hospital (BIH) Atrial Fibrillation database and the MIT-BIH Arrhythmia database as training data and verified the algorithm performance using RR intervals collected using an inexpensive consumer heart rate monitor device (Polar-H7) in a case-control study. RESULTS The SVM algorithm yielded excellent discrimination in the training data with a sensitivity of 99.2% and a specificity of 99.5% for AF. In the validation data, the SVM algorithm correctly identified AF in 79/79 cases; sensitivity 100% (95% CI 95.4%-100%) and non-AF in 328/336 cases; specificity 97.6% (95% CI 95.4%-99.0%). CONCLUSIONS An inexpensive wearable heart rate monitor and machine learning algorithm can be used to detect AF with very high accuracy and has the capability to transmit ECG data which could be used to confirm AF. It could potentially be used for intermittent screening or continuously for prolonged periods to detect paroxysmal AF. Further work could lead to cost-effective and accurate estimation of AF burden and improved risk stratification in AF.
Collapse
Affiliation(s)
- Mark Lown
- Primary Care & Population Sciences, Faculty of Medicine, University of Southampton, Southampton, England
- * E-mail:
| | | | - Chloë Brown
- Primary Care & Population Sciences, Faculty of Medicine, University of Southampton, Southampton, England
| | - Arthur M. Yue
- Cardiology and Electrophysiology, Southampton General Hospital, Southampton, England
| | - Benoy N. Shah
- Cardiology and Electrophysiology, Southampton General Hospital, Southampton, England
| | - Simon J. Corbett
- Cardiology and Electrophysiology, Southampton General Hospital, Southampton, England
| | - George Lewith
- Cardiology and Electrophysiology, Southampton General Hospital, Southampton, England
| | - Beth Stuart
- Primary Care & Population Sciences, Faculty of Medicine, University of Southampton, Southampton, England
| | - Michael Moore
- Primary Care & Population Sciences, Faculty of Medicine, University of Southampton, Southampton, England
| | - Paul Little
- Primary Care & Population Sciences, Faculty of Medicine, University of Southampton, Southampton, England
| |
Collapse
|
41
|
Inui T, Kohno H, Kawasaki Y, Matsuura K, Ueda H, Tamura Y, Watanabe M, Inage Y, Yakita Y, Wakabayashi Y, Matsumiya G. Use of a Smart Watch for Early Detection of Paroxysmal Atrial Fibrillation: Validation Study. JMIR Cardio 2020; 4:e14857. [PMID: 32012044 PMCID: PMC7003123 DOI: 10.2196/14857] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2019] [Revised: 11/14/2019] [Accepted: 12/01/2019] [Indexed: 12/15/2022] Open
Abstract
Background Wearable devices with photoplethysmography (PPG) technology can be useful for detecting paroxysmal atrial fibrillation (AF), which often goes uncaptured despite being a leading cause of stroke. Objective This study is the first part of a 2-phase study that aimed at developing a method for immediate detection of paroxysmal AF using PPG-integrated wearable devices. In this study, the diagnostic performance of 2 major smart watches, Apple Watch Series 3 and Fitbit (FBT) Charge HR Wireless Activity Wristband, each equipped with a PPG sensor, was compared, and the pulse rate data outputted from those devices were analyzed for precision and accuracy in reference to the heart rate data from electrocardiography (ECG) during AF. Methods A total of 40 subjects from patients who underwent cardiac surgery at a single center between September 2017 and March 2018 were monitored for postoperative AF using telemetric ECG and PPG devices. AF was diagnosed using a 12-lead ECG by qualified physicians. Each subject was given a pair of smart watches, Apple Watch and FBT, for simultaneous pulse rate monitoring. The heart rate of all subjects was also recorded on the telemetry system. Time series pulse rate trends and heart rate trends were created and analyzed for trend pattern similarities. Those trend data were then used to determine the accuracy of PPG-based pulse rate measurements in reference to ECG-based heart rate measurements during AF. Results Of the 20 AF events in group FBT, 6 (30%) showed a moderate or higher correlation (cross-correlation function>0.40) between pulse rate trend patterns and heart rate trend patterns. Of the 16 AF events in group Apple Watch (workout [W] mode), 12 (75%) showed a moderate or higher correlation between the 2 trend patterns. Linear regression analyses also showed a significant correlation between the pulse rates and the heart rates during AF in the subjects with Apple Watch. This correlation was not observed with FBT. The regression formula for Apple Watch W mode and FBT was X=14.203 + 0.841Y and X=58.225 + 0.228Y, respectively (where X denotes the mean of all average pulse rates during AF and Y denotes the mean of all corresponding average heart rates during AF), and the coefficient of determination (R2) was 0.685 and 0.057, respectively (P<.001 and .29, respectively). Conclusions In this validation study, the detection precision of AF and measurement accuracy during AF were both better with Apple Watch W mode than with FBT.
Collapse
Affiliation(s)
- Tomohiko Inui
- Department of Cardiovascular Surgery, University of Chiba, Chiba, Japan
| | - Hiroki Kohno
- Department of Cardiovascular Surgery, University of Chiba, Chiba, Japan
| | - Yohei Kawasaki
- Clinical Research Center, University of Chiba, Chiba, Japan
| | - Kaoru Matsuura
- Department of Cardiovascular Surgery, University of Chiba, Chiba, Japan
| | - Hideki Ueda
- Department of Cardiovascular Surgery, University of Chiba, Chiba, Japan
| | - Yusaku Tamura
- Department of Cardiovascular Surgery, University of Chiba, Chiba, Japan
| | - Michiko Watanabe
- Department of Cardiovascular Surgery, University of Chiba, Chiba, Japan
| | - Yuichi Inage
- Department of Cardiovascular Surgery, University of Chiba, Chiba, Japan
| | - Yasunori Yakita
- Department of Cardiovascular Surgery, University of Chiba, Chiba, Japan
| | | | - Goro Matsumiya
- Department of Cardiovascular Surgery, University of Chiba, Chiba, Japan
| |
Collapse
|
42
|
Millán CA, Girón NA, Lopez DM. Analysis of Relevant Features from Photoplethysmographic Signals for Atrial Fibrillation Classification. Int J Environ Res Public Health 2020; 17:E498. [PMID: 31941071 PMCID: PMC7013739 DOI: 10.3390/ijerph17020498] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Revised: 12/23/2019] [Accepted: 12/24/2019] [Indexed: 11/16/2022]
Abstract
Atrial Fibrillation (AF) is the most common cardiac arrhythmia found in clinical practice. It affects an estimated 33.5 million people, representing approximately 0.5% of the world's population. Electrocardiogram (ECG) is the main diagnostic criterion for AF. Recently, photoplethysmography (PPG) has emerged as a simple and portable alternative for AF detection. However, it is not completely clear which are the most important features of the PPG signal to perform this process. The objective of this paper is to determine which are the most relevant features for PPG signal analysis in the detection of AF. This study is divided into two stages: (a) a systematic review carried out following the Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies (PRISMA-DTA) statement in six databases, in order to identify the features of the PPG signal reported in the literature for the detection of AF, and (b) an experimental evaluation of them, using machine learning, in order to determine which have the greatest influence on the process of detecting AF. Forty-four features were found when analyzing the signal in the time, frequency, or time-frequency domains. From those 44 features, 27 were implemented, and through machine learning, it was found that only 11 are relevant in the detection process. An algorithm was developed for the detection of AF based on these 11 features, which obtained an optimal performance in terms of sensitivity (98.43%), specificity (99.52%), and accuracy (98.97%).
Collapse
Affiliation(s)
| | | | - Diego M. Lopez
- Telematics Engineering Research Group, Telematics Department, Universidad Del Cauca (Unicauca), Popayán 190002, Colombia; (C.A.M.); (N.A.G.)
| |
Collapse
|
43
|
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: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [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.
Collapse
|
44
|
Giebel GD, Gissel C. Accuracy of mHealth Devices for Atrial Fibrillation Screening: Systematic Review. JMIR Mhealth Uhealth 2019; 7:e13641. [PMID: 31199337 PMCID: PMC6598422 DOI: 10.2196/13641] [Citation(s) in RCA: 72] [Impact Index Per Article: 14.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2019] [Revised: 05/14/2019] [Accepted: 05/14/2019] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Mobile health (mHealth) devices can be used for the diagnosis of atrial fibrillation. Early diagnosis allows better treatment and prevention of secondary diseases like stroke. Although there are many different mHealth devices to screen for atrial fibrillation, their accuracy varies due to different technological approaches. OBJECTIVE We aimed to systematically review available studies that assessed the accuracy of mHealth devices in screening for atrial fibrillation. The goal of this review was to provide a comprehensive overview of available technologies, specific characteristics, and accuracy of all relevant studies. METHODS PubMed and Web of Science databases were searched from January 2014 until January 2019. Our systematic review was performed according to the Preferred Reporting Items for Systematic Review and Meta-Analyses. We restricted the search by year of publication, language, noninvasive methods, and focus on diagnosis of atrial fibrillation. Articles not including information about the accuracy of devices were excluded. RESULTS We found 467 relevant studies. After removing duplicates and excluding ineligible records, 22 studies were included. The accuracy of mHealth devices varied among different technologies, their application settings, and study populations. We described and summarized the eligible studies. CONCLUSIONS Our systematic review identifies different technologies for screening for atrial fibrillation with mHealth devices. A specific technology's suitability depends on the underlying form of atrial fibrillation to be diagnosed. With the suitable use of mHealth, early diagnosis and treatment of atrial fibrillation are possible. Successful application of mHealth technologies could contribute to significantly reducing the cost of illness of atrial fibrillation.
Collapse
Affiliation(s)
- Godwin Denk Giebel
- Health Economics, Department of Economics and Business, Justus Liebig University, Giessen, Germany
| | - Christian Gissel
- Health Economics, Department of Economics and Business, Justus Liebig University, Giessen, Germany
| |
Collapse
|
45
|
Abstract
OBJECTIVE This study proposes an algorithm for the detection of atrial fibrillation (AF), designed to operate on extended photoplethysmographic (PPG) signals recorded using a wrist-worn device of own design. APPROACH Robustness against false alarms is achieved by means of signal quality assessment and different techniques for suppression of ectopic beats, bigeminy, and respiratory sinus arrhythmia. The decision logic is based on our previously proposed RR interval-based AF detector, but modified to account for differences between interbeat intervals in the ECG and the PPG. The detector is evaluated on simulated PPG signals as well as on clinical PPG signals recorded during cardiac rehabilitation after myocardial infarction. MAIN RESULTS Analysis of the clinical signals showed that 1.5 false alarms were on average produced per day with a sensitivity of 72.0% and a specificity of 99.7% when 89.2% of the database was available for analysis, whereas as many as 15 when the RR interval-based AF detector, boosted by accelerometer information for signal quality assessment, was used. However, a sensitivity of 97.2% and a specificity of 99.6% were achieved when increasing the demands on signal quality so that 50% was available for analysis. SIGNIFICANCE The proposed detector offers promising performance and is particularly well-suited for implementation in low-power wearable devices, e.g. wrist-worn devices, with significance in mass screening applications.
Collapse
Affiliation(s)
- Andrius Sološenko
- Biomedical Engineering Institute, Kaunas University of Technology, Kaunas, Lithuania
| | | | | | | | | |
Collapse
|
46
|
Tamura T. Current progress of photoplethysmography and SPO 2 for health monitoring. Biomed Eng Lett 2019; 9:21-36. [PMID: 30956878 PMCID: PMC6431353 DOI: 10.1007/s13534-019-00097-w] [Citation(s) in RCA: 84] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2018] [Revised: 01/05/2019] [Accepted: 01/15/2019] [Indexed: 11/28/2022] Open
Abstract
A photoplethysmograph (PPG) is a simple medical device for monitoring blood flow and transportation of substances in the blood. It consists of a light source and a photodetector for measuring transmitted and reflected light signals. Clinically, PPGs are used to monitor the pulse rate, oxygen saturation, blood pressure, and blood vessel stiffness. Wearable unobtrusive PPG monitors are commercially available. Here, we review the principle issues and clinical applications of PPG for monitoring oxygen saturation.
Collapse
Affiliation(s)
- Toshiyo Tamura
- Future Robotics Institute, Wadeda University, Tokyo, Japan
| |
Collapse
|
47
|
Abstract
Background Wearable sensors (wearables) have been commonly integrated into a wide variety of commercial products and are increasingly being used to collect and process raw physiological parameters into salient digital health information. The data collected by wearables are currently being investigated across a broad set of clinical domains and patient populations. There is significant research occurring in the domain of algorithm development, with the aim of translating raw sensor data into fitness- or health-related outcomes of interest for users, patients, and health care providers. Objectives The aim of this review is to highlight a selected group of fitness- and health-related indicators from wearables data and to describe several algorithmic approaches used to generate these higher order indicators. Methods A systematic search of the Pubmed database was performed with the following search terms (number of records in parentheses): Fitbit algorithm (18), Apple Watch algorithm (3), Garmin algorithm (5), Microsoft Band algorithm (8), Samsung Gear algorithm (2), Xiaomi MiBand algorithm (1), Huawei Band (Watch) algorithm (2), photoplethysmography algorithm (465), accelerometry algorithm (966), ECG algorithm (8287), continuous glucose monitor algorithm (343). The search terms chosen for this review are focused on algorithms for wearable devices that dominated the commercial wearables market between 2014-2017 and that were highly represented in the biomedical literature. A second set of search terms included categories of algorithms for fitness-related and health-related indicators that are commonly used in wearable devices (e.g. accelerometry, PPG, ECG). These papers covered the following domain areas: fitness; exercise; movement; physical activity; step count; walking; running; swimming; energy expenditure; atrial fibrillation; arrhythmia; cardiovascular; autonomic nervous system; neuropathy; heart rate variability; fall detection; trauma; behavior change; diet; eating; stress detection; serum glucose monitoring; continuous glucose monitoring; diabetes mellitus type 1; diabetes mellitus type 2. All studies uncovered through this search on commercially available device algorithms and pivotal studies on sensor algorithm development were summarized, and a summary table was constructed using references generated by the literature review as described (Table 1). Conclusions Wearable health technologies aim to collect and process raw physiological or environmental parameters into salient digital health information. Much of the current and future utility of wearables lies in the signal processing steps and algorithms used to analyze large volumes of data. Continued algorithmic development and advances in machine learning techniques will further increase analytic capabilities. In the context of these advances, our review aims to highlight a range of advances in fitness- and other health-related indicators provided by current wearable technologies.
Collapse
|
48
|
Ko J, Kim NH. Evolution of Smart Health Wearables: Novel Application for Detection of Arrhythmia. Korean Circ J 2019; 49:446-447. [PMID: 31074213 PMCID: PMC6511535 DOI: 10.4070/kcj.2019.0078] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Accepted: 03/05/2019] [Indexed: 11/16/2022] Open
Affiliation(s)
- Jumsuk Ko
- Division of Cardiology, Department of Internal Medicine, Wonkwang University School of Medicine, Iksan, Korea
| | - Nam-Ho Kim
- Division of Cardiology, Department of Internal Medicine, Wonkwang University School of Medicine, Iksan, Korea
| |
Collapse
|