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Photoplethysmography based atrial fibrillation detection: a review
Open accessAbstract
Atrial fibrillation (AF) is a cardiac rhythm disorder associated with increased morbidity and mortality. It is the leading risk factor for cardioembolic stroke and its early detection is crucial in both primary and secondary stroke prevention. Continuous monitoring of cardiac rhythm is today possible thanks to consumer-grade wearable devices, enabling transformative diagnostic and patient management tools. Such monitoring is possible using low-cost easy-to-implement optical sensors that today equip the majority of wearables. These sensors record blood volume variations—a technology known as photoplethysmography (PPG)—from which the heart rate and other physiological parameters can be extracted to inform about user activity, fitness, sleep, and health. Recently, new wearable devices were introduced as being capable of AF detection, evidenced by large prospective trials in some cases. Such devices would allow for early screening of AF and initiation of therapy to prevent stroke. This review is a summary of a body of work on AF detection using PPG. A thorough account of the signal processing, machine learning, and deep learning approaches used in these studies is presented, followed by a discussion of their limitations and challenges towards clinical applications.
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“atrial fibrillation (AF), which can be identified from pulse wave signals due to the irregular heart rhythm produced by AF”
From Wearable Photoplethysmography for Cardiovascular Monitoring (2022) · cites this paper as [27]
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“Several techniques have been proposed to assess the irregularity of IBIs and the shape of pulse waves in order to detect AF from the PPG”
From Wearable Photoplethysmography for Cardiovascular Monitoring (2022) · cites this paper as [27]
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Rethinking the Inception Architecture for Computer Vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.308 · Cited by 32,138
Ror2 signaling regulates Golgi structure and transport through IFT20 for tumor invasiveness
Nishita, M., Park, S.-Y., Nishio, T., Kamizaki, K., Wang, Z., Tamada, K., Takumi, T., Hashimoto, R., Otani, H., Pazour, G. J., et al. (2017) · Scientific Reports · doi:10.1038/s41598-016-0028-x · Cited by 15,947
Goldberger, A. L., Amaral, L. A. N., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., Mietus, J. E., Moody, G. B., Peng, C.-K., & Stanley, H. E. (2000) · Circulation · doi:10.1161/01.cir.101.23.e215 · Cited by 14,809
Zhang, Q., Wang, C., Zheng, M., Li, Y., Li, J., Zhang, L., Shang, X., & Yan, C. (2014) · Cerebrovascular Diseases · doi:10.1159/000369778 · Cited by 11,609
2016 ESC Guidelines for the management of atrial fibrillation developed in collaboration with EACTS
Kirchhof, P. F., Benussi, S., Kotecha, D., Ahlsson, A., Atar, D., Casadei, B., Castellà, M., Diener, H., Heidbüchel, H., Hendriks, J., et al. (2016) · EP Europace · doi:10.1093/europace/euw295 · Cited by 6,438
2016 ESC Guidelines for the management of atrial fibrillation developed in collaboration with EACTS
Kirchhof, P., Benussi, S., Kotecha, D., Ahlsson, A., Atar, D., Casadei, B., Castella, M., Diener, H.-C., Heidbuchel, H., Hendriks, J., et al. (2016) · European Heart Journal · doi:10.1093/eurheartj/ehw210 · Cited by 6,418
Saito, T., & Rehmsmeier, M. (2015) · PLOS ONE · doi:10.1371/journal.pone.0118432 · Cited by 5,309
Kernan, W. N., Ovbiagele, B., Black, H. R., Bravata, D. M., Chimowitz, M. I., Ezekowitz, M. D., Fang, M. C., Fisher, M., Furie, K. L., Heck, D. V., et al. (2014) · Stroke · doi:10.1161/str.0000000000000024 · Cited by 5,170
The impact of the MIT-BIH Arrhythmia Database
Moody, G. B., & Mark, R. G. (2001) · IEEE Engineering in Medicine and Biology Magazine · doi:10.1109/51.932724 · Cited by 4,834
Research works printed but not linked: 5
- Singh, D. et al. Worldwide epidemiology of atrial fibrillation. Circulation 129, 837–847 (2013).
- Tripathi, S., San, U. C., Zachary, D., Lipton, C. & Nguyen, T. Q. Correction by projection: denoising images with generative adversarial networks. Preprint at http://arXiv.org/1803.04477v1 (2018).
- Rolnick, D., Veit, A., Belongie, S. & Shavit, N. Deep learning is robust to massive label noise. Preprint at http://arXiv.org/1705.10694 [cs.LG] (2017).
- Xu, M., Qian, F. & Pushp, S. Enabling cooperative inference of deep learning on wearables and smartphones. Preprint at http://arXiv.org/1712.03073, 1–21 (2017).
- Schack, T., Harb, Y. S., Muma, M. & Zoubir, A. M. Computationally Efficient Algorithm for Photoplethysmography-Based Atrial Fibrillation Detection Using Smartphones. In 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 104–108 (2017). · DOI
Cited by
Sorted by citations
Items 101–200 of 300
Feasibility of atrial fibrillation detection from a novel wearable armband device
Bashar, S. K., Hossain, M.-B., Lázaro, J. M., Ding, E., Noh, Y., Cho, C. H., McManus, D. D., Fitzgibbons, T. P., & Chon, K. H. (2021) · Cardiovascular Digital Health Journal · doi:10.1016/j.cvdhj.2021.05.004 · Cited by 17
Nie, G., Zhao, Q., Tang, G., Li, Y., & Hong, S. (2025) · Communications Medicine · doi:10.1038/s43856-025-01188-9 · Cited by 18
Participant-Aware Model Validation for Repeated-Measures Data: Comparative Cross-Validation Study
Karbalaie, A., Abtahi, F., & Häger, C. K. (2026) · JMIR AI · doi:10.2196/87728 · Cited by 21
Evaluating RGB channels in remote photoplethysmography: a comparative study with contact-based PPG
Ontiveros, R. C., Elgendi, M., Missale, G., & Menon, C. (2023) · Frontiers in Physiology · doi:10.3389/fphys.2023.1296277 · Cited by 16
Pulse oximetry in pediatric care: Balancing advantages and limitations
Al‐Beltagi, M., Saeed, N. K., Bediwy, A. S., & Elbeltagi, R. M. (2024) · World Journal of Clinical Pediatrics · doi:10.5409/wjcp.v13.i3.96950 · Cited by 20
Das, S. S. S., Shanto, S. K., Rahman, M., Islam, M. S., Rahman, A. H., Masud, M. M., & Ali, M. E. (2022) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3517247 · Cited by 22
Brundel, B. J. J. M., Ai, X., Hills, M. T., Kuipers, M. F., Lip, G. Y. H., & de Groot, N. M. S. (2016) · Nature Reviews Disease Primers · doi:10.1038/nrdp.2016.17 · Cited by 18
Continuous Atrial Fibrillation Monitoring From Photoplethysmography
Antiperovitch, P., MORTARA, D. W., Barrios, J. P., Avram, R., Yee, K., Khaless, A. N., Cristal, A. D., Tison, G. H., & Olgin, J. E. (2024) · JACC. Clinical electrophysiology · doi:10.1016/j.jacep.2024.01.008 · Cited by 16
Wearable remote monitoring for patients with COVID-19 in low-resource settings: case study
Châu, N. V. V., Hai, H. B., Greeff, H., Khanh, P. N. Q., Trieu, H. T., Khoa, L. D. V., Nguyen, C. N., Van, H. M. T., Yen, L. M., Tan, L. V., et al. (2021) · BMJ Innovations · doi:10.1136/bmjinnov-2021-000706 · Cited by 15
Yang, Z., Wang, H., Liu, B., & Lu, F. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3314282 · Cited by 14
Lee, H., Chung, H., Ko, H., Parisi, A., Busacca, A., Faes, L., Pernice, R., & Lee, J. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.107126 · Cited by 14
Photoplethysmograph based arrhythmia detection using morphological features
Neha, N., Sardana, H. K., Kanawade, R. V., & Dogra, N. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.104422 · Cited by 17
Noninvasive Hemoglobin Measurements With Photoplethysmography in Wrist
Лычагов, В. В., Semenov, V. M., Volkova, Е. K., Chernakov, D., Ahn, J., & Kim, J. Y. (2023) · IEEE Access · doi:10.1109/access.2023.3300293 · Cited by 13
Żyliński, M., Nassibi, A., & Mandic, D. P. (2023) · Sensors · doi:10.3390/s23177521 · Cited by 14
van Steijn, N. J., Pepplinkhuizen, S., Postema, P. G., Knops, R. E., & Winter, M. M. (2025) · Heart Rhythm · doi:10.1016/j.hrthm.2025.06.008 · Cited by 12
Sološenko, A., Paliakaitė, B., Marozas, V., & Sörnmo, L. (2022) · Frontiers in Physiology · doi:10.3389/fphys.2022.928098 · Cited by 13
Modeling and Estimation of Temporal Episode Patterns in Paroxysmal Atrial Fibrillation
Henriksson, M., Martín-Yebra, A., Butkuvienė, M., Rasmussen, J. G., Marozas, V., Petrėnas, A., Savelev, A. S., Platonov, P. G., & Sörnmo, L. (2020) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2020.2995563 · Cited by 14
Jain, P., Ding, C., Rudin, C. D., & Hu, X. (2024) · Harvard Data Science Review · doi:10.1162/99608f92.8636cb81 · Cited by 12
Self-citationLiang, H., Zhang, H., Wang, J., Shao, X., Wu, S., Lyu, S., Xu, W., Wang, L., Tan, J., Wang, J., et al. (2024) · Reviews in Cardiovascular Medicine · doi:10.31083/j.rcm2507257 · Cited by 12
A Novel Deep Ensemble Method for Selective Classification of Electrocardiograms
Argha, A., Alinejad‐Rokny, H., Baumgartner, M., Schreier, G., Celler, B. G., Redmond, S. J., Butcher, K., Ooi, S., & Lovell, N. H. (2024) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2024.3476088 · Cited by 14
Sawangjai, P., Seesawad, N., & Wilaiprasitporn, T. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3529546 · Cited by 13
vital_sqi: A Python package for physiological signal quality control
Khoa, L. D. V., Ho, H. B., Karolcik, S., Hernandez, B., Greeff, H., Nguyen, V. H., Phan, N. Q. K., Le, T. P., Thwaites, L., Georgiou, P., et al. (2022) · Frontiers in Physiology · doi:10.3389/fphys.2022.1020458 · Cited by 11
Sohn, J., Shin, H., Lee, J., & Kim, H. C. (2023) · Journal of Healthcare Informatics Research · doi:10.1007/s41666-023-00156-z · Cited by 11
Transformer-based heart language model with electrocardiogram annotations
Tudjarski, S., Gušev, M., & Kanoulas, E. (2025) · Scientific Reports · doi:10.1038/s41598-024-84270-x · Cited by 13
Parlato, S., Centracchio, J., Esposito, D., Bifulco, P., & Andreozzi, E. (2025) · Physical and Engineering Sciences in Medicine · doi:10.1007/s13246-025-01531-3 · Cited by 11
Luo, D. Y., Zhang, Z. W., Sibomana, O., & Salomon, I. (2025) · Annals of Medicine and Surgery · doi:10.1097/ms9.0000000000003155 · Cited by 11
Shuja, M. H., Shakil, F., Shuja, S. H., Hasan, M., Edhi, M., Abbasi, A. F., Jawaid, A., & Shakil, S. (2024) · Heart Views · doi:10.4103/heartviews.heartviews_103_24 · Cited by 14
Mobile health technologies in the diagnosis and management of atrial fibrillation
PASSMAN, R. S. (2021) · Current Opinion in Cardiology · doi:10.1097/hco.0000000000000930 · Cited by 11
Giggins, O. M., Doyle, J., Smith, S., Crabtree, D. R., & Fraser, M. (2022) · JMIR Formative Research · doi:10.2196/34280 · Cited by 12
Abdullah, S., & Kristoffersson, A. (2023) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2023.1285066 · Cited by 12
An Update on the Use of Artificial Intelligence in Cardiovascular Medicine
Rao, S. J., Iqbal, S. B., Isath, A. M., Virk, H. U. H., Wang, Z., Glicksberg, B. S., & Krittanawong, C. (2024) · Hearts · doi:10.3390/hearts5010007 · Cited by 12
Saggu, D. K., Madappa, N. U., Sarkar, S., Sathiyamoorthy, A., Dash, S., Veerendranath Reddy, P., Rajan, V., & Calambur, N. (2024) · Indian Pacing and Electrophysiology Journal · doi:10.1016/j.ipej.2024.08.003 · Cited by 10
Zhang, H., Zhu, L., Nathan, V., Kuang, J., Kim, J., Gao, J. A., & Olgin, J. E. (2021) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3463503 · Cited by 14
A review of patient-led data acquisition for atrial fibrillation detection to prevent stroke
Kareem, M., Lei, N., Ali, A., Ciaccio, E. J., Acharya, U. R., & Faust, O. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.102818 · Cited by 10
Digital health—high tech or high touch?
Gangl, C., & Krychtiuk, K. A. (2023) · Wiener Medizinische Wochenschrift · doi:10.1007/s10354-022-00991-6 · Cited by 11
Non-invasive continuous blood pressure prediction based on ECG and PPG fusion map
Wang, H., Han, M., Zhong, C., Wang, C., Chen, R., Zhang, G., Wang, J., & Wei, R. (2023) · Medical Engineering & Physics · doi:10.1016/j.medengphy.2023.104037 · Cited by 10
Automate, Illuminate, Predict: A Universal Framework for Integrating Wearable Sensors in Healthcare
O’Brien, M. K., Hohl, K., Lieber, R. L., & Jayaraman, A. (2024) · Digital Biomarkers · doi:10.1159/000540492 · Cited by 10
Wearable photoplethysmography devices
Charlton, P. H., & Marozas, V. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-823374-0.00011-6 · Cited by 10
Rinne, J. K. A., Miri, S., Oksala, N., Vehkaoja, A. T., & Kössi, J. A. O. (2022) · Journal of Clinical Monitoring and Computing · doi:10.1007/s10877-022-00854-w · Cited by 10
Fuster-Barceló, C., Guerrero-López, A., Cámara, C., & Lopez, P. P. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108325 · Cited by 9
Liu, Q., Yang, C., Yang, S., Kwong, C. F., Wang, J., & Zhou, N. (2024) · Physical and Engineering Sciences in Medicine · doi:10.1007/s13246-024-01445-6 · Cited by 13
Generative adversarial networks with fully connected layers to denoise PPG signals
Castro, I. A. A., Oliveira, H. P., Correia, R. G., Hayes‐Gill, B., Morgan, S. P., Korposh, S., Gomez, D., & Pereira, T. (2025) · Physiological Measurement · doi:10.1088/1361-6579/ada9c1 · Cited by 9
Self-citationNew Technologies for Detection and Management of Atrial Fibrillation
Hamad, A. K. (2021) · Journal of the Saudi Heart Association · doi:10.37616/2212-5043.1256 · Cited by 10
Atrial Fibrillation Detection With Spectral Manifolds in Low-Dimensional Latent Spaces
Bernal, C. P., Carrera, E. V., Melgarejo-Meseguer, F.-M., Gordillo, R., Garcı́a-Alberola, A., & Rojo‐Álvarez, J. L. (2023) · IEEE Access · doi:10.1109/access.2023.3317900 · Cited by 9
Zillner, L., Andreas, M., & Mach, M. (2024) · mHealth · doi:10.21037/mhealth-23-19 · Cited by 15
Smartwatch biomarkers and the path to clinical use
Picard, R. W., & Boyer, E. W. (2021) · Med · doi:10.1016/j.medj.2021.06.005 · Cited by 9
Khajehpiri, B., Granger, É., de Zambotti, M., Baker, F. C., Yüksel, D., & Forouzanfar, M. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3396052 · Cited by 8
From Wristbands to Implants: The Transformative Role of Wearables in Heart Failure Care
Gregório, C., Agostinho, J. R., Rigueira, J., Santos, R., Pinto, F. J., & Brito, D. A. (2024) · Healthcare · doi:10.3390/healthcare12242572 · Cited by 11
Ramesh, J., Solatidehkordi, Z., Aburukba, R., Sagahyroon, A. A., & Aloul, F. (2025) · Applied Sciences · doi:10.3390/app15094770 · Cited by 8
Itzhak, S. B., Ricon, S. S., Biton, S. B., Behar, J. A., & Sobel, J. (2022) · Physiological Measurement · doi:10.1088/1361-6579/ac6561 · Cited by 10
Ding, C., Guo, Z., Rudin, C. D., Xiao, R., Shah, A. J., Do, D. H., Lee, R. J., Clifford, G. D., Nahab, F. B., & Hu, X. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3360952 · Cited by 12
Self-citationSadeghi, P., Karimi, H., Lavafian, A., Rashedi, R., Samieefar, N., Shafiekhani, S., & Rezaei, N. (2024) · Expert Review of Clinical Immunology · doi:10.1080/1744666x.2024.2359019 · Cited by 9
The Role of Artificial Intelligence in Arrhythmia Monitoring
Siontis, K. C., & Friedman, P. A. (2021) · Cardiac Electrophysiology Clinics · doi:10.1016/j.ccep.2021.04.011 · Cited by 8
Atrial Fibrillation Detection on Low-Power Wearables using Knowledge Distillation
Faraone, A., Sigurthorsdottir, H., & Delgado-Gonzalo, R. (2021) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc46164.2021.9630957 · Cited by 8
Can we trust a smartwatch ECG ? Potential and limitations
Haverkamp, W., Butler, J., & Anker, S. D. (2021) · European Journal of Heart Failure · doi:10.1002/ejhf.2194 · Cited by 8
Yan, L., Long, Z., Qian, J., Lin, J., Xie, S. Q., & Sheng, B. (2024) · Sensors · doi:10.3390/s24092925 · Cited by 9
Gelen, M. A., Tuncer, T., Bayğın, M., Doğan, Ş., Barua, P. D., Tan, R., & Acharya, U. R. (2025) · Journal of Medical Systems · doi:10.1007/s10916-025-02169-0 · Cited by 7
Ikeda, T., Ashihara, T., Iwasaki, Y., Ono, M., Kagiyama, N., Kimura, T., KUSANO, K. F., Kohno, R., Saku, K., Sasano, T., et al. (2025) · Circulation Journal · doi:10.1253/circj.cj-24-0960 · Cited by 14
Mobile medical systems for equitable healthcare
Chan, J., Goel, M., Gollakota, S., & Nandakumar, R. (2025) · Nature Reviews Bioengineering · doi:10.1038/s44222-025-00330-5 · Cited by 8
Interference source-based quality assessment method for postauricular photoplethysmography signals
Qi, Y., Zhang, A., Ma, Y., Wang, H., & Li, J. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.104751 · Cited by 7
Photoplethysmography wave morphology in patients with atrial fibrillation
Basza, M., Waląg, D., Kowalczyk, W., Bożym, A., Ciurla, M., Krzyżanowska, M., Maciejewski, C., Bojanowicz, W., Soliński, M., & Kołtowski, Ł. (2023) · Physiological Measurement · doi:10.1088/1361-6579/acc725 · Cited by 8
Intelligent Electrocardiogram Acquisition Via Ubiquitous Photoplethysmography Monitoring
Liu, Z., Zhu, T., Lü, L., Zhang, Y., & Clifton, D. A. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3344187 · Cited by 7
Is machine learning the future for atrial fibrillation screening?
Sivanandarajah, P., Wu, H., Bajaj, N., Khan, S. N., & Ng, F. S. (2022) · Cardiovascular Digital Health Journal · doi:10.1016/j.cvdhj.2022.04.001 · Cited by 8
Wang, Z., Fan, J., Dai, Y. M., Zheng, H., Wang, P., Chen, H., & Wu, Z. (2024) · Sensors · doi:10.3390/s24165243 · Cited by 6
Goshvarpour, A., & Goshvarpour, A. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.107089 · Cited by 6
Recent advances in the tools and techniques for AI-aided diagnosis of atrial fibrillation
Islam, M. S., Islam, M. R., Sanjid-E-Elahi, Abedin, M. A., Dökeroğlu, T., & Rahman, M. (2025) · Biophysics Reviews · doi:10.1063/5.0217416 · Cited by 7
He, Y., Zhao, H., Wu, X., Zhou, J., Yin, A., Meng, D., Sun, W., & Guo, Q. (2025) · Advanced Science · doi:10.1002/advs.202506348 · Cited by 6
Artificial Intelligence-Driven Wearable Sensors for Cardiovascular Health Monitoring
Deng, H. T., Ma, B., Chen, G., Chi, J., Tong, J., Yu, F., Lü, B., Chen, Y., Zhao, C., Zhang, C., et al. (2026) · ACS Sensors · doi:10.1021/acssensors.5c04840 · Cited by 6
Raschdorf, K., Mohseni, A., Hogle, K., Cheung, A., So, K., Manouchehri, N., Khalili, M., Lingawi, S., Grunau, B. E., Kuo, C., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-31637-1 · Cited by 7
van Mourik, M. J. W., Keijsers, L. C. E. M., van der Velden, R. M. J., Vorstermans, B. J. G. H., Crijns, H. J. G. M., Muris, J. W. M., Linz, D., & Slok, A. H. M. (2025) · European Journal of Cardiovascular Nursing · doi:10.1093/eurjcn/zvae156 · Cited by 6
Mahajan, S., Garg, S., Sharma, R., Singh, Y., Chandola, N., Bhatia, T., & Bansal, B. (2023) · International Journal of Research in Medical Sciences · doi:10.18203/2320-6012.ijrms20230855 · Cited by 5
Wu, H., Hu, Q., Wang, D., Zhu, S., & Yang, C. (2025) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2025.108698 · Cited by 5
Tang, R., Yao, X., Chen, J., Sridar, S., He, X., Pu, Y., Wang, J., Wang, D., & Wang, S. (2021) · Macromolecular Rapid Communications · doi:10.1002/marc.202100134 · Cited by 5
Saglietto, A., Scarsoglio, S., Canova, D., Ferrari, G. M. D., Ridolfi, L., & Anselmino, M. (2023) · Scientific Reports · doi:10.1038/s41598-023-33952-z · Cited by 5
Wang, P., Zhang, H., Yang, M., Wang, J., Wang, C., & Jia, H. (2025) · IEEE Sensors Journal · doi:10.1109/jsen.2025.3614579 · Cited by 5
Amin, M. R., Alam, S., Khazaei, S., Azgomi, H. F., & Faghih, R. T. (2024) · IEEE Access · doi:10.1109/access.2024.3393515 · Cited by 4
Shin, H. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108952 · Cited by 4
Pal, R., Rudas, Á., Williams, T. M., Chiang, J. N., Barney, A. M., & Cannesson, M. P. (2025) · npj Cardiovascular Health · doi:10.1038/s44325-025-00096-0 · Cited by 4
Detection of Atrial Fibrillation in Compressively Sensed Electrocardiogram for Remote Monitoring
Abdelazez, M., Rajan, S., & Chan, A. D. C. (2022) · Frontiers in Electronics · doi:10.3389/felec.2022.906689 · Cited by 4
An analytical review for cloud computing in healthcare system based biosensors
Awsaj, M. K., Mashhadany, Y. I. M. A., & Fourati, L. C. (2022) · International Journal of Health Sciences · doi:10.53730/ijhs.v6ns9.12297 · Cited by 4
Assessing the Generalizability of a Deep Learning-based Automated Atrial Fibrillation Algorithm
Argha, A., Li, J., Magdy, J., Alinejad‐Rokny, H., Celler, B. G., Butcher, K., Ooi, S., & Lovell, N. H. (2023) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc40787.2023.10341108 · Cited by 4
Development and Optimisation of Textile-Based Optical Sensors for Cardiac Health Monitoring
Peiris, T., Shahidi, A. M., Koutsogeorgis, D. C., Hurley, W., de Oliveira, C. R. M., Dias, T., & Riley, T. H. (2024) · International Conference on the Challenges, Opportunities, Innovations and Applications in Electronic Textiles (E-Textiles) · doi:10.23919/e-textiles63767.2024.10914070 · Cited by 4
Digital health care and arrhythmia: a case of WPW syndrome in South Korea, 2020
Mun, E., Hong, J., Kwon, S., Lee, S. H., Kim, W., & Lee, C. (2021) · Annals of Occupational and Environmental Medicine · doi:10.35371/aoem.2021.33.e8 · Cited by 4
Deep Learning Classifier for Advancing Video Monitoring of Atrial Fibrillation
Bukum, K., Savur, C., & Tsouri, G. R. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00240 · Cited by 4
Efficient and Real-Time Approach for PQRST and Atrial Fibrillation Detection of ECG Signal
Ahmed, A. F., Al-Hamadani, A. A., & Al‐Obaidi, M. K. (2023) · International Conference on Telecommunications and Signal Processing (TSP) · doi:10.1109/tsp59544.2023.10197821 · Cited by 4
Balaraman, K., & Claret, S. P. A. (2023) · International Conference on Circuit, Power and Computing Technologies · doi:10.1109/iccpct58313.2023.10245666 · Cited by 4
New‐onset atrial fibrillation in a young patient detected by smartwatch
Samal, S., Singhania, N., Bansal, S. K., & Sahoo, A. (2020) · Clinical Case Reports · doi:10.1002/ccr3.2887 · Cited by 4
Das, S. S. S., Shanto, S. K., Rahman, M., Islam, M. S., Rahman, A. H., Masud, M. M., & Ali, M. E. (2020) · arXiv (Cornell University) · Cited by 4
Rantula, O., Lipponen, J. A., Halonen, J., Jäntti, H., Rissanen, T. T., Naukkarinen, N. S., Väliaho, E., Santala, O. E., Sedha, J., Martikainen, T. J., et al. (2025) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztaf055 · Cited by 4
Ikeda, T., Ashihara, T., Iwasaki, Y., Ono, M., Kagiyama, N., Kimura, T., KUSANO, K. F., Kohno, R., Saku, K., Sasano, T., et al. (2025) · Journal of Arrhythmia · doi:10.1002/joa3.70059 · Cited by 3
Costa, G. F., Lorenzi, L. J., Raymundo, T. M., Kort, H. S. M., & Castro, P. C. (2025) · BMJ Open · doi:10.1136/bmjopen-2024-097565 · Cited by 5
Bench, C. A., Desai, V., Moulaeifard, M., Strodthoff, N., Aston, P. J., & Thompson, A. (2025) · Machine Learning Health · doi:10.1088/3049-477x/ae0b74 · Cited by 3
Fibricheck detection capabilities for atrial fibrillation (FDA–AF): a multicenter validation study
Sollee, J., Cheema, B. S., Slotwiner, D. J., Volodarskiy, A., Desteghe, L., Buyck, C., Heidbüchel, H., Stavrakis, S., Pison, L., Nuyens, D., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02059-2 · Cited by 3
Валерьевич, Л. В., Semenov, V. M., Volkova, E., Chernakov, D., Ahn, J., & Kim, J. Y. (2023) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc40787.2023.10341173 · Cited by 3
Usanmaz, C. F., White, M. E., Valancius, M., & Wang, M. D. (2021) · IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ...) · doi:10.1109/bhi50953.2021.9508517 · Cited by 3
Atrial fibrillation: focus on monitoring strategies after cryptogenic stroke
BUONO, M. G. D., & SANNA, T. (2022) · Minerva Cardiology and Angiology · doi:10.23736/s2724-5683.21.05851-8 · Cited by 3
Wearable health monitoring: wave of the future or waste of time?
Hudock, N. L., Hughes, H. M., Shaheen, N., Ramadan, A., Parikh, K., Anamika, F., & Jain, R. (2024) · Global Cardiology Science and Practice · doi:10.21542/gcsp.2024.21 · Cited by 5
Introduction to cardiovascular signals and automated systems
Pachori, D., Dash, S., Tripathy, R. K., & Jain, T. K. (2024) · Elsevier eBooks · doi:10.1016/b978-0-44-314141-6.00006-2 · Cited by 3
Deep adaptative spectral zoom for improved remote heart rate estimation
Comas, J., Ruiz, A., & Sukno, F. M. (2024) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg59268.2024.10581863 · Cited by 3
Leveraging IoT Devices for Atrial Fibrillation Detection: A Comprehensive Study of AI Techniques
Pedrosa-Rodriguez, A., Cámara, C., & Lopez, P. P. (2024) · Applied Sciences · doi:10.3390/app14198945 · Cited by 3
Metadata
- Authors
- Pereira, T., Tran, N., Gadhoumi, K., Pelter, M. M., Do, D. H., Lee, R. J., Colorado, R., Meisel, K., & Hu, X.
- Affiliations
- University of California, San Francisco (US)
- University of California, Los Angeles (US)
- Venue
- npj Digital Medicine
- Published
- 2020-01-10
- Volume
- 3
- Issue
- 1
- Pages
- 3-3
- ISSN
- 2398-6352 (ISSN-L)
- PMC
- PMC6954115
- OpenAlex
- W2999926771
- Cited by
- 300
- References
- 102
- Citations by year
- 2026: 34 · 2025: 47 · 2024: 57 · 2023: 51 · 2022: 40 · 2021: 50 · 2020: 18
- FWCI
- 3.32
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- npj Digital Medicine (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- Funders
- National Institutes of Health (R01NHLBI128679)
- National Heart, Lung, and Blood Institute (R01NHLBI128679, R01HL128679)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
- Keywords
- atrial fibrillation detection, photoplethysmography, wearable devices, optical sensors, cardioembolic stroke prevention, signal processing, machine learning, deep learning, early screening
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