Comparison between electrocardiogram- and photoplethysmogram-derived features for atrial fibrillation detection in free-living conditions
Open accessAbstract
OBJECTIVE: Atrial fibrillation (AF) is the most commonly experienced arrhythmia and it increases the risk of stroke and heart failure. The challenge in detecting the presence of AF is the occasional and asymptomatic manifestation of the condition. Long-term monitoring can increase the sensitivity of detecting intermittent AF episodes, however it is either cumbersome or invasive and costly with electrocardiography (ECG). Photoplethysmography (PPG) is an unobtrusive measuring modality enabling heart rate monitoring, and promising results have been presented in detecting AF. However, there is still limited knowledge about the applicability of the PPG solutions in free-living conditions. The aim of this study was to compare the inter-beat interval derived features for AF detection between ECG and wrist-worn PPG in daily life. APPROACH: The data consisted of 24 h ECG, PPG, and accelerometer measurements from 27 patients (eight AF, 19 non-AF). In total, seven features (Shannon entropy, root mean square of successive differences (RMSSD), normalized RMSSD, pNN40, pNN70, sample entropy, and coefficient of sample entropy (CosEn)) were compared. Body movement was measured with the accelerometer and used with three different thresholds to exclude PPG segments affected by movement. MAIN RESULTS: CosEn resulted as the best performing feature from ECG with Cohens kappa 0.95. When the strictest movement threshold was applied, the same performance was obtained with PPG (kappa = 0.96). In addition, pNN40 and pNN70 reached similar results with the same threshold (kappa = 0.95 and 0.94), but were more robust with respect to movement artefacts. The coverage of PPG was 24.0%-57.6% depending on the movement threshold compared to 92.1% of ECG. SIGNIFICANCE: The inter-beat interval features derived from PPG are equivalent to the ones from ECG for AF detection. Movement artefacts substantially worsen PPG-based AF monitoring in free-living conditions, therefore monitoring coverage needs to be carefully selected. Wrist-worn PPG still provides a promising technology for long-term AF monitoring.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The percentage of unanalyzable PPG reached 76.0% by also accounting for the accelerometer-identified movement.
“However, the percentage of unanalyzable PPG reached 76.0% by also accounting for the accelerometer-identified movement.”
From Modeling of artifacts in the wrist photoplethysmogram: Application to the detection of life-threatening arrhythmias (2021) · cites this paper as [13]
The contradiction arises because sensitivity was calculated by including all arrhythmia episodes in our study, whereas only those episodes remaining after exclusion of artifact-corrupted PPG parts were included in [13].
“The contradiction arises since the sensitivity was calculated by including all arrhythmia episodes in our study, whereas only those episodes remaining after the exclusion of the artifact-corrupted PPG parts were included in [13].”
From Modeling of artifacts in the wrist photoplethysmogram: Application to the detection of life-threatening arrhythmias (2021) · cites this paper as [13]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
A Coefficient of Agreement for Nominal Scales
Cohen, J. (1960) · Educational and Psychological Measurement · doi:10.1177/001316446002000104 · Cited by 42,398 · see citing works
Physiological time-series analysis using approximate entropy and sample entropy
Richman, J. S., & Moorman, J. R. (2000) · American Journal of Physiology-Heart and Circulatory Physiology · doi:10.1152/ajpheart.2000.278.6.h2039 · Cited by 7,880 · see citing works
Guidelines for the management of atrial fibrillation
Authors/Task Force Members, Camm, A. J., Kirchhof, P. F., Lip, G. Y. H., Schotten, U., Savelieva, I., Ernst, S., Gelder, I. C. V., Al‐Attar, N., Hindricks, G., et al. (2010) · European Heart Journal · doi:10.1093/eurheartj/ehq278 · Cited by 5,875 · see citing works
Photoplethysmography and its application in clinical physiological measurement
Allen, J. (2007) · Physiological Measurement · doi:10.1088/0967-3334/28/3/r01 · Cited by 3,967 · see citing works
Cryptogenic stroke and underlying atrial fibrillation
Sanna, T., Diener, H.-C., Passman, R. S., Di Lazzaro, V., Bernstein, R. A., Morillo, C. A., Rymer, M. M., Thijs, V., Rogers, T., Beckers, F., et al. (2014) · New England Journal of Medicine · doi:10.1056/nejmoa1313600 · Cited by 2,161 · see citing works
Page, R. L., Wilkinson, W. E., Clair, W. K., McCarthy, E. A., & Pritchett, E. L. (1994) · Circulation · doi:10.1161/01.cir.89.1.224 · Cited by 627 · see citing works
Lake, D. E., & Moorman, J. R. (2010) · American Journal of Physiology-Heart and Circulatory Physiology · doi:10.1152/ajpheart.00561.2010 · Cited by 318 · see citing works
Automatic Real Time Detection of Atrial Fibrillation
Dash, S. S., Chon, K. H., Lu, S., & Raeder, E. A. (2009) · Annals of Biomedical Engineering · doi:10.1007/s10439-009-9740-z · Cited by 314 · see citing works
McManus, D. D., Lee, J., Maitas, O., Esa, N., Pidikiti, R., Carlucci, A., Harrington, J., Mick, E., & Chon, K. H. (2013) · Heart Rhythm · doi:10.1016/j.hrthm.2012.12.001 · Cited by 253 · see citing works
Chan, P., Wong, C., Poh, Y. C., Pun, L., Leung, W. W., Wong, Y., Wong, M. M., Poh, M., Chu, D. W., & Siu, C. (2016) · Journal of the American Heart Association · doi:10.1161/jaha.116.003428 · Cited by 240 · see citing works
Uncovering Atrial Fibrillation Beyond Short-Term Monitoring in Cryptogenic Stroke Patients
Brachmann, J., Morillo, C. A., Sanna, T., Lazzaro, V. D., Diener, H., Bernstein, R. A., Rymer, M., Ziegler, P., Liu, S., & PASSMAN, R. S. (2016) · Circulation Arrhythmia and Electrophysiology · doi:10.1161/circep.115.003333 · Cited by 183 · see citing works
A deep learning approach to monitoring and detecting atrial fibrillation using wearable technology
Shashikumar, S. P., Shah, A. J., Qiao, L., Clifford, G. D., & Nemati, S. (2017) · IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ...) · doi:10.1109/bhi.2017.7897225 · Cited by 167 · see citing works
Monitoring and detecting atrial fibrillation using wearable technology
Nemati, S., Ghassemi, M. M., Ambai, V., Isakadze, N., Levantsevych, O. M., Shah, A. J., & Clifford, G. D. (2016) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2016.7591456 · Cited by 144 · see citing works
Gooch, D. J. W. (2007) · Springer eBooks · doi:10.1007/978-0-387-30160-0_6196 · Cited by 115 · see citing works
Detection of atrial fibrillation episodes using a wristband device
Corino, V., Laureanti, R., Ferranti, L., Scarpini, G., Lombardi, F., & Mainardi, L. T. (2017) · Physiological Measurement · doi:10.1088/1361-6579/aa5dd7 · Cited by 95 · see citing works
Arrhythmia Discrimination using a Smart Phone
Chong, J. W., Esa, N., McManus, D. D., & Chon, K. H. (2015) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2015.2418195 · Cited by 79 · see citing works
Identification of Atrial Fibrillation by Quantitative Analyses of Fingertip Photoplethysmogram
Tang, S.-C., Huang, P.-W., Hung, C.-S., Shan, S.-M., Lin, Y.-H., Shieh, J., Lai, D., Wu, A.-Y. (., & Jeng, J. (2017) · Scientific Reports · doi:10.1038/srep45644 · Cited by 64 · see citing works
Impact of the presence of noise on RR interval-based atrial fibrillation detection
Oster, J., & Clifford, G. D. (2015) · Journal of Electrocardiology · doi:10.1016/j.jelectrocard.2015.08.013 · Cited by 60 · see citing works
Atrial Fibrillation Detection Using Photo:plethysmography and Acceleration Data at the Wrist
Bonomi, A., Schipper, F., Eerikäinen, L. M., Margarito, J., Aarts, R. M., Babaeizadeh, S., de Morree, H. M., & Dekker, L. (2016) · Computing in cardiology · doi:10.22489/cinc.2016.081-339 · Cited by 60 · see citing works
Self-citationAtrial fibrillation detection using a smart phone
Lee, J., Reyes, B. A., McManus, D. D., Mathias, O., & Chon, K. H. (2012) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2012.6346146 · Cited by 53 · see citing works
Ambulatory ECG Monitoring in Atrial Fibrillation Management
Rosero, S. Z., Kutyifa, V., Olshansky, B., & Zaręba, W. (2013) · Progress in Cardiovascular Diseases · doi:10.1016/j.pcad.2013.10.001 · Cited by 51 · see citing works
Using photoplethysmography in heart rate monitoring of patients with epilepsy
van Andel, J., Ungureanu, C., Aarts, R., Leijten, F., & Arends, J. (2015) · Epilepsy & Behavior · doi:10.1016/j.yebeh.2015.02.018 · Cited by 39 · see citing works
Self-citationReliable PPG-based algorithm in atrial fibrillation detection
Shan, S.-M., Tang, S., Huang, P.-W., Lin, Y., Huang, W.-H., Lai, D., & Wu, A.-Y. A. (2016) · IEEE Biomedical Circuits and Systems Conference (BioCAS) · doi:10.1109/biocas.2016.7833801 · Cited by 35 · see citing works
Optical heart rate monitoring module validation study
Valenti, G., & Westerterp, K. R. (2013) · 2013 IEEE International Conference on Consumer Electronics (ICCE) · doi:10.1109/icce.2013.6486856 · Cited by 32 · see citing works
Schäck, T., Harb, Y. S., Muma, M., & Zoubir, A. M. (2017) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2017.8036773 · Cited by 30 · see citing works
Wrist:Located Optical Device for Atrial Fibrillation Screening: a Clinical Study on Twenty Patients
Lemay, M., Fallet, S., Renevey, P., Solà, J., Solà, J., Leupi, C., Pruvot, E. J., & Vésin, J.-M. (2016) · Computing in cardiology · doi:10.22489/cinc.2016.200-350 · Cited by 23 · see citing works
Eerik�inen, L. M., Dekker, L., Bonomi, A., Vullings, R., Schipper, F., Margarito, J., de Morree, H. M., & Aarts, R. M. (2017) · Computing in cardiology · doi:10.22489/cinc.2017.050-177 · Cited by 11 · see citing works
Self-citationScreening of Atrial Fibrillation Using Wrist Photoplethysmography from a Fitbit Tracker
Pantelopoulos, A., Faranesh, A. Z., Milescu, A., Hosking, P., Venkatraman, S., & Heneghan, C. (2017) · Iproceedings · doi:10.2196/iproc.8447 · Cited by 6 · see citing works
(2014) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2014.2383834 · Cited by 4 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Photoplethysmography based atrial fibrillation detection: a review
Pereira, T., Tran, N., Gadhoumi, K., Pelter, M. M., Do, D. H., Lee, R. J., Colorado, R., Meisel, K., & Hu, X. (2020) · npj Digital Medicine · doi:10.1038/s41746-019-0207-9 · Cited by 300 · see citing works
Wearable Photoplethysmography for Cardiovascular Monitoring
Charlton, P. H., Kyriacou, P. A., Mant, J., Marozas, V., Chowienczyk, P., & Alastruey, J. (2022) · Proceedings of the IEEE · doi:10.1109/jproc.2022.3149785 · Cited by 250 · see citing works
The 2023 wearable photoplethysmography roadmap
Charlton, P. H., Allen, J., Bailón, R., Baker, S., Behar, J. A., Chen, F., Clifford, G. D., Clifton, D. A., Davies, H. J., Ding, C., et al. (2023) · Physiological Measurement · doi:10.1088/1361-6579/acead2 · Cited by 210 · see citing works
Diagnostic Features and Potential Applications of PPG Signal in Healthcare: A Systematic Review
Almarshad, M. A., Islam, M. S., Al-Ahmadi, S. A., & BaHammam, A. S. (2022) · Healthcare · doi:10.3390/healthcare10030547 · Cited by 200 · see citing works
Kalarus, Z. F., Mairesse, G. H., Sokal, A., Boriani, G., Średniawa, B., Casado-Arroyo, R., Wachter, R., Frommeyer, G., TRAYKOV, V. B., Dagres, N., et al. (2022) · EP Europace · doi:10.1093/europace/euac144 · Cited by 122 · see citing works
Scardulla, F., Cosoli, G., Spinsante, S., Poli, A., Iadarola, G., Pernice, R., Busacca, A., Pasta, S., Scalise, L., & D’Acquisto, L. (2023) · Measurement · doi:10.1016/j.measurement.2023.113150 · Cited by 120 · see citing works
Wearable Photoplethysmography for Cardiovascular Monitoring.
Charlton, P. H., Kyriacou, P. A., Mant, J., Marozas, V., Chowienczyk, P., & Alastruey, J. (2022) · KTUePubl (Repository of Kaunas University of Technology) · doi:10.17863/cam.81148 · Cited by 113 · see citing works
Dang, H., Sun, M., Zhang, G., Qi, X., Zhou, X., & Chang, Q. H. (2019) · IEEE Access · doi:10.1109/access.2019.2918792 · Cited by 103 · see citing works
Papini, G. B., Fonseca, P., van Gilst, M. M., Bergmans, J. W. M., Vullings, R., & Overeem, S. (2020) · Scientific Reports · doi:10.1038/s41598-020-69935-7 · Cited by 90 · see citing works
Self-citationWearable Armband Device for Daily Life Electrocardiogram Monitoring
Lázaro, J. M., Reljin, N., Hossain, M.-B., Noh, Y., Laguna, P., & Chon, K. H. (2020) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2020.2987759 · Cited by 81 · see citing works
Han, D., Bashar, S. K., Mohagheghian, F., Ding, E., Whitcomb, C., McManus, D. D., & Chon, K. H. (2020) · Sensors · doi:10.3390/s20195683 · Cited by 62 · see citing works
Detecting Atrial Fibrillation and Atrial Flutter in Daily Life Using Photoplethysmography Data
Eerikäinen, L. M., Bonomi, A., Schipper, F., Dekker, L., de Morree, H. M., Vullings, R., & Aarts, R. M. (2019) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2019.2950574 · Cited by 61 · see citing works
Self-citationHermans, A. N. L., van der Velden, R. M. J., Gawalko, M. M., Verhaert, D. V. M., Desteghe, L., Duncker, D., Manninger, M., Heidbüchel, H., Pisters, R., Hemels, M. E. W., et al. (2020) · Clinical Cardiology · doi:10.1002/clc.23469 · Cited by 57 · see citing works
Optimized Signal Quality Assessment for Photoplethysmogram Signals Using Feature Selection
Mohagheghian, F., Han, D., Peitzsch, A. G., Nishita, N., Ding, E., Dickson, E. L., DiMezza, D., Otabil, E. M., Noorishirazi, K., Scott, J., et al. (2022) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2022.3158582 · Cited by 57 · see citing works
Atrial fibrillation screening with photo-plethysmography through a smartphone camera
Verbrugge, F. H., Proesmans, T., Vijgen, J. M., Müllens, W., Rivero‐Ayerza, M., Herendael, H. V., Vandervoort, P. M., & Nuyens, D. (2019) · EP Europace · doi:10.1093/europace/euz119 · Cited by 50 · see citing works
van der Velden, R. M. J., Verhaert, D. V. M., Hermans, A. N. L., Duncker, D., Manninger, M., Betz, K., Gawalko, M. M., Desteghe, L., Pisters, R., Hemels, M. E. W., et al. (2021) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztab050 · Cited by 48 · see citing works
Detection of atrial fibrillation using a wrist-worn device
Sološenko, A., Petrėnas, A., Paliakaitė, B., Sörnmo, L., & Marozas, V. (2019) · Physiological Measurement · doi:10.1088/1361-6579/ab029c · Cited by 45 · see citing works
Mobile health technology in atrial fibrillation
Bonini, N., Vitolo, M., Imberti, J. F., Proietti, M., Romiti, G. F., Boriani, G., Johnsen, S. P., Guo, Y., & Lip, G. Y. H. (2022) · Expert Review of Medical Devices · doi:10.1080/17434440.2022.2070005 · Cited by 46 · see citing works
Detection and Monitoring of Viral Infections via Wearable Devices and Biometric Data
Goergen, C. J., Tweardy, M. J., Steinhubl, S. R., Wegerich, S., Singh, K., Mieloszyk, R. J., & Dunn, J. (2021) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-103020-040136 · Cited by 41 · see citing works
Considerations on Performance Evaluation of Atrial Fibrillation Detectors
Butkuvienė, M., Petrėnas, A., Sološenko, A., Martín-Yebra, A., Marozas, V., & Sörnmo, L. (2021) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2021.3067698 · Cited by 35 · see citing works
A computationally efficient algorithm for wearable sleep staging in clinical populations
Fonseca, P. M., Ross, M., Cerny, A., Anderer, P., van Meulen, F., Janssen, H. C. J. P., Pijpers, A., Dujardin, S., van Hirtum, P. V., van Gilst, M. M., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-36444-2 · Cited by 27 · see citing works
Saarinen, H. J., Joutsen, A. S., Korpi, K., Halkola, T., Nurmi, M., Hernesniemi, J. A., & Vehkaoja, A. T. (2023) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2023.1100127 · Cited by 27 · see citing works
Paliakaitė, B., Petrėnas, A., Sološenko, A., & Marozas, V. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.102421 · Cited by 24 · see citing works
Eerikäinen, L. M., Bonomi, A. G., Dekker, L. R. C., Vullings, R., & Aarts, R. M. (2020) · Cardiovascular Digital Health Journal · doi:10.1016/j.cvdhj.2020.03.001 · Cited by 24 · see citing works
Self-citationDeep CNN-based detection of cardiac rhythm disorders using PPG signals from wearable devices
Bulut, M. G., Ünal, S., Hammad, M. A., & Pławiak, P. (2025) · PLoS ONE · doi:10.1371/journal.pone.0314154 · Cited by 23 · see citing works
Shin, H., Sun, S., Lee, J., & Kim, H. C. (2021) · IEEE Access · doi:10.1109/access.2021.3078534 · Cited by 25 · see citing works
Papini, G. B., Fonseca, P. M., van Gilst, M. M., Bergmans, J. W. M., Vullings, R., & Overeem, S. (2020) · Physiological Measurement · doi:10.1088/1361-6579/ab9481 · Cited by 22 · see citing works
Self-citationMillán, C. A., Girón, N. A., & López, D. M. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17020498 · Cited by 21 · see citing works
A Review on Atrial Fibrillation Detection From Ambulatory ECG
Ma, C., Xiao, Z., Zhao, L., Biton, S. B., Behar, J. A., Long, X., Vullings, R., Aarts, R. M., Li, J., & Liu, C. (2023) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2023.3321792 · Cited by 20 · see citing works
Self-citationCheung, C. C., Gin, K. G., & Andrade, J. G. (2019) · JACC. Clinical electrophysiology · doi:10.1016/j.jacep.2019.02.008 · Cited by 15 · see citing works
Julián, P. A., Kontaxis, S., Rapalis, A., Marozas, V., Laguna, P., Bailón, R., Gil, E., & Lázaro, J. M. (2022) · Frontiers in Electronics · doi:10.3389/felec.2022.906324 · Cited by 14 · see citing works
Smart Watch Devices for Atrial Fibrillation Screening
Shamloo, A. S., Bollmann, A., Dagres, N., Arya, A., & Hindricks, G. (2020) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2019.10.063 · Cited by 14 · see citing works
Multi-night home assessment of sleep structure in OSA with and without insomnia
Wulterkens, B. M., Teuling, N. D., Hermans, L. W., Asin, J., Duis, N., Overeem, S., Fonseca, P. M., & van Gilst, M. M. (2024) · Sleep Medicine · doi:10.1016/j.sleep.2024.03.031 · Cited by 13 · see citing works
Sološenko, A., Paliakaitė, B., Marozas, V., & Sörnmo, L. (2022) · Frontiers in Physiology · doi:10.3389/fphys.2022.928098 · Cited by 13 · see citing works
Giggins, O. M., Doyle, J., Smith, S., Crabtree, D. R., & Fraser, M. (2022) · JMIR Formative Research · doi:10.2196/34280 · Cited by 9 · see citing works
Wearable photoplethysmography devices
Charlton, P. H., & Marozas, V. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-823374-0.00011-6 · Cited by 10 · see citing works
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 · see citing works
Chou, Y., Gu, J. J., Liu, J., Gu, Y., & Lin, J. (2019) · IEEE Access · doi:10.1109/access.2019.2940921 · Cited by 9 · see citing works
New 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 · see citing works
Interpretation der Photoplethysmographie: Schritt für Schritt
Betz, K., van der Velden, R. M. J., Gawalko, M. M., Hermans, A. N. L., Pluymaekers, N. A. H. A., Hillmann, H. A. K., Hendriks, J., Duncker, D., & Linz, D. (2021) · Herzschrittmachertherapie + Elektrophysiologie · doi:10.1007/s00399-021-00795-y · Cited by 8 · see citing works
Khundaqji, H., Hing, W. A., Furness, J. W., & Climstein, M. (2021) · PeerJ · doi:10.7717/peerj.12598 · Cited by 7 · see citing works
Chen, Y., Yang, X., Liu, X., Han, X., & Zhang, J. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.104662 · Cited by 7 · see citing works
Bester, M., Escorcia, M. J. A., Fonseca, P. M., Mollura, M., van Gilst, M. M., Barbieri, R., Mischi, M., van Laar, J. O. E. H., Vullings, R., & Joshi, R. (2023) · Scientific Reports · doi:10.1038/s41598-023-47980-2 · Cited by 6 · see citing works
Self-citationAtrial Fibrillation Detection Based on the Combination of Depth and Statistical Features of ECG
Li, M., He, G., & Zhu, B. (2019) · International Conference on Graphics and Signal Processing (ICGSP) · doi:10.1145/3338472.3338485 · Cited by 5 · see citing works
Chou, L., Liu, J., Gong, S., & Chou, Y. (2022) · Frontiers in Physiology · doi:10.3389/fphys.2022.1008111 · Cited by 4 · see citing works
Screening for Atrial Fibrillation and the Role of Digital Health Technologies
Richardson, E., Hall, A., & Mitchell, A. R. J. (2019) · IntechOpen eBooks · doi:10.5772/intechopen.88660 · Cited by 4 · see citing works
Bester, M., Moors, S., Joshi, R., Nichting, T. J., der Jagt, M. B. V. D. H., Oei, S. G., Mischi, M., Vullings, R., & van Laar, J. O. E. H. (2021) · Clinics and Practice · doi:10.3390/clinpract11010004 · Cited by 4 · see citing works
Self-citationRantula, 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 · see citing works
Feng, T., & Deng, Z. (2022) · Computational and Mathematical Methods in Medicine · doi:10.1155/2022/4596552 · Cited by 3 · see citing works
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 · see citing works
Eerikäinen, L. M., Bonomi, A., Schipper, F., Dekker, L., Vullings, R., de Morree, H. M., & Aarts, R. M. (2019) · Computing in cardiology · doi:10.22489/cinc.2019.086 · Cited by 3 · see citing works
Self-citationAtrial Fibrillation Episodes Detected Using Photoplethysmography
Eerikäinen, L. M., Bonomi, A. G., Dekker, L. R. C., & Aarts, R. M. (2020) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2019.10.064 · Cited by 2 · see citing works
Self-citationAtrial Fibrillation Detection System via Acoustic Sensing for Mobile Phones
Liu, X., Li, J., Liu, H., Yang, Z., Huang, Y., & Zhang, J. (2025) · ACM Transactions on Sensor Networks · doi:10.1145/3771548 · Cited by 2 · see citing works
Chu, J., Yang, W.-T., Chang, Y., & Yang, F.-L. (2022) · Diagnostics · doi:10.3390/diagnostics12061304 · Cited by 2 · see citing works
Bester, M., Nichting, T. J., Joshi, R., Aissati, L., Oei, S. G., Mischi, M., van Laar, J. O. E. H., & Vullings, R. (2024) · Journal of Clinical Medicine · doi:10.3390/jcm13082442 · Cited by 1 · see citing works
Self-citationA Novel Smartphone-based & Personalized Atrial Fibrillation Detection: A Preliminary Study
Tabei, F., Abohelwa, M., Davis, D. P., Sethi, P., NUGENT, K. M., & Chong, J. W. (2022) · IEEE Healthcare Innovations and Point of Care Technologies Conference (HI-POCT) · doi:10.1109/hi-poct54491.2022.9744074
Detection of atrial fibrillation using photoplethysmography signals: a systemic review
Chung, C. T., Roy, V. A. L., Tse, G., & Liu, H. (2024) · Elsevier eBooks · doi:10.1016/b978-0-44-314141-6.00009-8
A photoplethysmogram-based 1D-CNN algorithm for automated atrial fibrillation detection
John, A. (2025) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc58623.2025.11253262
Rantula, O., Lipponen, J. A., Halonen, J., Jäntti, H., Rissanen, T. T., Tarvainen, M. P., Naukkarinen, N. S., Väliaho, E., Santala, O. E., Sedha, J., et al. (2026) · JMIR mhealth and uhealth · doi:10.2196/82475 · Cited by 1 · see citing works
- Authors
- Eerikäinen, L. M., Bonomi, A. G., Schipper, F., Dekker, L. R. C., Vullings, R., de Morree, H. M., & Aarts, R. M.
- Affiliations
- Philips (Netherlands) (NL)
- Catharina Ziekenhuis (NL)
- Eindhoven University of Technology (NL)
- Philips Research
- Corresponding author
- Eerikäinen, Linda M
- Vullings, Rik
- ORCID
- Venue
- Physiological Measurement
- Publisher
- IOP Publishing
- Published
- 2018-08-01
- Volume
- 39
- Issue
- 8
- Pages
- 084001
- ISSN
- 0967-3334 (ISSN-L)
- 1361-6579
- OpenAlex
- W2847349893
- Cited by
- 61
- References
- 30
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 5 · 2023: 7 · 2022: 13 · 2021: 12 · 2020: 10 · 2019: 9
- FWCI
- 3.10
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Physiological Measurement (publishedVersion)
- PubMed (publishedVersion)
- TU/e Research Portal (submittedVersion, other-oa)
- TU/e Research Portal (submittedVersion)
- Funders
- Technische Universiteit Eindhoven
- 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, electrocardiogram, sample entropy, RMSSD, motion artifacts, wearable sensors, free-living conditions
- MeSH terms
- Activities of Daily Living, Adult, Aged, Atrial Fibrillation, Electrocardiography, Female, Heart Rate, Humans, Male, Middle Aged, Movement, ROC Curve, Signal Processing, Computer-Assisted, Photoplethysmography
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
