Photoplethysmography sampling frequency: pilot assessment of how low can we go to analyze pulse rate variability with reliability?
Open accessAbstract
Pulse rate variability (PRV) analysis appears as the first alternative to heart rate variability analysis for wearable devices; however, there is a constraint on computational load and energy consumption for the limited system resources available to the devices. Considering that adjustment of the sampling frequency is one of the strategies for reducing computational load and power consumption, this study aimed to investigate the influence of sampling frequency ( f s ) on PRV analysis and to find the minimum sampling frequency while maintaining reliability. We generated 5000, 2500, 1000, 500, 250, 100, 50, 25, 20, 15, 10, 5 Hz down-sampled photoplethysmograms from 10 kHz-sampled PPGs and derived time- and frequency-domain variables of the PRV. These included AVNN, SDNN, SDSD, RMSSD, NN50, pNN50, total power, VLF, LF, HF, LF/HF, nLF and nHF for each down-sampled signal. Derived variables were compared with heart rate variability of the 10 kHz-sampled electrocardiograms, and then statistically investigated using one-way ANOVA test and Bland–Altman analysis. As a result, significant differences ( P < 0.05) were found for SDNN, SDSD, RMSSD, NN50, pNN50, TP, HF, LF/HF, nLF and nHF, but not for AVNN, VLF and LF. Based on the post hoc tests, it was found that the NN50 and pNN50, SDSD and RMSSD, LF/HF and nHF, SDNN, TP and nLF analysis had significant differences at f s ⩽ 20 Hz, f s ⩽ 15 Hz, f s ⩽10 Hz; f s = 5 Hz, respectively. In other words, a significant difference was not seen for any variable if the f s was greater than 25 Hz. Consequently, our pilot study suggests that analysis of variability in the time and frequency domain from pulse rate obtained through PPG may be potentially as reliable as that derived from the analysis of the electrocardiogram, provided that f s ⩾25 Hz sampling frequency is used.
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?
Rapid irregularly conducted AF may produce variable pulse pressures that challenge detection.
“Rapid irregularly conducted AF may produce variable pulse pressures that challenge detection (Choi 2017).”
From 2021 ISHNE/HRS/EHRA/APHRS Expert Collaborative Statement on mHealth in Arrhythmia Management: Digital Medical Tools for Heart Rhythm Professionals: From the International Society for Holter and Noninvasive Electrocardiology/Heart Rhythm Society/European Heart Rhythm Association/Asia-Pacific Heart Rhythm Society (2021) · cites this paper as Choi 2017
The Limitations section (page unavailable) says arrhythmia could change pulse-wave characteristics, and §2.2 describes correcting missed PPG peak detections. The paper does not say that rapid, irregularly conducted atrial fibrillation produces variable pulse pressures or that those variations challenge detection; its volunteers were tested under normal resting conditions. (LLM, conf=0.78)
Experimental — accuracy not yet measuredThe PPG can be interpolated to increase pulse rate variability performance.
“Second, the PPG can be interpolated to increase performance [130], [131].”
From Wearable Photoplethysmography for Cardiovascular Monitoring (2022) · cites this paper as [130]
For pulse rate variability assessment, the PPG sampling rate should be sufficiently high, at least 25 Hz.
“First, the PPG sampling rate should be sufficiently high (at least 25 Hz [130]).”
From Wearable Photoplethysmography for Cardiovascular Monitoring (2022) · cites this paper as [130]
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
Heart Rate Variability: Standards of Measurement, Physiological Interpretation, and Clinical Use
Electrophysiology, T. F. O. T. E. S. O. C. T. N. A. (1996) · Circulation · doi:10.1161/01.cir.93.5.1043 · Cited by 17,159 · see citing works
Heart rate variability: Standards of measurement, physiological interpretation, and clinical use
Malik, M., Bigger, J. T., Camm, A. J., Kleiger, R. E., Malliani, A., Moss, A. J., & Schwartz, P. J. (1996) · European Heart Journal · doi:10.1093/oxfordjournals.eurheartj.a014868 · Cited by 15,289 · see citing works
Contributions to Probability and Statistics: Essays in Honor of Harold Hotelling.
David, F. N., Olkin, I., Ghurye, S. G., Hoeffding, W., Madow, W. G., & Mann, H. B. (1961) · Journal of the Royal Statistical Society Series A (General) · doi:10.2307/2984135 · Cited by 1,914 · see citing works
Saeed, M., Villarroel, M., Reisner, A. T., Clifford, G., Lehman, L.-W., Moody, G., Heldt, T., Kyaw, T. H., Moody, B., & Mark, R. G. (2011) · Critical Care Medicine · doi:10.1097/ccm.0b013e31820a92c6 · Cited by 1,162 · see citing works
Discrete Time Signal Processing
Chitode, J. S. (2011) · Cited by 798 · see citing works
Gil, E., Orini, M., Bailón, R., Vergara, J. M., Mainardi, L., & Laguna, P. (2010) · Physiological Measurement · doi:10.1088/0967-3334/31/9/015 · Cited by 461 · see citing works
Physiological parameter monitoring from optical recordings with a mobile phone
Scully, C. G., Lee, J., Meyer, J., Gorbach, A. M., Granquist-Fraser, D., Mendelson, Y., & Chon, K. H. (2012) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2011.2163157 · Cited by 452 · see citing works
Lü, G., Yang, F., Taylor, J. A., & Stein, J. F. (2009) · Journal of Medical Engineering & Technology · doi:10.3109/03091900903150998 · Cited by 324 · see citing works
Towards Measuring Stress with Smartphones and Wearable Devices During Workday and Sleep
Muaremi, A., Arnrich, B., & Tröster, G. (2013) · BioNanoScience · doi:10.1007/s12668-013-0089-2 · Cited by 302 · see citing works
Lu, S. F., Zhao, H., Ju, K., Shin, K., Lee, M., Shelley, K. H., & Chon, K. H. (2007) · Journal of Clinical Monitoring and Computing · doi:10.1007/s10877-007-9103-y · Cited by 284 · see citing works
Adaptive threshold method for the peak detection of photoplethysmographic waveform
Shin, H. S., Lee, C., & Lee, M. (2009) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2009.10.006 · Cited by 211 · see citing works
Self-citationGregoski, M. J., Mueller, M., Vertegel, A. A., Shaporev, A., Jackson, B. B., Frenzel, R. M., Sprehn, S. M., & Treiber, F. A. (2012) · International Journal of Telemedicine and Applications · doi:10.1155/2012/696324 · Cited by 181 · see citing works
Investigating a smartphone imaging unit for photoplethysmography
Jonathan, E., & Leahy, M. J. (2010) · Physiological Measurement · doi:10.1088/0967-3334/31/11/n01 · Cited by 178 · see citing works
A novel method to detect Heart Beat Rate using a mobile phone
Pelegris, P., Banitsas, K. A., Orbach, T., & Kostas Marias, A. (2010) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2010.5626580 · Cited by 126 · see citing works
Heathers, J. A. J. (2013) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2013.05.017 · Cited by 122 · see citing works
Garde, A., Karlen, W., Ansermino, J. M., & Dumont, G. A. (2014) · PLoS ONE · doi:10.1371/journal.pone.0086427 · Cited by 114 · see citing works
Smartphone-Derived Heart-Rate Variability and Training Load in a Women’s Soccer Team
Flatt, A. A., & Esco, M. R. (2015) · International Journal of Sports Physiology and Performance · doi:10.1123/ijspp.2014-0556 · Cited by 97 · see citing works
Charlot, K., Cornolo, J., Brugniaux, J. V., Richalet, J., & Pichon, A. (2009) · Physiological Measurement · doi:10.1088/0967-3334/30/12/005 · Cited by 88 · see citing works
Hejjel, L., & Röth, E. (2004) · Physiological Measurement · doi:10.1088/0967-3334/25/6/006 · Cited by 85 · see citing works
Assessment of pulse rate variability by the method of pulse frequency demodulation
Hayano, J., Barros, A. K., Kamiya, A., Ohte, N., & Yasuma, F. (2005) · BioMedical Engineering OnLine · doi:10.1186/1475-925x-4-62 · Cited by 80 · see citing works
Is 50 Hz high enough ECG sampling frequency for accurate HRV analysis?
Mahdiani, S., Jeyhani, V., Peltokangas, M., & Vehkaoja, A. T. (2015) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2015.7319746 · Cited by 70 · see citing works
Ziemssen, T., Gasch, J., & Ruediger, H. (2008) · Journal of Clinical Monitoring and Computing · doi:10.1007/s10877-008-9117-0 · Cited by 67 · see citing works
Using a Smartphone to Measure Heart Rate Changes during Relived Happiness and Anger
Lakens, D. (2013) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2013.3 · Cited by 64 · see citing works
Deriving heart period variability from blood pressure waveforms
McKinley, P. S., Shapiro, P. A., Bagiella, E., Myers, M. M., Meersman, R. E. D., Grant, I., & Sloan, R. P. (2003) · Journal of Applied Physiology · doi:10.1152/japplphysiol.01110.2002 · Cited by 60 · see citing works
Carrasco, S., Gonzàlez, R., Jimenez, J. E., Román, R., Medina, V., & Azpiroz, J. (1998) · Journal of Medical Engineering & Technology · doi:10.3109/03091909809032542 · Cited by 30 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Soft wearable devices for deep-tissue sensing
Lin, M., Hu, H., Zhou, S., & Xu, S. H. (2022) · Nature Reviews Materials · doi:10.1038/s41578-022-00427-y · Cited by 349 · 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
Krittanawong, C., Rogers, A. J., Johnson, K. W., Wang, Z., Turakhia, M. P., Halperin, J. L., & Narayan, S. M. (2020) · Nature Reviews Cardiology · doi:10.1038/s41569-020-00445-9 · Cited by 237 · see citing works
Kim, K. B., & Baek, H. J. (2023) · Electronics · doi:10.3390/electronics12132923 · Cited by 238 · see citing works
Mejía‐Mejía, E., May, J. M., Torres, R., & Kyriacou, P. A. (2020) · Physiological Measurement · doi:10.1088/1361-6579/ab998c · Cited by 160 · see citing works
Blood Pressure Estimation Using Photoplethysmogram Signal and Its Morphological Features
Hasanzadeh, N., Ahmadi, M. M., & Mohammadzade, H. (2019) · IEEE Sensors Journal · doi:10.1109/jsen.2019.2961411 · Cited by 144 · see citing works
Personalized Blood Pressure Estimation Using Photoplethysmography: A Transfer Learning Approach
Leitner, J., Chiang, P.-H., & Dey, S. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3085526 · Cited by 140 · see citing works
Can Photoplethysmography Replace Arterial Blood Pressure in the Assessment of Blood Pressure?
Martínez, G., Howard, N., Abbott, D., Lim, K. I., Ward, R. K., & Elgendi, M. (2018) · Journal of Clinical Medicine · doi:10.3390/jcm7100316 · Cited by 133 · see citing works
Heart Rate Variability in the Perinatal Period: A Critical and Conceptual Review
Chiera, M., Cerritelli, F., Casini, A., Barsotti, N., Boschiero, D., Cavigioli, F., Corti, C. G., & Manzotti, A. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.561186 · Cited by 128 · 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
Hussain, L. (2018) · Cognitive Neurodynamics · doi:10.1007/s11571-018-9477-1 · Cited by 105 · see citing works
A Real-time PPG Quality Assessment Approach for Healthcare Internet-of-Things
Naeini, E. K., Azimi, I., Rahmani, A. M., Liljeberg, P., & Dutt, N. D. (2019) · Procedia Computer Science · doi:10.1016/j.procs.2019.04.074 · Cited by 90 · see citing works
Hussain, L., Awan, I. A., Aziz, W., Saeed, S., Ali, A., Zeshan, F., & Kwak, K. S. (2020) · BioMed Research International · doi:10.1155/2020/4281243 · Cited by 86 · see citing works
Long-Term IoT-Based Maternal Monitoring: System Design and Evaluation
Sarhaddi, F., Azimi, I., Labbaf, S., Niela‐Vilén, H., Dutt, N. D., Axelin, A., Liljeberg, P., & Rahmani, A. M. (2021) · Sensors · doi:10.3390/s21072281 · Cited by 85 · see citing works
Contactless Vital Signs Monitoring From Videos Recorded With Digital Cameras: An Overview
Molinaro, N., Schena, E., Silvestri, S., Bonotti, F., Aguzzi, D., Viola, E., Buccolini, F., & Massaroni, C. (2022) · Frontiers in Physiology · doi:10.3389/fphys.2022.801709 · Cited by 85 · see citing works
A comprehensive accuracy assessment of Samsung smartwatch heart rate and heart rate variability
Sarhaddi, F., Kazemi, K., Azimi, I., Cao, R., Niela‐Vilén, H., Axelin, A., Liljeberg, P., & Rahmani, A. M. (2022) · PLoS ONE · doi:10.1371/journal.pone.0268361 · Cited by 82 · see citing works
Wireless and battery-free platforms for collection of biosignals
Stuart, T., Cai, L., Burton, A., & Gutruf, P. (2021) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2021.113007 · Cited by 76 · see citing works
Varma, N., Cygankiewicz, I., Turakhia, M. P., Heidbuchel, H., Hu, Y.-F., Chen, L. Y., Couderc, J.-P., Cronin, E. M., Estep, J. D., Grieten, L., et al. (2021) · Circulation: Arrhythmia and Electrophysiology · doi:10.1161/circep.120.009204 · Cited by 74 · see citing works
Béres, S., & Hejjel, L. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.102589 · Cited by 70 · see citing works
Kim, D.-K., Kim, Y., Kim, H., & Kim, D. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3172514 · Cited by 67 · see citing works
Li, L., & Zhang, Y. (2021) · Transportation · doi:10.1007/s11116-021-10240-1 · Cited by 63 · see citing works
Sarhaddi, F., Azimi, I., Axelin, A., Niela‐Vilén, H., Liljeberg, P., & Rahmani, A. M. (2022) · JMIR mhealth and uhealth · doi:10.2196/33458 · Cited by 56 · see citing works
Varma, N., Cygankiewicz, I., Turakhia, M. P., Heidbüchel, H., Hu, Y., Chen, L. Y., Couderc, J., Cronin, E. M., Estep, J. D., Grieten, L., et al. (2021) · Annals of Noninvasive Electrocardiology · doi:10.1111/anec.12795 · Cited by 50 · see citing works
Evaluation of the Possible Use of PPG Waveform Features Measured at Low Sampling Rate
Fujita, D., & Suzuki, A. (2019) · IEEE Access · doi:10.1109/access.2019.2914498 · Cited by 48 · see citing works
Niela‐Vilén, H., Auxier, J. N., Ekholm, E. M. K., Sarhaddi, F., Mehrabadi, M. A., Mahmoudzadeh, A., Azimi, I., Liljeberg, P., Rahmani, A. M., & Axelin, A. (2021) · PLoS ONE · doi:10.1371/journal.pone.0246494 · Cited by 47 · see citing works
Mejía‐Mejía, E., May, J. M., Elgendi, M., & Kyriacou, P. A. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00447-y · Cited by 42 · see citing works
Peláez‐Coca, M. D., Hernando, A., Lázaro, J. M., & Gil, E. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3099208 · Cited by 41 · see citing works
A Deep Learning–based PPG Quality Assessment Approach for Heart Rate and Heart Rate Variability
Naeini, E. K., Sarhaddi, F., Azimi, I., Liljeberg, P., Dutt, N. D., & Rahmani, A. M. (2023) · ACM Transactions on Computing for Healthcare · doi:10.1145/3616019 · Cited by 39 · see citing works
Quality Assessment for the Photoplethysmogram (PPG)
Orphanidou, C. (2017) · SpringerBriefs in bioengineering · doi:10.1007/978-3-319-68415-4_3 · Cited by 39 · see citing works
Varma, N., Cygankiewicz, I., Turakhia, M. P., Heidbüchel, H., Hu, Y., Chen, L. Y., Couderc, J., Cronin, E. M., Estep, J. D., Grieten, L., et al. (2021) · Journal of Arrhythmia · doi:10.1002/joa3.12461 · Cited by 37 · see citing works
Mohebbian, M. R., Dinh, A., Wahid, K. A., & Alam, M. S. (2019) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2019.101682 · Cited by 35 · see citing works
PPG Derived Heart Rate Estimation During Intensive Physical Exercise
Motin, M. A., Karmakar, C. K., & Palaniswami, M. S. (2019) · IEEE Access · doi:10.1109/access.2019.2913148 · Cited by 35 · see citing works
Béres, S., Holczer, L., & Hejjel, L. (2019) · Measurement Science Review · doi:10.2478/msr-2019-0030 · Cited by 33 · see citing works
Non-Contact Blood Pressure Estimation From Radar Signals by a Stacked Deformable Convolution Network
Qiu, Y., Ma, X., Li, X., Fan, S., Deng, Z., & Huang, X. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3400961 · Cited by 29 · see citing works
Hu, X., Sgherza, T. R., Nothrup, J. B., Fresco, D. M., Naragon‐Gainey, K., & Bylsma, L. M. (2024) · Behavior Research Methods · doi:10.3758/s13428-024-02387-3 · Cited by 29 · see citing works
Silva, R., Salvador, G. F. D., Bota, P. J., Fred, A. L. N., & Silva, H. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07212-6 · Cited by 26 · see citing works
Optimizing sampling rate of wrist-worn optical sensors for physiologic monitoring
Bent, B., & Dunn, J. (2020) · Journal of Clinical and Translational Science · doi:10.1017/cts.2020.526 · Cited by 27 · see citing works
Zaunseder, S., Vehkaoja, A. T., Fleischhauer, V., & Antink, C. H. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.103538 · Cited by 26 · see citing works
Mejía‐Mejía, E., May, J. M., & Kyriacou, P. A. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.106724 · Cited by 26 · see citing works
Enhancing the Robustness of Smartphone Photoplethysmography: A Signal Quality Index Approach
Liu, I., Ni, S., & Peng, K. (2020) · Sensors · doi:10.3390/s20071923 · Cited by 26 · see citing works
Effects of hot-humid exposure on human cognitive performance under sustained multi-tasks
Zhu, H., Hu, M., Hu, S., Wang, H., & Guan, J. (2022) · Energy and Buildings · doi:10.1016/j.enbuild.2022.112704 · Cited by 24 · see citing works
Feasibility of assessing ultra-short-term pulse rate variability from video recordings
Finžgar, M., & Podržaj, P. (2020) · PeerJ · doi:10.7717/peerj.8342 · Cited by 22 · see citing works
Martins, D., Davies, C., Micheli, A. D., Oliver, D., Krawczun-Rygmaczewska, A., Fusar‐Poli, P., & Paloyelis, Y. (2020) · Translational Psychiatry · doi:10.1038/s41398-020-00890-7 · Cited by 24 · see citing works
Regression analysis for detecting epileptic seizure with different feature extracting strategies
Hussain, L., Saeed, S., Idris, A., Awan, I. A., Shah, S. A., Majid, A., Ahmed, B., & Chaudhary, Q.-A. (2019) · Biomedizinische Technik/Biomedical Engineering · doi:10.1515/bmt-2018-0012 · Cited by 21 · see citing works
Mohapatra, P., Premkumar, P. S., & Sivaprakasam, M. (2018) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2017.2786677 · Cited by 20 · see citing works
Establishing best practices in photoplethysmography signal acquisition and processing
Charlton, P. H., Pilt, K., & Kyriacou, P. A. (2022) · Physiological Measurement · doi:10.1088/1361-6579/ac6cc4 · Cited by 23 · see citing works
Fioravanti, V. B. O., Freitas, P. G., Rodrigues, P. G., de Lima, R. G., Lucafo, G. D., Cabello, F. C., Seidel, I., & Penatti, O. A. B. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105689 · Cited by 19 · see citing works
Reali, P., Lolatto, R., Coelli, S., Tartaglia, G., & Bianchi, A. M. (2022) · Sensors · doi:10.3390/s22041428 · Cited by 19 · see citing works
Baigutanova, A., Park, S., Constantinides, M., Lee, S., Quercia, D., & Cha, M. (2025) · Scientific Data · doi:10.1038/s41597-025-05801-3 · Cited by 18 · see citing works
Baek, H. J., Shin, J., Jin, G., & Cho, J. (2017) · Journal of Medical Systems · doi:10.1007/s10916-017-0842-0 · Cited by 17 · see citing works
Prada, E. J. A., Gallego, C. A. B., & García, J. F. C. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.102606 · Cited by 16 · see citing works
Varma, N., Cygankiewicz, I., Turakhia, M. P., Heidbuchel, H., Hu, Y., Chen, L. Y., Couderc, J.-P., Cronin, E. M., Estep, J. D., Grieten, L., et al. (2021) · Cardiovascular Digital Health Journal · doi:10.1016/j.cvdhj.2020.11.004 · Cited by 16 · see citing works
RapidHRV: an open-source toolbox for extracting heart rate and heart rate variability
Kirk, P. A., Bryan, A. D., Garfinkel, S. N., & Robinson, O. J. (2022) · PeerJ · doi:10.7717/peerj.13147 · Cited by 15 · see citing works
Varma, N., Cygankiewicz, I., Turakhia, M. P., Heidbüchel, H., Hu, Y., Chen, L. Y., Couderc, J., Cronin, E. M., Estep, J. D., Grieten, L., et al. (2021) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztab001 · Cited by 16 · see citing works
Shintomi, A., Izumi, S., Yoshimoto, M., & Kawaguchi, H. (2022) · Healthcare Technology Letters · doi:10.1049/htl2.12023 · Cited by 14 · see citing works
A Mobile Mental Health Monitoring System: A Smart Glove
Fraiwan, L., Basmaji, T., & Hassanin, O. (2018) · International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) · doi:10.1109/sitis.2018.00043 · Cited by 14 · see citing works
Stress Detection Using Smartphone Extracted Photoplethysmography
Panganiban, F. C., & de Leon, F. A. (2021) · IEEE Region 10 Symposium (TENSYMP) · doi:10.1109/tensymp52854.2021.9550905 · Cited by 13 · see citing works
Zhou, Y., Lindsey, B., Snyder, S., Bell, E. M., Reider, L., Vignos, M. F., Bar-Kochba, E., Mousavi, A. S., Parreira, J. D., Hanley, C. L., et al. (2024) · Physiological Measurement · doi:10.1088/1361-6579/ad252d · Cited by 13 · see citing works
Towards pulse rate parametrization during free-living activities using smart wristband
Rapalis, A., Petrėnas, A., Šimaitytė, M., Bailón, R., & Marozas, V. (2018) · Physiological Measurement · doi:10.1088/1361-6579/aac24a · Cited by 12 · see citing works
Gingl, Z., Makan, G., Mellár, J., Vadai, G., & Mingesz, R. (2019) · IEEE Access · doi:10.1109/access.2019.2926519 · Cited by 12 · see citing works
Derivation of Breathing Metrics From a Photoplethysmogram at Rest: Machine Learning Methodology
Prinable, J., Jones, P. W., Boland, D., Thamrin, C., & McEwan, A. L. (2020) · JMIR mhealth and uhealth · doi:10.2196/13737 · Cited by 11 · see citing works
Biosignal Compression Toolbox for Digital Biomarker Discovery
Bent, B., Lu, B., Kim, J., & Dunn, J. (2021) · Sensors · doi:10.3390/s21020516 · Cited by 11 · see citing works
Niela‐Vilén, H., Ekholm, E. M. K., Sarhaddi, F., Azimi, I., Rahmani, A. M., Liljeberg, P., Pasanen, M., & Axelin, A. (2023) · Sexual & Reproductive Healthcare · doi:10.1016/j.srhc.2023.100820 · Cited by 10 · see citing works
Sarhaddi, F., Azimi, I., Niela‐Vilén, H., Axelin, A., Liljeberg, P., & Rahmani, A. M. (2023) · JMIR Formative Research · doi:10.2196/47950 · Cited by 16 · 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
Firoozabadi, R., Helfenbein, E. D., & Babaeizadeh, S. (2017) · Journal of Electrocardiology · doi:10.1016/j.jelectrocard.2017.08.020 · Cited by 10 · see citing works
The quest for raw signals: a quality review of publicly available photoplethysmography datasets
Wolling, F., & Van Laerhoven, K. (2020) · Proceedings of the Third Workshop on Data: Acquisition To Analysis · doi:10.1145/3419016.3431485 · Cited by 9 · see citing works
Li, X., Hu, C., Meng, A., Guo, Y., Chen, Y., & Dang, R. (2022) · Health Science Reports · doi:10.1002/hsr2.477 · Cited by 9 · see citing works
Hassan, H., Zulkifli, M. S. A., Suhaime, M. A. M., Kaidi, H. M., & Bakar, R. A. (2021) · IEEE Symposium on Industrial Electronics & Applications (ISIEA) · doi:10.1109/isiea51897.2021.9509987 · Cited by 8 · see citing works
Thermal comfort under weightlessness: A physiological prediction
Zhu, H., Huang, L., Yu, C. W., & Su, H. (2020) · Indoor and Built Environment · doi:10.1177/1420326x20935279 · Cited by 8 · see citing works
Zaman, M. S., & Morshed, B. I. (2021) · Measurement · doi:10.1016/j.measurement.2021.109214 · Cited by 8 · see citing works
Development of a prototype for the analysis of multiple responses of the autonomic nervous system
Naranjo, D., Cattaneo, R., & Mesin, L. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.102994 · Cited by 7 · see citing works
Sultan, M. A., & Saadeh, W. (2023) · IEEE Open Journal of Engineering in Medicine and Biology · doi:10.1109/ojemb.2023.3329728 · Cited by 7 · see citing works
Fewer Samples for a Longer Life Span
Wolling, F., & Laerhoven, K. V. (2018) · International Workshop on Sensor-based Activity Recognition and Interaction (iWOAR) · doi:10.1145/3266157.3266209 · Cited by 7 · see citing works
Meng, Z., Yang, X., Liu, X., Wang, D., & Han, X. (2022) · Physiological Measurement · doi:10.1088/1361-6579/ac9d7f · Cited by 6 · 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
Kiselev, A. R., Posnenkova, O. M., Karavaev, A. S., Shvartz, V. A., Novikov, M. Y., & Gridnev, V. I. (2024) · Biomedicines · doi:10.3390/biomedicines12092088 · Cited by 6 · see citing works
Charlton, P. H., Prada, E. J. A., Mant, J., & Kyriacou, P. A. (2025) · Physiological Measurement · doi:10.1088/1361-6579/adb89e · Cited by 8 · see citing works
Casanova‐Lizón, A., Sarabia, J. M., Pastor, D., Javaloyes, A., Peña‐González, I., & Moya‐Ramón, M. (2023) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph20021528 · Cited by 6 · see citing works
Luo, W., Sharma, V., & Young, D. J. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3329100 · Cited by 6 · see citing works
Unity in Diversity: Sampling Strategies in Wearable Photoplethysmography
Wolling, F., Heimes, S., & Laerhoven, K. V. (2019) · IEEE Pervasive Computing · doi:10.1109/mprv.2019.2926613 · Cited by 6 · see citing works
High Sampling Rate Smartphone-PPG via Built-in Rolling Shutter Image Sensor
Rachim, V. P., Baek, J., Kim, Y. S., Kim, Y., & Park, S. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3201910 · Cited by 6 · see citing works
Choi, A., & Shin, H. (2017) · Physiological Measurement · doi:10.1088/1361-6579/aa930b · Cited by 6 · see citing works
Self-citationPredicting maternal social loneliness by passive sensing with wearable devicesPreprint
Sarhaddi, F., Azimi, I., Niela‐Vilén, H., Axelin, A., Liljeberg, P., & Rahmani, A. M. (2022) · medRxiv · doi:10.1101/2022.10.10.22280910 · Cited by 7 · see citing works
Spectral analysis for pulse rate variability assessment from simulated photoplethysmographic signals
Mejía‐Mejía, E., & Kyriacou, P. A. (2022) · Frontiers in Physiology · doi:10.3389/fphys.2022.966130 · Cited by 5 · see citing works
Inequity aversion toward AI counterparts
Borthakur, D., Diep, P., & Plaks, J. E. (2025) · Scientific Reports · doi:10.1038/s41598-025-22673-0 · Cited by 5 · see citing works
Varma, N., Cygankiewicz, I., & Turakhia, M. P. (2021) · Russian Journal of Cardiology · doi:10.15829/1560-4071-2021-4420 · Cited by 6 · see citing works
A fast permutation entropy for pulse rate variability online analysis with one-sample recursion
Zhan, J., Gan, Z., Chou, L., Hu, L., Zhou, Y., Yang, H., & Chou, Y. (2023) · Medical Engineering & Physics · doi:10.1016/j.medengphy.2023.104050 · Cited by 5 · see citing works
Shin, H. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108952 · Cited by 4 · see citing works
Self-citationAdvanced analyses of physiological signals in the neonatal intensive care unit
Huvanandana, J., Thamrin, C., Tracy, M. B., Hinder, M. K., Nguyen, C., & McEwan, A. L. (2017) · Physiological Measurement · doi:10.1088/1361-6579/aa8a13 · Cited by 4 · see citing works
Analysis and Comparison of Heart Rate Variability Signals Derived from PPG and ECG Sensors
Królak, A., & Pilecka, E. (2021) · Lecture notes in networks and systems · doi:10.1007/978-3-030-83704-4_2 · 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
Poulsen, M. B., Julu, P. O. O., Røikjer, J., Drewes, A. M., & Brock, C. (2025) · Frontiers in Endocrinology · doi:10.3389/fendo.2025.1653881 · Cited by 3 · see citing works
Mejía‐Mejía, E., Budidha, K., Abay, T. Y., May, J. M., & Kyriacou, P. A. (2020) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc44109.2020.9175856 · Cited by 3 · see citing works
An Online–Offline Machine Learning Approach for Estimating Driver States Using Multi-Sensory Data
Jiao, Y., & Yu, J. (2026) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2026.3652470 · Cited by 2 · see citing works
Toward Automatic and Remote Monitoring of the Pain Experience: An Internet of Things (IoT) Approach
Rodríguez, J. J. R., García, J. F. C., & Prada, E. J. A. (2020) · Communications in computer and information science · doi:10.1007/978-3-030-42520-3_16 · Cited by 2 · see citing works
Yokoyama, M., Miyazawa, T., Hanawa, H., Hirata, K., Yokoyama, H., Kubota, K., Fujino, T., Kanemura, N., Kaneko, H., Adili, A., et al. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105841 · Cited by 2 · see citing works
Chang, C.-K., Chen, Y., & Juan, C.-H. (2024) · Progress in brain research · doi:10.1016/bs.pbr.2024.04.002 · Cited by 2 · see citing works
Pulse Wave Harmony: Ancient Wisdoms for Modern AgePreprint
Celka, P., Samten, L., Brucet, M., & Alabdulgader, A. (2019) · Preprints.org · doi:10.20944/preprints201906.0204.v1 · Cited by 2 · see citing works
- Authors
- Choi, A., & Shin, H.
- Affiliations
- Gachon University (KR)
- Chonnam National University (KR)
- Corresponding author
- Choi, A
- Shin, H
- Venue
- Physiological Measurement
- Publisher
- IOP Publishing
- Published
- 2017-02-23
- Volume
- 38
- Issue
- 3
- Pages
- 586-600
- ISSN
- 0967-3334 (ISSN-L)
- 1361-6579
- OpenAlex
- W2586184806
- Cited by
- 116
- References
- 25
- Citations by year
- 2026: 8 · 2025: 5 · 2024: 8 · 2023: 12 · 2022: 23 · 2021: 24 · 2020: 14 · 2019: 11 · 2018: 6 · 2017: 5
- FWCI
- 4.44
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Physiological Measurement (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Ministry of Science, ICT & Future Planning (NRF-2015R1C1A1A02036535)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Keywords
- photoplethysmography, pulse rate variability, sampling frequency, downsampling, wearable devices, heart rate variability, signal reliability, power consumption, computational load, time and frequency domain analysis, electrocardiogram comparison
- MeSH terms
- Female, Heart Rate, Humans, Male, Pilot Projects, Signal Processing, Computer-Assisted, Photoplethysmography, Young Adult
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
