Photoplethysmogram Signal Quality Evaluation by Unsupervised Learning Approach
Abstract
The following topics are dealt with: feature extraction; medical signal processing; image classification; convolutional neural nets; learning (artificial intelligence); image segmentation; medical image processing; particle swarm optimisation; control system synthesis; support vector machines.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
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?
A clean BVP signal from the Empatica E4 without noise from motion artifacts has a smooth quasi-periodic signature, which can be separated from random noise or motion artifacts by analyzing the entropy of the signal spectrum.
“A clean BVP signal from the Empatica E4 without noise from motion artifacts has a smooth quasi-periodic signature, which can be separated from random noise or, more importantly, from motion artifacts by analyzing the entropy of the signal spectrum.”
From Data quality evaluation in wearable monitoring (2022) · cites this paper as [33]
Cites
Approximate entropy as a measure of system complexity.
Pincus, S. M. (1991) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.88.6.2297 · Cited by 5,811 · see citing works
Detecting dynamical changes in time series using the permutation entropy
Cao, Y., Tung, W., Gao, J., Protopopescu, V., & Hively, L. M. (2004) · Physical Review E · doi:10.1103/physreve.70.046217 · Cited by 545 · see citing works
Orphanidou, C., Bonnici, T., Charlton, P., Clifton, D., Vallance, D., & Tarassenko, L. (2014) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2014.2338351 · Cited by 368 · see citing works
Optimal Signal Quality Index for Photoplethysmogram Signals
Elgendi, M. (2016) · Bioengineering · doi:10.3390/bioengineering3040021 · Cited by 338 · see citing works
Characterization of EEG—A comparative study
Kannathal, N., Acharya, U. R., Lim, C. M., & Puthusserypady, S. K. (2005) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2005.06.005 · Cited by 285 · see citing works
Dynamic time warping and machine learning for signal quality assessment of pulsatile signals
Li, Q., & Clifford, G. D. (2012) · Physiological Measurement · doi:10.1088/0967-3334/33/9/1491 · Cited by 274 · see citing works
Application of Higuchi's fractal dimension from basic to clinical neurophysiology: A review
Kesić, S., & Spasić, S. Z. (2016) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2016.05.014 · Cited by 246 · see citing works
Use of Sample Entropy Approach to Study Heart Rate Variability in Obstructive Sleep Apnea Syndrome
Al-Angari, H. M., & Sahakian, A. V. (2007) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2006.889772 · Cited by 243 · see citing works
Signal quality measures for pulse oximetry through waveform morphology analysis
Sukor, J. A., Redmond, S. J., & Lovell, N. H. (2011) · Physiological Measurement · doi:10.1088/0967-3334/32/3/008 · Cited by 166 · see citing works
Photoplethysmogram signal quality estimation using repeated Gaussian filters and cross-correlation
Karlen, W., Kobayashi, K., Ansermino, J. M., & Dumont, G. A. (2012) · Physiological Measurement · doi:10.1088/0967-3334/33/10/1617 · Cited by 123 · see citing works
Wu, B., & Wang, K.-C. (2005) · IEEE Transactions on Speech and Audio Processing · doi:10.1109/tsa.2005.851909 · Cited by 118 · see citing works
Drift counteraction with multiple self-organising maps for an electronic nose
Zuppa, M. (2004) · Sensors and Actuators B Chemical · doi:10.1016/j.snb.2003.10.029 · Cited by 112 · see citing works
Comparison of fractal dimension estimation algorithms for epileptic seizure onset detection
Polychronaki, G. E., Ktonas, P. Y., Gatzonis, S., Siatouni, A., Asvestas, P. A., Tsekou, H., Sakas, D. E., & Nikita, K. S. (2010) · Journal of Neural Engineering · doi:10.1088/1741-2560/7/4/046007 · Cited by 106 · see citing works
Dao, D. K., Salehizadeh, S. M. A., Noh, Y., Chong, J. W., Cho, C. H., McManus, D., Darling, C. E., Mendelson, Y., & Chon, K. H. (2016) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2016.2612059 · Cited by 104 · see citing works
An Algorithm for Real-Time Pulse Waveform Segmentation and Artifact Detection in Photoplethysmograms
Fischer, C., Dömer, B., Wibmer, T., & Penzel, T. (2016) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2016.2518202 · Cited by 91 · 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
A Supervised Approach to Robust Photoplethysmography Quality Assessment
Pereira, T., Gadhoumi, K., Ma, M., Liu, X., Xiao, R., Colorado, R. A., Keenan, K. J., Meisel, K. M., & Hu, X. (2019) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2019.2909065 · Cited by 80 · see citing works
Real-Time PPG Signal Quality Assessment System for Improving Battery Life and False Alarms
Vadrevu, S., & Manikandan, M. S. (2019) · IEEE Transactions on Circuits & Systems II Express Briefs · doi:10.1109/tcsii.2019.2891636 · Cited by 79 · see citing works
Roy, M. S., Gupta, R., Chandra, J. K., Sharma, K. D., & Talukdar, A. (2018) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2018.2829488 · Cited by 76 · see citing works
Self-citationCouceiro, R., Carvalho, P., Paiva, R. P., Henriques, J. M. O., & Muehlsteff, J. (2014) · Physiological Measurement · doi:10.1088/0967-3334/35/12/2369 · Cited by 64 · see citing works
Evaluation of the signal quality of wrist-based photoplethysmography
Pradhan, N., Rajan, S., & Adler, A. (2019) · Physiological Measurement · doi:10.1088/1361-6579/ab225a · Cited by 50 · see citing works
Photoplethysmography Assessments in Cardiovascular Disease
Allen, J., Overbeck, K., Stansby, G. P., & Murray, A. (2006) · Measurement and Control · doi:10.1177/002029400603900303 · Cited by 37 · see citing works
Effect of Trends on Detrended Fluctuation Analysis of Precipitation Series
Yue, J., Zhao, X., & Shang, P. (2010) · Mathematical Problems in Engineering · doi:10.1155/2010/749894 · Cited by 34 · see citing works
Hierarchical disaster image classification for situation report enhancement
Yang, Y., Ha, H.-Y., Fleites, F. C., Chen, S., & Luis, S. (2011) · IEEE International Conference on Information Reuse and Integration (IRI) · doi:10.1109/iri.2011.6009543 · Cited by 32 · see citing works
Roy, B., & Gupta, R. (2019) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2019.101676 · Cited by 30 · see citing works
Self-citationPeng, F., Liu, H., & Wang, W. (2015) · Physiological Measurement · doi:10.1088/0967-3334/36/10/2159 · Cited by 24 · see citing works
Wander, J. D., & Morris, D. (2014) · Physiological Measurement · doi:10.1088/0967-3334/35/12/2543 · Cited by 21 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Photoplethysmography signal processing and synthesis
Mejía-Mejía, E., Allen, J., Budidha, K., El-Hajj, C., Kyriacou, P. A., & Charlton, P. H. (2022) · Photoplethysmography · doi:10.1016/b978-0-12-823374-0.00015-3 · Cited by 138 · see citing works
Data quality evaluation in wearable monitoring
Böttcher, S., Vieluf, S., Bruno, E., Joseph, B., Epitashvili, N., Biondi, A., Zabler, N., Glasstetter, M., Dümpelmann, M., Van Laerhoven, K., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-25949-x · Cited by 91 · see citing works
Deep convolutional neural network-based signal quality assessment for photoplethysmogram
Shin, H. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105430 · Cited by 42 · see citing works
Langlais, É. L., Thériault-Lauzier, P., Marquis‐Gravel, G., Kulbay, M., So, D. Y. F., Tanguay, J., Ly, H. Q., Gallo, R., Lesage, F., & Avram, R. (2022) · Journal of Cardiovascular Translational Research · doi:10.1007/s12265-022-10260-x · Cited by 31 · see citing works
A Survey of Photoplethysmography and Imaging Photoplethysmography Quality Assessment Methods
Desquins, T., Bousefsaf, F., Pruski, A., & Maaoui, C. (2022) · Applied Sciences · doi:10.3390/app12199582 · Cited by 33 · see citing works
Feli, M., Azimi, I., Anzanpour, A., Rahmani, A. M., & Liljeberg, P. (2023) · Smart Health · doi:10.1016/j.smhl.2023.100390 · Cited by 29 · see citing works
Xu, H., Yan, W., Lan, K., Ma, C., Wu, D., Wu, A., Yang, Z., Wang, J., Zang, Y., Yan, M., et al. (2021) · JMIR mhealth and uhealth · doi:10.2196/25415 · Cited by 26 · see citing works
Photoplethysmography signal quality assessment using attractor reconstruction analysis
Schmith, J., Kelsch, C., Cunha, B., Prade, L. R., Martins, E. A., Keller, A. L., & de Figueiredo, R. M. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105142 · Cited by 21 · see citing works
Personalized Continuous Blood Pressure Tracking Through Single Channel PPG in Wearable Scenarios
Zhang, Y., Zhou, C., Ren, X., Wang, Q., Wang, H., Xiang, T., Qiu, S., Zhang, Y., & Ye, X. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3535788 · Cited by 20 · see citing works
Roy, M. S., Gupta, R., & Sharma, K. D. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3212750 · Cited by 20 · see citing works
Self-citationPrada, E. J. A., & García, J. F. C. (2024) · Sensors · doi:10.3390/s24227193 · Cited by 18 · see citing works
Prasun, P., Mukhopadhyay, S., & Gupta, R. (2021) · Measurement Science and Technology · doi:10.1088/1361-6501/ac2d5b · Cited by 15 · see citing works
Self-citationOvadia‐Blechman, Z., Hauptman, Y., Rabin, N., Wiezman, G., Hoffer, O., Gertz, S. D., Gavish, B., & Gavish, L. (2023) · Frontiers in Physiology · doi:10.3389/fphys.2023.1175470 · Cited by 14 · see citing works
Non-invasive Fetal ECG Signal Quality Assessment based on Unsupervised Learning Approach
Shi, X., Yamamoto, K., Ohtsuki, T. O., Matsui, Y., & Owada, K. (2022) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc48229.2022.9870908 · Cited by 12 · see citing works
Design of a Realtime Photoplethysmogram Signal Quality Checker for Wearables and Edge Computing
Banerjee, T., Gavas, R. D., Mithun, B. S., Karmakar, S., Ramakrishnan, R. K., & Pal, A. (2022) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc48229.2022.9871741 · Cited by 8 · see citing works
Unsupervised Learning-Based Non-Invasive Fetal ECG Muti-Level Signal Quality Assessment
Shi, X., Yamamoto, K., Ohtsuki, T. O., Matsui, Y., & Owada, K. (2023) · Bioengineering · doi:10.3390/bioengineering10010066 · Cited by 8 · see citing works
Real-Time PPG-to-ECG Reconstruction Model With On-Device Recalibration Facility
Murmu, N., Gupta, R., & Sharma, K. D. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3450120 · Cited by 6 · see citing works
Self-citationShi, X., Niida, N., Yamamoto, K., Ohtsuki, T. O., Matsui, Y., & Owada, K. (2023) · Sensors · doi:10.3390/s23249698 · Cited by 4 · see citing works
Sabatini, A., Bacco, L., Lotini, L., Palombo, G., Tomaso, G. D., Sabbadini, R., Merone, M., & Vollero, L. (2023) · IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0&IoT) · doi:10.1109/metroind4.0iot57462.2023.10180134 · Cited by 3 · see citing works
Bose, A., Mukherjee, S., Chandra, J. K., Gupta, R., & Ghosh, D. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3351256 · Cited by 3 · see citing works
Self-citationUnsupervised Representation Learning-based Doppler Ultrasound Signal Quality Assessment
Shi, X., Yamamoto, K., Ohtsuki, T. O., Matsui, Y., & Owada, K. (2022) · Globecom · doi:10.1109/globecom48099.2022.10000857 · Cited by 2 · see citing works
Sardar, P., Gupta, R., & Mukhopadhyay, S. (2022) · IEEE Silchar Subsection Conference (SILCON) · doi:10.1109/silcon55242.2022.10028787 · Cited by 1 · see citing works
Self-citationAutomated estimation of blood pressure using PPG recordings: an updated review
Liu, H. (2024) · Elsevier eBooks · doi:10.1016/b978-0-44-314141-6.00014-1 · Cited by 1 · see citing works
AI Regulations in Medical Devices
Khinvasara, T. (2026) · Springer handbooks · doi:10.1007/978-3-032-01636-2_40
- Authors
- Roy, M. S., Gupta, R., & Sharma, K. D.
- Affiliations
- University of Calcutta (IN)
- Venue
- 2020 IEEE Applied Signal Processing Conference (ASPCON)
- Publisher
- IEEE
- Published
- 2020-10-07
- Pages
- 6-10
- OpenAlex
- W3111412575
- Cited by
- 24
- References
- 27
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 4 · 2023: 6 · 2022: 9 · 2021: 3
- FWCI
- 8.17
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Applied Signal Processing Conference (ASPCON) (publishedVersion)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Physical Sciences › Computer Science › Signal Processing › Biometric Identification and Security
- Keywords
- photoplethysmogram, signal quality evaluation, unsupervised learning, feature extraction, biomedical signal processing, image classification, convolutional neural networks, image segmentation, particle swarm optimization, support vector machine, controller synthesis
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