An optimal filter for short photoplethysmogram signals

Photoplethysmography and its application in clinical physiological measurement

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A Method for Removing Imaging Artifact from Continuous EEG Recorded during Functional MRI

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On the Analysis of Fingertip Photoplethysmogram Signals

Elgendi, M. (2012) · Current Cardiology Reviews · doi:10.2174/157340312801215782 · Cited by 1,206 · see citing works

Aortic Stiffness

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Motion Artifact Reduction in Photoplethysmography Using Independent Component Analysis

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Printed multifunctional flexible device with an integrated motion sensor for health care monitoring

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Optimal Signal Quality Index for Photoplethysmogram Signals

Elgendi, M. (2016) · Bioengineering · doi:10.3390/bioengineering3040021 · Cited by 338 · see citing works

Systolic Peak Detection in Acceleration Photoplethysmograms Measured from Emergency Responders in Tropical Conditions

Elgendi, M., Norton, I., Brearley, M. B., Abbott, D., & Schuurmans, D. (2013) · PLoS ONE · doi:10.1371/journal.pone.0076585 · Cited by 282 · see citing works

Two-Stage Approach for Detection and Reduction of Motion Artifacts in Photoplethysmographic Data

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Optical blood pressure estimation with photoplethysmography and FFT-based neural networks

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High-performance flexible energy storage and harvesting system for wearable electronics

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Revisiting QRS Detection Methodologies for Portable, Wearable, Battery-Operated, and Wireless ECG Systems

Elgendi, M., Eskofier, B. M., Dokos, S., & Abbott, D. (2014) · PLoS ONE · doi:10.1371/journal.pone.0084018 · Cited by 224 · see citing works

The difference in pulse transit time to the toe and finger measured by photoplethysmography

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A new, short-recorded photoplethysmogram dataset for blood pressure monitoring in China

Liang, Y., Chen, Z., Liu, G., & Elgendi, M. (2018) · Scientific Data · doi:10.1038/sdata.2018.20 · Cited by 215 · see citing works

Digital Photoplethysmography for Assessment of Arterial Stiffness: Repeatability and Comparison with Applanation Tonometry

von Wowern, E., Östling, G., Nilsson, P. M., & Olofsson, P. (2015) · PLOS ONE · doi:10.1371/journal.pone.0135659 · Cited by 134 · see citing works

Weighing Scale-Based Pulse Transit Time is a Superior Marker of Blood Pressure than Conventional Pulse Arrival Time

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Toward Generating More Diagnostic Features from Photoplethysmogram Waveforms

Elgendi, M., Liang, Y., & Ward, R. K. (2018) · Diseases · doi:10.3390/diseases6010020 · Cited by 107 · see citing works

Signal and Image Denoising Using Wavelet Transform

Ergen, B. (2012) · InTech eBooks · doi:10.5772/36434 · Cited by 105 · see citing works

Review of ‘the Potential Role of Arterial Stiffness in the Pathogenesis of Alzheimer’s Disease’

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Detection of a and b waves in the acceleration photoplethysmogram

Elgendi, M., Norton, I., Brearley, M. B., Abbott, D., & Schuurmans, D. (2014) · BioMedical Engineering OnLine · doi:10.1186/1475-925x-13-139 · Cited by 100 · see citing works

New photoplethysmogram indicators for improving cuffless and continuous blood pressure estimation accuracy

Lin, W.-H., Wang, H., Samuel, O. W., Liu, G., Huang, Z., & Li, G. (2018) · Physiological Measurement · doi:10.1088/1361-6579/aaa454 · Cited by 78 · see citing works

Adaptive Noise Cancellation Using Accelerometers for the PPG Signal from Forehead

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Identification of Atrial Fibrillation by Quantitative Analyses of Fingertip Photoplethysmogram

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Estimation of distributed arterial mechanical properties using a wave propagation model in a reverse way.

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Beat-to-beat ambulatory blood pressure estimation based on random forest

He, R., Huang, Z., Ji, L., Wu, J., Li, H., & Zhang, Z. (2016) · Wearable and Implantable Body Sensor Networks · doi:10.1109/bsn.2016.7516258 · Cited by 54 · see citing works

Investigation of peripheral photoplethysmographic morphology changes induced during a hand-elevation study

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Signal Quality Estimation With Multichannel Adaptive Filtering in Intensive Care Settings

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A New Wristband Wearable Sensor Using Adaptive Reduction Filter to Reduce Motion Artifact

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The effect of vascular changes on the photoplethysmographic signal at different hand elevations

Hickey, M., Phillips, J. P., & Kyriacou, P. A. (2015) · Physiological Measurement · doi:10.1088/0967-3334/36/3/425 · Cited by 43 · see citing works

Study of Artifact-Resistive Technology Based on a Novel Dual Photoplethysmography Method for Wearable Pulse Rate Monitors

Zhou, C., Feng, J., Hu, J. H., & Ye, X. (2015) · Journal of Medical Systems · doi:10.1007/s10916-015-0412-2 · Cited by 38 · see citing works

Photoplethysmography for the Assessment of Haemorheology

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Cuff-less blood pressure measurement using pulse arrival time and a Kalman filter

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A multimodal approach to reduce false arrhythmia alarms in the intensive care unit

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TERMA Framework for Biomedical Signal Analysis: An Economic-Inspired Approach

Elgendi, M. (2016) · Biosensors · doi:10.3390/bios6040055 · Cited by 26 · see citing works

Blockwise PPG Enhancement Based on Time-Variant Zero-Phase Harmonic Notch Filtering

Park, C., Shin, H., & Lee, B. (2017) · Sensors · doi:10.3390/s17040860 · Cited by 26 · see citing works

A comb filter based signal processing method to effectively reduce motion artifacts from photoplethysmographic signals

Peng, F., Liu, H., & Wang, W. (2015) · Physiological Measurement · doi:10.1088/0967-3334/36/10/2159 · Cited by 24 · see citing works

On Time Domain Analysis of Photoplethysmogram Signals for Monitoring Heat Stress

Elgendi, M., Fletcher, R., Norton, I., Brearley, M. B., Abbott, D., Lovell, N. H., & Schuurmans, D. (2015) · Sensors · doi:10.3390/s151024716 · Cited by 21 · see citing works

Motion artifacts reduction from PPG using cyclic moving average filter

Lee, J. (2014) · Technology and Health Care · doi:10.3233/thc-140798 · Cited by 21 · see citing works

Interval set classifiers using support vector machines

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Age-related factors that confound peripheral pulse timing characteristics in Caucasian children

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A new movement artifact detector for photoplethysmographic signals

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A Six-Step Framework on Biomedical Signal Analysis for Tackling Noncommunicable Diseases: Current and Future Perspectives

Elgendi, M., Howard, N., Lovell, N. H., Cichocki, A. S., Brearley, M. B., Abbott, D., & Adatia, I. (2016) · JMIR Biomedical Engineering · doi:10.2196/biomedeng.6401 · Cited by 18 · see citing works

Less Is More in Biosignal Analysis: Compressed Data Could Open the Door to Faster and Better Diagnosis

Elgendi, M. (2018) · Diseases · doi:10.3390/diseases6010018 · Cited by 18 · see citing works

Eventogram: A Visual Representation of Main Events in Biomedical Signals

Elgendi, M. (2016) · Bioengineering · doi:10.3390/bioengineering3040022 · Cited by 17 · see citing works

PPG-BP Database

Liang, Y., Liu, G., Chen, Z., & Elgendi, M. (2017) · Figshare · doi:10.6084/m9.figshare.5459299 · Cited by 16 · see citing works

Gender differences in tibial microvascular flow responses to head down tilt and lower body negative pressure

Siamwala, J. H., Macias, B. R., Lee, P. C., & Hargens, A. R. (2017) · Physiological Reports · doi:10.14814/phy2.13143 · Cited by 13 · see citing works

Towards Investigating Global Warming Impact on Human Health Using Derivatives of Photoplethysmogram Signals

Elgendi, M., Norton, I., Brearley, M. B., Fletcher, R., Abbott, D., Lovell, N. H., & Schuurmans, D. (2015) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph121012776 · Cited by 10 · see citing works

The photoplethysmographic amplitude to pulse pressure ratio can track sudden changes in vascular compliance and resistance during liver graft reperfusion

Kim, W., Kim, J.-W., Moon, Y., Kim, S., Hwang, G., & Shin, W. (2017) · Medicine · doi:10.1097/md.0000000000007045 · Cited by 10 · see citing works

Photoplethysmographic signal processing using adaptive sum comb filter for pulse delay measurement; pp. 78–94

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Dual-Mode Additive Noise Rejection in Wearable Photoplethysmography

Patterson, J. A. C., & Yang, G. (2012) · doi:10.1109/bsn.2012.15 · Cited by 6 · see citing works

Peripheral arterial volume distensibility changes with applied external pressure: significant difference between arteries with different compliance

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Proceedings of the 21st IEEE Instrumentation and Measurement Technology Conference

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A Comparative Analysis of Adaptive IIR Filtering Techniques using LabVIEW

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A simple method for evaluating the balance of the autonomic nervous system using photoplethysmography

Tsuchiya, M., & Yokoyama, M. (2017) · Microsystem Technologies · doi:10.1007/s00542-017-3388-7 · Cited by 3 · see citing works

The use of photoplethysmography for assessing hypertension

Elgendi, M., Fletcher, R., Liang, Y., Howard, N., Lovell, N. H., Abbott, D., Lim, K., & Ward, R. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0136-7 · Cited by 625 · see citing works

Estimating Blood Pressure from the Photoplethysmogram Signal and Demographic Features Using Machine Learning Techniques

Chowdhury, M. H., Shuzan, M. N. I., Chowdhury, M. E. H., Mahbub, Z. B., Uddin, M. M., Khandakar, A., & Reaz, M. B. I. (2020) · Sensors · doi:10.3390/s20113127 · Cited by 264 · see citing works

Current progress of photoplethysmography and SPO2 for health monitoring

TAMURA, T. (2019) · Biomedical Engineering Letters · doi:10.1007/s13534-019-00097-w · Cited by 260 · 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

Photoplethysmography and Deep Learning: Enhancing Hypertension Risk Stratification

Liang, Y., Chen, Z., Ward, R. K., & Elgendi, M. (2018) · Biosensors · doi:10.3390/bios8040101 · Cited by 170 · see citing works

A review of machine learning in hypertension detection and blood pressure estimation based on clinical and physiological data

Martinez-Ríos, E. A., Montesinos, L., Alfaro-Ponce, M., & Pecchia, L. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.102813 · Cited by 147 · see citing works

Cuffless Single-Site Photoplethysmography for Blood Pressure Monitoring

Hosanee, M., Chan, G., Welykholowa, K., Cooper, R., Kyriacou, P. A., Zheng, D., Allen, J., Abbott, D., Menon, C., Lovell, N. H., et al. (2020) · Journal of Clinical Medicine · doi:10.3390/jcm9030723 · Cited by 136 · see citing works

PPG2ABP: Translating photoplethysmogram (PPG) signals to arterial blood pressure (ABP) waveforms

Ibtehaz, N., Mahmud, S., Chowdhury, M. E. H., Khandakar, A., Salman Khan, M., Ayari, M. A., Tahir, A. M., & Rahman, M. S. (2022) · Bioengineering · doi:10.3390/bioengineering9110692 · Cited by 133 · see citing works

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

Hypertension Assessment via ECG and PPG Signals: An Evaluation Using MIMIC Database

Liang, Y., Chen, Z., Ward, R. K., & Elgendi, M. (2018) · Diagnostics · doi:10.3390/diagnostics8030065 · Cited by 132 · see citing works

PulseDB: A large, cleaned dataset based on MIMIC-III and VitalDB for benchmarking cuff-less blood pressure estimation methods

Wang, W., Mohseni, P., Kilgore, K. L., & Najafizadeh, L. (2023) · Frontiers in Digital Health · doi:10.3389/fdgth.2022.1090854 · Cited by 112 · see citing works

Organic Multi-Channel Optoelectronic Sensors for Wearable Health Monitoring

Khan, Y., Han, D., Ting, J., Ahmed, M., Nagisetty, R., & Arias, A. C. (2019) · IEEE Access · doi:10.1109/access.2019.2939798 · Cited by 111 · see citing works

Blood Pressure Morphology Assessment from Photoplethysmogram and Demographic Information Using Deep Learning with Attention Mechanism

Aguirre, N. A., Grall‐Maës, E., Cymberknop, L. J., & Armentano, R. L. (2021) · Sensors · doi:10.3390/s21062167 · Cited by 110 · see citing works

MLP-BP: A novel framework for cuffless blood pressure measurement with PPG and ECG signals based on MLP-Mixer neural networks

Huang, B., Chen, W., Lin, C., Juang, C., & Wang, J. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.103404 · Cited by 105 · see citing works

A Method for Stress Detection Using Empatica E4 Bracelet and Machine-Learning Techniques

Campanella, S., Altaleb, A., Belli, A., Pierleoni, P., & Palma, L. (2023) · Sensors · doi:10.3390/s23073565 · Cited by 101 · see citing works

Hypertension Assessment Using Photoplethysmography: A Risk Stratification Approach

Liang, Y., Chen, Z., Ward, R. K., & Elgendi, M. (2018) · Journal of Clinical Medicine · doi:10.3390/jcm8010012 · Cited by 99 · see citing works

How Effective Is Pulse Arrival Time for Evaluating Blood Pressure? Challenges and Recommendations from a Study Using the MIMIC Database

Liang, Y., Abbott, D., Howard, N., Lim, K. I., Ward, R. K., & Elgendi, M. (2019) · Journal of Clinical Medicine · doi:10.3390/jcm8030337 · Cited by 94 · see citing works

SleepPPG-Net: A Deep Learning Algorithm for Robust Sleep Staging From Continuous Photoplethysmography

Kotzen, K., Charlton, P. H., Salabi, S., Amar, L., Landesberg, A., & Behar, J. A. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3225363 · Cited by 80 · see citing works

Analysis on Four Derivative Waveforms of Photoplethysmogram (PPG) for Fiducial Point Detection

Suboh, M. Z., Jaafar, R., Nayan, N. A., Harun, N. H., & Mohamad, M. S. F. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2022.920946 · Cited by 75 · see citing works

Non-invasive arterial blood pressure measurement and SpO2 estimation using PPG signal: a deep learning framework

Chu, Y.-P., Tang, K.-C., Hsu, Y.-C., Huang, T.-T., Wang, D.-L., Li, W.-T., Savitz, S., Jiang, X.-Q., & Shams, S. (2023) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-023-02215-2 · Cited by 73 · see citing works

Noninvasive Classification of Blood Pressure Based on Photoplethysmography Signals Using Bidirectional Long Short-Term Memory and Time-Frequency Analysis

Tjahjadi, H., Ramli, K., & Murfi, H. (2020) · IEEE Access · doi:10.1109/access.2020.2968967 · Cited by 71 · see citing works

A Refined Blood Pressure Estimation Model Based on Single Channel Photoplethysmography

Zhang, Y., Ren, X., Xiao, L., Ye, X., & Zhou, C. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3206477 · Cited by 61 · see citing works

Deep generative model with domain adversarial training for predicting arterial blood pressure waveform from photoplethysmogram signal

Qin, K., Huang, W. X., & Zhang, T. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.102972 · Cited by 60 · see citing works

Non-invasive cuff-less blood pressure estimation using a hybrid deep learning model

Yang, S., Zhang, Y., Cho, S., Correia, R. G., & Morgan, S. P. (2021) · Optical and Quantum Electronics · doi:10.1007/s11082-020-02667-0 · Cited by 59 · see citing works

Multimodal Photoplethysmography-Based Approaches for Improved Detection of Hypertension

Welykholowa, K., Hosanee, M., Chan, G., Cooper, R. E., Kyriacou, P. A., Zheng, D., Allen, J., Abbott, D., Menon, C., Lovell, N. H., et al. (2020) · Journal of Clinical Medicine · doi:10.3390/jcm9041203 · Cited by 51 · see citing works

Filtering-induced time shifts in photoplethysmography pulse features measured at different body sites: the importance of filter definition and standardization

Liu, H., Allen, J., Khalid, S. G., Chen, F., & Zheng, D. (2021) · Physiological Measurement · doi:10.1088/1361-6579/ac0a34 · Cited by 50 · see citing works

High‐Performing Acid‐Free PEDOT:PSS‐Based Organic Photodiodes for Cardiovascular Disease Diagnosis

Zhao, Y., Kim, B. G., Jang, W., Park, J. H., & Wang, D. H. (2023) · Advanced Functional Materials · doi:10.1002/adfm.202309271 · Cited by 46 · see citing works

BiGRU-attention for Continuous blood pressure trends estimation through single channel PPG

Liu, Z., Zhang, Y., & Zhou, C. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107795 · Cited by 41 · 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

Progress in Data Acquisition of Wearable Sensors

Liu, Z., Kong, J., Qu, M., Zhao, G., & Zhang, C. (2022) · Biosensors · doi:10.3390/bios12100889 · Cited by 41 · see citing works

A continuous cuffless blood pressure measurement from optimal PPG characteristic features using machine learning algorithms

Nishan, A., Raju, S. M. T. U., Hossain, M. I., Dipto, S. A., Uddin, S. M. T., Sijan, A., Chowdhury, M. A. S., Ahmad, A., & Khan, M. M. H. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e27779 · Cited by 39 · see citing works

Determinants of photoplethysmography signal quality at the wrist

Charlton, P. H., Marozas, V., Mejía‐Mejía, E., Kyriacou, P. A., & Mant, J. (2025) · PLOS Digital Health · doi:10.1371/journal.pdig.0000585 · Cited by 38 · see citing works

A Machine Learning Approach to Predict Diabetes Using Short Recorded Photoplethysmography and Physiological Characteristics

Hettiarachchi, C., & Chitraranjan, C. D. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-21642-9_41 · Cited by 36 · see citing works

Advancing PPG Signal Quality and Know-How Through Knowledge Translation—From Experts to Student and Researcher

Huthart, S., Elgendi, M., Zheng, D., Stansby, G. P., & Allen, J. (2020) · Frontiers in Digital Health · doi:10.3389/fdgth.2020.619692 · Cited by 35 · see citing works

Attention-based residual improved U-Net model for continuous blood pressure monitoring by using photoplethysmography signal

Yu, M., Huang, Z., Zhu, Y., Zhou, P., & Zhu, J. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.103581 · Cited by 35 · see citing works

Estimation of phase distortions of the photoplethysmographic signal in digital IIR filtering

Lapitan, D. G., Рогаткин, Д. А., Molchanova, E. A., & Tarasov, A. P. (2024) · Scientific Reports · doi:10.1038/s41598-024-57297-3 · Cited by 34 · see citing works

A review: Blood pressure monitoring based on PPG and circadian rhythm

Chen, G., Zou, L., & Ji, Z. (2024) · APL Bioengineering · doi:10.1063/5.0206980 · Cited by 34 · see citing works

PPGMotion: Model-based detection of motion artifacts in photoplethysmography signals

Maity, A. K., Veeraraghavan, A., & Sabharwal, A. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.103632 · Cited by 32 · see citing works

Development of a novel light-sensitive PPG model using PPG scalograms and PPG-NET learning for non-invasive hypertension monitoring

Fahoum, A. S. A., Al-Omari, A., Omari, G. A., & Zyout, A. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e39745 · Cited by 32 · see citing works

SpO 2 Measurement: Non-Idealities and Ways to Improve Estimation Accuracy in Wearable Pulse Oximeters

Berwal, D., Kuruba, A., Shaikh, A. M., Udupa, A., & Baghini, M. S. (2022) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3170069 · Cited by 32 · see citing works

Nanopores‐templated CNT/PDMS Microcolumn Substrate for the Fabrication of Wearable Triboelectric Nanogenerator Sensors to Monitor Human Pulse and Blood Pressure

Zhang, T., Yao, C., Xu, X., Liu, Z., Liu, Z., Sun, T., Huang, S., Huang, X., Farah, S., Shi, P., et al. (2024) · Advanced Materials Technologies · doi:10.1002/admt.202400749 · Cited by 29 · see citing works

A Survey on Denoising Techniques of PPG Signal

Mishra, B., & Nirala, N. (2020) · International Conference for Innovation in Technology (INOCON) · doi:10.1109/inocon50539.2020.9298358 · Cited by 26 · see citing works

PPG and EDA dataset collected with Empatica E4 for stress assessment

Campanella, S., Altaleb, A., Belli, A., Pierleoni, P., & Palma, L. (2024) · Data in Brief · doi:10.1016/j.dib.2024.110102 · Cited by 26 · see citing works

Blood pressure estimation with complexity features from electrocardiogram and photoplethysmogram signals

Yang, S., Zaki, W. S. W., Morgan, S. P., Cho, S., Correia, R. G., & Zhang, Y. (2020) · Optical and Quantum Electronics · doi:10.1007/s11082-020-2260-7 · Cited by 25 · see citing works

Classification of cardiac disorders using 1D local ternary patterns based on pulse plethysmograph signals

Aziz, S., Awais, M., Khan, M. U., Iqtidar, K., & Qamar, U. (2021) · Expert Systems · doi:10.1111/exsy.12664 · Cited by 24 · see citing works

Wearable Wrist Photoplethysmography for Optimal Monitoring of Vital Signs: A Unified Perspective on Pulse Waveforms

Long, N. M. H., & Chung, W. (2022) · IEEE photonics journal · doi:10.1109/jphot.2022.3153506 · Cited by 24 · see citing works

Assessment of Blood Pressure Using Only a Smartphone and Machine Learning Techniques: A Systematic Review

Haugg, F., Elgendi, M., & Menon, C. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2022.894224 · Cited by 23 · see citing works

A novel interpretable feature set optimization method in blood pressure estimation using photoplethysmography signals

Liu, J., Hu, S., Xiao, Z., Hu, Q., Wang, D., & Yang, C. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105184 · Cited by 23 · see citing works

A novel method for continuous blood pressure estimation based on a single-channel photoplethysmogram signal

Hu, Q., Deng, X., Wang, A., & Yang, C. (2020) · Physiological Measurement · doi:10.1088/1361-6579/abc8dd · Cited by 23 · 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

Assessing Elevated Blood Glucose Levels Through Blood Glucose Evaluation and Monitoring Using Machine Learning and Wearable Photoplethysmography Sensors: Algorithm Development and Validation

Shi, B., Dhaliwal, S. S., soo, M., chan, C., Wong, J., Lam, N. W. C., Zhou, E., Paitimusa, V., Loke, K. Y., Chin, J., et al. (2023) · JMIR AI · doi:10.2196/48340 · Cited by 22 · see citing works

Comparison of Different Machine Learning Techniques for the Cuffless Estimation of Blood Pressure using PPG Signals

Kilickaya, S., Guner, A., & Dal, B. (2020) · International Congress on Human-Computer Interaction, Optimization and Robotic Applications (ICHORA) · doi:10.1109/hora49412.2020.9152602 · Cited by 22 · see citing works

Exploring the limitations of blood pressure estimation using the photoplethysmography signal

Dias, F. M., Cárdenas, D. A. C., de Toledo, M. A. F., Oliveira, F. A. C., Ribeiro, E., Krieger, J. E., & Gutierrez, M. A. (2025) · Physiological Measurement · doi:10.1088/1361-6579/adcb86 · Cited by 21 · see citing works

Design and implementation of a photoplethysmography acquisition system with an optimized artificial neural network for accurate blood pressure measurement

Pandey, R. K., Lin, T.-Y., & Chao, P. C.-P. (2021) · Microsystem Technologies · doi:10.1007/s00542-020-05109-9 · Cited by 20 · see citing works

The Relevance of Calibration in Machine Learning-Based Hypertension Risk Assessment Combining Photoplethysmography and Electrocardiography

Cano, J., Fácila, L., Gracia-Baena, J. M., Zangróniz, R., Alcaraz, R., & Rieta, J. J. (2022) · Biosensors · doi:10.3390/bios12050289 · Cited by 20 · see citing works

Blood Pressure Estimation From Photoplethysmography by Considering Intra- and Inter-Subject Variabilities: Guidelines for a Fair Assessment

Costa, T. B. D. S., Dias, F. M., Cárdenas, D. A. C., de Toledo, M. A. F., de Lima, D. M., Krieger, J. E., & Gutierrez, M. A. (2023) · IEEE Access · doi:10.1109/access.2023.3284458 · Cited by 20 · see citing works

Comparison of Machine Learning Models for Predicting Interstitial Glucose Using Smart Watch and Food Log

Ali, H., Niazi, I. K., White, D. E., Akhter, M. N., & Madanian, S. (2024) · Electronics · doi:10.3390/electronics13163192 · Cited by 20 · see citing works

Predicting Blood Pressures for Pregnant Women by PPG and Personalized Deep Learning

Nguyen, D. H., Chao, P. C.-P., Yan, H. F., Tu, T.-Y., Cheng, C.-H., & Phan, T.-P. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3386707 · Cited by 19 · see citing works

Human Activity Recognition Algorithm with Physiological and Inertial Signals Fusion: Photoplethysmography, Electrodermal Activity, and Accelerometry

Gilmore, J., & Nasseri, M. (2024) · Sensors · doi:10.3390/s24103005 · Cited by 19 · see citing works

Validation of a new device for photoplethysmographic measurement of multi-site arterial pulse wave velocity

Sondej, T., Jannasz, I., Sieczkowski, K., Dobrowolski, A. P., Obiała, K., Targowski, T., & Olszewski, R. (2021) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2021.11.001 · Cited by 19 · see citing works

Towards a machine-learning assisted non-invasive classification of dengue severity using wearable PPG data: a prospective clinical study

Karolcik, S., Manginas, V., Chanh, H. Q., Daniels, J., Giang, N. T., Huyen, V. N. T., Hoang, M. T. V., Phan Nguyen Quoc, K., Hernandez, B., Ming, D. K., et al. (2024) · eBioMedicine · doi:10.1016/j.ebiom.2024.105164 · Cited by 18 · see citing works

PPG EduKit: An Adjustable Photoplethysmography Evaluation System for Educational Activities

Morillo, Á. S., Cause, J. L., Baciu, V.-E., da Silva, B., Naranjo, J. C. G., & Stiens, J. H. (2022) · Sensors · doi:10.3390/s22041389 · Cited by 18 · see citing works

Optimal digital filter selection for remote photoplethysmography (rPPG) signal conditioning

Guler, S., Golparvar, A. J., Öztürk, Ö., Doğan, H., & Yapici, M. K. (2023) · Biomedical Physics & Engineering Express · doi:10.1088/2057-1976/acaf8a · Cited by 18 · see citing works

PPGFeat: a novel MATLAB toolbox for extracting PPG fiducial points

Abdullah, S., Hafid, A., Folke, M., Lindén, M., & Kristoffersson, A. (2023) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2023.1199604 · Cited by 18 · see citing works

Reconfigurable Architectures with High-Frequency Noise Suppression for Wearable ECG Devices

Jerard, V. J. M., Thilagaraj, M., Pandiaraj, K., Easwaran, M., Govindan, P., & Elamaran, V. (2021) · Journal of Healthcare Engineering · doi:10.1155/2021/1552641 · Cited by 18 · see citing works

A hybrid denoising approach for PPG signals utilizing variational mode decomposition and improved wavelet thresholding

Hu, Q., Li, M., Jiang, L., & Liu, M. (2024) · Technology and Health Care · doi:10.3233/thc-231996 · Cited by 17 · see citing works

DNN-BP: a novel framework for cuffless blood pressure measurement from optimal PPG features using deep learning model

Raju, S. M. T. U., Dipto, S. A., Hossain, M. I., Chowdhury, M. A. S., Haque, F., Nashrah, A. T., Nishan, A., Khan, M. M. H., Hashem, M. M. A., & Hashem, M. M. A. (2024) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-024-03157-1 · Cited by 17 · see citing works

Reduced graphene oxide/silk fibroin/cellulose nanocrystal-based wearable sensors with high efficiency and durability for physiological monitoring

Cho, H., Lee, G., Tsukruk, V. V., & Kim, S. (2023) · Cellulose · doi:10.1007/s10570-023-05289-3 · Cited by 16 · see citing works

Wearable Blood Pressure Monitoring Devices: Understanding Heterogeneity in Design and Evaluation

Cheung, M. Y., Sabharwal, A., Coté, G. L., & Veeraraghavan, A. (2024) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2024.3434344 · Cited by 16 · see citing works

A Touch Enabled Hemodynamic and Metabolic Monitor

Djassemi, O., Saha, T., Nandhakumar, P., Khan, M. I., Fishman, H., Earney, S., Moonla, C., Xu, Y., Thai, H., Morales‐Fermin, S., et al. (2025) · Advanced Science · doi:10.1002/advs.202502138 · Cited by 16 · see citing works

A New Adaptive Readout System For a New OLED OPD Flexible Patch PPG Sensor

Pandey, R. K., Pribadi, E. F., & Chao, P. C.-P. (2019) · Proceedings of IEEE Sensors/Proceedings of IEEE Sensors ... · doi:10.1109/sensors43011.2019.8956825 · Cited by 14 · see citing works

Non-invasive modeling of heart rate and blood pressure from a photoplethysmography by using machine learning techniques

Nidigattu, G. R., Mattela, G., & Jana, S. (2020) · International Conference on Communication Systems & Networks (COMSNETS) · doi:10.1109/comsnets48256.2020.9027457 · Cited by 14 · see citing works

Nonlinear features of photoplethysmography signals for Non-invasive blood pressure estimation

Shoeibi, F., Najafiaghdam, E., & Ebrahimi, A. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105067 · Cited by 14 · see citing works

Hypertension assessment based on feature extraction using a photoplethysmography signal and its derivatives

Yao, L.-P., & Wei-zhang, L. (2020) · Physiological Measurement · doi:10.1088/1361-6579/aba537 · Cited by 14 · see citing works

Experimental Analysis on the Effect of Contact Pressure and Activity Level as Influencing Factors in PPG Sensor Performance

Scardulla, F., Cosoli, G., Gnoffo, C., Antognoli, L., Bongiorno, F., Diana, G., Scalise, L., D’Acquisto, L., & Arnesano, M. (2025) · Sensors · doi:10.3390/s25144477 · Cited by 12 · see citing works

A Novel Fiducial Point Extraction Algorithm to Detect C and D Points from the Acceleration Photoplethysmogram (CnD)

Abdullah, S., Hafid, A., Folke, M., Lindén, M., & Kristoffersson, A. (2023) · Electronics · doi:10.3390/electronics12051174 · Cited by 12 · see citing works

Expert diagnostic system for detection of hypertension and diabetes mellitus using discrete wavelet decomposition of photoplethysmogram signal and machine learning technique

Khan, M. A., Singh, B. K., & Nirala, N. (2023) · Medicine in Novel Technology and Devices · doi:10.1016/j.medntd.2023.100251 · Cited by 12 · see citing works

Calibration-free Blood Pressure Assessment Using An Integrated Deep Learning Method

Han, C., Gu, M., Yu, F., Huang, R., Huang, X., & Cui, L. (2020) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm49941.2020.9313586 · Cited by 12 · see citing works

Justification of the Photoplethysmography Sensor Configuration by Monte Carlo Modeling of the Pulse Waveform

Lapitan, D. G., Tarasov, A. P., & Рогаткин, Д. А. (2022) · Journal of Biomedical Photonics & Engineering · doi:10.18287/jbpe22.08.030306 · Cited by 11 · see citing works

Hardware/Software Co-design of an ECG- PPG Preprocessor: A Qualitative & Quantitative Analysis

Chowdhury, A., Das, D., Cheung, R. C. C., & Chowdhury, M. H. (2023) · International Conference on Electrical, Computer and Communication Engineering · doi:10.1109/ecce57851.2023.10101536 · Cited by 10 · see citing works

Automated Assessment of Cardiovascular Sufficiency Using Non-Invasive Physiological Data

Li, X., Pinsky, M. R., & Dubrawski, A. W. (2022) · Sensors · doi:10.3390/s22031024 · Cited by 10 · see citing works

Intra-beat biomarker for accurate continuous non-invasive blood pressure monitoring

Abiri, A., Chou, E., Qian, C., Rinehart, J. B., & Khine, M. (2022) · Scientific Reports · doi:10.1038/s41598-022-19096-6 · Cited by 10 · see citing works

LEAF-Net: A real-time fine-grained quality assessment system for physiological signals using lightweight evolutionary attention fusion

Liu, J., Hu, S., Wang, Y., Hu, Q., Wang, D., Xiang, W., Feng, X., & Yang, C. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.126995 · Cited by 9 · see citing works

Validation of an Open-Source Smartwatch for Continuous Monitoring of Physical Activity and Heart Rate in Adults

Ravanelli, N., Lefebvre, K., Brough, A., Paquette, S., & Lin, W. (2025) · Sensors · doi:10.3390/s25092926 · Cited by 9 · see citing works

HYPE: Predicting Blood Pressure from Photoplethysmograms in a Hypertensive Population

Sasso, A. M., Datta, S., Jeitler, M., Steckhan, N., Keßler, C. S., Michalsen, A., Bert Arnrich, A., & Böttinger, E. P. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59137-3_29 · Cited by 9 · see citing works

Noninvasive Non-Contact SpO2 Monitoring Using an Integrated Polarization-Sensing CMOS Imaging Sensor

Sarkar, M., & Assaad, M. M. (2022) · Sensors · doi:10.3390/s22207796 · Cited by 9 · see citing works

Schrödinger spectrum and slim CNN architecture-based signal quality estimation for Photoplethysmogram signals

Sarkar, S., & Ghosh, A. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106240 · Cited by 9 · see citing works

Personalized interpretable prediction of perceived sleep quality: Models with meaningful cardiovascular and behavioral features

Moebus, M., & Holz, C. (2024) · PLoS ONE · doi:10.1371/journal.pone.0305258 · Cited by 9 · see citing works

Respiratory Events Estimation From PPG Signals Using a Simple Peak Detection Algorithm

Suleman, M., Motaman, K., Hasanpoor, Y., Ghamari, M., Alipour, K., & Zadeh, M. (2022) · National and International Iranian Conference on Biomedical Engineering (ICBME) · doi:10.1109/icbme57741.2022.10052943 · Cited by 9 · see citing works

Discussion of Cuffless Blood Pressure Prediction Using Plethysmograph Based on a Longitudinal Experiment: Is the Individual Model Necessary?

Kido, K., Chen, Z., Huang, M., TAMURA, T., Chen, W., Ono, N., Takeuchi, M., Altaf‐Ul‐Amin, M., & Kanaya, S. (2021) · Life · doi:10.3390/life12010011 · Cited by 9 · see citing works

HIPPO

Yin, Z., Liyanage, M., Ottun, A.-R. O., Paul, S., Zuniga, A., Nurmi, P., & Flores, H. (2022) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3570344 · Cited by 8 · see citing works

Current Developments in Cuff-Free Non-invasive Continuous Blood Pressure Estimation Using Photoplethysmography

Sarkar, S., & Pahuja, S. K. (2024) · Biomedical Materials & Devices · doi:10.1007/s44174-023-00146-0 · Cited by 7 · see citing works

The MSPTDfast photoplethysmography beat detection algorithm: design, benchmarking, and open-source distribution

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

Design of a Multi-Sensors Wearable System for Continuous Home Monitoring of People With Parkinson’s

Sica, M., Varnosfaderani, O. T., Crowe, C., Kenny, L., Bocchino, A., O’Flynn, B., Mueller, D. S., Tedesco, S. A., Timmons, S., & Barton, J. J. (2024) · IEEE Access · doi:10.1109/access.2024.3375871 · Cited by 8 · see citing works

Rehabilitation Assessment System for Stroke Patients Based on Fusion-Type Optoelectronic Plethysmography Device and Multi-Modality Fusion Model: Design and Validation

Yan, L., Long, Z., Qian, J., Lin, J., Xie, S. Q., & Sheng, B. (2024) · Sensors · doi:10.3390/s24092925 · Cited by 8 · see citing works

Development of IoT Based Cuffless Blood Pressure Measurement System

Norsuriati, M. S., Sobri, M. S. N. M., Hafiszah, H. Z., Adon, M. N., Suhaimizan, W. Z. W., Vajravelu, A., & Mahadi, A. J. M. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2071/1/012030 · Cited by 8 · see citing works

Heart Rate Detection from the Supratrochlear Vessels using a Virtual Reality Headset integrated PPG Sensor

Gnacek, M., Garrido-Leal, D., Lopez, R., Seiss, E., Κωστούλας, Θ., Balaguer‐Ballester, E., Mavridou, I. I., & Nduka, C. C. (2020) · Companion Publication of the International Conference on Multimodal Interaction (ICMI Companion) · doi:10.1145/3395035.3425323 · Cited by 8 · see citing works

Reconstruction of Missing Electrocardiography Signals from Photoplethysmography Data Using Deep Neural Network

Guo, Y., Tang, Q., Li, S., & Chen, Z. (2024) · Bioengineering · doi:10.3390/bioengineering11040365 · Cited by 7 · see citing works

Non-invasive prediction of cholesterol levels from photoplethysmogram (PPG)-based features using machine learning techniques: a proof-of-concept study

Prada, E. J. A., Ojeda, A. V. V., & Ojeda, M. Y. V. (2025) · Cogent Engineering · doi:10.1080/23311916.2025.2467153 · Cited by 7 · see citing works

Exploring supervised machine learning models to estimate blood pressure using non-fiducial features of the photoplethysmogram (PPG) and its derivatives

Prada, E. J. A., & Mosquera, C. D. C. (2025) · Physical and Engineering Sciences in Medicine · doi:10.1007/s13246-025-01593-3 · Cited by 7 · see citing works

Optimal filtering and smoothing thresholds for high-frequency photoplethysmography signals

Karolcik, S., & Georgiou, P. (2024) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas58744.2024.10558515 · Cited by 3 · see citing works

IIR Based Digital Filter Design for Denoising the Photoplethysmography Signal

Eris, O., Eris, S. B., Bílgín, C., & Bozkurt, M. R. (2021) · Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu53274.2021.9477993 · Cited by 4 · see citing works

Comparative Analysis of Photoplethysmography Signal Quality from Right and Left Index Fingers

Kanwal, K., Asif, M., Khalid, S. G., Wasi, S., Zafar, F., Kiran, I., & Abdullah, S. (2023) · Traitement du signal · doi:10.18280/ts.400537 · Cited by 3 · see citing works

Optimal digital filter selection for remote photoplethysmography (rPPG) signal conditioning

Guler, S., Golparvar, A. J., Öztürk, Ö., Doğan, H., & Yapici, M. K. (2023) · Biomedical Physics & Engineering Express · doi:10.1088/2057-1976/acaf8a · Cited by 18 · see citing works

Improving Photoplethysmograph Signal Quality using Butterworth Filters: Denoising and SNR Evaluation Analysis

Nashichah, R., Lusiana, L., & Wahyuningtya, D. T. (2025) · Medika Teknika Jurnal Teknik Elektromedik Indonesia · doi:10.18196/mt.v7i1.26693