The Analysis of PPG Morphology: Investigating the Effects of Aging on Arterial Compliance

Photoplethysmography and its application in clinical physiological measurement

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Is Pulse Pressure Useful in Predicting Risk for Coronary Heart Disease?

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Determination of age-related increases in large artery stiffness by digital pulse contour analysis

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Arterial Stiffness: A New Cardiovascular Risk Factor?

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Utility of the Photoplethysmogram in Circulatory Monitoring

Reisner, A. T., Shaltis, P., McCombie, D. B., Asada, H. H., Warner, D. S., & Warner, M. A. A. (2008) · Anesthesiology · doi:10.1097/aln.0b013e31816c89e1 · Cited by 365

Arterial stiffness and central blood pressure, as determined by pulse wave analysis, in rheumatoid arthritis

Klocke, R., Cockcroft, J. R., Taylor, G. J., Hall, I. R., & Blake, D. R. (2003) · Annals of the Rheumatic Diseases · doi:10.1136/ard.62.5.414 · Cited by 190

Validation of a device to measure arterial pulse wave velocity by a photoplethysmographic method

Loukogeorgakis, S., Dawson, R., Phillips, N., Martyn, C. N., & Greenwald, S. E. (2002) · Physiological Measurement · doi:10.1088/0967-3334/23/3/309 · Cited by 164

Detection and Processing Techniques of FECG Signal for Fetal Monitoring

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Microvascular blood flow and skin temperature changes in the fingers following a deep inspiratory gasp

Allen, J., Frame, J. R., & Murray, A. (2002) · Physiological Measurement · doi:10.1088/0967-3334/23/2/312 · Cited by 86

Analysis of the Effect of Ageing on Rising Edge Characteristics of the Photoplethysmogram using a Modified Windkessel Model

Zahedi, E., Chellappan, K., Ali, M. A. M., & Singh, H. (2007) · Cardiovascular Engineering An International Journal · doi:10.1007/s10558-007-9037-5 · Cited by 60

A review on wearable photoplethysmography sensors and their potential future applications in health care

Ghamari, M. (2018) · International Journal of Biosensors & Bioelectronics · doi:10.15406/ijbsbe.2018.04.00125 · Cited by 945

Photoplethysmogram Analysis and Applications: An Integrative Review

Park, J., Seok, H. S., Kim, S.-S., & Shin, H. (2022) · Frontiers in Physiology · doi:10.3389/fphys.2021.808451 · Cited by 514

Blood Pressure Estimation from Photoplethysmogram Using a Spectro-Temporal Deep Neural Network

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Sources of inaccuracy in photoplethysmography for continuous cardiovascular monitoring

Fine, J., Branan, K. L., Rodriguez, A. J., Boonya-ananta, T., Ajmal, Ramella-Roman, J. C., McShane, M. J., & Coté, G. L. (2021) · Biosensors · doi:10.3390/bios11040126 · Cited by 348

Assessing hemodynamics from the photoplethysmogram to gain insights into vascular age: a review from VascAgeNet

Charlton, P. H., Paliakaitė, B., Pilt, K., Bachler, M., Zanelli, S., Kulin, D., Allen, J., Hallab, M., Bianchini, E., Mayer, C. C., et al. (2021) · American Journal of Physiology-Heart and Circulatory Physiology · doi:10.1152/ajpheart.00392.2021 · Cited by 166

Assessment of Non-Invasive Blood Pressure Prediction from PPG and rPPG Signals Using Deep Learning

Schrumpf, F., Frenzel, P., Aust, C., Osterhoff, G., & Fuchs, M. (2021) · Sensors · doi:10.3390/s21186022 · Cited by 152

Vascular ageing: moving from bench towards bedside

Climie, R. E., Alastruey, J., Mayer, C. C., Schwarz, A., Laučytė-Cibulskienė, A., Voicehovska, J., Bianchini, E., Bruno, R. M., Charlton, P. H., Grillo, A., et al. (2023) · European Journal of Preventive Cardiology · doi:10.1093/eurjpc/zwad028 · Cited by 149

XGBoost Regression of the Most Significant Photoplethysmogram Features for Assessing Vascular Aging

Shin, H. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3151091 · Cited by 81

Advanced Uses of Pulse Oximetry for Monitoring Mechanically Ventilated Patients

Tusman, G., Böhm, S. H., & Suárez-Sipmann, F. (2016) · Anesthesia & Analgesia · doi:10.1213/ane.0000000000001283 · Cited by 72

Prediction of vascular aging based on smartphone acquired PPG signals

Dall’Olio, L., Curti, N., Remondini, D., Harb, Y. S., Asselbergs, F. W., Castellani, G., & Uh, H. (2020) · Scientific Reports · doi:10.1038/s41598-020-76816-6 · Cited by 70

Integrated non-invasive biochemical and biophysical sensing systems for health and performance monitoring: A systems perspective

Yokus, M. A., & Daniele, M. A.-A. (2021) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2021.113249 · Cited by 62

New aging index using signal features of both photoplethysmograms and acceleration plethysmograms

Ahn, J. M. (2017) · Healthcare Informatics Research · doi:10.4258/hir.2017.23.1.53 · Cited by 58

Non-Invasive Classification of Blood Glucose Level for Early Detection Diabetes Based on Photoplethysmography Signal

Susana, E., Ramli, K., Murfi, H., & Apriantoro, N. H. (2022) · Information · doi:10.3390/info13020059 · Cited by 59

Identification of Coronary Artery Diseases Using Photoplethysmography Signals and Practical Feature Selection Process

Fahoum, A. S. A., Al-Haija, A. O. A., & Alshraideh, H. A. (2023) · Bioengineering · doi:10.3390/bioengineering10020249 · Cited by 59

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

Hickey, M., Phillips, J. P., & Kyriacou, P. A. (2016) · Journal of Clinical Monitoring and Computing · doi:10.1007/s10877-015-9761-0 · Cited by 49

Wrist Photoplethysmography Signal Quality Assessment for Reliable Heart Rate Estimate and Morphological Analysis

Moscato, S., Giudice, S. L., Massaro, G., & Chiari, L. (2022) · Sensors · doi:10.3390/s22155831 · Cited by 51

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

Surgical pleth index monitoring in perioperative pain management: usefulness and limitations

Oh, S. K., Won, Y. J., & Lim, B. G. (2023) · Korean Journal of Anesthesiology · doi:10.4097/kja.23158 · Cited by 43

Photoplethysmography—new applications for an old technology: a sleep technology review

Ryals, S. M., Chiang, A. A.-P., Schutte-Rodin, S., Chandrakantan, A., Verma, N., Holfinger, S. J., Abbasi-Feinberg, F., Bandyopadhyay, A., Baron, K. G., Bhargava, S., et al. (2022) · Journal of Clinical Sleep Medicine · doi:10.5664/jcsm.10300 · Cited by 43

Bilateral Photoplethysmography Analysis for Peripheral Arterial Stenosis Screening With a Fractional-Order Integrator and Info-Gap Decision-Making

Wu, J., Li, C., Ho, Y., Wu, M., Huang, P., & Lin, C. (2015) · IEEE Sensors Journal · doi:10.1109/jsen.2015.2513899 · Cited by 41

Data-driven assessment of cardiovascular ageing through multisite photoplethysmography and electrocardiography

Chiarelli, A. M., Bianco, F., Perpetuini, D., Bucciarelli, V., Filippini, C., Cardone, D., Zappasodi, F., Gallina, S., & Merla, A. (2019) · Medical Engineering & Physics · doi:10.1016/j.medengphy.2019.07.009 · Cited by 40

Multitask deep label distribution learning for blood pressure prediction

Qin, K., Huang, W. X., & Zhang, T. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.02.019 · Cited by 39

Cuff-Less Blood Pressure Prediction Based on Photoplethysmography and Modified ResNet

Qin, C., Li, Y., Liu, C., & Ma, X. (2023) · Bioengineering · doi:10.3390/bioengineering10040400 · Cited by 39

Differences in photoplethysmography morphological features and feature time series between two opposite emotions: Happiness and sadness

Li, F., Yang, L., Shi, H., & Liu, C. (2017) · Artery Research · doi:10.1016/j.artres.2017.02.003 · Cited by 38

Multimodal Finger Pulse Wave Sensing: Comparison of Forcecardiography and Photoplethysmography Sensors

Andreozzi, E., Sabbadini, R., Centracchio, J., Bifulco, P., Irace, A., Breglio, G., & Riccio, M. (2022) · Sensors · doi:10.3390/s22197566 · Cited by 38

An Organic Electrochemical Transistor Integrated Photodetector for High Quality Photoplethysmogram Signal Acquisition

Zhong, Y., Koklu, A., Villalva, D. R., Zhang, Y., Hernandez, L. H., Moser, M., Hallani, R. K., McCulloch, I. A., Baran, D., & Inal, S. (2022) · Advanced Functional Materials · doi:10.1002/adfm.202211479 · Cited by 33

Photoplethysmogram based vascular aging assessment using the deep convolutional neural network

Shin, H., Noh, G., & Choi, B. (2022) · Scientific Reports · doi:10.1038/s41598-022-15240-4 · Cited by 37

Advances in Cuffless Continuous Blood Pressure Monitoring Technology Based on PPG Signals

Qin, C., Wang, X., Xu, G., & Ma, X. (2022) · BioMed Research International · doi:10.1155/2022/8094351 · Cited by 31

Digital processing of ECG and PPG signals for study of arterial parameters for cardiovascular risk assessment

Dutt, D. N., & Shruthi, S. (2015) · International Conference on Communications and Signal Processing (ICCSP) · doi:10.1109/iccsp.2015.7322766 · Cited by 30

Advances in PPG Sensors Data Acquisition With Light-to-Digital Converters: A Review

Chettri, N., Aprile, A., Bonizzoni, E., & Malcovati, P. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3420170 · Cited by 29

Robust blood pressure estimation from finger photoplethysmography using age-dependent linear models

Xing, X., Ma, Z., Zhang, M., Gao, X., Li, Y., Song, M., & Dong, W. (2020) · Physiological Measurement · doi:10.1088/1361-6579/ab755d · Cited by 29

Scenario Adaptive Cuffless Blood Pressure Estimation by Integrating Cardiovascular Coupling Effects

Qiu, S., Zhang, Y., Lau, S.-K., & Zhao, N. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3227235 · Cited by 27

Multi-Site Photoplethysmographic and Electrocardiographic System for Arterial Stiffness and Cardiovascular Status Assessment

Perpetuini, D., Chiarelli, A. M., Maddiona, L., Rinella, S., Bianco, F., Bucciarelli, V., Gallina, S., Perciavalle, V., Vinciguerra, V., Merla, A., et al. (2019) · Sensors · doi:10.3390/s19245570 · Cited by 28

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

Continuous Vital Sign Analysis to Predict Secondary Neurological Decline After Traumatic Brain Injury

Melinosky, C., Yang, S., Hu, P., Li, H.-C., Miller, C. H. T., Khan, I. R., MacKenzie, C. R., Chang, W.-T. W., Parikh, G. Y., Stein, D. M., et al. (2018) · Frontiers in Neurology · doi:10.3389/fneur.2018.00761 · Cited by 26

Effect of surgical pleth index-guided remifentanil administration on perioperative outcomes in elderly patients: a prospective randomized controlled trial

Won, Y. J., Oh, S. K., Lim, B. G., Kim, Y. S., Lee, D. Y., & Lee, J. H. (2023) · BMC Anesthesiology · doi:10.1186/s12871-023-02011-5 · Cited by 22

Aging Index using Photoplethysmography for a Healthcare Device: Comparison with Brachial-Ankle Pulse Wave Velocity

Hong, K. S., Park, K. T., & Ahn, J. M. (2015) · Healthcare Informatics Research · doi:10.4258/hir.2015.21.1.30 · Cited by 21

Estimation of systolic blood pressure by Random Forest using heart sounds and a ballistocardiogram

González-Landaeta, R., Ramirez, B., & Mejia, J. (2022) · Scientific Reports · doi:10.1038/s41598-022-22205-0 · Cited by 24

Quality Assessment and Morphological Analysis of Photoplethysmography in Daily Life

Moscato, S., Palmerini, L., Palumbo, P., & Chiari, L. (2022) · Frontiers in Digital Health · doi:10.3389/fdgth.2022.912353 · Cited by 21

An algorithm to detect dicrotic notch in arterial blood pressure and photoplethysmography waveforms using the iterative envelope mean method

Pal, R., Rudas, Á., Kim, S., Chiang, J. N., Barney, A. M., & Cannesson, M. P. (2024) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2024.108283 · Cited by 22

Deep Learning based non-invasive diabetes predictor using Photoplethysmography signals

Srinivasan, V. B., & Foroozan, F. (2021) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco54536.2021.9616351 · Cited by 19

Enhancing diffuse correlation spectroscopy pulsatile cerebral blood flow signal with near-infrared spectroscopy photoplethysmography

Wu, K.-C., Martin, A., Renna, M., Robinson, M. B., Ozana, N. N., Carp, S. A., & Franceschini, M. A. (2023) · Neurophotonics · doi:10.1117/1.nph.10.3.035008 · Cited by 17

Artificial intelligence-derived photoplethysmography age as a digital biomarker for cardiovascular health

Nie, G., Zhao, Q., Tang, G., Li, Y., & Hong, S. (2025) · Communications Medicine · doi:10.1038/s43856-025-01188-9 · Cited by 18

Precision Heart Rate Estimation Using a PPG Sensor Patch Equipped with New Algorithms of Pre-Quality Checking and Hankel Decomposition

Thakur, S., Chao, P. C.-P., & Tsai, C.-H. (2023) · Sensors · doi:10.3390/s23136180 · Cited by 16

Applied machine learning for blood pressure estimation using a small, real-world electrocardiogram and photoplethysmogram dataset

Wong, M. K. F., Hei, H., Lim, S. Z., & Ng, E. Y. K. (2022) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2023045 · Cited by 18

Photoplethysmography Based Arrhythmia Detection and Classification

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Vascular Aging Estimation Based on Artificial Neural Network Using Photoplethysmogram Waveform Decomposition: Retrospective Cohort Study

Park, J., & Shin, H. (2022) · JMIR Medical Informatics · doi:10.2196/33439 · Cited by 15

The Impact of Contact Force on Signal Quality Indices in Photoplethysmography Measurements

Cause, J. L., Morillo, Á. S., Naranjo, J. C. G., Stiens, J. H., & da Silva, B. (2024) · Applied Sciences · doi:10.3390/app14135704 · Cited by 14

Personalized PPG Normalization Based on Subject Heartbeat in Resting State Condition

Gasparini, F., Grossi, A., Giltri, M., & Bandini, S. (2022) · Signals · doi:10.3390/signals3020016 · Cited by 13

Diagnosis of Peripheral Artery Disease Using Backflow Abnormalities in Proximal Recordings of Accelerometer Contact Microphone (ACM)

Shokouhmand, A., Wen, H., Khan, S., Puma, J. A., Patel, A., Green, P., Ayazi, F., & Tavassolian, N. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3218595 · Cited by 13

Peripheral arterial disease screening for hemodialysis patients using a fractional‐order integrator and transition probability decision‐making model

Wu, J., Li, C., Chen, G., Ho, Y., & Lin, C. (2017) · IET Systems Biology · doi:10.1049/iet-syb.2016.0046 · Cited by 13

Stretchable Photodiodes with Polymer-Engineered Semiconductor Nanowires for Wearable Photoplethysmography

Mao, P., Li, H., Shan, X., Davis, M., Tang, T., Zhang, Y., Tong, X., Xin, Y., Cheng, J., Li, L., et al. (2023) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.3c04494 · Cited by 12

Source–Detector Spectral Pairing-Related Inaccuracies in Pulse Oximetry: Evaluation of the Wavelength Shift

Tsiakaka, O., Gosselin, B. J., & Feruglio, S. (2020) · Sensors · doi:10.3390/s20113302 · Cited by 18

Methods of Extracting Feature from Photoplethysmogram Waveform for Non-Invasive Diagnostic Applications

Hamid, H. A., & Nayan, N. A. (2020) · International Journal of Online and Biomedical Engineering (iJOE) · doi:10.3991/ijoe.v16i09.13577 · Cited by 13

On the use of indexes derived from photoplethysmographic (PPG) signals for postoperative pain assessment: A narrative review

Prada, E. J. A., & Valencia, R. M. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.104335 · Cited by 12

A Multimodal System for Comprehensive Cardiovascular Monitoring Using ECG, PCG, and PPG Signal Fusion

Tran, K. T., Tran, T. T. T., Huynh, D. N., Le, N. K., Le, C. D., Mai, H. X., Linh, H. Q., & Nguyen, T.-L. (2025) · Sensors · doi:10.3390/s25216708 · Cited by 12

Myoelectric Pattern Recognition Performance Enhancement Using Nonlinear Features

Islam, M. J., Ahmad, S., Haque, F., Reaz, M. B. I., Bhuiyan, M. A. S., Minhad, K. N., & Islam, M. R. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/6414664 · Cited by 9

Remote photoplethysmography with consumer smartphone reveals temporal differences between glabrous and nonglabrous skin: Pilot in vivo study

Burton, T., Saiko, G., Cao, M., & Douplik, A. (2022) · Journal of Biophotonics · doi:10.1002/jbio.202200187 · Cited by 10

Anesthesiologist as Physiologist: Discussion and Examples of Clinical Waveform Analysis

Alian, A. A. (2016) · Anesthesia & Analgesia · doi:10.1213/ane.0000000000001468 · Cited by 10

Hardware Prototype for Wrist-Worn Simultaneous Monitoring of Environmental, Behavioral, and Physiological Parameters

Haghi, M., Danyali, S., Thurow, K., Warnecke, J. M., Wang, J., & Deserno, T. M. (2020) · Applied Sciences · doi:10.3390/app10165470 · Cited by 9

Noninvasive hemodynamic indices of vascular aging: an in silico assessment

Hong, J., Nandi, M., Charlton, P. H., & Alastruey, J. (2023) · American Journal of Physiology-Heart and Circulatory Physiology · doi:10.1152/ajpheart.00454.2023 · Cited by 11

Clinical validation of comprehensive time- and frequency-domain photoplethysmography features from a single-sensor system for non-invasive assessment of vascular load and systolic blood pressure

Lin, C.-N., Chang, C.-P., Lin, J., Chang, J.-N., Hung, Y.-C., & Ko, P.-Y. (2025) · Frontiers in Physiology · doi:10.3389/fphys.2025.1695391 · Cited by 9

Multifunctional Photoplethysmography Sensor Design for Respiratory and Cardiovascular Diagnosis

Uguz, D. U., Venema, B., Leonhardt, S. K., & Teichmann, D. (2018) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-981-10-9038-7_167 · Cited by 9

Comparison of Six Shallow and Deep Regressors in Continuous Blood Pressure and Heart Rate Estimation Using Single-Channel Photoplethysmograms

Kanoga, S., Hoshino, T., Kamei, S., Kobayashi, T., Ohmori, T., Uchiyama, M., & Tada, M. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105029 · Cited by 9

Advancing PPG-based cf-PWV estimation with an integrated CNN-BiLSTM-Attention model

Abrisham, K. P., Alipour, K., Tarvirdizadeh, B., & Ghamari, M. (2024) · Signal Image and Video Processing · doi:10.1007/s11760-024-03496-4 · Cited by 9

Age-Related Changes in the Characteristics of the Elderly Females Using the Signal Features of an Earlobe Photoplethysmogram

Seo, J.-W., Choi, J.-M., Lee, K. H., & Kim, J. U. (2021) · Sensors · doi:10.3390/s21237782 · Cited by 8

Evaluation of PPG Feature Values Toward Biometric Authentication Against Presentation Attacks

Hinatsu, S., Suzuki, D., Ishizuka, H., Ikeda, S., & Oshiro, O. (2022) · IEEE Access · doi:10.1109/access.2022.3167667 · Cited by 8

Data-Driven Guided Attention for Analysis of Physiological Waveforms With Deep Learning

Martinez, J., Nowroozilarki, Z., Jafari, R., & Mortazavi, B. J. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3199199 · Cited by 8

Using the ear photoplethysmographic waveform as an early indicator of central hypovolemia in healthy volunteers utilizing LBNP induced hypovolemia model

Eid, A.-M., Elgamal, M. H., Fiol, A. G., Shelley, K. H., Wu, H., & Alian, A. A. (2023) · Physiological Measurement · doi:10.1088/1361-6579/acd165 · Cited by 8

One-Minute Finger Pulsation Measurement for Diabetes Rapid Screening with 1.3% to 13% False-Negative Prediction Rate

Chu, J., Yang, W.-T., Hsieh, T., & Yang, F.-L. (2021) · Biomedical Statistics and Informatics · doi:10.11648/j.bsi.20210601.12 · Cited by 8

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

P2E-LGAN: PPG to ECG Reconstruction Methodology using LSTM based Generative Adversarial Network

Kumari, R., Sarkar, S., Dutta, D., Das, P., & Acharyya, A. (2024) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas58744.2024.10558493 · Cited by 7

Mobile medical systems for equitable healthcare

Chan, J., Goel, M., Gollakota, S., & Nandakumar, R. (2025) · Nature Reviews Bioengineering · doi:10.1038/s44222-025-00330-5 · Cited by 8

Development of integrated photoplethysmographic recording circuit for trans-nail pulse-wave monitoring system

Qian, Z., Takezawa, Y., Shimokawa, K., Kino, H., Fukushima, T., Kiyoyama, K., & Tanaka, T. (2018) · Japanese Journal of Applied Physics · doi:10.7567/jjap.57.04fm11 · Cited by 7

Review of Non-Invasive Blood Glucose Level Estimation based on Photoplethysmography and Artificial Intelligent Technology

Susana, E., & Ramli, K. (2021) · International Conference on Quality in Research (QiR): International Symposium on Electrical and Computer Engineering · doi:10.1109/qir54354.2021.9716164 · Cited by 7

Optimal Design of a New Strain-Type Sensor for Cuff-Less Blood Pressure Measurement via Finite Element Modeling and Taguchi Method

Tu, T.-Y., & Chao, P. C.-P. (2019) · IEEE Sensors Journal · doi:10.1109/jsen.2019.2927993 · Cited by 6

PPGTempStitch: A MATLAB Toolbox for Augmenting Annotated Photoplethsmogram Signals

Tang, Q., Chen, Z., Menon, C., Ward, R. K., & Elgendi, M. (2021) · Sensors · doi:10.3390/s21124007 · Cited by 6

Evaluation of Cardiorespiratory Function During Cardiopulmonary Exercise Testing in Untreated Hypertensive and Healthy Subjects

Zhang, Y., Jiang, Z., Qi, L., Xu, L., Sun, X., Chu, X., Liu, Y., Zhang, T., & Greenwald, S. E. (2018) · Frontiers in Physiology · doi:10.3389/fphys.2018.01590 · Cited by 6

Toward the monitoring of the spinal cord: A feasibility study

Tsiakaka, O., & Feruglio, S. (2018) · Microelectronics Journal · doi:10.1016/j.mejo.2018.01.026 · Cited by 6

Optical Biosensors—Principles of Operation and Applications

Błachowicz, T., Ehrmann, G., Stepula, E., & Ehrmann, A. (2026) · Micromachines · doi:10.3390/mi17050579 · Cited by 5

Deep Learning-Based Estimation of Arterial Stiffness from PPG Spectrograms: A Novel Approach for Non-Invasive Cardiovascular Diagnostics

Abrisham, K. P., Alipour, K., Tarvirdizadeh, B., & Ghamari, M. (2024) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc53108.2024.10782553 · Cited by 6

Reliability of Pulse Rate Variability in Elderly Men and Women: an Application of Cross-Mapping Approach

Nardelli, M., Greco, A., Vanello, N., & Scilingo, E. P. (2021) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc46164.2021.9630550 · Cited by 5

Design of a Two-Channel Forehead PPG Sensor

Du, Y. H., Sun, C., Zhang, M., & Mai, S. (2023) · IEEE Biomedical Circuits and Systems Conference (BioCAS) · doi:10.1109/biocas58349.2023.10388984 · Cited by 5

Photoplethysmographic assessment of arterial stiffness and endothelial function

Zheng, D., Yao, Y., Morrison, I., & Greenwald, S. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-823374-0.00007-4 · Cited by 7

Hypothesis Scoring for Confidence-Aware Blood Pressure Estimation With Particle Filters

Martinez, J., Passage, B., Mortazavi, B. J., & Jafari, R. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3289192 · Cited by 6

Biological Assessments by Innovative Use of Multi-Wavelength Photoplethysmographic Signals Time Differences

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Vascular Markers among Young Women with CVD Risk Factors

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