The DOI matches its Crossref record; 134 of the 136 references it cites were found in a registry or an index.
Advances in Photopletysmography Signal Analysis for Biomedical Applications
Open accessAbstract
Heart Rate Variability (HRV) is an important tool for the analysis of a patient’s physiological conditions, as well a method aiding the diagnosis of cardiopathies. Photoplethysmography (PPG) is an optical technique applied in the monitoring of the HRV and its adoption has been growing significantly, compared to the most commonly used method in medicine, Electrocardiography (ECG). In this survey, definitions of these technique are presented, the different types of sensors used are explained, and the methods for the study and analysis of the PPG signal (linear and nonlinear methods) are described. Moreover, the progress, and the clinical and practical applicability of the PPG technique in the diagnosis of cardiovascular diseases are evaluated. In addition, the latest technologies utilized in the development of new tools for medical diagnosis are presented, such as Internet of Things, Internet of Health Things, genetic algorithms, artificial intelligence and biosensors which result in personalized advances in e-health and health care. After the study of these technologies, it can be noted that PPG associated with them is an important tool for the diagnosis of some diseases, due to its simplicity, its cost–benefit ratio, the easiness of signals acquisition, and especially because it is a non-invasive technique.
Overview
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
Verification
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?
In the citing paper
“PPG signals are low-amplitude signals and have a normal pulse frequency within the range of 0.5 to 4 Hz [195,196].”
From Sources of inaccuracy in photoplethysmography for continuous cardiovascular monitoring (2021) · cites this paper as [196]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Chobanian, A. V. (2003) · JAMA · doi:10.1001/jama.289.19.2560 · Cited by 25,386
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
Akselrod, S., Gordon, D., Ubel, F. A., Shannon, D. C., Berger, A. C., & Cohen, R. J. (1981) · Science · doi:10.1126/science.6166045 · Cited by 5,079
Photoplethysmography and its application in clinical physiological measurement
Allen, J. (2007) · Physiological Measurement · doi:10.1088/0967-3334/28/3/r01 · Cited by 3,967
The Internet of Things for Health Care: A Comprehensive Survey
Islam, S. M. R., Kwak, D., Kabir, M. H., Hossain, M., & Kwak, K.-S. (2015) · IEEE Access · doi:10.1109/access.2015.2437951 · Cited by 3,199
Heart rate variability: A review
Rajendra Acharya, U., Paul Joseph, K., Kannathal, N., Lim, C. M., & Suri, J. S. (2006) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-006-0119-0 · Cited by 2,820
Mendis, S., Davis, S. M., & Norrving, B. G. (2015) · Stroke · doi:10.1161/strokeaha.115.008097 · Cited by 1,023
Towards fog-driven IoT eHealth: Promises and challenges of IoT in medicine and healthcare
Farahani, B., Firouzi, F., Chang, V. I. C., Badaroglu, M., Constant, N. P., & Mankodiya, K. (2017) · Future Generation Computer Systems · doi:10.1016/j.future.2017.04.036 · Cited by 991
Heart Rate Variability in Athletes
AUBERT, A. E., Seps, B., & Beckers, F. (2003) · Sports Medicine · doi:10.2165/00007256-200333120-00003 · Cited by 981
Flagged first, then by citations
2 of the 136 references couldn’t be matched to a known paper.
Research works printed but not linked: 9
- Paschoal, 2003, Controle autonômico cardíaco durante a execução de atividade física dinâmica de baixa intensidade, Rev. Soc. Cardiol. Estado de São Paulo, 13, S1
- Corrêa, L.A.F. (2006). Sistema não Invasivo de Monitorização da Pressão Arterial e da Onda de Pulso Utilizando a Fotopletismografia. [Ph.D. Thesis, Programa de Pós Graduação em Engenharia Biomédica, Universidade Federal do Rio de Janeiro, UFRJ].
- Kilsztajn, 2003, Health services, expenses and aging of the Brazilian population, Rev. Bras. Study Popul., 20, 93
- Martins, R.M.S. (2010). Fotopletismografia para Monitorização Cardíaca para aplicação no Pulso. [Master’s Thesis, Universidade de Coimbra].
- Khaled, 2006, Employing Time-Domain Methods and Poincaré Plot of Heart Rate Variability Signals to Detect Congestive Heart Failure, BIME J., 6, 35
- Puri, C., Ukil, A., Bandyopadhyay, S., Singh, R., Pal, A., and Mandana, K. (2016, January 25–30). iCarMa: Inexpensive Cardiac Arrhythmia Management–An IoT Healthcare Analytics Solution. Proceedings of the First IoT of Health ’16 Workshop on IoT-enabled Healthcare and Wellness Technologies and Systems, Singapore.
- Pantoni, 2007, Study of heart rate autonomic modulation at rest in elderly patients with chronic obstructive pulmonary disease, Braz. J. Phys. Ther., 11, 35
- Vilhegas, L.Z. (2007). Development of a Prototype for Monitoring Oxygen Saturation and Heart Rate for Rodents. [Master’s Thesis, Título de Engenharia Elétrica, Escola Politécnica].
- Rodrigues, 2018, Enabling Technologies for the Internet of Health Things, IEEE Access, 6, 13129, 10.1109/ACCESS.2017.2789329 · DOI
Cited by
Sorted by citations
Items 201–290 of 290
Reduction of Motion Artifact in PPG signal with CDS-LMS Filter
Lee, W., Park, P., & Cho, S. (2022) · IEEE Asia Pacific Conference on Circuits and Systems (APCCAS) · doi:10.1109/apccas55924.2022.10090402 · Cited by 2
de Pedro-Carracedo, J., Clemente, J., Fuentes-Jiménez, D., Cabrera-Umpiérrez, M. F., & Gonzalez-Marcos, A. P. (2023) · Applied Sciences · doi:10.3390/app13158902 · Cited by 4
A new e-health cloud-based system for cardiovascular risk assessment
Tatsis, G., Baldoumas, G., Christofilakis, V., Kostarakis, P., Varotsos, P. A., Sarlis, N. V., Skordas, E. S., Bechlioulis, A., Michalis, L. K., & Naka, K. Κ. (2023) · Frontiers in Electronics · doi:10.3389/felec.2023.1315132 · Cited by 2
Palanisamy, S., & Rajaguru, H. (2024) · Diagnostics · doi:10.3390/diagnostics14202287 · Cited by 2
AI and Wearable Biometric Devices: Improving Real-Time Health
Alahmed, Y., Omar, D., Hesham, H., Alahmhad, Y., & Sharo, A. A. (2025) · Lecture notes in civil engineering · doi:10.1007/978-3-031-92754-6_2 · Cited by 2
Early detection of mental disorder signs using photoplethysmogram : A review
Awang, A., & Nayan, N. A. (2021) · Journal of Discrete Mathematical Sciences and Cryptography · doi:10.1080/09720529.2021.2009191 · Cited by 2
Digital Volume Pulse Analysis to Differentiate Diabetic From Non-Diabetic Subjects
Qawqzeh, Y. (2019) · Communications in Mathematics and Applications · doi:10.26713/cma.v10i4.1266 · Cited by 3
Stratification of cardiopathies using photoplethysmographic signals
de Moraes, J. L., de Oliveira, T. L., Rocha, M. X., de Vasconcelos, G. G., & de Alexandria, A. R. (2020) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2020.100417 · Cited by 2
Self-citationSharma, R. (2020) · International Journal of Advanced Trends in Computer Science and Engineering · doi:10.30534/ijatcse/2020/138942020 · Cited by 2
Development of a digital finger photoplethysmogram sensor
Kulminskiy, D. D., Kurbako, A. V., Skazkina, V. V., Prokhorov, M. D., Пономаренко, В. И., Kiselev, A. R., Bezruchko, B. P., & Karavaev, A. S. (2021) · Izvestiya of Saratov University Physics · doi:10.18500/1817-3020-2021-21-1-58-68 · Cited by 2
Automated, Portable, and Low-Cost System for Home Screening of Peripheral Arterial Disease
Forghani, N., Maghooli, K., Dabanloo, N. J., Vasheghani‐Farahani, A., & Forouzanfar, M. (2021) · IEEE International Symposium on Medical Measurements and Applications (MeMeA) · doi:10.1109/memea52024.2021.9478761 · Cited by 2
Assessment of Blood Viscosity from Photoplethysmogram
Sulochana, H., & Safwaana, A. (2022) · International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT) · doi:10.1109/iceeict53079.2022.9768640 · Cited by 2
Low Power Flexible Sensor for Ambient Light-based Blood Oxygen Saturation Measurement
Anabestani, H., Sadaghiani, S. M., & Bhadra, S. (2023) · Proceedings of IEEE Sensors/Proceedings of IEEE Sensors ... · doi:10.1109/sensors56945.2023.10325136 · Cited by 2
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 1
Towards Autonomous Physiological Signal Extraction From Thermal Videos Using Deep Learning
Das, K., Abouelenien, M., Burzo, M. G., Elson, J., Prakah-Asante, K. O., & Maranville, C. W. (2023) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3577190.3614123 · Cited by 1
A Brief Review on Recent Advancements of MEMS Devices for Telecommunication Applications
Behera, B. (2024) · Recent Advances in Computer Science and Communications · doi:10.2174/0126662558342193241211063716 · Cited by 1
de Leon, M. F., Camic, C. L., Herring, M. J., & Hill, C. M. (2025) · Sports · doi:10.3390/sports13020049 · Cited by 1
High-quality photoplethysmography signals using a new non-contact method
Hernandez, S. D., & Паез, Г. (2025) · Biophotonics in Exercise Science, Sports Medicine, Health Monitoring Technologies, and Wearables · doi:10.1117/12.3044105 · Cited by 1
Agrawal, P., Kumari, R., Das, P., Sarkar, S., & Acharyya, A. (2025) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas56072.2025.11043726 · Cited by 1
Heartid: A Deep Learning Model for Biometrics Using Multimodal ECG and PPG Signals
Ruan, B., Zhao, Z., Zhang, Y., Deng, Y., Li, G., Jiao, P., & Zhang, X. (2025) · International Conference on Communications · doi:10.1109/icc52391.2025.11161386 · Cited by 1
Song, D., Zou, F., Wang, L., & Wang, H. (2025) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2025.111164 · Cited by 3
Jiao, Y. (2026) · Sensor Review · doi:10.1108/sr-08-2025-0630 · Cited by 1
Detection of anomalies in physiological signals using artificial neural network
Jayashree, K., Bhat, G. V., Hiremath, S., & Shrishail, M. H. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-33082-7.00010-3 · Cited by 1
Wearable monitoring of muscle health across fatigue, injury and recovery in sports
Chang, N., Wu, D., Song, Y., Jin, B., Su, L., Tang, D., Yao, T., & Liu, H. (2026) · Soft Science · doi:10.20517/ss.2025.132 · Cited by 1
Low cost Electronic Instrumentation Solutions for Cardiovascular Parameters Measurement
Acef, L., BOUZERGUI, F., Benouar, S., Hafid, A., & Ferroukhi, M. (2019) · International Conference on Advanced Electrical Engineering (ICAEE) · doi:10.1109/icaee47123.2019.9014818 · Cited by 1
Celka, P., Brucet, M., Granqvist, N., & Schwabl, H. (2021) · Cardiology & Vascular Research · doi:10.33425/2639-8486.s1-1006
Drowsiness Detection System Using PPG Sensor’s Measured Physiological Parameter
Tripathi, J., Chand, S., Kumar, B., Ghansiyal, A., & Nema, A. (2021) · Advances in intelligent systems and computing · doi:10.1007/978-981-16-2597-8_53 · Cited by 1
Kalinichenko, A. N., Олегович, А. Н., & Anisimov, A. A. (2022) · IEEE International Conference on Soft Computing and Measurements (SCM) · doi:10.1109/scm55405.2022.9794865 · Cited by 1
Non-contact cardiac activity monitoring using optical speckle-tolerant pulsed laser vibrometer
Wang, C.-C., TRIVEDI, S. B., Jin, F., Zientara, G. P., Choa, F., & Khurgin, J. B. (2023) · Diagnostic and Therapeutic Applications of Light in Cardiology · doi:10.1117/12.2648096 · Cited by 1
Mishra, H. M., Ahmed, B., Shuja, M., Qtaishat, A., & Kumar, M. (2023) · International Conference on Information Systems and Computer Networks (ISCON) · doi:10.1109/iscon57294.2023.10111959 · Cited by 1
Sharma, R., & Kang, S. S. (2024) · IEEE Region 10 Symposium (TENSYMP) · doi:10.1109/tensymp61132.2024.10752296 · Cited by 1
Clinical Application of AI-ECG
Xue, Z.-K., Chen, K., Li, X.-M., Liu, T., Xie, J., Guo, S., Song, W.-H., Chu, H., Fu, G., Zhang, N., et al. (2024) · Springer eBooks · doi:10.1007/978-981-97-8359-5_3 · Cited by 1
Variational-statistical and spectral analysis of photoplethysmograms
Isupov, I. B., & Zatrudina, R. S. (2019) · Saratov Fall Meeting: Computations and Data Analysis: from Nanoscale Tools to Brain Functions (SFM) · doi:10.1117/12.2523349 · Cited by 1
A Heart Rate Measuring Trainer Kit as a Medical Electronics Practice Learning Media
Anugrah, D., & Waluyanti, S. (2019) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1413/1/012004 · Cited by 1
Simonyan, M. A., Kiselev, A. R., Karavaev, A. S., Ishbulatov, Y. M., Gridnev, V. I., & Posnenkova, O. M. (2020) · Scientific School Dynamics of Complex Networks and their Applications (DCNA) · doi:10.1109/dcnair50402.2020.9216757 · Cited by 1
Multi-sensory portable information system for monitoring and analysis of cardiac data
Gospodinov, M. V., Gospodinova, E. P., & Georgieva-Tsaneva, G. N. (2019) · doi:10.33422/researchconf.2019.12.896 · Cited by 1
Detection of special points in a photoplethysmogram
Gribkov, V. Y., Igor, I., & Zatrudina, R. S. (2021) · Saratov Fall Meeting: Computations and Data Analysis: from Nanoscale Tools to Brain Functions (SFM) · doi:10.1117/12.2591113 · Cited by 1
Poli, A., Cosoli, G., Verdenelli, L., Scardulla, F., D’Acquisto, L., Spinsante, S., & Scalise, L. (2022) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-030-99197-5_17 · Cited by 1
Remote Monitoring Device for People Under Self-Quarantine Due to COVID-19
Bahri, A. M. A., Yammahi, A. M. A., Marzooqi, A. Y. A., Khanum, N. U. M., & Jarndal, A. (2022) · Advances in Science and Engineering Technology International Conferences (ASET) · doi:10.1109/aset53988.2022.9734881 · Cited by 1
Affordable Custom made Physiological Measurement Instrumentation Based on Raspberry PI
Hafid, A., Medjek, M., Bediaf, R., & Difallah, S. (2022) · International Conference on Image and Signal Processing and their Applications (ISPA) · doi:10.1109/ispa54004.2022.9786316 · Cited by 1
Kumar, A., & Praveen, V. (2024) · Research Square · doi:10.21203/rs.3.rs-4014054/v1 · Cited by 1
Detection of Heart Rate Using Reflectance Mode Photoplethysmography
Chakraborty, D., & Jeeva, J. B. (2019) · Innovations in Power and Advanced Computing Technologies (i-PACT) · doi:10.1109/i-pact44901.2019.8960204
Commodity Sensors, Physiological Signals, Research Opportunities, and Practical Issues
Stanford, M. W., & Stanford, V. M. (2019) · IEEE Signal Processing in Medicine and Biology Symposium · doi:10.1109/spmb47826.2019.9037855
Body Sensors System for Physiological Data Long-term Monitoring
Georgieva-Tsaneva, G. N. (2020) · International Conference on Computer Systems and Technologies (CompSysTech) · doi:10.1145/3407982.3408009
Silber, H. A., Win, T. T., Monti, J., & Galiatsatos, P. (2020) · Physiological Measurement · doi:10.1088/1361-6579/abb790
Development of a simulation model of a photoplethysmographic signal under psychoemotional stress
Yavorska, E., Strembitska, O., Strembitskyi, M., & Паньків, І. М. (2021) · Eastern-European Journal of Enterprise Technologies · doi:10.15587/1729-4061.2021.227001
Cardiac Arrhythmia Detection While Driving
Badawi, H. A. A., Megdar, M. A. A., Yousif, M. A. Z., Mustafa, E. M. M., & Abdalrheem, N. O. M. (2021) · International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE) · doi:10.1109/iccceee49695.2021.9429627
Sensor Data Analytics for Health Care
Moharir, M., Srikanth, N., & Pavan, K. R. (2021) · Studies in big data · doi:10.1007/978-981-16-0415-7_5
Deformation of Heartbeat Pulse Waveform Caused by Sensor Binding ForcePreprint
He, J., & Wu, W. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.10014
The Prediction of Atherosclerosis Index Based on Photoplethysmograph
Li, Y., Xie, F., Ren, X., & Cao, F. (2021) · Scientific Programming · doi:10.1155/2021/2234514
Munasingha, S. C., Priyankara, K. K., Liyanagoonawardena, S. N., Charith, W. V., Pinto, C. S., Wickremasinghe, K., Constantine, G. R., & Jayasinghe, S. (2021) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc46164.2021.9629748
AN IOT-BASED REMOTELY HEALTH MONITORING SYSTEM USING LOCATION PRIVACY MECHANISM FOR WBAN MODEL
(2020) · International Journal of Engineering Sciences & Research Technology · doi:10.29121/ijesrt.v9.i11.2020.2
al., M. K. E. A. M. K. E., & TJPRC (2020) · International journal of mechanical and production engineering research and development · doi:10.24247/ijmperdjun2020658
Analysis and Classification of Physiological Signals for Emotion Detection
Khan, G., Shaw, S. K., Aggarwal, S., Gupta, A. K., Haldar, S., Adhikari, S., & Sen, S. (2022) · Advances in intelligent systems and computing · doi:10.1007/978-981-19-0182-9_8
Oshin, O. B., & Garg', E. P. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4101749
Pansini, P. F., Gonçalves, A. K., Rebuli, G. D. S., Vidoto, L. V., de Mello, J. P. V., Soares, A. R., Filho, V. Z., Guizardi, P., Tozetti, L. F., & Rodrigues, R. M. (2022) · Technical Papers ... Rio Oil & Gas · doi:10.48072/2525-7579.rog.2022.321
Simonyan, M. A., Shvartz, V. A., Karavaev, A. S., & Skazkina, V. V. (2022) · Biomedical & Pharmacology Journal · doi:10.13005/bpj/2459
A Novel IoT-based Monitoring Device for Effective Prediction of Heart Failure
Bhongade, R. A., Goyal, J., & Sharma, S. V. K. (2023) · Cardiometry · doi:10.18137/cardiometry.2023.26.309314
Design of drunk detection device using non-linear analysis of the heart rate variability method
Utama, J., & Aminudin, Y. M. (2023) · AIP conference proceedings · doi:10.1063/5.0129267
The Non-Invasive HbA1c Measurement Device: A Narrative Review
Indahsari, I., & Koesoema, A. P. (2023) · doi:10.1109/iceei59426.2023.10346672
Smart Watch-based Stress Recognition to Control Difficulty Levels in Games
Gorgan, R.-A., & Sabou, A. (2023) · RoCHI - International Conference on Human-Computer Interaction (RoCHI) · doi:10.37789/rochi.2023.1.1.7
Soula, M., Messas, N.-I., Aridhi, S., Urbinelli, R., & Guyon, A. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e29127
Affordable Internet of Things Sleep Monitor System
Ciufudean, C., & Buzduga, C. (2024) · Advances in business information systems and analytics book series · doi:10.4018/979-8-3693-3354-9.ch005
Spectrum Filtering to Extract Pulse Rate Variability from Signals Recorded by Wearable Devices
Prucnal, M. A., Adamczyk, K., & Polak, A. G. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-64629-4_19
Guidotti, S., Torelli, P., Ambiveri, G., Fiduccia, A., Castaldo, M., & Pruneti, C. A. (2024) · Neurological Sciences · doi:10.1007/s10072-024-07739-7
State-of-the-Art Analysis of Cuffless Methods for Blood Pressure Measurement
Burbach, S., Brück, R., & Keil, A. (2024) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-031-62502-2_15
A non-invasive heart rate prediction method using a convolutional approach
Karapinar, E., & Sevinç, E. (2024) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-024-03217-6
Design of an Analog Front-End (AFE) for Photoplethysmography (PPG) Acquisition System
Fathy, A. M., Mohamed, A. M., Abdelrahman, A. K., Mohamed, N. S., Saber, N. M., Mohamed, E. A., Ebrahim, R. M., & Eid, A. M. (2024) · Advanced Sciences and Technology Journal. · doi:10.21608/astj.2024.302774.1005
Estimation of vital parameters from photoplethysmography using deep learning architecture
Sulochana, C. H., Dharshini, S. L. S., & Blessy, S. A. P. S. (2025) · Signal Image and Video Processing · doi:10.1007/s11760-024-03669-1
Artificial intelligence at the point-of-care in cardio-oncology
Guo, J., & DeCara, J. M. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-29829-5.00009-8
ARTIFICIAL INTELLIGENCE IN CARDIOLOGY: A BIBLIOMETRIC ANALYSIS OF BRAZILIAN RESEARCH
Sígolo, B. D. O. O., Avallone, M. F. M., Medeiros, R. D. O., Galhardi, C. M., de Araújo, V. C. E., Yasuda, P. V. C., Donadai, K. C. E. D. V., Mascarin, A. M. N., dos Santos, J. P., Orso, L. F., et al. (2025) · Interciencia · doi:10.59671/ctqfe
Real-Time Physiological Monitoring System for High-Risk Industrial Operations
Castañeira, M., Abuin, N., Rubio, D., Madrid, F., Roberti, M., Caggioli, M., & Ponce, S. (2025) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-032-06401-1_50
PROSPECTS OF PHOTOPLETHYSMOGRAPHY AS A SCREENING METHOD FOR VEGETATIVE VASCULAR DYSFUNCTION
Д., Г. Н., Bobrik, Y. V., & Rybalko, S. (2025) · Таврический медико-биологический вестник · doi:10.29039/2070-8092-2025-28-2-71-78
An Inexpensive Internet of Thing Sleep Monitoring System
Pramanik, S., Bag, S., Obaid, A. J., Roy, A., & Rakshit, P. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-4094-4.ch011
Under Display Ambient Light Sensor for Noninvasive Vital Signs Monitoring
Picozzi, N., Gumiero, A., Milanetto, C., & Sabri, A. (2025) · IEEE Sensors Journal · doi:10.1109/jsen.2025.3645926
An Enhanced Conditional GAN-based PPG Data Augmentation for Cardiovascular Disease Detection
Tandon, A., Kumar, V., & Choudhury, T. (2025) · International Conference on Contemporary Computing · doi:10.1109/ic366947.2025.11290631
Rényi Entropy Based Cognitive Load Evaluation via Photoplethysmography
Milivojević, M., Dujković, D. M., & Gavrovska, A. M. (2025) · Telecommunications Forum (TELFOR) · doi:10.1109/telfor67910.2025.11314305
Tang, T. Y., Piers, S., Gistelinck, L., Daniëls, N., Moerkerke, M., Steyaert, J. G., Ortibus, E., Naulaers, G., Alaerts, K., & Boets, B. (2026) · Pediatric Research · doi:10.1038/s41390-025-04613-w
Reflective Multi-PD PPG Sensor for Health Monitoring Under Indoor Ambient Light
Anabestani, H., Žilić, Ž., & Bhadra, S. (2025) · Proceedings of IEEE Sensors/Proceedings of IEEE Sensors ... · doi:10.1109/sensors59705.2025.11331285
Baigarayeva, Z. Y., Boltaboyeva, A., Иманбек, Б., Kozhamberdiyeva, M., & Bekturganova, A. (2026) · Bulletin of Shakarim University Technical Sciences · doi:10.53360/2788-7995-2025-4(20)-27
A temporal fusion light-transformer network for heart rate estimation
Sun, J., Liu, J., Zhang, J., Yuan, X., Hu, S., Liu, G. B., & Zhu, H. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.109982
Adaptive Hybrid Model for Enhanced PPG Signal Processing (HARMONY)
Ivković, R., Spalević, P., Kunicina, N., & Dragić, R. (2025) · IEEE International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON) · doi:10.1109/rtucon67996.2025.11414961
Systolic Arterial Pressure in Dogs Pharmacologically Induced to Vasoconstriction and Vasodilation
Vaccarin, C. V., Floriano, B. P., Thomas, L., Fishborn, J. C., Bonomo, L., Schneider, M., Farret, M. H., Freitas, G. C., & Soares, A. V. (2026) · ACTA SCIENTIAE VETERINARIAE · doi:10.22456/1679-9216.150175
Tagnithammou, T. (2021) · theses.fr (ABES) · doi:10.70675/21d622d4z2013z47dfzbdb0z90efc051fd6f
Détection précoce de l’endormissement en situation de conduite automobile
Chevallier, B. (2023) · theses.fr (ABES) · doi:10.70675/362f0c4fz2d31z4174z9d1ez9568a9de4582
Infrared-Based Detection of Muscle Contraction for Control Signal Generation
Feneșan, A. C., Ianoși-Andreeva-Dimitrova, A., & Mândru, S.-D. (2026) · doi:10.3390/engproc2026148011
Guo, R., Gong, W., Song, E., Zhang, C., Wang, Y., Ming, H., Chen, Z., Peng, Z., He, Z., & Zhang, Q. (2026) · Advanced Materials · doi:10.1002/adma.202521971
Nigot, A., Giblewska, W., Sondej, T., & Szczepaniak, Z. R. (2026) · Sensors · doi:10.3390/s26196311
Photoplethysmography-based glucose estimation with feature selectionPreprint
Singha, S., & Chowdhury, A. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.10.07.757242
Metadata
- Authors
- Moraes, J. L., Rocha, M. X., Vasconcelos, G. G., Vasconcelos Filho, J. E., De Albuquerque, V. H. C., & Alexandria, A. R.
- Affiliations
- Instituto Federal de Educação, Ciência e Tecnologia do Ceará (BR)
- Hospital de Messejana (BR)
- Programa de Pós-Graduação em Informática Aplicada, Laboratório de Bioinformática, Universidade de Fortaleza, Fortaleza 60811-905, Ceará, Brazil
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2018-06-09
- Volume
- 18
- Issue
- 6
- Pages
- 1894
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC6022166
- OpenAlex
- W2808345783
- Cited by
- 292
- References
- 136
- Citations by year
- 2026: 12 · 2025: 31 · 2024: 37 · 2023: 38 · 2022: 49 · 2021: 46 · 2020: 38 · 2019: 38 · 2018: 3
- FWCI
- 2.55
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Conselho Nacional de Desenvolvimento Científico e Tecnológico (304315/2017-6)
- Fundação Cearense de Apoio ao Desenvolvimento Científico e Tecnológico (no number)
- 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, heart rate variability, PPG signal analysis, cardiovascular disease diagnosis, non-invasive monitoring, PPG sensor, Internet of Medical Things, artificial intelligence, genetic algorithm, biosensors, e-health, Internet of Things
- MeSH terms
- Blood Pressure, Heart Rate, Humans, Signal Processing, Computer-Assisted, Photoplethysmography, Telemedicine
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
