Cited by 292Non-Invasive Vital Sign MonitoringHeart Rate Variability and Autonomic ControlECG Monitoring and Analysis

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 access

Moraes, Jermana L., Rocha, Matheus X., Vasconcelos, Glauber G., Vasconcelos Filho, José E., De Albuquerque, Victor Hugo C., Alexandria, Auzuir R.·2018·Sensors

DOI 10.3390/s18061894PMC PMC6022166

Open this paper in CoChat — free account Open PDF

Abstract

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.

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

Crossref · 10.3390/s18061894

Cites

134 of 134 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure<SUBTITLE>The JNC 7 Report</SUBTITLE>
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
Heart rate variability: standards of measurement, physiological interpretation and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology.
(1996) · PubMed · Cited by 5,666
Power Spectrum Analysis of Heart Rate Fluctuation: A Quantitative Probe of Beat-to-Beat Cardiovascular Control
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
Organizational Update
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
A Quantitative Systematic Review of Normal Values for Short-Term Heart Rate Variability in Healthy Adults: REVIEW OF SHORT-TERM HRV VALUES
NUNAN, D., SANDERCOCK, G. R. H., & BRODIE, D. A. (2010) · Pacing and Clinical Electrophysiology · doi:10.1111/j.1540-8159.2010.02841.x · Cited by 874
Software for advanced HRV analysis
Niskanen, J., Tarvainen, M. P., Ranta-aho, P. O., & Karjalainen, P. A. (2004) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2004.03.004 · Cited by 789
Untitled work
Vanderlei (2009) · doi:10.1590/s0102-76382009000200018 · Cited by 744
Photophysical processes in recent medical laser developments: A review
Boulnois, J.-L. (1986) · Lasers in Medical Science · doi:10.1007/bf02030737 · Cited by 663
Functional assessment of heart rate variability: physiological basis and practical applications
Pumprla, J., Howorka, K., Groves, D., Chester, M., & Nolan, J. (2002) · International Journal of Cardiology · doi:10.1016/s0167-5273(02)00057-8 · Cited by 629
Unsupervised skin tissue segmentation for remote photoplethysmography
Bobbia, S., Macwan, R., Benezeth, Y., Mansouri, A., & Dubois, J. (2017) · Pattern Recognition Letters · doi:10.1016/j.patrec.2017.10.017 · Cited by 619
Photoplethysmography Revisited: From Contact to Noncontact, From Point to Imaging
Sun, Y., & Thakor, N. V. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2476337 · Cited by 589
The Autonomic Nervous System and Heart Failure
Florea, V. G., & Cohn, J. N. (2014) · Circulation Research · doi:10.1161/circresaha.114.302589 · Cited by 587
Heart Rate Assessment Immediately after Birth
Phillipos, E., Solevåg, A. L., Pichler, G., Aziz, K., van Os, S., O’Reilly, M., Cheung, P., & Schmölzer, G. M. (2015) · Neonatology · doi:10.1159/000441940 · Cited by 531
Validity of the Polar S810 Heart Rate Monitor to Measure R-R Intervals at Rest
Gamelin, F.-X., Berthoin, S., & Bosquet, L. (2006) · Medicine & Science in Sports & Exercise · doi:10.1249/01.mss.0000218135.79476.9c · Cited by 502
Multiparameter Respiratory Rate Estimation From the Photoplethysmogram
Karlen, W., Raman, S., Ansermino, J. M., & Dumont, G. A. (2013) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2013.2246160 · Cited by 486
Continuous blood pressure measurement by using the pulse transit time: comparison to a cuff-based method
Gesche, H., Grosskurth, D., Küchler, G., & Patzak, A. R. (2011) · European Journal of Applied Physiology · doi:10.1007/s00421-011-1983-3 · Cited by 472
Photoplethysmography pulse rate variability as a surrogate measurement of heart rate variability during non-stationary conditions.
Gil, E., Orini, M., Bailón, R., Vergara, J. M., Mainardi, L., & Laguna, P. (2010) · Physiological Measurement · doi:10.1088/0967-3334/31/9/015 · Cited by 461
Enabling Technologies for the Internet of Health Things
Rodrigues, J. J. P. C., Segundo, D. B. R., Junqueira, H. A., Sabino, M. H., Prince, R. M., Al-Muhtadi, J. F., & de Albuquerque, V. H. C. (2018) · IEEE Access · doi:10.1109/access.2017.2789329 · Cited by 428
Vagal modulation of heart rate during exercise: effects of age and physical fitness
Tulppo, M. P., Mäkikallio, T. H., Seppänen, T., Laukkanen, R. M. T., & Huikuri, H. V. (1998) · American Journal of Physiology-Heart and Circulatory Physiology · doi:10.1152/ajpheart.1998.274.2.h424 · Cited by 413
ECG Signal Denoising By Wavelet Transform Thresholding
Alfaouri, M., & Daqrouq, K. O. (2008) · American Journal of Applied Sciences · doi:10.3844/ajassp.2008.276.281 · Cited by 401
Breathing Rate Estimation From the Electrocardiogram and Photoplethysmogram: A Review
Charlton, P. H., Birrenkott, D. A., Bonnici, T., Pimentel, M. A. F., Johnson, A. E. W., Alastruey, J., Tarassenko, L., Watkinson, P. J., Beale, R., & Clifton, D. A. (2018) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2017.2763681 · Cited by 379
Wiley Encyclopedia of Biomedical Engineering
Çetin, A. E., & Gerek, O. N. (2006) · Wiley eBooks · doi:10.1002/9780471740360 · Cited by 369
A Novel Approach for Motion Artifact Reduction in PPG Signals Based on AS-LMS Adaptive Filter
Ram, M. R., Madhav, K. V., Krishna, E. H., Komalla, N. R., & Reddy, K. A. (2011) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2011.2175832 · Cited by 345
Heart rate recovery after exercise: relations to heart rate variability and complexity
Javorka, M., Žila, I., Balhárek, T., & Javorka, K. (2002) · Brazilian Journal of Medical and Biological Research · doi:10.1590/s0100-879x2002000800018 · Cited by 297
Pulse wave analysis and pulse wave velocity
Davies, J. I., & Struthers, A. D. (2003) · Journal of Hypertension · doi:10.1097/00004872-200303000-00004 · Cited by 292
Evaluation of Carotid-Femoral Pulse Wave Velocity
Millasseau, S., Stewart, A. D., Patel, S. J., Redwood, S. R., & Chowienczyk, P. (2005) · Hypertension · doi:10.1161/01.hyp.0000154229.97341.d2 · Cited by 289
Photoplethysmography
Alian, A. A., & Shelley, K. H. (2014) · Best Practice & Research Clinical Anaesthesiology · doi:10.1016/j.bpa.2014.08.006 · Cited by 289
A Reference Model for Internet of Things Middleware
da Cruz, M. A. A., Rodrigues, J. J. P. C., Al-Muhtadi, J., Korotaev, V. V., & de Albuquerque, V. H. C. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2796561 · Cited by 281
Neural control of the heart: Recent concepts and clinical correlations
Palma, J.-A., & Benarroch, E. E. (2014) · Neurology · doi:10.1212/wnl.0000000000000605 · Cited by 237
Artifact-resistant power-efficient design of finger-ring plethysmographic sensors
Rhee, S., Yang, B.-H., & Asada, H. H. (2001) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.930904 · Cited by 235
Wavelet transform feature extraction from human PPG, ECG, and EEG signal responses to ELF PEMF exposures: A pilot study
Cvetković, D., Übeylı, E. D., & Ćosić, I. (2007) · Digital Signal Processing · doi:10.1016/j.dsp.2007.05.009 · Cited by 227
Photoplethysmographic derivation of respiratory rate: A review of relevant physiology
Meredith, D. J., Clifton, D., Charlton, P., Brooks, J., Pugh, C. W., & Tarassenko, L. (2012) · Journal of Medical Engineering & Technology · doi:10.3109/03091902.2011.638965 · Cited by 226
Heart rate estimation using facial video: A review
Hassan, M. A., Malik, A. S., David Fofi, B., Saad, M. N. M., Karasfi, B., Ali, Y. S., & Mériaudeau, F. (2017) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2017.07.004 · Cited by 183
A reliable IoT system for Personal Healthcare Devices
Woo, M. W., Lee, J., & Park, K. (2017) · Future Generation Computer Systems · doi:10.1016/j.future.2017.04.004 · Cited by 182
ECG arrhythmia classification based on optimum-path forest
Luz, E. J. D. S., Nunes, T. M., de Albuquerque, V. H. C., Papa, J. P., & Menotti, D. (2012) · Expert Systems with Applications · doi:10.1016/j.eswa.2012.12.063 · Cited by 177
An Armband Wearable Device for Overnight and Cuff-Less Blood Pressure Measurement
Zheng, Y.-L., Yan, B. P., Zhang, Y., & Poon, C. C. Y. (2014) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2014.2318779 · Cited by 176
Nonlinear systems in medicine.
Higgins, J. P. (2002) · PubMed Central · Cited by 158
An Automated Remote Cloud-Based Heart Rate Variability Monitoring System
Hussein, A. F., Arunkumar, N., Burbano-Fernandez, M. F., Ramírez-González, G., Abdulhay, E., & de Albuquerque, V. H. C. (2018) · IEEE Access · doi:10.1109/access.2018.2831209 · Cited by 137
Heart rate variability with deep breathing as a clinical test of cardiovagal function
Shields, R. W. (2009) · Cleveland Clinic Journal of Medicine · doi:10.3949/ccjm.76.s2.08 · Cited by 131
Comparison of Time‐Domain Short‐Term Heart Interval Variability Analysis Using a Wrist‐Worn Heart Rate Monitor and the Conventional Electrocardiogram
Porto, L. G. G., & Junqueira, L. F. (2008) · Pacing and Clinical Electrophysiology · doi:10.1111/j.1540-8159.2009.02175.x · Cited by 130
Noninvasive cuffless blood pressure estimation using pulse transit time and Hilbert–Huang transform
Choi, Y., Zhang, Q., & Ko, S. (2012) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2012.09.005 · Cited by 123
Body Sensor Network for Mobile Health Monitoring, a Diagnosis and Anticipating System
Wannenburg, J., & Malekian, R. (2015) · IEEE Sensors Journal · doi:10.1109/jsen.2015.2464773 · Cited by 122
Heart rate variability in bipolar disorder: A systematic review and meta-analysis
Faurholt‐Jepsen, M., Kessing, L. V., & Munkholm, K. (2016) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2016.12.007 · Cited by 120
Adaptive blood pressure estimation from wearable PPG sensors using peripheral artery pulse wave velocity measurements and multi-channel blind identification of local arterial dynamics
McCombie, D. B., Reisner, A. T., & Asada, H. H. (2006) · 2006 International Conference of the IEEE Engineering in Medicine and Biology Society · doi:10.1109/iembs.2006.260590 · Cited by 118
Reduction of motion artifacts from photoplethysmographic recordings using a wavelet denoising approach
Lee, C. M., & Zhang, Y. (2004) · doi:10.1109/apbme.2003.1302650 · Cited by 114
Multichannel reflective PPG earpiece sensor with passive motion cancellation
Wang, L., Lo, B. P., & Yang, G.-Z. (2007) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2007.910900 · Cited by 114
The Periodic Moving Average Filter for Removing Motion Artifacts from PPG Signals
Lee, H.-W., Lee, J., Jung, W.-G., & Lee, G.-K. (2007) · International Journal of Control Automation and Systems · Cited by 114
Effective Features to Classify Big Data Using Social Internet of Things
Lakshmanaprabu, S. K., Shankar, K., Khanna, A. K., Gupta, D. K., Rodrigues, J. J. P. C., Pinheiro, P. R., & de Albuquerque, V. H. C. (2018) · IEEE Access · doi:10.1109/access.2018.2830651 · Cited by 113
Photoplethysmography detection of lower limb peripheral arterial occlusive disease: a comparison of pulse timing, amplitude and shape characteristics
Allen, J., Oates, C. P., Lees, T. A., & Murray, A. (2005) · Physiological Measurement · doi:10.1088/0967-3334/26/5/018 · Cited by 112
Adaptive reduction of motion artifact from photoplethysmographic recordings using a variable step-size LMS filter
Chan, K. W., & Zhang, Y. (2003) · Proceedings of IEEE Sensors/Proceedings of IEEE Sensors ... · doi:10.1109/icsens.2002.1037314 · Cited by 104
An Electronic Patch for Wearable Health Monitoring by Reflectance Pulse Oximetry
Haahr, R. G., Duun, S., Toft, M. H., Belhage, B., Larsen, J. O., Birkelund, K., & Thomsen, E. V. (2011) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2011.2164247 · Cited by 100
A Wearable Reflectance Pulse Oximeter for Remote Physiological Monitoring
Mendelson, Y., Duckworth, R. J., & Comtois, G. (2006) · doi:10.1109/iembs.2006.260137 · Cited by 99
An Ultra Low Power Pulse Oximeter Sensor Based on Compressed Sensing
Baheti, P. K., & Garudadri, H. (2009) · Wearable and Implantable Body Sensor Networks · doi:10.1109/bsn.2009.32 · Cited by 97
Discrimination power of long-term heart rate variability measures for chronic heart failure detection
Melillo, P., Fusco, R., Sansone, M., Bracale, M., & Pecchia, L. (2011) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-010-0728-5 · Cited by 93
Similarity in bilateral photoplethysmographic peripheral pulse wave characteristics at the ears, thumbs and toes
Allen, J., & Murray, A. (2000) · Physiological Measurement · doi:10.1088/0967-3334/21/3/303 · Cited by 93
The effect of venous pulsation on the forehead pulse oximeter wave form as a possible source of error in Spo2 calculation
Shelley, K. H., Tamai, D., Jablonka, D., Gesquiere, M., Stout, R. G., & Silverman, D. G. (2005) · Anesthesia & Analgesia · doi:10.1213/01.ane.0000145063.01043.4b · Cited by 91
Design of filter to reject motion artifact of pulse oximetry
Lee, J., Jung, W.-G., Kang, I., Kim, Y., & Lee, G.-K. (2003) · Computer Standards & Interfaces · doi:10.1016/s0920-5489(03)00077-1 · Cited by 91
Heart rate variability analysis
Hejjel, L., & Gál, I. (2001) · Acta Physiologica Hungarica · doi:10.1556/aphysiol.88.2001.3-4.4 · Cited by 89
Robust automated cardiac arrhythmia detection in ECG beat signals
de Albuquerque, V. H. C., Nunes, T. M., Pereira, D. R., Luz, E. J. D. S., Menotti, D., Papa, J. P., & Tavares, J. M. R. S. (2016) · Neural Computing and Applications · doi:10.1007/s00521-016-2472-8 · Cited by 88
Toward Ubiquitous Blood Pressure Monitoring via Pulse Transit Time: Predictions on Maximum Calibration Period and Acceptable Error Limits
Mukkamala, R., & Hahn, J. (2017) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2017.2756018 · Cited by 86
Single-source PPG-based local pulse wave velocity measurement: a potential cuffless blood pressure estimation technique
Nabeel, P. M., Jayaraj, J., & Sivaprakasam, M. (2017) · Physiological Measurement · doi:10.1088/1361-6579/aa9550 · Cited by 80
Validation of a novel and existing algorithms for the estimation of pulse transit time: advancing the accuracy in pulse wave velocity measurement
Vardoulis, O., Papaioannou, T. G., & Stergiopulos, N. (2013) · American Journal of Physiology-Heart and Circulatory Physiology · doi:10.1152/ajpheart.00963.2012 · Cited by 75
Evaluation of wavelets for reduction of motion artifacts in photoplethysmographic signals
Raghuram, M., Madhav, K. V., Krishna, E. H., & Reddy, K. A. (2010) · International Conference on Information Science, Signal Processing and their Applications (ISSPA) · doi:10.1109/isspa.2010.5605443 · Cited by 73
Feedback from the heart: Emotional learning and memory is controlled by cardiac cycle, interoceptive accuracy and personality
Pfeifer, G., Garfinkel, S. N., van Praag, C. G., Sahota, K., Betka, S., & Critchley, H. (2017) · Biological Psychology · doi:10.1016/j.biopsycho.2017.04.001 · Cited by 72
A Robust Fusion Model for Estimating Respiratory Rate From Photoplethysmography and Electrocardiography
Birrenkott, D. A., Pimentel, M. A. F., Watkinson, P. J., & Clifton, D. A. (2018) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2017.2778265 · Cited by 71
Pulse-Glasses: An unobtrusive, wearable HR monitor with Internet-of-Things functionality
Constant, N. P., Douglas-Prawl, O., Johnson, S., & Mankodiya, K. (2015) · Wearable and Implantable Body Sensor Networks · doi:10.1109/bsn.2015.7299350 · Cited by 70
Comparison of foot finding methods for deriving instantaneous pulse rates from photoplethysmographic signals
Hemon, M. C., & Phillips, J. P. (2015) · Journal of Clinical Monitoring and Computing · doi:10.1007/s10877-015-9695-6 · Cited by 67
Analysis and characterization of photo-plethysmographic signal
Bhattacharya, J., Kanjilal, P. P., & Muralidhar, V. (2001) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.900243 · Cited by 66
Reduction of Periodic Motion Artifacts in Photoplethysmography
Wijshoff, R. W. C. G. R., Mischi, M., & Aarts, R. M. (2016) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2016.2553060 · Cited by 66
Design and development of non invasive glucose measurement system
Paul, B., Manuel, M. P., & Alex, Z. C. (2012) · International Symposium on Physics and Technology of Sensors · doi:10.1109/ispts.2012.6260873 · Cited by 63
Correlates of the shift in heart rate variability with an active postural change in a healthy population sample: The Atherosclerosis Risk In Communities study
Carnethon, M. R., Liao, D., Evans, G. W., Cascio, W. E., Chambless, L. E., & HEISS, G. M. (2002) · American Heart Journal · doi:10.1067/mhj.2002.121928 · Cited by 63
Pulse Transit Time Measurement Using Seismocardiogram, Photoplethysmogram, and Acoustic Recordings: Evaluation and Comparison
Yang, C., & Tavassolian, N. (2017) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2017.2696703 · Cited by 62
Wearable Multichannel Photoplethysmography Framework for Heart Rate Monitoring During Intensive Exercise
Lee, H., Chung, H., Ko, H., & Lee, J. (2018) · IEEE Sensors Journal · doi:10.1109/jsen.2018.2801385 · Cited by 61
Characterization and adaptive filtering of motion artifacts in pulse oximetry using accelerometers
Relente, A. R., & Sison, L. G. (2002) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2002.1106644 · Cited by 60
Continuous blood pressure measurement using pulse transit time
Hennig, A., & Patzak, A. R. (2013) · Somnologie - Schlafforschung und Schlafmedizin · doi:10.1007/s11818-013-0617-x · Cited by 58
Geometric index of heart rate variability in chronic obstructive pulmonary disease
de Carvalho, T. D., Pastre, C. M., Rossi, R. C., de Abreu, L. C., Valenti, V. E., & Vanderlei, L. C. M. (2011) · Revista Portuguesa de Pneumologia · doi:10.1016/j.rppnen.2011.10.001 · Cited by 55
Noise-resistant pulse oximetry using a synthetic reference signal
Coetzee, F. M., & Elghazzawi, Z. F. (2000) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.855928 · Cited by 53
Adaptive Pulse Width Control and Sampling for Low Power Pulse Oximetry
Gubbi, S., & Amrutur, B. S. (2014) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2014.2326712 · Cited by 46
Índices geométricos de variabilidade da frequência cardíaca na doença pulmonar obstrutiva crônica
de Carvalho, T. D., Pastre, C. M., Rossi, R. C., de Abreu, L. C., Valenti, V. E., & Vanderlei, L. C. M. (2011) · Revista Portuguesa de Pneumologia · doi:10.1016/j.rppneu.2011.06.007 · Cited by 45
SmartStuff: A case study of a smart water bottle
Jovanov, E., Nallathimmareddygari, V. R., & Pryor, J. E. (2016) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2016.7592170 · Cited by 45
SFST: A robust framework for heart rate monitoring from photoplethysmography signals during physical activities
Zhao, D., Sun, Y., Wan, S., & Feng, W. (2016) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2016.12.005 · Cited by 45
Non-contact remote estimation of cardiovascular parameters
Fan, Q., & Li, K. (2017) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2017.09.022 · Cited by 44
Standard Terminologies for Photoplethysmogram Signals
Elgendi, M. (2012) · Current Cardiology Reviews · doi:10.2174/157340312803217184 · Cited by 43
Heart rate variability is decreased in chronic kidney disease but may improve with hemoglobin normalization
Furuland, H., Linde, T., Englund, A., & Wíkström, B. (2008) · Journal of Nephrology · doi:10.1093/joneph/21.1.45 · Cited by 42
pSnakes: A new radial active contour model and its application in the segmentation of the left ventricle from echocardiographic images
de Alexandria, A. R., Cortez, P. C., Bessa, J. A., Félix, J. H. D. S., de Abreu, J. S., & de Albuquerque, V. H. C. (2014) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2014.05.009 · Cited by 41
Self-citation
Photoplethysmography as a single source for analysis of sleep‐disordered breathing in patients with severe cardiovascular disease
Amir, O., Barak‐Shinar, D., Henry, A., & Smart, F. W. (2011) · Journal of Sleep Research · doi:10.1111/j.1365-2869.2011.00927.x · Cited by 39
Aortic Stiffness is an Independent Predictor of Stroke in Hypertensive Patients
Pereira, T. S., Maldonado, J. M. P., Pereira, L., & Conde, J. (2013) · Arquivos Brasileiros de Cardiologia · doi:10.5935/abc.20130079 · Cited by 39
Spectral Analysis of Pulse Transit Time Variability and Its Coherence with Other Cardiovascular Variabilities
Ma, H. T., & Zhang, Y. (2006) · doi:10.1109/iembs.2006.259365 · Cited by 37
Photoplethysmography beat detection and pulse morphology quality assessment for signal reliability estimation
Papini, G. B., Fonseca, P. M., Aubert, X. L., Overeem, S., Bergmans, J. W. M., & Vullings, R. (2017) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2017.8036776 · Cited by 36
Dual-tree complex wavelet transform for motion artifact reduction of PPG signals
Raghuram, M., Madhav, K. V., Krishna, E. H., Komalla, N. R., Sivani, K., & Reddy, K. A. (2012) · IEEE International Symposium on Medical Measurements and Applications (MeMeA) · doi:10.1109/memea.2012.6226643 · Cited by 34
Detection of cardiovascular risk from a photoplethysmographic signal using a matching pursuit algorithm
Sommermeyer, D., Zou, D., Ficker, J. H., Randerath, W. J., Fischer, C., Penzel, T., Sanner, B. M., Hedner, J. A., & Grote, L. (2015) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-015-1410-8 · Cited by 33
Determinants of Daytime Blood Pressure in Relation to Obstructive Sleep Apnea in Men
Lam, J., Yan, C. S. W., Lai, A. Y. K., Tam, S., Fong, D., LAM, B. K., & Ip, M. (2009) · Lung · doi:10.1007/s00408-009-9161-7 · Cited by 31
Effects of vagus nerve stimulation on heart rate variability in children with epilepsy
Hırfanoğlu, T., Serdaroğlu, A., Çetin, İ. İ., Kurt, G., Çapraz, İ., Ekici, F., Arhan, E., & Bilir, E. (2018) · Epilepsy & Behavior · doi:10.1016/j.yebeh.2018.01.036 · Cited by 30
Frequency analysis of photoplethysmogram and its derivatives
Elgendi, M., Fletcher, R., Norton, I., Brearley, M. B., Abbott, D., Lovell, N. H., & Schuurmans, D. (2015) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2015.09.021 · Cited by 28
Respiratory rate estimation using respiratory sinus arrhythmia from photoplethysmography
Karlen, W., Brouse, C. J., Cooke, E. M., Ansermino, J. M., & Dumont, G. A. (2011) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2011.6090282 · Cited by 26
Arterial Stiffness: Pathophysiological and Genetic Aspects
Alvim, R. D. O., Santos, P. C. J. L., Bortolotto, L. A., Mill, J. G., & Pereira, A. D. C. (2017) · International Journal of Cardiovascular Sciences · doi:10.5935/2359-4802.20170053 · Cited by 25
Assessment of bilateral photoplethysmography for lower limb peripheral vascular occlusive disease using color relation analysis classifier
Lin, C. (2010) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2010.06.014 · Cited by 24
Smart diagnostic algorithms for automated detection of childhood pneumonia in resource-constrained settings
Naydenova, E., Tsanas, A., Casals‐Pascual, C., & Vos, M. D. (2015) · IEEE Global Humanitarian Technology Conference (GHTC) · doi:10.1109/ghtc.2015.7344000 · Cited by 24
A new signal decomposition to estimate breathing rate and heart rate from photoplethysmography signal
Li, D., Zhao, H., & Dou, S. (2015) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2015.03.008 · Cited by 23
Motion artefact minimization from photoplethysmography based non-invasive hemoglobin sensor based on an envelope filtering algorithm
Yuan, H., Memon, S. F., Newe, T., Lewis, E., & Leen, G. (2017) · Measurement · doi:10.1016/j.measurement.2017.10.060 · Cited by 22
Classification of the quality of wristband-based photoplethysmography signals
Pradhan, N., Rajan, S., Adler, A., & Redpath, C. J. (2017) · IEEE International Symposium on Medical Measurements and Applications (MeMeA) · doi:10.1109/memea.2017.7985887 · Cited by 21
Estudo da modulação autonômica da freqüência cardíaca em repouso de pacientes idosos com doença pulmonar obstrutiva crônica
Pantoni, C. B. F., Reis, M., Martins, L., Catai, A., Costa, D., & Borghi‐Silva, A. (2007) · Brazilian Journal of Physical Therapy · doi:10.1590/s1413-35552007000100007 · Cited by 20
Towards the Development of Wearable Blood Pressure Sensors: A Photo-Plethysmograph Approach Using Conducting Polymer Actuators
Asada, H. H., Reisner, A. T., Shaltis, P., & McCombie, D. B. (2005) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2005.1615379 · Cited by 18
A Novel Headset with a Transmissive PPG Sensor for Heart Rate Measurement
Shin, K., Kim, Y., Bae, S., Park, K., & Kim, S. (2009) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-540-92841-6_127 · Cited by 17
Home-Telecare of the elderly living alone using an new designed ear-wearable sensor
Wang, C., & Zheng, Y. (2008) · IEEE/EMBS International Summer School on Medical Devices and Biosensors (ISSS-MDBS) · doi:10.1109/issmdbs.2008.4575019 · Cited by 16
A motion-tolerant approach for monitoring SpO2 and heart rate using photoplethysmography signal with dual frame length processing and multi-classifier fusion
Fan, F., Yan, Y., Tang, Y., & Zhang, H. (2017) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2017.10.017 · Cited by 16
Comparison of methods for determining pulse arrival time from Doppler and photoplethysmography signals
Phillips, J. P., & Kyriacou, P. A. (2014) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2014.6944453 · Cited by 15
Correlated Poincaré indices for measuring heart rate variability.
Smith, A.-L., Reynolds, K. J., & Owen, H. (2007) · PubMed · Cited by 15
Pulse oximetry and PPG measurements in plastic surgery
Pickett, J. A., Amoroso, P., Nield, D. V., & Jones, D. P. (2002) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.1997.758831 · Cited by 12
ECG based human authentication with using Generalized Hurst Exponent
Karegar, F. P., Fallah, A., & Rashidi, S. (2017) · Iranian Conference on Electrical Engineering · doi:10.1109/iraniancee.2017.7985480 · Cited by 11
A new fibre optic pulse oximeter probe for monitoring splanchnic organ arterial blood oxygen saturation
Hickey, M., Samuels, N., Randive, N., Langford, R. M., & Kyriacou, P. A. (2011) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2011.03.019 · Cited by 10
Renal Regulation of Potassium, Calcium, Phosphate, and Magnesium; Integration of Renal Mechanisms for Control of Blood Volume and Extracellular Fluid Volume
(2012) · Elsevier eBooks · doi:10.1016/b978-1-4160-5451-1.00029-3 · Cited by 8
Plethysmography
Madhavan, G. (2005) · Biomedical Instrumentation & Technology · doi:10.2345/0899-8205(2005)39[367:p]2.0.co;2 · Cited by 8
Laser Oximetry: A Novel Noninvasive Method to Determine Changes in Penile Hemodynamics in an Anesthetized Rabbit Model
Choi, S., Min, K. S., Kim, N. N., Munarriz, R. M., Goldstein, I., & Traish, A. M. (2002) · Journal of Andrology · doi:10.1002/j.1939-4640.2002.tb02625.x · Cited by 7
Measurement and Analysis Possibilities of Pulse Wave Signals
Borik, Š., & Čáp, I. (2013) · Advances in Electrical and Electronic Engineering · doi:10.15598/aeee.v11i6.759 · Cited by 7
Proposta de um sistema telemétrico para aquisição de sinais fisiológicos
Schwarz, L. (2007) · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · Cited by 6
Increased variation of the response index of nociception during noxious stimulation in patients during general anaesthesia
Sarén-Koivuniemi, T. J. M., Yli‐Hankala, A., & van Gils, M. (2010) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2010.10.001 · Cited by 6
Heart rate variability using photoplethysmography with green wavelength
Ruiz, L. M., Manzo-Martínez, A., Casimiro, E., Cardenas, E., & Gonzalez, R. (2014) · IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC) · doi:10.1109/ropec.2014.7036296 · Cited by 4
Determination of Blood Vessels Expandability; Multichannel Photoplethysmography
Peter, L., Vorek, I., Massot, B., Bryjová, I., & Urbanczyk, T. (2016) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2016.12.048 · Cited by 4
An ultra-low power, self-organizing wireless network and non-invasive biomedical instrumentation
Rhee, S., & Liu, S. (2002) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2002.1053033 · Cited by 3
Visualization and Analysis of Mental States Based on Photoplethysmogram
Horio, K., & Li, Y. (2009) · International Conference on Innovative Computing, Information and Control · doi:10.1109/icicic.2009.381 · Cited by 3
DETECÇÃO EM TEMPO REAL DA FREQUÊNCIA CARDÍACA DE PESSOAS POR MEIO DA ANÁLISE DE VARIAÇÕES TEMPORAIS EM VÍDEOS
Rodrigues, K. A. S., Pereira, M. H. R., & Pádua, F. L. C. (2016) · e-xacta · doi:10.18674/exacta.v9i1.1666 · Cited by 2
Sistema móvel de monitoramento e treinamento para ciclista com smartphone android
Rosero, G., & Fernando, Ó. (2012) · Cited by 1
Photoplethysmography technology and its feature visualization for cognitive stimulation assessment
Pham, T. D., & Oyama-Higa, M. (2015) · IEEE International Conference on Industrial Technology (ICIT) · doi:10.1109/icit.2015.7125348 · Cited by 1
Cardiovascular risk in eutrophic young subjects: influence of corporal fat and sympathetic activity
Rubira, M. C., Rubira, A. P. F. D. A., Soares, P. P. D. S., Medeiros, L. G., Neves, G. A., & Consolim‐Colombo, F. M. (2011) · ConScientiae Saúde · doi:10.5585/conssaude.v10i2.2578 · Cited by 1
Monitoramento remoto preventivo de pacientes com doenças cardiovasculares utilizando dispositivo móvel como agente inteligente
Silva, M. D. C. (2012) · Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) · Cited by 1
NON-INVASIVE WEARABLE SENSORS FOR RESPIRATION MONITORING
Kukkapalli, R. (2016) · Digital Repository at the University of Maryland (University of Maryland College Park) · doi:10.13016/m2dn1v-8bya · Cited by 1
Development of an acquisition circuit of multiple biological signals for integration into a wearable bracelet
Rincón, L. M. (2017) · Cited by 1

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

Sorted by citations

Contact-Based Methods for Measuring Respiratory Rate
Massaroni, C., Nicolò, A., Presti, D. L., Sacchetti, M., Silvestri, S., & Schena, E. (2019) · Sensors · doi:10.3390/s19040908 · Cited by 465
Organic Light‐Emitting Diodes: Pushing Toward the Limits and Beyond
Song, J., Lee, H., Jeong, E. G., Choi, K. C., & Yoo, S. (2020) · Advanced Materials · doi:10.1002/adma.201907539 · Cited by 417
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
Photoplethysmography based atrial fibrillation detection: a review
Pereira, T., Tran, N., Gadhoumi, K., Pelter, M. M., Do, D. H., Lee, R. J., Colorado, R., Meisel, K., & Hu, X. (2020) · npj Digital Medicine · doi:10.1038/s41746-019-0207-9 · Cited by 300
RETRACTED ARTICLE: Hybrid optimization with cryptography encryption for medical image security in Internet of Things
Elhoseny, M., Shankar, K., Lakshmanaprabu, S. K., Maseleno, A., & Arunkumar, N. (2018) · Neural Computing and Applications · doi:10.1007/s00521-018-3801-x · Cited by 241
Retracted

See all 292

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
ORCID: Moraes, Jermana L.
Vasconcelos, Glauber G.
De Albuquerque, Victor Hugo C.
Alexandria, Auzuir R.
Venue: Sensors
Publisher: MDPI AG
Published: 2018-06-09
Volume: 18
Issue: 6
Pages: 1894
ISSN: 1424-8220 (ISSN-L)
DOI: 10.3390/s18061894
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: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
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

Advances in Photopletysmography Signal Analysis for Biomedical Applications

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks