Cited by 123Non-Invasive Vital Sign MonitoringECG Monitoring and AnalysisHeart Rate Variability and Autonomic Control

The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.

Photoplethysmogram signal quality estimation using repeated Gaussian filters and cross-correlation

Karlen, W, Kobayashi, K, Ansermino, J M, Dumont, G A·2012·Physiological Measurement

Open this paper in CoChat — free account

Abstract

Pulse oximeters are monitors that noninvasively measure heart rate and blood oxygen saturation (SpO2). Unfortunately, pulse oximetry is prone to artifacts which negatively impact the accuracy of the measurement and can cause a significant number of false alarms. We have developed an algorithm to segment pulse oximetry signals into pulses and estimate the signal quality in real time. The algorithm iteratively calculates a signal quality index (SQI) ranging from 0 to 100. In the presence of artifacts and irregular signal morphology, the algorithm outputs a low SQI number. The pulse segmentation algorithm uses the derivative of the signal to find pulse slopes and an adaptive set of repeated Gaussian filters to select the correct slopes. Cross-correlation of consecutive pulse segments is used to estimate signal quality. Experimental results using two different benchmark data sets showed a good pulse detection rate with a sensitivity of 96.21% and a positive predictive value of 99.22%, which was equivalent to the available reference algorithm. The novel SQI algorithm was effective and produced significantly lower SQI values in the presence of artifacts compared to SQI values during clean signals. The SQI algorithm may help to guide untrained pulse oximeter users and also help in the design of advanced algorithms for generating smart alarms.

Integrity signals

Registry

Crossref · 10.1088/0967-3334/33/10/1617

Cites

18 of 18 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

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?

Use of a peripheral perfusion index derived from the pulse oximetry signal as a noninvasive indicator of perfusion
Lima, A. P., Beelen, P., & Bakker, J. (2002) · Critical Care Medicine · doi:10.1097/00003246-200206000-00006 · Cited by 479
Motion Artifact Reduction in Photoplethysmography Using Independent Component Analysis
Kim, B., & Yoo, S. K. (2006) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2005.869784 · Cited by 400
The peripheral pulse wave: Information overlooked
Murray, W. B., & Foster, P. A. (1996) · The Journal of Clinical Monitoring · doi:10.1007/bf02077634 · Cited by 231
An Automatic Beat Detection Algorithm for Pressure Signals
Aboy, M., McNames, J., Thong, T., Tsunami, D., Ellenby, M. S., & Goldstein, B. (2005) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2005.855725 · Cited by 216
Signal processing methods for pulse oximetry
Rusch, T. L., Sankar, R. T., & Scharf, J. E. (1996) · Computers in Biology and Medicine · doi:10.1016/0010-4825(95)00049-6 · Cited by 185
The Effect of Motion on Pulse Oximetry and Its Clinical Significance
Petterson, M. T., Begnoche, V. L., & Graybeal, J. M. (2007) · Anesthesia & Analgesia · doi:10.1213/01.ane.0000278134.47777.a5 · Cited by 177
Signal quality measures for pulse oximetry through waveform morphology analysis
Sukor, J. A., Redmond, S. J., & Lovell, N. H. (2011) · Physiological Measurement · doi:10.1088/0967-3334/32/3/008 · Cited by 166
Improved elimination of motion artifacts from a photoplethysmographic signal using a Kalman smoother with simultaneous accelerometry
Lee, B., Han, J., Baek, H. J., Shin, J. H., Park, K. S., & Yi, W. J. (2010) · Physiological Measurement · doi:10.1088/0967-3334/31/12/003 · Cited by 155
CapnoBase: Signal database and tools to collect, share and annotate respiratory signals
Karlen, W., Turner, M., Cooke, E., Dumont, G. A., & Ansermino, J. M. (2010) · Repository for Publications and Research Data (ETH Zurich) · doi:10.3929/ethz-b-000087887 · Cited by 114
Kalman Filter Based Adaptive Reduction Of Motion Artifact From Photoplethysmographic Signal
Seyedtabaii, S., & Seyedtabaii, L. (2008) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.1333655 · Cited by 59

Show all 18 references

Research works printed but not linked: 9

2003, ANSI/AAMI EC57
Farooq U Jang D G Park J H Park S H, 2010, Annu. Int. Conf. IEEE Engineering in Medicine and Biology Society, 4582
Karlen W Petersen C Gow J Ansermino J M Dumont G A Fred A Filipe J Gamboa H, 2012, BIOSTEC 2011: 4th Int. Joint Conf. on Biomedical Engineering Systems and Technologies, 31
Karlen W Turner M Cooke E Dumont G A Ansermino J M, 2010, STA: Annu. Meet. of the Society for Technology in Anesthesia, 25
Lovell N H Redmond S J Basilakis J Celler B G, 2010, 12th IEEE Int. Conf. on e-Health Networking Applications and Services (Healthcom), 81
Madhav K V Ram M R Krishna E H Reddy K N Reddy K A, 2011, Int. Conf. on Communications and Signal Processing (ICCSP 2011), 452
Seyedtabaii S, 2008, World Acad. Sci. Eng. Technol., 37, 173
Weng J Ye Z Weng J, 2005, 27th Annu. Int. Conf. IEEE Engineering in Medicine and Biology Society, 41
Wood L B Asada H H, 2006, 28th Annu. Int. Conf. of the IEEE Engineering in Medicine and Biology Society, 3525

Sorted by citations

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
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
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
Heart Rate Variability (HRV) and Pulse Rate Variability (PRV) for the Assessment of Autonomic Responses
Mejía‐Mejía, E., Budidha, K., Abay, T. Y., May, J. M., & Kyriacou, P. A. (2020) · Frontiers in Physiology · doi:10.3389/fphys.2020.00779 · Cited by 178
Assessing mental stress from the photoplethysmogram: a numerical study
Charlton, P. H., Celka, P., Farukh, B., Chowienczyk, P., & Alastruey, J. (2018) · Physiological Measurement · doi:10.1088/1361-6579/aabe6a · Cited by 131
A standardized validity assessment protocol for physiological signals from wearable technology: Methodological underpinnings and an application to the E4 biosensor
van Lier, H. G., Pieterse, M. E., Garde, A., Postel, M. G., de Haan, H. A., Vollenbroek-Hutten, M. M. R., Schraagen, J. M., & Noordzij, M. L. (2019) · Behavior Research Methods · doi:10.3758/s13428-019-01263-9 · Cited by 143
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
Development of a Screening Tool for Sleep Disordered Breathing in Children Using the Phone Oximeter™
Garde, A., Dehkordi, P., Karlen, W., Wensley, D., Ansermino, J. M., & Dumont, G. A. (2014) · PLoS ONE · doi:10.1371/journal.pone.0112959 · Cited by 122
Photoplethysmograhic sensors, potential and limitations: Is it time for regulation? A comprehensive review
Scardulla, F., Cosoli, G., Spinsante, S., Poli, A., Iadarola, G., Pernice, R., Busacca, A., Pasta, S., Scalise, L., & D’Acquisto, L. (2023) · Measurement · doi:10.1016/j.measurement.2023.113150 · Cited by 134
Wearable Photoplethysmography for Cardiovascular Monitoring.
Charlton, P. H., Kyriacou, P. A., Mant, J., Marozas, V., Chowienczyk, P., & Alastruey, J. (2022) · KTUePubl (Repository of Kaunas University of Technology) · doi:10.17863/cam.81148 · Cited by 113
A Robust Motion Artifact Detection Algorithm for Accurate Detection of Heart Rates From Photoplethysmographic Signals Using Time–Frequency Spectral Features
Dao, D. K., Salehizadeh, S. M. A., Noh, Y., Chong, J. W., Cho, C. H., McManus, D., Darling, C. E., Mendelson, Y., & Chon, K. H. (2016) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2016.2612059 · Cited by 104
SeismoWatch
Carek, A. M., Conant, J., Joshi, A. N., Kang, H., & Inan, O. T. (2017) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3130905 · Cited by 103
Sleep Apnea Detection Based on Thoracic and Abdominal Movement Signals of Wearable Piezoelectric Bands
Lin, Y.-Y., Wu, H., Hsu, C.-A., Huang, P.-C., Huang, Y.-H., & Lo, Y. (2016) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2016.2636778 · Cited by 104
A Supervised Approach to Robust Photoplethysmography Quality Assessment
Pereira, T., Gadhoumi, K., Ma, M., Liu, X., Xiao, R., Colorado, R. A., Keenan, K. J., Meisel, K. M., & Hu, X. (2019) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2019.2909065 · Cited by 80
Photoplethysmograph signal reconstruction based on a novel hybrid motion artifact detection–reduction approach. Part I: Motion and noise artifact detection
Chong, J. W., Dao, D. K., Salehizadeh, S. M. A., McManus, D. D., Darling, C. E., Chon, K. H., & Mendelson, Y. (2014) · Annals of Biomedical Engineering · doi:10.1007/s10439-014-1080-y · Cited by 79
Improving Photoplethysmographic Measurements Under Motion Artifacts Using Artificial Neural Network for Personal Healthcare
Roy, M. S., Gupta, R., Chandra, J. K., Sharma, K. D., & Talukdar, A. (2018) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2018.2829488 · Cited by 76
On-Device Integrated PPG Quality Assessment and Sensor Disconnection/Saturation Detection System for IoT Health Monitoring
Reddy, G. N. K., Manikandan, M. S., & Murty, N. V. L. N. (2020) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2020.2971132 · Cited by 73
A Multimodal Data Fusion Technique for Heartbeat Detection in Wearable IoT Sensors
John, A., Redmond, S. J., Cardiff, B., & John, D. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3093112 · Cited by 70
Classification of Photoplethysmographic Signal Quality with Deep Convolution Neural Networks for Accurate Measurement of Cardiac Stroke Volume
Liu, S.-H., Li, R.-X., Wang, J.-J., Chen, W., & Su, C.-H. (2020) · Applied Sciences · doi:10.3390/app10134612 · Cited by 58
Optimized Signal Quality Assessment for Photoplethysmogram Signals Using Feature Selection
Mohagheghian, F., Han, D., Peitzsch, A. G., Nishita, N., Ding, E., Dickson, E. L., DiMezza, D., Otabil, E. M., Noorishirazi, K., Scott, J., et al. (2022) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2022.3158582 · Cited by 57
Implementation of Artifact Detection in Critical Care: A Methodological Review
Nizami, S., Green, J. R., & McGregor, C. (2013) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2013.2243724 · Cited by 52
Development of an Ambulatory Biofeedback App to Enhance Emotional Awareness in Patients with Borderline Personality Disorder: Multicycle Usability Testing Study
Derks, Y. P., Klaassen, R., Westerhof, G. J., Bohlmeijer, E. T., & Noordzij, M. L. (2019) · JMIR mHealth and uHealth · doi:10.2196/13479 · Cited by 45
Deep learning approaches for plethysmography signal quality assessment in the presence of atrial fibrillation
Pereira, T., Ding, C., Gadhoumi, K., Tran, N., Colorado, R. A., Meisel, K. M., & Hu, X. (2019) · Physiological Measurement · doi:10.1088/1361-6579/ab5b84 · Cited by 53
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
Pulse rate variability compared with heart rate variability in children with and without sleep disordered breathing
Dehkordi, P., Garde, A., Karlen, W., Wensley, D., Ansermino, J. M., & Dumont, G. A. (2013) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2013.6611059 · Cited by 52
Differential effects of the blood pressure state on pulse rate variability and heart rate variability in critically ill patients
Mejía‐Mejía, E., May, J. M., Elgendi, M., & Kyriacou, P. A. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00447-y · Cited by 42
A supervised machine learning semantic segmentation approach for detecting artifacts in plethysmography signals from wearables
Guo, Z., Ding, C., Hu, X., & Rudin, C. D. (2021) · Physiological Measurement · doi:10.1088/1361-6579/ac3b3d · Cited by 40
Tiny-PPG: A lightweight deep neural network for real-time detection of motion artifacts in photoplethysmogram signals on edge devices
Zheng, Y., Wu, C., Cai, P., Zhong, Z., Huang, H., & Jiang, Y. (2023) · Internet of Things · doi:10.1016/j.iot.2023.101007 · Cited by 40
Signal quality measure for pulsatile physiological signals using morphological features: Applications in reliability measure for pulse oximetry
Sabeti, E., Reamaroon, N., Mathis, M. R., Gryak, J., Sjoding, M. W., & Najarian, K. (2019) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2019.100222 · Cited by 40
Quality Assessment for the Photoplethysmogram (PPG)
Orphanidou, C. (2017) · SpringerBriefs in bioengineering · doi:10.1007/978-3-319-68415-4_3 · Cited by 39
Adaptive template matching of photoplethysmogram pulses to detect motion artefact
Lim, P. K., Ng, S., Lovell, N. H., Yu, Y. P., Tan, M. P., McCombie, D. B., Lim, E., & Redmond, S. J. (2018) · Physiological Measurement · doi:10.1088/1361-6579/aadf1e · Cited by 39
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
Pediatric pulse oximetry-based OSA screening at different thresholds of the apnea-hypopnea index with an expression of uncertainty for inconclusive classifications
Garde, A., Hoppenbrouwer, X. L. R., Dehkordi, P., Zhou, G., Rollinson, A. U., Wensley, D., Dumont, G. A., & Ansermino, J. M. (2018) · Sleep Medicine · doi:10.1016/j.sleep.2018.08.027 · Cited by 36
Dynamic ROI based on K-means for remote photoplethysmography
Feng, L., Po, L.-M., Xu, X., Li, Y., Cheung, T. C.-H., Cheung, K.-W., & Fang, Y. (2015) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2015.7178182 · Cited by 38
Classification of Photoplethysmographic Signal Quality with Fuzzy Neural Network for Improvement of Stroke Volume Measurement
Liu, S.-H., Wang, J.-J., Chen, W., Pan, K., & Su, C.-H. (2020) · Applied Sciences · doi:10.3390/app10041476 · Cited by 35
Classification of blood pressure in critically ill patients using photoplethysmography and machine learning
Mejía‐Mejía, E., May, J. M., Elgendi, M., & Kyriacou, P. A. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106222 · Cited by 36
Pulse oximetry recorded from the Phone Oximeter for detection of obstructive sleep apnea events with and without oxygen desaturation in children
Garde, A., Dehkordi, P., Wensley, D., Ansermino, J. M., & Dumont, G. A. (2015) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2015.7320174 · Cited by 33
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
Sinus or not: a new beat detection algorithm based on a pulse morphology quality index to extract normal sinus rhythm beats from wrist-worn photoplethysmography recordings
Papini, G. B., Fonseca, P. M., Eerikäinen, L. M., Overeem, S., Bergmans, J. W. M., & Vullings, R. (2018) · Physiological Measurement · doi:10.1088/1361-6579/aae7f8 · Cited by 32
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
A Survey of Photoplethysmography and Imaging Photoplethysmography Quality Assessment Methods
Desquins, T., Bousefsaf, F., Pruski, A., & Maaoui, C. (2022) · Applied Sciences · doi:10.3390/app12199582 · Cited by 35
Computationally efficient algorithm for photoplethysmography-based atrial fibrillation detection using smartphones
Schäck, T., Harb, Y. S., Muma, M., & Zoubir, A. M. (2017) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2017.8036773 · Cited by 30
On-Board Signal Quality Assessment Guided Compression of Photoplethysmogram for Personal Health Monitoring
Alam, S., Gupta, R., & Sharma, K. D. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3067238 · Cited by 30
On-Device Reliability Assessment and Prediction of Missing Photoplethysmographic Data Using Deep Neural Networks
Roy, M. S., Roy, B., Gupta, R., & Sharma, K. D. (2020) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2020.3028935 · Cited by 29
Respiratory rate and pulse oximetry derived information as predictors of hospital admission in young children in Bangladesh: a prospective observational study
Garde, A., Zhou, G., Raihana, S., Dunsmuir, D. T., Karlen, W., Dekhordi, P., Huda, T. M., Arifeen, S. E., Larson, C. P., Kissoon, N. “., et al. (2016) · BMJ Open · doi:10.1136/bmjopen-2016-011094 · Cited by 27
Identifying individual sleep apnea/hypoapnea epochs using smartphone-based pulse oximetry
Garde, A., Dekhordi, P., Ansermino, J. M., & Dumont, G. A. (2016) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2016.7591408 · Cited by 27
PQR signal quality indexes: A method for real-time photoplethysmogram signal quality estimation based on noise interferences
Song, J., Li, D., Ma, X., Teng, G., & Wei, J. (2018) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2018.05.020 · Cited by 25
Modeling of artifacts in the wrist photoplethysmogram: Application to the detection of life-threatening arrhythmias
Paliakaitė, B., Petrėnas, A., Sološenko, A., & Marozas, V. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.102421 · Cited by 24
Photoplethysmogram Signal Quality Evaluation by Unsupervised Learning Approach
Roy, M. S., Gupta, R., & Sharma, K. D. (2020) · 2020 IEEE Applied Signal Processing Conference (ASPCON) · doi:10.1109/aspcon49795.2020.9276733 · Cited by 24
Photoplethysmography in dogs and cats: a selection of alternative measurement sites for a pet monitor
Cugmas, B., Štruc, E., & Spīgulis, J. (2018) · Physiological Measurement · doi:10.1088/1361-6579/aaf433 · Cited by 24
Evaluation of cardiac modulation in children in response to apnea/hypopnea using the Phone Oximeter ™
Dehkordi, P., Garde, A., Karlen, W., Petersen, C. L., Wensley, D., Dumont, G. A., & Ansermino, J. M. (2016) · Physiological Measurement · doi:10.1088/0967-3334/37/2/187 · Cited by 22
Sleep stage classification in children using photoplethysmogram pulse rate variability
Dehkordi, P., Garde, A., Karlen, W., Wensley, D., Ansermino, J. M., & Dumont, G. A. (2014) · Computing in cardiology · Cited by 22
A combined segmenting and non-segmenting approach to signal quality estimation for ambulatory photoplethysmography
Wander, J. D., & Morris, D. (2014) · Physiological Measurement · doi:10.1088/0967-3334/35/12/2543 · Cited by 22
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 20
Fuzzy-Inspired Photoplethysmography Signal Classification with Bio-Inspired Optimization for Analyzing Cardiovascular Disorders
Prabhakar, S. K., Rajaguru, H., & Kim, S. (2020) · Diagnostics · doi:10.3390/diagnostics10100763 · Cited by 17
In vivo evaluation of a novel, wrist-mounted arterial pressure sensing device versus the traditional hand-held tonometer
Vardoulis, O., Saponas, T. S., Morris, D., Villar, N., Smith, G., Patel, S., & Tan, D. S. (2016) · Medical Engineering & Physics · doi:10.1016/j.medengphy.2016.06.022 · Cited by 16
LSTM-based real-time signal quality assessment for blood volume pulse analysis
Gao, H., Zhang, C., Pei, S., & Wu, X. (2023) · Biomedical Optics Express · doi:10.1364/boe.477143 · Cited by 15
Real-time multi-class signal quality assessment of photoplethysmography using machine learning technique
Prasun, P., Mukhopadhyay, S., & Gupta, R. (2021) · Measurement Science and Technology · doi:10.1088/1361-6501/ac2d5b · 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
A novel diversity method for smartphone camera-based heart rhythm signals in the presence of motion and noise artifacts
Tabei, F., Zaman, R., Foysal, K. H., Kumar, R., Kim, Y., & Chong, J. W. (2019) · PLoS ONE · doi:10.1371/journal.pone.0218248 · Cited by 13
A Generalized Signal Quality Estimation Method for IoT Sensors
John, A., Cardiff, B., & John, D. (2020) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas45731.2020.9180546 · Cited by 14
A Pulse Signal Preprocessing Method Based on the Chauvenet Criterion
Ni, W., Qi, J., Liu, L., & Li, S. (2019) · Computational and Mathematical Methods in Medicine · doi:10.1155/2019/2067196 · Cited by 14
A Cohort Study of Morbidity, Mortality and Health Seeking Behavior following Rural Health Center Visits by Children under 12 in Southwestern Uganda
Wiens, M. O., Gan, H., Barigye, C., Zhou, G., Kumbakumba, E., Kabakyenga, J. K., Kissoon, N. “., Ansermino, J. M., Karlen, W., Larson, C. P., et al. (2015) · PLoS ONE · doi:10.1371/journal.pone.0118055 · Cited by 13
Resource-Efficient Derivative PPG-Based Signal Quality Assessment Using One-Dimensional CNN With Optimal Hyperparameters for Quality-Aware PPG Analysis
Sivanjaneyulu, Y., Manikandan, M. S., Boppu, S., & Cenkeramaddi, L. R. (2024) · IEEE Access · doi:10.1109/access.2024.3464231 · Cited by 12
On-Device Multi-Level Signal Quality Aware Compression for Energy-Efficient Wearable PPG Sensing
Alam, S., Gupta, R., & Sharma, K. D. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3234171 · Cited by 12
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 13
Night to night variability of pulse oximetry features in children at home and at the hospital
Hoppenbrouwer, X. L. R., Rollinson, A. U., Dunsmuir, D. T., Ansermino, J. M., Dumont, G. A., Nijeweme-d’Hollosy, W. O., Veltink, P. H., & Garde, A. (2021) · Physiological Measurement · doi:10.1088/1361-6579/ac278e · Cited by 10
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
MLFusion: Multilevel Data Fusion using CNNs for atrial fibrillation detection
John, A., Parhi, K. K., Cardiff, B., & John, D. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.109901 · Cited by 10
Data Integrity–Based Methodology and Checklist for Identifying Implementation Risks of Physiological Sensing in Mobile Health Projects: Quantitative and Qualitative Analysis
Zhang, J., Tüshaus, L., Martínez, N. N., Moreo, M., Verástegui, H., Hartinger, S. M., Mäusezahl, D., & Karlen, W. (2018) · JMIR mhealth and uhealth · doi:10.2196/11896 · Cited by 9
Robust Assessment of Photoplethysmogram Signal Quality in the Presence of Atrial Fibrillation
Pereira, T., Gadhoumi, K., Ma, M., Colorado, R. A., Keenan, K. J., Meisel, K. M., & Hu, X. (2018) · Computing in cardiology · doi:10.22489/cinc.2018.254 · Cited by 8
Physiologically driven, altitude-adaptive model for the interpretation of pediatric oxygen saturation at altitudes above 2,000 m a.s.l.
Tüshaus, L., Moreo, M., Zhang, J., Hartinger, S. M., Mäusezahl, D., & Karlen, W. (2019) · Journal of Applied Physiology · doi:10.1152/japplphysiol.00478.2018 · Cited by 8
Influence of Photoplethysmogram Signal Quality on Pulse Arrival Time during Polysomnography
Rinkevičius, M., Charlton, P. H., Bailón, R., & Marozas, V. (2023) · Sensors · doi:10.3390/s23042220 · Cited by 11
Sleep/Wake Classification Using Cardiorespiratory Features Extracted from Photoplethysmogram
Dehkordi, P., Garde, A., Dumont, G. A., & Ansermino, J. M. (2016) · Computing in cardiology · doi:10.22489/cinc.2016.294-147 · Cited by 8
Reconstruction of Corrupted and Lost Segments from Photoplethysmographic Data Using Recurrent Neural Network
Roy, M. S., Bag, P., & Gupta, R. (2019) · IEEE Region 10 Symposium (TENSYMP) · doi:10.1109/tensymp46218.2019.8971035 · Cited by 7
Automated Segmentation of the Systolic and Diastolic Phases in Wrist Pulse Signal Using Long Short‐Term Memory Network
Huang, L., Yan, J., Cai, S., Guo, R. J., Yan, H., & Wang, Y. (2022) · BioMed Research International · doi:10.1155/2022/2766321 · Cited by 7
Recognition of correct finger placement for photoplethysmographic imaging
Karlen, W., Lim, J. M.-Y., Ansermino, J. M., Dumont, G. A., & Scheffer, C. (2013) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2013.6611288 · Cited by 7
Identification of Pulse Onset on Cerebral Blood Flow Velocity Waveforms: A Comparative Study
Asgari, S., Canac, N., Hamilton, R., & Scalzo, F. (2019) · BioMed Research International · doi:10.1155/2019/3252178 · Cited by 7
Segmentation of Motion Artifacts in Wearable PPG Signals Using Lightweight Neural Networks
Bolpagni, M., Campanella, S., Gabrielli, S., & Palma, L. (2025) · IEEE Sensors Journal · doi:10.1109/jsen.2025.3561604 · Cited by 6
COVID-19 Detection Using Photoplethysmography and Neural Networks
Lombardi, S., Francia, P., Deodati, R., Calamai, I., Luchini, M., Spina, R., & Bocchi, L. (2023) · Sensors · doi:10.3390/s23052561 · Cited by 8
Learning based Quality Indicator Aiding Heart Rate Estimation in Wrist-Worn PPG
Lutin, E., Biswas, D., Simoes-Capela, N., Hoof, C. A. V., & Helleputte, N. V. (2021) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc46164.2021.9630910 · Cited by 6
A Novel Abnormal Segments Detection Method for Photopletysmography Signal
Teng, Y., Ge, L., & Chou, Y. (2018) · Chinese Control Conference (CCC) · doi:10.23919/chicc.2018.8483815 · Cited by 6
Cuffless Monitoring of Blood Pressure Using Photoplethysmography Signal: A Comprehensive Review of Artificial Intelligence and Edge Computing Solutions
Pankaj, Maan, P., Kumar, M., Kumar, A., & Komaragiri, R. (2025) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-025-10415-4 · Cited by 11
OPRA: A fast on-line signal quality estimator for pulsatile signals
Pflugradt, M., Moeller, B., & Orglmeister, R. (2015) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2015.10.183 · Cited by 5
Correntropy-Based Pulse Rate Variability Analysis in Children with Sleep Disordered Breathing
Garde, A., Dehkordi, P., Ansermino, J. M., & Dumont, G. A. (2017) · Entropy · doi:10.3390/e19060282 · Cited by 5
Airflow from nasal pulse oximetry in the screening of obstructive sleep apnea
Hoppenbrouwer, X. L. R., Fabius, T. M., Eijsvogel, M., de Jongh, F. H., & Garde, A. (2019) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2019.8857586 · Cited by 5
Signal Quality Analysis in Pulse Oximetry: Modelling and Detection of Motion Artifact
Clarke, G. (2015) · doi:10.22215/etd/2015-10956 · Cited by 5
Comparative Study on the Generalization Ability of Machine Learning and Deep Learning Algorithms for Quality Assessment of Wearable PPG Recordings
Mula, S., Zangróniz, R., Martín, Ó. A., Rieta, J. J., & Alcaraz, R. (2025) · IEEE Access · doi:10.1109/access.2025.3604652 · Cited by 4
Signal completion using generative adversarial networks for enhanced photoplethysmography measurement accuracy
Shin, H. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108952 · Cited by 4
Non-invasive Estimation of Oxygen Saturation Level in Blood
Devi, C. N., & Alex, Z. C. (2017) · Indian Journal of Science and Technology · doi:10.17485/ijst/2017/v10i5/108778 · Cited by 4
Photoplethysmogram analysis and applications: An Integrative Review (Preprint)
Shin, H., Park, J., Seok, H. S., & Kim, S. (2020) · JMIR Biomedical Engineering · doi:10.2196/25567 · Cited by 4
Recognising Cardiac Abnormalities in Wearable Device Photoplethysmography (PPG) with Deep LearningPreprint
Whiting, S., Moreland, S., Costello, J., Colopy, G. W., & McCann, C. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.04077 · Cited by 4
Physiology and enabling technologies for quantitative assessment of survivability during cold water immersion and rewarming: a review
Shahrbabak, S. M., Bouzid, Z., Inan, O. T., & Hahn, J. (2025) · Progress in Biomedical Engineering · doi:10.1088/2516-1091/ae0c4b · Cited by 3
Remote physiological monitoring of neck blood vessels with a high-speed camera
Cao, M., Saiko, G., & Douplik, A. (2025) · Advanced Optical Technologies · doi:10.3389/aot.2025.1536415 · Cited by 3
On-Device Signal Quality Guided and Embedded Physiologic Information for High Fidelity Continuous PPG Compression
Bose, A., Mukherjee, S., Chandra, J. K., Gupta, R., & Ghosh, D. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3351256 · Cited by 3
Pulse2AI: An Adaptive Framework to Standardize and Process Pulsatile Wearable Sensor Data for Clinical Applications
Huang, S., Jafari, R., & Mortazavi, B. J. (2024) · IEEE Open Journal of Engineering in Medicine and Biology · doi:10.1109/ojemb.2024.3398444 · Cited by 3
Case Study on Measurement of SpO2 from PPG Signals in the Presence of Motion Artifact
Subhagya, D. S., Keshava Murthy., C., Aruna, N., Janardhan, L., & Ramakrishna, H. S. (2017) · International Conference on Recent Advances in Electronics and Communication Technology (ICRAECT) · doi:10.1109/icraect.2017.71 · Cited by 3
Motion Artifact Mitigation for Wearable Pulse Oximetry
Williamson, J. R., Patel, T., Singh, N., Siegel, A. M., Telfer, B. A., Trebicka, R., Welsh, B., & Hoyt, R. W. (2018) · Wearable and Implantable Body Sensor Networks · doi:10.1109/bsn.2018.8329662 · Cited by 3
Alexithymia in borderline personality pathology: From theory to a biosensor application
Derks, Y. P. M. J. (2022) · doi:10.3990/1.9789464217896 · Cited by 2
Systolic Blood Pressure in Anesthetized Bitches Measured with Photoplethysmography
Vaccarin, C. V., Gasparotto, J. C., Vargas, D., Libardoni, R. D. N., Velasquez, O. G., da Silva, C. T., Segat, H. J., Lopes, C., Müller, D. C. D. M., & Soares, A. V. (2022) · ACTA SCIENTIAE VETERINARIAE · doi:10.22456/1679-9216.124686 · Cited by 1

Authors: Karlen, W., Kobayashi, K., Ansermino, J. M., & Dumont, G. A.
Affiliations: University of British Columbia (CA)
Corresponding author: Karlen, W
ORCID: Karlen, W
Ansermino, J M
Dumont, G A
Venue: Physiological Measurement
Publisher: IOP Publishing
Published: 2012-09-18
Volume: 33
Issue: 10
Pages: 1617-1629
ISSN: 0967-3334 (ISSN-L)
1361-6579
DOI: 10.1088/0967-3334/33/10/1617
OpenAlex: W2160347936
Cited by: 123
References: 18
Citations by year: 2026: 10 · 2025: 8 · 2024: 7 · 2023: 10 · 2022: 12 · 2021: 11 · 2020: 10 · 2019: 11 · 2018: 13 · 2017: 7 · 2016: 9 · 2015: 6 · 2014: 6 · 2013: 3
FWCI: 2.74
Type: journal article
Language: en
Open access: closed
Locations: Physiological Measurement (publishedVersion)
PubMed (publishedVersion)
Funders: Hirosaki University
Topics: Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
Keywords: photoplethysmogram, pulse oximetry, signal quality estimation, pulse detection, Gaussian filtering, cross correlation function, signal artifacts, real-time monitoring, smart alarms, heart rate monitoring
MeSH terms: Adolescent, Adult, Aged, Algorithms, Child, Child, Preschool, Humans, Infant, Middle Aged, Quality Control, Signal Processing, Computer-Assisted, Normal Distribution, Databases, Factual, Photoplethysmography, Young Adult

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

Photoplethysmogram signal quality estimation using repeated Gaussian filters and cross-correlation

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks