A Robust Fusion Model for Estimating Respiratory Rate From Photoplethysmography and Electrocardiography
Open accessAbstract
OBJECTIVE: Respiratory rate (RR) estimation algorithms based on the photoplethymogram (PPG) and electrocardiogram (ECG) lack clinical robustness. This is because the PPG and ECG respiratory modulations are dependent on patient physiology, regardless of general signal quality. The present work describes an RR estimation algorithm using respiratory quality indices (RQIs) that assess the presence or absence of the PPG- and ECG-derived respiratory modulations. METHODS: Six respiratory waveforms are derived from the amplitude modulation, frequency modulation, and baseline wander of the PPG and ECG. The respiratory quality of each modulation is assessed by using RQIs based on the fast Fourier transform, autoregression, and autocorrelation. The individual RQIs are fused to obtain a single RQI per modulation per time window. Based on a tunable threshold, the RQIs are used to discard poor modulations and weight the remaining modulations to provide a single RR estimation per time window. RESULTS: The proposed method was tested on two independent datasets and found that using a conservative threshold, the mean absolute error was 0.71 $\pm$ 0.89 and 3.12 $\pm$ 4.39 brpm while discarding only 1.3% and 23.2% of all time windows, for each dataset, respectively. CONCLUSION: These errors are either better than or comparable to current methods, and the number of windows discarded is far lower demonstrating improved robustness. SIGNIFICANCE: This work describes a novel preprocessing algorithm that can be implemented in conjunction with other RR estimation techniques to improve robustness by specifically considering the quality of the respiratory information.
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
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?
A key challenge for RR algorithms is to make them robust to signal artifact and only output values associated with a high degree of confidence, particularly when used with wearable data.
“A key challenge is to make algorithms robust to signal artifact and only output values associated with a high degree of confidence”
From Wearable Photoplethysmography for Cardiovascular Monitoring (2022) · cites this paper as [89]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Goldberger, A. L., Amaral, L. A. N., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., Mietus, J. E., Moody, G. B., Peng, C.-K., & Stanley, H. E. (2000) · Circulation · doi:10.1161/01.cir.101.23.e215 · Cited by 14,809 · see citing works
EEG analysis based on time domain properties
Hjorth, B. (1970) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(70)90143-4 · Cited by 1,504 · see citing works
Saeed, M., Villarroel, M., Reisner, A. T., Clifford, G., Lehman, L.-W., Moody, G., Heldt, T., Kyaw, T. H., Moody, B., & Mark, R. G. (2011) · Critical Care Medicine · doi:10.1097/ccm.0b013e31820a92c6 · Cited by 1,162 · see citing works
Yasuma, F., & Hayano, J.-I. (2004) · Chest · doi:10.1378/chest.125.2.683 · Cited by 907 · see citing works
Respiratory rate: the neglected vital sign
Cretikos, M. A., Bellomo, R., Hillman, K., Chen, J., Finfer, S. R., & Flabouris, A. (2008) · The Medical Journal of Australia · doi:10.5694/j.1326-5377.2008.tb01825.x · Cited by 903 · see citing works
Clinical Antecedents to In-Hospital Cardiopulmonary Arrest
Schein, R. M. H., Hazday, N., Pena, M., Ruben, B. H., & Sprung, C. L. (1990) · CHEST Journal · doi:10.1378/chest.98.6.1388 · Cited by 902 · see citing works
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 · see citing works
Physiological values and procedures in the 24 h before ICU admission from the ward
Goldhill, D. R., White, S. A., & Sumner, A. (1999) · Anaesthesia · doi:10.1046/j.1365-2044.1999.00837.x · Cited by 447 · see citing works
Respiratory rate predicts cardiopulmonary arrest for internal medicine inpatients.
Fieselmann, J. F., Hendryx, M. S., Helms, C. M., & Wakefield, D. S. (1993) · Journal of General Internal Medicine · doi:10.1007/bf02600071 · Cited by 399 · see citing works
Toward a Robust Estimation of Respiratory Rate From Pulse Oximeters
Pimentel, M. A. F., Johnson, A. E. W., Charlton, P. H., Birrenkott, D. A., Watkinson, P., Tarassenko, L., & Clifton, D. A. (2016) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2016.2613124 · Cited by 391 · see citing works
Self-citationEl-Amrawy, F., & Nounou, M. I. (2015) · Healthcare Informatics Research · doi:10.4258/hir.2015.21.4.315 · Cited by 287 · see citing works
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 · see citing works
Self-citationWhy don't nurses monitor the respiratory rates of patients?
Hogan, J. (2006) · British Journal of Nursing · doi:10.12968/bjon.2006.15.9.21087 · Cited by 204 · see citing works
Novel Wireless Devices for Cardiac Monitoring
Walsh, J. A., Topol, E. J., & Steinhubl, S. R. (2014) · Circulation · doi:10.1161/circulationaha.114.009024 · Cited by 175 · see citing works
Long-term effect of introducing an early warning score on respiratory rate charting on general wards
McBride, J., Knight, D., Piper, J., & Smith, G. B. (2005) · Resuscitation · doi:10.1016/j.resuscitation.2004.10.015 · Cited by 154 · see citing works
Missed opportunities? An observational study of vital sign measurements
Leuvan, C. V., & Mitchell, I. (2008) · Critical Care and Resuscitation · doi:10.1016/s1441-2772(23)01322-4 · Cited by 142 · see citing works
Charlton, P. H., Bonnici, T., Tarassenko, L., Alastruey, J., Clifton, D. A., Beale, R. J., & Watkinson, P. (2017) · Physiological Measurement · doi:10.1088/1361-6579/aa670e · Cited by 140 · see citing works
Self-citationData Fusion for Improved Respiration Rate Estimation
Nemati, S., Malhotra, A., & Clifford, G. D. (2010) · EURASIP Journal on Advances in Signal Processing · doi:10.1155/2010/926305 · Cited by 115 · see citing works
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 · see citing works
Tutorial on Univariate Autoregressive Spectral Analysis
Takalo, R., Hytti, H., & Ihalainen, H. (2005) · Journal of Clinical Monitoring and Computing · doi:10.1007/s10877-005-7089-x · Cited by 74 · see citing works
Respiratory rates in emergency department patients
Hooker, E. A., O’Brien, D. J., Danzl, D. F., Barefoot, J. A., & Brown, J. E. (1989) · Journal of Emergency Medicine · doi:10.1016/0736-4679(89)90257-6 · Cited by 73 · see citing works
Chemla, D., Young, J., Badilini, F., Maison‐Blanche, P., Affres, H., Lecarpentier, Y., & Chanson, P. (2005) · International Journal of Cardiology · doi:10.1016/j.ijcard.2004.12.018 · Cited by 70 · see citing works
Effects of Varying Respiratory Waveforms on Gas Exchange
Bergman, N. A. (1967) · Anesthesiology · doi:10.1097/00000542-196703000-00022 · Cited by 65 · see citing works
Comparison of respiratory-induced variations in photoplethysmographic signals
Li, J., Jin, J., Chen, X., Sun, W., & Guo, P. (2010) · Physiological Measurement · doi:10.1088/0967-3334/31/3/009 · Cited by 47 · see citing works
Respiratory rate estimation during triage of children in hospitals
Shah, S. A., Fleming, S., Thompson, M., & Tarassenko, L. (2015) · Journal of Medical Engineering & Technology · doi:10.3109/03091902.2015.1105316 · Cited by 44 · see citing works
Pimentel, M. A. F., Santos, M., Springer, D., & Clifford, G. D. (2015) · Physiological Measurement · doi:10.1088/0967-3334/36/8/1717 · Cited by 41 · see citing works
Self-citationLarson, T. S., & Brady, W. J. (2008) · The American Journal of Emergency Medicine · doi:10.1016/j.ajem.2007.12.003 · Cited by 36 · see citing works
Surveillance of Patients in the Waiting Area of the Department of Emergency Medicine
Hubner, P., Schober, A., Sterz, F. R., Stratil, P., Wallmueller, C., Testori, C., Grassmann, D., Lebl, N., Ohrenberger, I., Herkner, H., et al. (2015) · Medicine · doi:10.1097/md.0000000000002322 · Cited by 17 · see citing works
Mobile phone-based rheumatic heart disease detection
Springer, D. (2015) · Oxford University Research Archive (ORA) (University of Oxford) · doi:10.5287/ora-kebg0rn99 · Cited by 5 · see citing works
Flagged first, then by citations
Research works printed but not linked: 3
- P. Charlton et al., "Respiratory rate estimation from the electrocardiogram and photoplethysmogram: A review," IEEE Rev. Biomed. Eng. [Online]. Available: http://ieeexplore.ieee.org/document/8081839/
- D. Birrenkott, "Respiratory quality indices for ECG-and PPG-derived respiratory data," D.Phil transfer of status report, Dept. of Eng. Science, University of Oxford, Oxford, U.K., 2015.
- D. Meredith et al., "Monitoring of vital signs during haemodialysis," in Proc. Brit. Renal Assoc. Conf., 2010, p. 355.
Cited by
Sorted by citations
The Importance of Respiratory Rate Monitoring: From Healthcare to Sport and Exercise
Nicolò, A., Massaroni, C., Schena, E., & Sacchetti, M. (2020) · Sensors · doi:10.3390/s20216396 · Cited by 451 · see citing works
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 · see citing works
Self-citationAdvances in Photopletysmography Signal Analysis for Biomedical Applications
Moraes, J. L., Rocha, M. X., Vasconcelos, G. G., Vasconcelos Filho, J. E., De Albuquerque, V. H. C., & Alexandria, A. R. (2018) · Sensors · doi:10.3390/s18061894 · Cited by 292 · see citing works
Recent development of respiratory rate measurement technologies
Liu, H., Allen, J., Zheng, D., & Chen, F. (2019) · Physiological Measurement · doi:10.1088/1361-6579/ab299e · Cited by 267 · see citing works
Wearable Photoplethysmography for Cardiovascular Monitoring
Charlton, P. H., Kyriacou, P. A., Mant, J., Marozas, V., Chowienczyk, P., & Alastruey, J. (2022) · Proceedings of the IEEE · doi:10.1109/jproc.2022.3149785 · Cited by 250 · see citing works
The 2023 wearable photoplethysmography roadmap
Charlton, P. H., Allen, J., Bailón, R., Baker, S., Behar, J. A., Chen, F., Clifford, G. D., Clifton, D. A., Davies, H. J., Ding, C., et al. (2023) · Physiological Measurement · doi:10.1088/1361-6579/acead2 · Cited by 210 · see citing works
Self-citationA review of wearable and unobtrusive sensing technologies for chronic disease management
Guo, Y., Liu, X., Peng, S., Jiang, X., Xu, K., Chen, C., Wang, Z., Dai, C., & Chen, W. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.104163 · Cited by 137 · see citing works
Photoplethysmography signal processing and synthesis
Mejía-Mejía, E., Allen, J., Budidha, K., El-Hajj, C., Kyriacou, P. A., & Charlton, P. H. (2022) · Photoplethysmography · doi:10.1016/b978-0-12-823374-0.00015-3 · Cited by 138 · see citing works
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 · see citing works
Deep learning for predicting respiratory rate from biosignals
Kumar, A., Ritam, M., Han, L., Guo, S., & Chandra, R. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105338 · Cited by 109 · see citing works
Remote Healthcare for Elderly People Using Wearables: A Review
Olmedo-Aguirre, J. O., Reyes-Campos, J., Alor‐Hernández, G., Machorro-Cano, I., Rodríguez-Mazahua, L., & Sánchez-Cervantes, J. L. (2022) · Biosensors · doi:10.3390/bios12020073 · Cited by 104 · see citing works
RespNet: A deep learning model for extraction of respiration from photoplethysmogram
Ravichandran, V., Murugesan, B., Vaishali, B., Ram, K., Preejith, S. P., Joseph, J., & Sivaprakasam, M. (2019) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2019.8856301 · Cited by 58 · see citing works
Breathing Rate Estimation Using Kalman Smoother With Electrocardiogram and Photoplethysmogram
Khreis, S., Ge, D., Rahman, H. A., & Carrault, G. (2019) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2019.2923448 · Cited by 52 · see citing works
Gazi, A. H., Wittbrodt, M. T., Harrison, A. B., Sundararaj, S., Gurel, N. Z., Nye, J. A., Shah, A. J., Vaccarino, V., Bremner, J. D., & Inan, O. T. (2021) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2021.3108135 · Cited by 50 · see citing works
Liu, H., Chen, F., Hartmann, V., Khalid, S. G., Hughes, S., & Zheng, D. (2020) · Physiological Measurement · doi:10.1088/1361-6579/abaaf0 · Cited by 49 · see citing works
Chan, M., Ganti, V. G., & Inan, O. T. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3144990 · Cited by 36 · see citing works
Roy, B., Roy, A., Chandra, J. K., & Gupta, R. (2020) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2020.3043506 · Cited by 34 · see citing works
Aqajari, S. A. H., Cao, R., Zargari, A. H. A., & Rahmani, A. M. (2021) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc46164.2021.9629984 · Cited by 31 · see citing works
Baker, S., Xiang, W., & Atkinson, I. (2021) · PLOS ONE · doi:10.1371/journal.pone.0249843 · Cited by 29 · see citing works
RF-Based Noncontact Respiratory Rate Monitoring With Parametric Spectral Estimation
Uysal, C., & Filik, T. (2019) · IEEE Sensors Journal · doi:10.1109/jsen.2019.2927536 · Cited by 22 · see citing works
Reconstruction of the respiratory signal through ECG and wrist accelerometer data
Leube, J., Zschocke, J., Kluge, M., Pelikan, L., Graf, A., Glos, M., Müller, A., Bartsch, R. P., Penzel, T., & Kantelhardt, J. W. (2020) · Scientific Reports · doi:10.1038/s41598-020-71539-0 · Cited by 18 · see citing works
A Portable Multi-Modal Cushion for Continuous Monitoring of a Driver’s Vital Signs
Linschmann, O., Uguz, D. U., Romanski, B., Baarlink, I., Gunaratne, P., Leonhardt, S. K., Walter, M., & Lueken, M. (2023) · Sensors · doi:10.3390/s23084002 · Cited by 17 · see citing works
Bernstein’s Theory of Levels and Its Application for Assessing the Human Operator State
Игоревич, С. С. (2019) · Studies in systems, decision and control · doi:10.1007/978-3-030-12072-6_25 · Cited by 16 · see citing works
Gazi, A. H., Sundararaj, S., Harrison, A. B., Gurel, N. Z., Wittbrodt, M. T., Shah, A. J., Vaccarino, V., Bremner, J. D., & Inan, O. T. (2021) · IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ...) · doi:10.1109/bhi50953.2021.9508534 · Cited by 16 · see citing works
Multimodal fusion for robust respiratory rate estimation in wearable sensing
John, A., Wang, H., Cardiff, B., Parhi, K. K., & John, D. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103253 · Cited by 15 · see citing works
Hurtado, D. E., Chavez, J. A. P., Mansilla, R., Lopez, R., & Hoffman, A. C. A. (2020) · IEEE Access · doi:10.1109/access.2020.3045603 · Cited by 15 · see citing works
A review on multi-sensor data fusion for wearable health monitoring
John, A., James, A. P., Cardiff, B., & John, D. (2026) · Information Fusion · doi:10.1016/j.inffus.2026.104319 · Cited by 32 · see citing works
Commercial Wearables for the Management of People with Autism Spectrum Disorder: A Review
Hernández-Capistrán, J., Alor‐Hernández, G., Marín‐Vega, H., Bustos-López, M., Sánchez-Morales, L. N., & Sánchez-Cervantes, J. L. (2024) · Biosensors · doi:10.3390/bios14110556 · Cited by 14 · see citing works
Respiratory Rate Estimation During Walking Using a Wearable Patch With Modality Attentive Fusion
Chan, M., Gazi, A. H., Aydemir, V. B., Soliman, M. M., Ozmen, G. C., Richardson, K. L. S., Abdallah, C. A., Nikbakht, M. R., Nichols, C. J., & Inan, O. T. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3324931 · Cited by 10 · see citing works
Wearable photoplethysmography devices
Charlton, P. H., & Marozas, V. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-823374-0.00011-6 · Cited by 10 · see citing works
Estimation of Tidal Volume Using Load Cells on a Hospital Bed
Jung, H., Kimball, J. P., Receveur, T., Gazi, A. H., Agdeppa, E. D., & Inan, O. T. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3141209 · Cited by 10 · see citing works
Continuous respiratory rate monitoring through temporal fusion of ECG and PPG signals
Lin, Y., Song, X., Zhao, Y., Zhang, C., & Ding, X. (2025) · PLoS ONE · doi:10.1371/journal.pone.0325307 · Cited by 7 · see citing works
Gazi, A. H., Sanchez-Perez, J. A., Saks, G., Alday, E. A. P., Haffar, A., Ahmed, H., Herraka, D., Tarlapally, N., Smith, N. L., Bremner, J. D., et al. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3397589 · Cited by 7 · see citing works
Wireless Tag Sensor Network for Apnea Detection and Posture Recognition Using LSTM
Saddaoui, R., Massine, G. A. N. A., Hamiche, H., & Laghrouche, M. (2024) · IEEE Embedded Systems Letters · doi:10.1109/les.2024.3410024 · Cited by 7 · see citing works
Dong, X., Wang, Z., Cao, L., Chen, Z., & Liang, Y. (2023) · Diagnostics · doi:10.3390/diagnostics13050913 · Cited by 6 · see citing works
Roy, B., Gupta, R., & Chandra, J. K. (2019) · IEEE Region 10 Symposium (TENSYMP) · doi:10.1109/tensymp46218.2019.8971202 · Cited by 6 · see citing works
Ganeshmurthy, M. S., Periyasamy, R., & Joshi, D. (2025) · Physical and Engineering Sciences in Medicine · doi:10.1007/s13246-025-01582-6 · Cited by 5 · see citing works
Estimation of Respiratory Signals from Remote Photoplethysmography of RGB Facial Videos
Seo, H., Kim, S., & Lee, E. C. (2025) · Electronics · doi:10.3390/electronics14112152 · Cited by 5 · see citing works
Gazi, A. H., Jung, H., Kimball, J. P., & Inan, O. T. (2022) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc48229.2022.9871873 · Cited by 5 · see citing works
Le, T.-D., Macabiau, C., Albert, K., Jouvet, P. A., & Noumeir, R. (2024) · IEEE Access · doi:10.1109/access.2024.3488595 · Cited by 4 · see citing works
Covariance intersection to improve the robustness of the photoplethysmogram derived respiratory rate
Zhang, J., Scebba, G., & Karlen, W. (2020) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc44109.2020.9175943 · Cited by 4 · see citing works
Extracting Fractional Inspiratory Time from Electrocardiograms
Nyamukuru, M., & Odame, K. M. (2021) · IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies · doi:10.1109/chase52844.2021.00020 · Cited by 4 · see citing works
Robust Estimation of Respiratory Rate from Photoplethysmogram with Respiration Quality Analysis
Sultan, M. A., & Saadeh, W. (2022) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas48785.2022.9937671 · Cited by 4 · see citing works
Filtering Parameters Selection Method and Peaks Extraction for ECG and PPG Signals
Bastos, L., do Rosário, D. L., Cerqueira, E., dos Santos, A. L., & Nogueira, M. (2019) · IEEE Latin-American Conference on Communications · doi:10.1109/latincom48065.2019.8937861 · Cited by 4 · see citing works
Covariance intersection to improve the robustness of the photoplethysmogram derived respiratory rate
Zhang, J., Scebba, G., & Karlen, W. (2020) · Repository for Publications and Research Data (ETH Zurich) · doi:10.3929/ethz-b-000419723 · Cited by 4 · see citing works
Extraction of Respiratory Signals and Respiratory Rates from the Photoplethysmogram
Xiao, S., Yang, P., Liu, L., Zhang, Z., & Wu, J. (2020) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-030-64991-3_13 · Cited by 4 · see citing works
Branan, K. L., Kurian, R., McMurray, J. P., Erraguntla, M., Gutiérrez‐Osuna, R., & Coté, G. L. (2025) · Biosensors · doi:10.3390/bios15080493 · Cited by 3 · see citing works
An Optical Signal Simulator for the Characterization of Photoplethysmographic Devices
Pittella, E., Testa, O., Podestà, L., & Piuzzi, E. (2024) · Sensors · doi:10.3390/s24031008 · Cited by 3 · see citing works
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 · see citing works
Breathing rate estimation based on multiple linear regression
Li, W., & Chen, Z. (2021) · Computer Methods in Biomechanics & Biomedical Engineering · doi:10.1080/10255842.2021.1977801 · Cited by 3 · see citing works
Castrejón-Peralta, C., Montiel-Pérez, J. Y., Gante-Díaz, S. A., Vázquez, J. A. C., Rubín-Alvarado, A. A., Reyes-Vera, Z., Torres-Delgadillo, J. M., Sossa, H., Villegas, O. O. V., & Sánchez, V. G. C. (2026) · Applied Sciences · doi:10.3390/app16021126 · Cited by 2 · see citing works
Rathore, K. S., Vijayarangan, S., Preejith, S. P., & Sivaprakasam, M. (2023) · Sensors · doi:10.3390/s23031599 · Cited by 4 · see citing works
Ribeiro, G., & Postolache, O. A. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-41456-5_55 · Cited by 2 · see citing works
Zhang, C., Wei, S., Ge, D., Zeng, Y., Zhu, G., Zhou, X., & Liu, F. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.107006 · Cited by 2 · see citing works
Xiao, Z., Vos, M. D., Chatzichristos, C., Dong, K., Jiang, Y., Wang, Z., Zhang, Y., Ding, F., Yang, C., Li, J., et al. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128360 · Cited by 2 · see citing works
Mohammadzadeh, F., Nam, C. S., & Lobaton, E. J. (2018) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2018.8512276 · Cited by 2 · see citing works
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 · see citing works
Multi-frequency Model Fusion for Robust Breathing Rate Estimation
Khreis, S., Ge, D., Zhu, J., & Carrault, G. (2019) · Computing in cardiology · doi:10.22489/cinc.2019.041 · Cited by 1 · see citing works
Estimation of Respiratory Rate from a Corrupted PPG Signal using Time-Frequency Spectrogram
Pankaj, Kumar, A., Komaragiri, R., & Kumar, M. (2023) · International Conference on Signal Processing and Integrated Networks (SPIN) · doi:10.1109/spin57001.2023.10117416 · Cited by 1 · see citing works
Sanchez-Perez, J. A., Gazi, A. H., Mabrouk, S. A., Rahman, F. N., Seith, A., Saks, G., Sundararaj, S., Erbrick, R., Harrison, A. B., Modak, M., et al. (2023) · Wearable and Implantable Body Sensor Networks · doi:10.1109/bsn58485.2023.10331181 · Cited by 1 · see citing works
A contribution to unobtrusive video-based measurement of respiratory signals
Mateu-Mateus, M. (2020) · doi:10.5821/dissertation-2117-334684 · Cited by 1 · see citing works
Saddaoui, R., Laghrouche, M., Hamiche, H., Zidelmal, Z., & Aoughlis, S. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4009443 · Cited by 1 · see citing works
Respiratory Signal Extraction from ECG Signal
Dong, K., Zhao, L., & Liu, C. (2020) · Springer eBooks · doi:10.1007/978-981-15-3824-7_13
A Multi-Task Learning Model for Estimating Fractional Inspiratory Time from ElectrocardiographyPreprint
Nyamukuru, M., & Odame, K. M. (2020) · arXiv (Cornell University)
Choi, C., Hwang, J., Lee, J., Ko, B.-H., Kim, Y.-H., & Choo, H. (2021) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc46164.2021.9630993
State feedback control for human inspiratory system
Alkurawy, L. E. J., Mohammed, K. G., & Ismael, A. I. (2022) · International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering · doi:10.11591/ijece.v12i3.pp2405-2413
Fourier Analysis Based Respiration Rate Estimation Using Corrupted Photoplethysmogram Signal
Pankaj, Kumar, A., Komaragiri, R., & Kumar, M. (2022) · International Conference on Multimedia, Signal Processing and Communication Technologies (IMPACT) · doi:10.1109/impact55510.2022.10029065
A Survey of Fusion Frameworks and Algorithms for Physiological Monitoring
John, A., Cardiff, B., & John, D. (2026) · Springer eBooks · doi:10.1007/978-3-031-96724-5_2
Ho, M. N., & Nguyen, K. (2026) · PLoS ONE · doi:10.1371/journal.pone.0353203 · Cited by 1 · see citing works
Digital Twin of the Lung from Wearable Biosignals for Real-Time Respiratory Monitoring
Acharya, P., Sulake, N. R., Chakraborty, S., Mandal, S., & Chakraborty, S. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-38186-6_48
Next‐Generation Brain Health Monitoring Enabled by Integrated Wearable AI Infrastructure
Masrur, M. A., Khan, B., Khalid, R. T., Sumaira, & Abdullah, S. (2026) · Smart Medicine · doi:10.1002/smmd.70053
- Authors
- Birrenkott, D. A., Pimentel, M. A. F., Watkinson, P. J., & Clifton, D. A.
- Affiliations
- University of Oxford (GB)
- Oxford University Hospitals NHS Trust (GB)
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2018-09
- Volume
- 65
- Issue
- 9
- Pages
- 2033-2041
- ISSN
- 0018-9294 (ISSN-L)
- 1558-2531
- OpenAlex
- W2776923905
- Cited by
- 71
- References
- 29
- Citations by year
- 2026: 6 · 2025: 6 · 2024: 7 · 2023: 8 · 2022: 11 · 2021: 9 · 2020: 13 · 2019: 8 · 2018: 2 · 2017: 1
- FWCI
- 2.95
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- IEEE Transactions on Biomedical Engineering (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Wellcome Trust (WT 088877/Z/09/Z)
- The Rhodes Trust
- 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
- respiratory rate estimation, photoplethysmography, electrocardiogram, multimodal signal fusion, amplitude modulation, frequency modulation, baseline wander, signal quality assessment, autoregression, autocorrelation, fast Fourier transform, robustness
- MeSH terms
- Algorithms, Electrocardiography, Humans, Signal Processing, Computer-Assisted, Photoplethysmography, Respiratory Rate
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
