{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T13:51:16Z","timestamp":1778507476493,"version":"3.51.4"},"reference-count":59,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,2,11]],"date-time":"2025-02-11T00:00:00Z","timestamp":1739232000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["SCC CNS-1831918"],"award-info":[{"award-number":["SCC CNS-1831918"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002341","name":"Research Council of Finland","doi-asserted-by":"publisher","award":["316810"],"award-info":[{"award-number":["316810"]}],"id":[{"id":"10.13039\/501100002341","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002341","name":"Research Council of Finland","doi-asserted-by":"publisher","award":["316811"],"award-info":[{"award-number":["316811"]}],"id":[{"id":"10.13039\/501100002341","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.fr","clinicalkey.jp","clinicalkey.com.au","clinicalkey.es","clinicalkey.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computers in Biology and Medicine"],"published-print":{"date-parts":[[2025,4]]},"DOI":"10.1016\/j.compbiomed.2025.109798","type":"journal-article","created":{"date-parts":[[2025,2,12]],"date-time":"2025-02-12T12:05:22Z","timestamp":1739361922000},"page":"109798","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":9,"special_numbering":"C","title":["Multitask learning approach for PPG applications: Case studies on signal quality assessment and physiological parameters estimation"],"prefix":"10.1016","volume":"188","author":[{"given":"Mohammad","family":"Feli","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0919-8661","authenticated-orcid":false,"given":"Kianoosh","family":"Kazemi","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5003-299X","authenticated-orcid":false,"given":"Iman","family":"Azimi","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9392-3589","authenticated-orcid":false,"given":"Pasi","family":"Liljeberg","sequence":"additional","affiliation":[]},{"given":"Amir M.","family":"Rahmani","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"2","key":"10.1016\/j.compbiomed.2025.109798_b1","doi-asserted-by":"crossref","first-page":"282","DOI":"10.3390\/electronics3020282","article-title":"Wearable photoplethysmographic sensors\u2014past and present","volume":"3","author":"Tamura","year":"2014","journal-title":"Electronics"},{"issue":"3","key":"10.1016\/j.compbiomed.2025.109798_b2","doi-asserted-by":"crossref","first-page":"R1","DOI":"10.1088\/0967-3334\/28\/3\/R01","article-title":"Photoplethysmography and its application in clinical physiological measurement","volume":"28","author":"Allen","year":"2007","journal-title":"Physiol. Meas."},{"issue":"4","key":"10.1016\/j.compbiomed.2025.109798_b3","first-page":"195","article-title":"A review on wearable photoplethysmography sensors and their potential future applications in health care","volume":"4","author":"Castaneda","year":"2018","journal-title":"Int. J. Biosens. Bioelectron."},{"key":"10.1016\/j.compbiomed.2025.109798_b4","series-title":"2006 International Conference of the IEEE Engineering in Medicine and Biology Society","first-page":"4289","article-title":"Comparison of heart rate variability signal features derived from electrocardiography and photoplethysmography in healthy individuals","author":"Bolanos","year":"2006"},{"issue":"7","key":"10.1016\/j.compbiomed.2025.109798_b5","doi-asserted-by":"crossref","first-page":"1946","DOI":"10.1109\/TBME.2013.2246160","article-title":"Multiparameter respiratory rate estimation from the photoplethysmogram","volume":"60","author":"Karlen","year":"2013","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"11","key":"10.1016\/j.compbiomed.2025.109798_b6","first-page":"45","article-title":"A real time analysis of PPG signal for measurement of SpO2 and pulse rate","volume":"36","author":"Bagha","year":"2011","journal-title":"Int. J. Comput. Appl."},{"key":"10.1016\/j.compbiomed.2025.109798_b7","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.bspc.2018.08.022","article-title":"Blood pressure estimation from appropriate and inappropriate PPG signals using a whole-based method","volume":"47","author":"Mousavi","year":"2019","journal-title":"Biomed. Signal Process. Control."},{"issue":"11","key":"10.1016\/j.compbiomed.2025.109798_b8","doi-asserted-by":"crossref","first-page":"zsaa098","DOI":"10.1093\/sleep\/zsaa098","article-title":"Deep learning enables sleep staging from photoplethysmogram for patients with suspected sleep apnea","volume":"43","author":"Korkalainen","year":"2020","journal-title":"Sleep"},{"key":"10.1016\/j.compbiomed.2025.109798_b9","doi-asserted-by":"crossref","first-page":"47777","DOI":"10.1109\/ACCESS.2021.3060441","article-title":"Stress detection with single PPG sensor by orchestrating multiple denoising and peak-detecting methods","volume":"9","author":"Heo","year":"2021","journal-title":"IEEE Access"},{"issue":"6","key":"10.1016\/j.compbiomed.2025.109798_b10","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0298949","article-title":"Objective monitoring of loneliness levels using smart devices: A multi-device approach for mental health applications","volume":"19","author":"Jafarlou","year":"2024","journal-title":"PLoS One"},{"issue":"1","key":"10.1016\/j.compbiomed.2025.109798_b11","doi-asserted-by":"crossref","first-page":"19896","DOI":"10.1038\/s41598-024-70773-0","article-title":"Preterm birth risk stratification through longitudinal heart rate and HRV monitoring in daily life","volume":"14","author":"Feli","year":"2024","journal-title":"Sci. Rep."},{"key":"10.1016\/j.compbiomed.2025.109798_b12","doi-asserted-by":"crossref","first-page":"258","DOI":"10.3389\/fpubh.2017.00258","article-title":"An overview of heart rate variability metrics and norms","volume":"5","author":"Shaffer","year":"2017","journal-title":"Front. Public Heal."},{"issue":"6","key":"10.1016\/j.compbiomed.2025.109798_b13","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0157557","article-title":"Inverse correlation between heart rate variability and heart rate demonstrated by linear and nonlinear analysis","volume":"11","author":"Kazmi","year":"2016","journal-title":"PLoS One"},{"key":"10.1016\/j.compbiomed.2025.109798_b14","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1023\/A:1022312815649","article-title":"The effects of specific respiratory rates on heart rate and heart rate variability","volume":"28","author":"Song","year":"2003","journal-title":"Appl. Psychophys. Biof."},{"issue":"12","key":"10.1016\/j.compbiomed.2025.109798_b15","doi-asserted-by":"crossref","first-page":"5586","DOI":"10.1109\/TKDE.2021.3070203","article-title":"A survey on multi-task learning","volume":"34","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.compbiomed.2025.109798_b16","series-title":"Using multitask learning to improve 12-lead electrocardiogram classification","author":"Hughes","year":"2018"},{"key":"10.1016\/j.compbiomed.2025.109798_b17","series-title":"2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society","first-page":"341","article-title":"Adversarial multi-task learning for robust end-to-end ECG-based heartbeat classification","author":"Shahin","year":"2020"},{"key":"10.1016\/j.compbiomed.2025.109798_b18","series-title":"Healthcare","first-page":"1000","article-title":"An ECG classification method based on multi-task learning and cot attention mechanism","volume":"vol. 11","author":"Geng","year":"2023"},{"issue":"7","key":"10.1016\/j.compbiomed.2025.109798_b19","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6579\/acdfb5","article-title":"CS-based multi-task learning network for arrhythmia reconstruction and classification using ECG signals","volume":"44","author":"Tang","year":"2023","journal-title":"Physiol. Meas."},{"issue":"5","key":"10.1016\/j.compbiomed.2025.109798_b20","doi-asserted-by":"crossref","first-page":"1595","DOI":"10.3390\/s21051595","article-title":"An adaptive weight learning-based multitask deep network for continuous blood pressure estimation using electrocardiogram signals","volume":"21","author":"Fan","year":"2021","journal-title":"Sensors"},{"issue":"1","key":"10.1016\/j.compbiomed.2025.109798_b21","doi-asserted-by":"crossref","first-page":"13539","DOI":"10.1038\/s41598-021-92997-0","article-title":"Combined deep CNN\u2013LSTM network-based multitasking learning architecture for noninvasive continuous blood pressure estimation using difference in ECG-PPG features","volume":"11","author":"Jeong","year":"2021","journal-title":"Sci. Rep."},{"key":"10.1016\/j.compbiomed.2025.109798_b22","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2021.108048","article-title":"PulseNet: A multitask learning network for remote heart rate estimation","volume":"239","author":"Yin","year":"2022","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.compbiomed.2025.109798_b23","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2021.104447","article-title":"A neonatal dataset and benchmark for non-contact neonatal heart rate monitoring based on spatio-temporal neural networks","volume":"106","author":"Huang","year":"2021","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.compbiomed.2025.109798_b24","series-title":"2022 IEEE 10th International Conference on Serious Games and Applications for Health (SeGAH)","first-page":"1","article-title":"MRNet-A deep learning based multitasking model for respiration rate estimation in practical settings","author":"Rathore","year":"2022"},{"key":"10.1016\/j.compbiomed.2025.109798_b25","doi-asserted-by":"crossref","first-page":"426","DOI":"10.1016\/j.inffus.2023.02.019","article-title":"Multitask deep label distribution learning for blood pressure prediction","volume":"95","author":"Qin","year":"2023","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.compbiomed.2025.109798_b26","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2022.103891","article-title":"PPG-based blood pressure estimation can benefit from scalable multi-scale fusion neural networks and multi-task learning","volume":"78","author":"Hu","year":"2022","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.compbiomed.2025.109798_b27","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2024.106378","article-title":"Advancing cuffless blood pressure estimation: A PPG-based multi-task learning model for enhanced feature extraction and fusion","volume":"95","author":"Xiao","year":"2024","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.compbiomed.2025.109798_b28","series-title":"2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society","first-page":"6398","article-title":"A survey of remote optical photoplethysmographic imaging methods","author":"McDuff","year":"2015"},{"issue":"1","key":"10.1016\/j.compbiomed.2025.109798_b29","doi-asserted-by":"crossref","DOI":"10.1002\/aisy.202300270","article-title":"Multitask learning for automated sleep staging and wearable technology integration","volume":"6","author":"Chih","year":"2024","journal-title":"Adv. Intell. Syst."},{"issue":"1","key":"10.1016\/j.compbiomed.2025.109798_b30","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1109\/TBME.2018.2830366","article-title":"ECG-based concentration recognition with multi-task regression","volume":"66","author":"Kaji","year":"2018","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"10.1016\/j.compbiomed.2025.109798_b31","series-title":"2015 11th International Conference on Signal-Image Technology & Internet-Based Systems","first-page":"42","article-title":"Applying non linear approach for ECG denoising and waves localization","author":"Beya","year":"2015"},{"key":"10.1016\/j.compbiomed.2025.109798_b32","series-title":"International Conference on Bio-Inspired Systems and Signal Processing","first-page":"156","article-title":"Electrocardiogram signal analysing-delineation and localization of ECG component","volume":"vol. 5","author":"Beya","year":"2016"},{"key":"10.1016\/j.compbiomed.2025.109798_b33","series-title":"Signal Quality Assessment in Physiological Monitoring: State of the Art and Practical Considerations","author":"Orphanidou","year":"2017"},{"issue":"11","key":"10.1016\/j.compbiomed.2025.109798_b34","first-page":"1910","article-title":"Real-time PPG signal quality assessment system for improving battery life and false alarms","volume":"66","author":"Vadrevu","year":"2019","journal-title":"IEEE Trans. Circuits Syst. II Express Briefs"},{"issue":"9","key":"10.1016\/j.compbiomed.2025.109798_b35","doi-asserted-by":"crossref","first-page":"6351","DOI":"10.1109\/TIM.2020.2971132","article-title":"On-device integrated PPG quality assessment and sensor disconnection\/saturation detection system for IoT health monitoring","volume":"69","author":"Reddy","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"3","key":"10.1016\/j.compbiomed.2025.109798_b36","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1109\/JBHI.2019.2909065","article-title":"A supervised approach to robust photoplethysmography quality assessment","volume":"24","author":"Pereira","year":"2019","journal-title":"IEEE J. Biomed. Heal. Inform."},{"key":"10.1016\/j.compbiomed.2025.109798_b37","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.procs.2021.03.025","article-title":"Lightweight photoplethysmography quality assessment for real-time IoT-based health monitoring using unsupervised anomaly detection","volume":"184","author":"Mahmoudzadeh","year":"2021","journal-title":"Procedia Comput. Sci."},{"key":"10.1016\/j.compbiomed.2025.109798_b38","article-title":"An energy-efficient semi-supervised approach for on-device photoplethysmogram signal quality assessment","volume":"28","author":"Feli","year":"2023","journal-title":"Smart Heal."},{"key":"10.1016\/j.compbiomed.2025.109798_b39","series-title":"2023 IEEE International Conference on Bioinformatics and Biomedicine","first-page":"1895","article-title":"End-to-end PPG processing pipeline for wearables: From quality assessment and motion artifacts removal to HR\/HRV feature extraction","author":"Feli","year":"2023"},{"key":"10.1016\/j.compbiomed.2025.109798_b40","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1016\/j.procs.2019.04.074","article-title":"A real-time PPG quality assessment approach for healthcare Internet-of-Things","volume":"151","author":"Naeini","year":"2019","journal-title":"Procedia Comput. Sci."},{"key":"10.1016\/j.compbiomed.2025.109798_b41","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2022.105430","article-title":"Deep convolutional neural network-based signal quality assessment for photoplethysmogram","volume":"145","author":"Shin","year":"2022","journal-title":"Comput. Biol. Med."},{"issue":"6","key":"10.1016\/j.compbiomed.2025.109798_b42","doi-asserted-by":"crossref","first-page":"7833","DOI":"10.1109\/JSEN.2019.2923982","article-title":"A review of deep learning models for time series prediction","volume":"21","author":"Han","year":"2019","journal-title":"IEEE Sensors J."},{"issue":"4","key":"10.1016\/j.compbiomed.2025.109798_b43","doi-asserted-by":"crossref","first-page":"969","DOI":"10.3390\/s20040969","article-title":"Deep learning in physiological signal data: A survey","volume":"20","author":"Rim","year":"2020","journal-title":"Sensors"},{"key":"10.1016\/j.compbiomed.2025.109798_b44","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2024.111926","article-title":"Time-series weather prediction in the Red sea using ensemble transformers","volume":"164","author":"Hittawe","year":"2024","journal-title":"Appl. Soft Comput."},{"issue":"4","key":"10.1016\/j.compbiomed.2025.109798_b45","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3616019","article-title":"A deep learning\u2013based PPG quality assessment approach for heart rate and heart rate variability","volume":"4","author":"Naeini","year":"2023","journal-title":"ACM Trans. Comput. Heal."},{"key":"10.1016\/j.compbiomed.2025.109798_b46","first-page":"2002","article-title":"Frequency bands effects on QRS detection","volume":"2003","author":"Elgendi","year":"2010","journal-title":"Biosignals"},{"key":"10.1016\/j.compbiomed.2025.109798_b47","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.cmpb.2017.06.018","article-title":"Heart rate variability metrics for fine-grained stress level assessment","volume":"148","author":"Pereira","year":"2017","journal-title":"Comput. Methods Programs Biomed."},{"issue":"1\u20134","key":"10.1016\/j.compbiomed.2025.109798_b48","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/0167-2789(92)90242-F","article-title":"Nonlinear total variation based noise removal algorithms","volume":"60","author":"Rudin","year":"1992","journal-title":"Phys. D Nonlinear Phenom."},{"issue":"3","key":"10.1016\/j.compbiomed.2025.109798_b49","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3130969","article-title":"Sleepmonitor: Monitoring respiratory rate and body position during sleep using smartwatch","volume":"1","author":"Sun","year":"2017","journal-title":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."},{"issue":"11","key":"10.1016\/j.compbiomed.2025.109798_b50","article-title":"Sleep tracking of a commercially available smart ring and smartwatch against medical-grade actigraphy in everyday settings: instrument validation study","volume":"8","author":"Mehrabadi","year":"2020","journal-title":"JMIR MHealth UHealth"},{"key":"10.1016\/j.compbiomed.2025.109798_b51","series-title":"Gear sport 42mm smartwatch","year":"2025"},{"key":"10.1016\/j.compbiomed.2025.109798_b52","series-title":"Shimmer3 ECG unit","year":"2025"},{"key":"10.1016\/j.compbiomed.2025.109798_b53","series-title":"2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society","first-page":"4668","article-title":"Estimation of respiration rate and sleeping position using a wearable accelerometer","author":"Doheny","year":"2020"},{"issue":"4","key":"10.1016\/j.compbiomed.2025.109798_b54","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6579\/abf01f","article-title":"Estimation of respiratory rate and effort from a chest-worn accelerometer using constrained and recursive principal component analysis","volume":"42","author":"Schipper","year":"2021","journal-title":"Physiol. Meas."},{"issue":"17","key":"10.1016\/j.compbiomed.2025.109798_b55","doi-asserted-by":"crossref","first-page":"10000","DOI":"10.1109\/JSEN.2020.2990864","article-title":"PP-Net: A deep learning framework for PPG-based blood pressure and heart rate estimation","volume":"20","author":"Panwar","year":"2020","journal-title":"IEEE Sensors J."},{"key":"10.1016\/j.compbiomed.2025.109798_b56","series-title":"2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society","first-page":"1899","article-title":"PPGnet: Deep network for device independent heart rate estimation from photoplethysmogram","author":"Shyam","year":"2019"},{"key":"10.1016\/j.compbiomed.2025.109798_b57","series-title":"2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society","first-page":"5948","article-title":"Respiratory rate estimation using PPG: A deep learning approach","author":"Bian","year":"2020"},{"key":"10.1016\/j.compbiomed.2025.109798_b58","series-title":"2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society","first-page":"744","article-title":"An end-to-end and accurate PPG-based respiratory rate estimation approach using cycle generative adversarial networks","author":"Aqajari","year":"2021"},{"issue":"8","key":"10.1016\/j.compbiomed.2025.109798_b59","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1093\/chemse\/bjy045","article-title":"Automated analysis of breathing waveforms using BreathMetrics: a respiratory signal processing toolbox","volume":"43","author":"Noto","year":"2018","journal-title":"Chem. Senses"}],"container-title":["Computers in Biology and Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0010482525001489?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0010482525001489?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,4,21]],"date-time":"2025-04-21T16:21:12Z","timestamp":1745252472000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0010482525001489"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4]]},"references-count":59,"alternative-id":["S0010482525001489"],"URL":"https:\/\/doi.org\/10.1016\/j.compbiomed.2025.109798","relation":{},"ISSN":["0010-4825"],"issn-type":[{"value":"0010-4825","type":"print"}],"subject":[],"published":{"date-parts":[[2025,4]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Multitask learning approach for PPG applications: Case studies on signal quality assessment and physiological parameters estimation","name":"articletitle","label":"Article Title"},{"value":"Computers in Biology and Medicine","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compbiomed.2025.109798","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 The Author(s). Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"109798"}}