{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T10:03:33Z","timestamp":1768817013394,"version":"3.49.0"},"reference-count":49,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,4]],"date-time":"2023-01-04T00:00:00Z","timestamp":1672790400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology Projects of the Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM)","award":["HRTP202231"],"award-info":[{"award-number":["HRTP202231"]}]},{"name":"Science and Technology Projects of the Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM)","award":["62001404"],"award-info":[{"award-number":["62001404"]}]},{"DOI":"10.13039\/501100001809","name":"National Nature Science Foundation of China","doi-asserted-by":"publisher","award":["HRTP202231"],"award-info":[{"award-number":["HRTP202231"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Nature Science Foundation of China","doi-asserted-by":"publisher","award":["62001404"],"award-info":[{"award-number":["62001404"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>As one of the most remarkable indicators of physiological health, heart rate (HR) has become an unfailing investigation for researchers. Unlike many existing methods, this article proposes an approach to implement short-time HR estimation from electrocardiography in time series missing patterns. Benefiting from the rapid development of deep learning, we adopted a bidirectional long short-term memory model (Bi-LSTM) and temporal convolution network (TCN) to recover complete heartbeat signals from those with durations are less than one cardiac cycle, and the estimated HR from recovered segment combining the input and the predicted output. We also compared the performance of Bi-LSTM and TCN in PhysioNet dataset. Validating the method over a resting heart rate range of 60\u2013120 bpm in the database without significant arrhythmias and a corresponding range of 30\u2013150 bpm in the database with arrhythmias, we found that networks provide an estimated approach for incomplete signals in a fixed format. These results are consistent with real heartbeats in the normal heartbeat dataset (\u03b3 &gt; 0.7, RMSE &lt; 10) and in the arrhythmia database (\u03b3 &gt; 0.6, RMSE &lt; 30), verifying that HR could be estimated by models in advance. We also discussed the short-time limits for the predictive model. It could be used for physiological purposes such as mobile sensing in time-constrained scenarios, and providing useful insights for better time series analyses in missing data patterns.<\/jats:p>","DOI":"10.3390\/s23020597","type":"journal-article","created":{"date-parts":[[2023,1,5]],"date-time":"2023-01-05T02:28:53Z","timestamp":1672885733000},"page":"597","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Heart Rate Estimation from Incomplete Electrocardiography Signals"],"prefix":"10.3390","volume":"23","author":[{"given":"Yawei","family":"Song","sequence":"first","affiliation":[{"name":"School of Electronic Science and Engineering (National Model Microelectronics College), Xiamen University, Xiamen 361005, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7109-6351","authenticated-orcid":false,"given":"Jia","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Electronic Science and Engineering (National Model Microelectronics College), Xiamen University, Xiamen 361005, China"},{"name":"Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen 361005, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rongxin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Underwater Acoustic Communication and Marine Information Technology, Xiamen University, Ministry of Education, Xiamen 361005, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Lazazzera, R., Laguna, P., Gil, E., and Carrault, G. 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