{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T18:30:09Z","timestamp":1772044209656,"version":"3.50.1"},"reference-count":24,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,2,23]],"date-time":"2021-02-23T00:00:00Z","timestamp":1614038400000},"content-version":"vor","delay-in-days":53,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006261","name":"Taif University","doi-asserted-by":"publisher","award":["TURSP-2020\/231"],"award-info":[{"award-number":["TURSP-2020\/231"]}],"id":[{"id":"10.13039\/501100006261","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>Low heart rate causes a risk of death, heart disease, and cardiovascular diseases. Therefore, monitoring the heart rate is critical because of the heart\u2019s function to discover its irregularity to detect the health problems early. Rapid technological advancement (e.g., artificial intelligence and stream processing technologies) allows healthcare sectors to consolidate and analyze massive health\u2010based data to discover risks by making more accurate predictions. Therefore, this work proposes a real\u2010time prediction system for heart rate, which helps the medical care providers and patients avoid heart rate risk in real time. The proposed system consists of two phases, namely, an offline phase and an online phase. The offline phase targets developing the model using different forecasting techniques to find the lowest root mean square error. The heart rate time\u2010series dataset is extracted from Medical Information Mart for Intensive Care (MIMIC\u2010II). Recurrent neural network (RNN), long short\u2010term memory (LSTM), gated recurrent units (GRU), and bidirectional long short\u2010term memory (BI\u2010LSTM) are applied to heart rate time series. For the online phase, Apache Kafka and Apache Spark have been used to predict the heart rate in advance based on the best developed model. According to the experimental results, the GRU with three layers has recorded the best performance. Consequently, GRU with three layers has been used to predict heart rate 5 minutes in advance.<\/jats:p>","DOI":"10.1155\/2021\/5535734","type":"journal-article","created":{"date-parts":[[2021,2,23]],"date-time":"2021-02-23T21:06:11Z","timestamp":1614114371000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":47,"title":["Real\u2010Time System Prediction for Heart Rate Using Deep Learning and Stream Processing Platforms"],"prefix":"10.1155","volume":"2021","author":[{"given":"Abdullah","family":"Alharbi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wael","family":"Alosaimi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Radhya","family":"Sahal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6184-7107","authenticated-orcid":false,"given":"Hager","family":"Saleh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,2,23]]},"reference":[{"key":"e_1_2_9_1_2","volume-title":"Heart Disease","author":"World Health Organization","year":"2020"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1038\/nrcardio.2010.165"},{"key":"e_1_2_9_3_2","doi-asserted-by":"crossref","unstructured":"MasumS. ChivertonJ. P. LiuY. andVuksanovicB. Investigation of machine learning techniques in forecasting of blood pressure time series data Proceedings of the International Conference on Innovative Techniques and Applications of Artificial Intelligence December 2019 Cambridge UK Springer 269\u2013282.","DOI":"10.1007\/978-3-030-34885-4_21"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2013.2291900"},{"key":"e_1_2_9_5_2","doi-asserted-by":"publisher","DOI":"10.12693\/aphyspola.132.451"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/tbcas.2019.2892297"},{"key":"e_1_2_9_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.07.012"},{"key":"e_1_2_9_8_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10916-019-1243-3"},{"key":"e_1_2_9_9_2","doi-asserted-by":"crossref","unstructured":"BianM. PengB. WangW. andDongJ. An accurate LSTM based video heart rate estimation method Proceedings of the Chinese Conference on Pattern Recognition and Computer Vision (PRCV) November 2019 Xi\u2019an China Springer 409\u2013417.","DOI":"10.1007\/978-3-030-31726-3_35"},{"key":"e_1_2_9_10_2","doi-asserted-by":"crossref","unstructured":"ShyamA. RavichandranV. PreejithS. JosephJ. andSivaprakasamM. PPGnet: deep network for device independent heart rate estimation from photoplethysmogram Proceedings of the 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) July 2019 Berlin Germany IEEE 1899\u20131902 https:\/\/doi.org\/10.1109\/EMBC.2019.8856989.","DOI":"10.1109\/EMBC.2019.8856989"},{"key":"e_1_2_9_11_2","unstructured":"Z. Zhang \u201cSpc \u201dhttp:\/\/h:\/\/sites.google.com\/site\/researchbyzhang\/ieeespcup2015 2020."},{"key":"e_1_2_9_12_2","doi-asserted-by":"crossref","unstructured":"HuangB. ChangC.-M. LinC.-L. ChenW. JuangC.-F. andWuX. 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