{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T15:04:24Z","timestamp":1791299064408,"version":"4.1.0"},"reference-count":37,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"23","content-domain":{"domain":["www.ahajournals.org"],"crossmark-restriction":true},"short-container-title":["JAHA"],"published-print":{"date-parts":[[2021,12,7]]},"abstract":"<jats:sec xml:lang=\"en\">\n            <jats:title>Background<\/jats:title>\n            <jats:p xml:lang=\"en\">Accurate detection of arrhythmic events in the intensive care units (ICU) is of paramount significance in providing timely care. However, traditional ICU monitors generate a high rate of false alarms causing alarm fatigue. In this work, we develop an algorithm to improve life threatening arrhythmia detection in the ICUs using a deep learning approach.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec xml:lang=\"en\">\n            <jats:title>Methods and Results<\/jats:title>\n            <jats:p xml:lang=\"en\">This study involves a total of 953 independent life\u2010threatening arrhythmia alarms generated from the ICU bedside monitors of 410 patients. Specifically, we used the ECG (4 channels), arterial blood pressure, and photoplethysmograph signals to accurately detect the onset and offset of various arrhythmias, without prior knowledge of the alarm type. We used a hybrid convolutional neural network based classifier that fuses traditional handcrafted features with features automatically learned using convolutional neural networks. Further, the proposed architecture remains flexible to be adapted to various arrhythmic conditions as well as multiple physiological signals. Our hybrid\u2010 convolutional neural network approach achieved superior performance compared with methods which only used convolutional neural network. We evaluated our algorithm using 5\u2010fold cross\u2010validation for 5 times and obtained an accuracy of 87.5%\u00b10.5%, and a score of 81%\u00b10.9%. Independent evaluation of our algorithm on the publicly available PhysioNet 2015 Challenge database resulted in overall classification accuracy and score of 93.9% and 84.3%, respectively, indicating its efficacy and generalizability.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec xml:lang=\"en\">\n            <jats:title>Conclusions<\/jats:title>\n            <jats:p xml:lang=\"en\">Our method accurately detects multiple arrhythmic conditions. Suitable translation of our algorithm may significantly improve the quality of care in ICUs by reducing the burden of false alarms.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1161\/jaha.121.023222","type":"journal-article","created":{"date-parts":[[2021,12,2]],"date-time":"2021-12-02T12:34:30Z","timestamp":1638448470000},"update-policy":"https:\/\/doi.org\/10.1161\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":28,"title":["Real\u2010Time Arrhythmia Detection Using Hybrid Convolutional Neural Networks"],"prefix":"10.1161","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9971-9335","authenticated-orcid":false,"given":"Sandeep Chandra","family":"Bollepalli","sequence":"first","affiliation":[{"name":"Cardiovascular Research Center Massachusetts General Hospital Boston MA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6234-367X","authenticated-orcid":false,"given":"Rahul K.","family":"Sevakula","sequence":"additional","affiliation":[{"name":"Cardiovascular Research Center Massachusetts General Hospital Boston MA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wan\u2010Tai M.","family":"Au\u2010Yeung","sequence":"additional","affiliation":[{"name":"Cardiovascular Research Center Massachusetts General Hospital Boston MA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohamad B.","family":"Kassab","sequence":"additional","affiliation":[{"name":"Cardiovascular Research Center Massachusetts General Hospital Boston MA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0679-860X","authenticated-orcid":false,"given":"Faisal M.","family":"Merchant","sequence":"additional","affiliation":[{"name":"Cardiology Division Emory University School of Medicine Atlanta GA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"George","family":"Bazoukis","sequence":"additional","affiliation":[{"name":"Second Department of Cardiology Evangelismos General Hospital of Athens Athens Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Richard","family":"Boyer","sequence":"additional","affiliation":[{"name":"Anesthesia Department Massachusetts General Hospital Boston MA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eric M.","family":"Isselbacher","sequence":"additional","affiliation":[{"name":"Healthcare Transformation Lab Massachusetts General Hospital Boston MA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5006-1547","authenticated-orcid":false,"given":"Antonis A.","family":"Armoundas","sequence":"additional","affiliation":[{"name":"Cardiovascular Research Center Massachusetts General Hospital Boston MA"},{"name":"Institute for Medical Engineering and Science Massachusetts Institute of Technology Cambridge MA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"276","reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0110274"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1097\/00003246-199704000-00010"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jelectrocard.2012.08.050"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/0169-2607(91)90039-V"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/0169-2607(90)90026-6"},{"key":"e_1_3_2_7_2","first-page":"832","article-title":"Signal\u2010quality indices for the electrocardiogram and photoplethysmogram: derivation and applications to wireless monitoring","volume":"19","author":"Orphanidou C","year":"2015","unstructured":"Orphanidou C, Bonnici T, Charlton P, Clifton D, Vallance D, Tarassenko L. 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