{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T15:01:15Z","timestamp":1780498875034,"version":"3.54.1"},"reference-count":22,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2026,1,26]],"date-time":"2026-01-26T00:00:00Z","timestamp":1769385600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,1,26]],"date-time":"2026-01-26T00:00:00Z","timestamp":1769385600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Cardiovasc electrophysiol"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Ventricular tachycardia (VT) is a life\u2010threatening arrhythmia that requires both relatively rapid and accurate detection in intensive care units (ICUs). Continuous monitoring systems play a crucial role in detecting them. However, previous studies have reported that nearly 9 out of 10 arrhythmia alarms in ICUs tend to be false positives, which usually transpire to a well\u2010documented phenomenon called \u201calarm fatigue\u201d that leads to desensitization, delayed responses, and increased cognitive burden on healthcare providers.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We developed a deep learning based, one\u2010dimensional convolutional neural network (1D\u2010CNN) to classify VT alarms using multiple raw waveform inputs, including two electrocardiogram (ECG) leads, photoplethysmogram (PPG) and arterial blood pressure (ABP) signals. The model was trained using the publicly available VTaC Arrhythmia Benchmark Dataset. We used the 10\u2010second waveform segments that preceded each VT alarm, pre\u2010processed and then used them to train the machine learning model to correctly classify the VT alarm.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>On the test set, the model achieved an area under the receiver operating characteristic curve of 0.901, overall accuracy of 83.22%, F1\u2010score of 73.3%, sensitivity of 77.53%, specificity of 85.63%, and positive predictive value of 69.57%. The model successfully detected over three\u2010quarters of them while significantly reducing false positive rates for the detection of VT.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This study demonstrates that a deep learning based 1D\u2010CNN model using short segments of raw waveform data can achieve robust performance in distinguishing true and false VT alarms.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/jce.70271","type":"journal-article","created":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T04:52:22Z","timestamp":1769489542000},"page":"705-713","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Role of a Deep\u2010Learning Based Convolutional Neural Network Model for Real\u2010Time Ventricular Tachycardia Alarm Classification"],"prefix":"10.1111","volume":"37","author":[{"given":"Unmesh","family":"Khanolkar","sequence":"first","affiliation":[{"name":"Division of Cardiac Electrophysiology University of Toledo Toledo Ohio USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ashish","family":"Yadav","sequence":"additional","affiliation":[{"name":"Division of Cardiac Electrophysiology University of Toledo Toledo Ohio USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Avdhesh","family":"Mann","sequence":"additional","affiliation":[{"name":"NYU Tandon School of Engineering New York New York USA"},{"name":"Indian Institute of Technology Mandi India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Scott","family":"Pappada","sequence":"additional","affiliation":[{"name":"Department of Bioengineering University of Toledo Toledo Ohio USA"},{"name":"Department of Anaesthesia University of Toledo Toledo Ohio USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abhishek","family":"Deshmukh","sequence":"additional","affiliation":[{"name":"Mayo Clinic Rochester Minnesota USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abhishek","family":"Maan","sequence":"additional","affiliation":[{"name":"Division of Cardiac Electrophysiology University of Toledo Toledo Ohio USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,1,26]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1161\/CIR.0000000000000261"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.2345\/0899-8205-46.4.268"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1097\/NCI.0b013e3182a903f9"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0110274"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.4037\/ajcc2019314"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.2147\/TCRM.S126191"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-018-0268-3"},{"key":"e_1_2_8_9_1","unstructured":"L.Lehman B.Moody L.McCullum et al. (2024). VTaC: A Benchmark Dataset of Ventricular Tachycardia Alarms From ICU Monitors (version 1.0). PhysioNet. RRID:SCR_007345 https:\/\/doi.org\/10.13026\/8td2-g363."},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1161\/01.CIR.101.23.e215"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.sigpro.2020.107914"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.104224"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/S2589-7500(20)30107-2"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(19)31721-0"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065721300011"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.2979450"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1007\/s13246-019-00815-9"},{"key":"e_1_2_8_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.103866"},{"key":"e_1_2_8_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2024.3490187"},{"key":"e_1_2_8_20_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-07761-9"},{"key":"e_1_2_8_21_1","doi-asserted-by":"publisher","DOI":"10.3390\/e20030190"},{"key":"e_1_2_8_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC.2019.8857034"},{"key":"e_1_2_8_23_1","unstructured":"C.Kechris Time\u2010Series Saliency Maps: Explaining Model Across Multiple Domains. Published in 05\/2025 https:\/\/arxiv.org\/html\/2505.13100v1."}],"container-title":["Journal of Cardiovascular Electrophysiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/jce.70271","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1111\/jce.70271","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/jce.70271","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T14:02:43Z","timestamp":1780495363000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1111\/jce.70271"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,26]]},"references-count":22,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,4]]}},"alternative-id":["10.1111\/jce.70271"],"URL":"https:\/\/doi.org\/10.1111\/jce.70271","archive":["Portico"],"relation":{"has-preprint":[{"id-type":"doi","id":"10.22541\/au.175826004.46360597\/v1","asserted-by":"object"}]},"ISSN":["1045-3873","1540-8167"],"issn-type":[{"value":"1045-3873","type":"print"},{"value":"1540-8167","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,26]]},"assertion":[{"value":"2025-09-16","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-01-19","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-01-26","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}