{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T16:30:21Z","timestamp":1784997021876,"version":"3.55.0"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,8]]},"abstract":"<jats:p>Life-threatening ventricular arrhythmias (VAs) detection on intracardiac electrograms (IEGMs) is essential to Implantable Cardioverter Defibrillators (ICDs). However, current VAs detection methods count on a variety of heuristic detection criteria, and require frequent manual interventions to personalize criteria parameters for each patient to achieve accurate detection. In this work, we propose a one-dimensional convolutional neural network (1D-CNN) based life-threatening VAs detection on IEGMs. The network architecture is elaborately designed to satisfy the extreme resource constraints of the ICD while maintaining high detection accuracy. We further propose a meta-learning algorithm with a novel patient-wise training tasks formatting strategy to personalize the 1D-CNN. The algorithm generates a well-generalized model initialization containing across-patient knowledge, and performs a quick adaptation of the model to the specific patient's IEGMs. In this way, a new patient could be immediately assigned with personalized 1D-CNN model parameters using limited input data. Compared with the conventional VAs detection method, the proposed method achieves 2.2% increased sensitivity for detecting VAs rhythm and 8.6% increased specificity for non-VAs rhythm.<\/jats:p>","DOI":"10.24963\/ijcai.2021\/359","type":"proceedings-article","created":{"date-parts":[[2021,8,11]],"date-time":"2021-08-11T07:00:49Z","timestamp":1628665249000},"page":"2606-2613","source":"Crossref","is-referenced-by-count":15,"title":["Learning to Learn Personalized Neural Network for Ventricular Arrhythmias Detection on Intracardiac EGMs"],"prefix":"10.24963","author":[{"given":"Zhenge","family":"Jia","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Pittsburgh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhepeng","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Pittsburgh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feng","family":"Hong","sequence":"additional","affiliation":[{"name":"Singular Medical Co., Ltd."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lichuan","family":"PING","sequence":"additional","affiliation":[{"name":"Singular Medical Co., Ltd"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiyu","family":"Shi","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University of Notre Dame"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingtong","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Pittsburgh"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"10584","event":{"name":"Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}","theme":"Artificial Intelligence","location":"Montreal, Canada","acronym":"IJCAI-2021","number":"30","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2021,8,19]]},"end":{"date-parts":[[2021,8,27]]}},"container-title":["Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2021,8,11]],"date-time":"2021-08-11T07:02:49Z","timestamp":1628665369000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2021\/359"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2021,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2021\/359","relation":{},"subject":[],"published":{"date-parts":[[2021,8]]}}}