{"type":"ScholarlyArticle","title":"Learning to Learn Personalized Neural Network for Ventricular Arrhythmias Detection on Intracardiac EGMs","authors":[{"name":"Zhenge Jia","slug":"zhenge-jia","given":"Zhenge","family":"Jia","affiliation":""},{"name":"Zhepeng Wang","slug":"zhepeng-wang","given":"Zhepeng","family":"Wang","affiliation":""},{"name":"Feng Hong","slug":"feng-hong","given":"Feng","family":"Hong","affiliation":""},{"name":"Lichuan PING","slug":"lichuan-ping","given":"Lichuan","family":"PING","affiliation":""},{"name":"Yiyu Shi","slug":"yiyu-shi","given":"Yiyu","family":"Shi","affiliation":""},{"name":"Jingtong Hu","slug":"jingtong-hu","given":"Jingtong","family":"Hu","affiliation":""}],"abstract":"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.","doi":"10.24963/ijcai.2021/359","url":"https://ijcai.org/proceedings/2021/359","pdf":"https://ijcai.org/proceedings/2021/0359.pdf","markdown":"https://ijcai.org/proceedings/2021/359.md","bibtex":"https://ijcai.org/proceedings/2021/bibtex/359","volume":{"slug":"2021","title":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2021","url":"https://ijcai.org/proceedings/2021/","year":2021,"location":"Montreal","dates":"19-27 August 2021","editors":"Prof. Zhi-Hua Zhou","isbn":"978-0-9992411-9-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2021"},"section":"Machine Learning","track":"Main Track","number":359,"pages":{"first":2606,"last":2613},"keywords":["Machine Learning: Transfer, Adaptation, Multi-task Learning","Machine Learning Applications: Bio/Medicine","Multidisciplinary Topics and Applications: Biology and Medicine"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}