Learning to Learn Personalized Neural Network for Ventricular Arrhythmias Detection on Intracardiac EGMs

Learning to Learn Personalized Neural Network for Ventricular Arrhythmias Detection on Intracardiac EGMs

Zhenge Jia, Zhepeng Wang, Feng Hong, Lichuan PING, Yiyu Shi, Jingtong Hu

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence
Main Track. Pages 2606-2613. https://doi.org/10.24963/ijcai.2021/359

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.
Keywords:
Machine Learning: Transfer, Adaptation, Multi-task Learning
Multidisciplinary Topics and Applications: Biology and Medicine
Machine Learning Applications: Bio/Medicine