文献类型：期刊文献

英文题名：Unsupervised Identification and Replay-based Detection (UIRD) for New Category Anomaly Detection in ECG Signal

作者：Shi, Zhangyue[1]; Wang, Zekai[2]; Li, Yuxuan[3]

机构：[1] St. Louis, MO, United States; [2] Charles F. Dolan School of Business, Fairfield University, Fairfield, CT, United States; [3] School of Business, East China University of Science and Technology, Shanghai, China

年份：2025

外文期刊名：arXiv

收录：EI(收录号：20250422160)

语种：英文

外文关键词：Biomedical signal processing - Classification (of information) - Digital storage - Electrocardiograms - Heart - Learning systems - Pattern recognition - Sampling - Signal detection - Unsupervised learning

摘要：In clinical practice, automatic analysis of electrocardiogram (ECG) is widely applied to identify irregular heart rhythms and other electrical anomalies of the heart, enabling timely intervention and potentially improving clinical outcomes. However, due to the limited samples in certain types of ECG signals, the class imbalance issues pose a challenge for ECG-based detection. In addition, as the volume of patient data grows, long-term storage of all historical data becomes increasingly burdensome as training samples to recognize new patterns and classify existing ECG signals accurately. Therefore, to enhance the performance of anomaly detection while addressing storage limitations, we propose a pseudo-replay based semi-supervised continual learning framework, which consists of two components: unsupervised identification and replay-based detection. For unsupervised identification, an unsupervised generative adversarial network (GAN)-based framework is integrated to detect novel patterns. Besides, instead of directly storing all historical data, a pseudo replay-based learning strategy is proposed which utilizes a generator to learn the data distribution for each individual task. When a new task arises, the generator synthesizes pseudo data representative of previous learnt classes, enabling the model to detect both the existed patterns and the newly presented anomalies. The effectiveness of the proposed framework is validated in four public ECG datasets, which leverages supervised classification problems for anomaly detection. The experimental results show that the developed approach is very promising in identifying novel anomalies while maintaining good performance on detecting existing ECG signals. Copyright ? 2025, The Authors. All rights reserved.