关键词: 深度学习, 生理时间序列, 异常检测

Abstract:

Physiological signals usually cover useful information such as bioelectrical activity, temperature and pressure of the body, monitoring their numerical fluctuations can help to detect or warn the risk of clinical events in advance. Deep models are hierarchical machine learning models containing multi-level nonlinear transformations, which have significant advantages in feature extraction and modeling, and have great application prospects in the field of computer-aided diagnosis. With the advancement of continuous physiological parameter monitoring technology, the utility of deep models in the detection of physiological electrical signal abnormalities has gradually increased and the research focus has expanded to clinical applications. This paper reviews the research progress of depth models in physiological electrical signal abnormality detection. Firstly, the advantages and shortcomings of classical signal abnormality detection methods are analyzes from the perspective of clinical applications, and the current modeling approaches of depth models are described briefly. Then, the modeling principles and latest applications of classical models are summarized from the perspective of discriminative and generative models, while the training architecture and training strategies of deep models are discussed. Finally, this paper summarizes and discusses the three aspects of abnormality detection in clinical applications, the research progress of deep models and the availability of physiological datasets, and provides an outlook on future research.

Key words: deep learning, physiological time series, abnormality detection