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

摘要：

生理信号通常涵盖机体的生物电活动、温度、压力等关键信息，监测其数值波动有助于预警临床事件风险。深度模型是包含多级非线性变换的层级机器学习模型，在特征提取与建模方面优势显著，在计算机辅助诊断领域有着巨大的应用前景。随着连续生理参数监测技术的进步，深度模型在生理电信号异常检测中的效用逐渐提高，研究重点也向临床应用领域拓展。报告了深度模型在生理电信号异常检测中的研究进展。从临床应用出发，分析了经典信号异常检测方法的优势与不足，简述了当前深度模型的建模方式。从判别模型和生成模型的角度总结了经典模型的建模原理及最新应用，同时讨论了深度模型的训练架构和训练策略。结合异常检测在临床中的应用、深度模型的研究进展以及生理数据集的可用性三方面进行总结与讨论，并对未来研究进行展望。

关键词: 深度学习, 生理时间序列, 异常检测