基于深度学习的生理异常检测研究综述  

Review of Deep Learning Based Physiological Abnormality Detection Research

作　　者：麻琛彬 张政波[1] 王晶[3] MA Chenbin;ZHANG Zhengbo;WANG Jing(Center for Artificial Intelligence in Medicine,Medical Innovation Research Department,PLA General Hospital,Beijing 100853,China;School of Biological Science and Medical Engineering,Beihang University,Beijing 100191,China;School of Computer and Information Technology,Beijing Jiaotong University,Beijing 100044,China)

机构地区：[1]解放军总医院医学创新研究部医学人工智能研究中心,北京100853 [2]北京航空航天大学生物与医学工程学院,北京100191 [3]北京交通大学计算机与信息技术学院,北京100044

出　　处：《计算机工程与应用》2021年第10期10-25,共16页Computer Engineering and Applications

基　　金：国家自然科学基金面上项目(61471398);保健专项(16BJZ23);北京市科委医药协同科技创新研究项目(Z18110000s1918023);解放军总医院大数据研发项目(2018MBD-009)。

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。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.

关 键 词：深度学习 生理时间序列 异常检测 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]