基于自注意力机制和单导联心电信号的自动睡眠分期算法#br#

计算机与现代化 ›› 2022, Vol. 0 ›› Issue (12): 50-59.

基于自注意力机制和单导联心电信号的自动睡眠分期算法#br#

(1.中国科学院空天信息创新研究院,北京100190;2.中国科学院大学电子电气与通信工程学院,北京100049)

出版日期:2023-01-04 发布日期:2023-01-04
作者简介:栗伟松(1998—),女，河南上蔡人，硕士研究生，研究方向：医疗健康物联网技术，E-mail: liweisong19@mails.ucas.ac.cn; 汤敏芳(1996—),女,博士研究生,研究方向:医疗健康物联网技术,E-mail: 1293227181@qq.com; 何征岭(1993—),男,博士研究生,研究方向:医疗健康物联网技术,E-mail: hezhengling15@mails.ucas.ac.cn; 王鹏(1995—),男,硕士,研究方向:医疗健康物联网技术,E-mail: wangpeng01@aircas.ac.cn; 杜利东(1981—),男,副研究员,硕士生导师,研究方向:微纳制造技术,E-mail: lddu@mail.ie.ac.cn; 方震(1976—),男,研究员,博士生导师,研究方向:医疗健康物联网技术,E-mail: zf@mail.ie.ac.cn; 通信作者：陈贤祥(1979—),男,副研究员,硕士生导师,研究方向:医疗健康物联网技术,E-mail: xxchen@mail.ie.ac.cn。
基金资助:
国家重点研发计划项目(2020YFC2003703)

Automatic Sleep Staging Algorithm Based on Self-attention Mechanism and Single Lead ECG

(1.Aerospace Information Research Institution, Chinese Academy of Sciences, Beijing 100190, China； 2.School of
Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China)

Online:2023-01-04 Published:2023-01-04

摘要/Abstract

摘要： 基于手工标记或传统机器学习方法实现睡眠分期过程复杂且效率低下，深度神经网络因其强大的提取复杂特征的能力改善了睡眠分期结果，但仍存在忽略片段内部信息相关性的问题。针对此问题，本文提出一种基于自注意力机制和单导联心电信号的自动睡眠分期算法，利用卷积模块、双向门控循环单元及自注意力机制实现特征自动提取与分类。在开源睡眠心脏健康研究数据库(SHHS1、SHHS2)、动脉粥样硬化的多民族研究数据库(MESA)和美国麻省理工的多导睡眠数据库(MITBPD)中分别选取1000、1000、1000和16名受试者的单导联心电信号数据进行训练和测试，得到模型睡眠四分类(觉醒、快速眼动期、浅睡眠和深睡眠)结果，分类准确率分别达到75.77%(kappa＝0.63)、81.01%(kappa＝066)、82.79%(kappa＝0.71)和76.22%(kappa＝0.58)，优于基于传统机器学习算法的睡眠分期结果，验证了提出模型的有效性。

关键词: 自动睡眠分期, 单导联心电信号, 卷积网络, 门控循环单元, 自注意力机制

Abstract: The realization of sleep staging based on manual labeling or traditional machine learning methods is complex and inefficient. Deep neural network improves the results of sleep staging because of its powerful ability to extract complex features, but there are still some problems, such as ignoring the correlation of internal information. To solve this problem, this paper proposes an automatic sleep staging algorithm based on self-attention mechanism and single lead ECG signal, realizing feature extraction and classification automatically by using convolution module, bidirectional gated recurrent unit and self-attention mechanism. In the open database Sleep Heart Health Study database (SHHS1, SHHS2), Multi-Ethnic Study of Atherosclerosis database (MESA) and MIT-BIH Polysomnographic database (MITBPD), the single lead ECG data of 1000, 1000, 1000 and 16 subjects are randomly selected for training and testing. The experimental results show that the accuracy of the four sleep classifications (wake, rapid eye movement, light sleep and deep sleep) of the model is 75.77% (kappa＝0.63), 81.01% (kappa =0.66), 8279% (kappa＝0.71) and 76.22% (kappa＝0.58) respectively, which is better than the sleep staging results based on the traditional machine learning algorithms, verifying the validity of the model.

Key words: automatic sleep staging, single lead electrocardiogram, convolution network, gated recurrent unit, self-attention mechanism

栗伟松, 汤敏芳, 何征岭, 王鹏, 杜利东, 方震, 陈贤祥. 基于自注意力机制和单导联心电信号的自动睡眠分期算法#br#[J]. 计算机与现代化, 2022, 0(12): 50-59.

LI Wei-song, TANG Min-fang, HE Zheng-ling, WANG Peng, DU Li-dong, FANG Zhen, CHEN Xian-xiang. Automatic Sleep Staging Algorithm Based on Self-attention Mechanism and Single Lead ECG[J]. Computer and Modernization, 2022, 0(12): 50-59.

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