一种基于深度学习的家用式不宁腿综合症早期诊断系统  

A Domestic Diagnosis System for Early Restless Legs Syndrome Based on Deep Learning

作　　者：周平[1] 黄罗杰 赵庆贤 肖文锦 李思雨[1] ZHOU Ping;HUANG Luojie;ZHAO Qingxian;XIAO Wenjin;LI Siyu(School of Biological Science & Medical Engineering, Southeast University, Nanjing, 210096)

机构地区：[1]东南大学生物科学与医学工程学院

出　　处：《中国医疗器械杂志》2019年第2期79-82,共4页Chinese Journal of Medical Instrumentation

基　　金：国家自然科学基金面上项目(61771130);江苏高校品牌专业建设工程(PPZY2015B51)

摘　　要：不宁腿综合症是一种常见的睡眠障碍疾病。该文提出一种基于深度学习的家用式不宁腿综合症诊断系统,适用于症状不稳定的早期患者进行日常诊断。该系统硬件部分安装于床体,基于加速度传感器实现非接触式的无感睡眠体动信号采集;软件部分利用深度学习进行信号分类识别——基于Keras框架构建全连接前馈网络,实现共7种睡眠体动类型识别,综合分类准确率可达97.83%。该系统根据上述检测结果评估睡眠过程中周期肢动指数和觉醒指数,评估结果可以作为不宁腿综合症早期诊断的依据。Restless legs syndrome, as a common sleep disorder, has nowadays long been diagnosed by self-rating scale and polysomnography. In this paper, a domestic diagnosis system for early restless legs syndrome based on deep learning is proposed, which is suitable for early patients with unstable symptoms in routine diagnosis. The hardware system is installed in the bed. And the non-contact sleeping dynamic signal acquisition is realized based on the acceleration sensors. The software system uses deep learning to classify and recognize the signals. A Fully Connected Feedforward Network based on Keras framework is constructed to recognize seven kinds of activities during sleeping. The accuracy of comprehensive classification is 97.83%. Based on former results, the periodic limb movement index and awakening index were evaluated to make the diagnosis of restless legs syndrome.

关 键 词：不宁腿综合症 睡眠体动信号 加速度传感器 深度学习 

分 类 号：TP391.5[自动化与计算机技术—计算机应用技术] R443.8[自动化与计算机技术—计算机科学与技术]