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.
引文
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