基于光电容积脉搏波信号的卷积神经网络用于睡眠呼吸暂停综合征检测。
Convolutional neural network based on photoplethysmography signals for sleep apnea syndrome detection.
作者信息
Jiang Xinge, Ren YongLian, Wu Hua, Li Yanxiu, Liu Feifei
机构信息
School of Information Science and Electrical Engineering, Shandong Jiaotong University, Jinan, China.
School of Science, Shandong Jianzhu University, Jinan, China.
出版信息
Front Neurosci. 2023 Jul 21;17:1222715. doi: 10.3389/fnins.2023.1222715. eCollection 2023.
INTRODUCTION
The current method of monitoring sleep disorders is complex, time-consuming, and uncomfortable, although it can provide scientifc guidance to ensure worldwide sleep quality. This study aims to seek a comfortable and convenient method for identifying sleep apnea syndrome.
METHODS
In this work, a one-dimensional convolutional neural network model was established. To classify this condition, the model was trained with the photoplethysmographic (PPG) signals of 20 healthy people and 39 sleep apnea syndrome (SAS) patients, and the influence of noise on the model was tested by anti-interference experiments.
RESULTS AND DISCUSSION
The results showed that the accuracy of the model for SAS classifcation exceeds 90%, and it has some antiinterference ability. This paper provides a SAS detection method based on PPG signals, which is helpful for portable wearable detection.
引言
目前监测睡眠障碍的方法复杂、耗时且令人不适，尽管它能提供科学指导以确保全球范围内的睡眠质量。本研究旨在寻找一种舒适便捷的方法来识别睡眠呼吸暂停综合征。
方法
在这项工作中，建立了一个一维卷积神经网络模型。为了对这种情况进行分类，该模型使用20名健康人和39名睡眠呼吸暂停综合征（SAS）患者的光电容积脉搏波描记法（PPG）信号进行训练，并通过抗干扰实验测试噪声对模型的影响。
结果与讨论
结果表明，该模型对SAS分类的准确率超过90%，并且具有一定的抗干扰能力。本文提供了一种基于PPG信号的SAS检测方法，有助于便携式可穿戴检测。
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