论文标题
使用卷积神经网络自动化阻塞性睡眠呼吸暂停诊断
Automate Obstructive Sleep Apnea Diagnosis Using Convolutional Neural Networks
论文作者
论文摘要
从一夜之间的多聚会摄影（PSG）录音中确定睡眠问题的严重程度在诊断和治疗睡眠障碍（例如阻塞性睡眠呼吸暂停（OSA））中起着重要作用。传统上，该分析是由专家通过视觉检查来完成的，这可能是乏味，耗时的，并且容易出现主观错误。解决方案之一是使用卷积神经网络（CNN），其中卷积和合并层作为特征提取器的行为表现，并且使用了一些完全连接的（FCN）层来对OSA严重性进行最终预测。在本文中，介绍了具有1D卷积和FCN层进行分类的CNN体系结构。该项目的PSG数据来自克利夫兰儿童的睡眠和健康研究数据库，分类结果证实了拟议的CNN方法的有效性。提出的1D CNN模型可在无需手动预处理PSG信号（例如特征提取和功能还原）的情况下实现出色的分类结果。
Identifying sleep problem severity from overnight polysomnography (PSG) recordings plays an important role in diagnosing and treating sleep disorders such as the Obstructive Sleep Apnea (OSA). This analysis traditionally is done by specialists manually through visual inspections, which can be tedious, time-consuming, and is prone to subjective errors. One of the solutions is to use Convolutional Neural Networks (CNN) where the convolutional and pooling layers behave as feature extractors and some fully-connected (FCN) layers are used for making final predictions for the OSA severity. In this paper, a CNN architecture with 1D convolutional and FCN layers for classification is presented. The PSG data for this project are from the Cleveland Children's Sleep and Health Study database and classification results confirm the effectiveness of the proposed CNN method. The proposed 1D CNN model achieves excellent classification results without manually preprocesssing PSG signals such as feature extraction and feature reduction.
