利用卷积神经网络从原始心电图和 SpO2 信号实时检测阻塞性睡眠呼吸暂停。

Tanmoy Paul, Omiya Hassan, Syed K Islam, Abu S M Mosa
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引用次数: 0

摘要

阻塞性睡眠呼吸暂停是一种睡眠障碍,与许多健康并发症有关,严重的呼吸暂停甚至可以致命。通宵多导睡眠图是诊断呼吸暂停的黄金标准,但这种方法昂贵、耗时,而且需要睡眠专家进行人工分析。最近有许多研究表明,人工智能可用于实时检测呼吸暂停。但这些研究大多采用数据预处理和特征提取技术,推理时间较长,导致实时检测系统效率低下。本研究提出了一种单一卷积神经网络架构,可从心电图(ECG)和血氧饱和度(SpO2)信号中自动提取空间特征并检测呼吸暂停。利用 10 秒的片段,该网络对心电图和 SpO2 信号进行呼吸暂停分类的准确率分别为 94.2% 和 96%。此外,两个模型的总体性能一致,AUC 得分为 0.99。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Real-Time Obstructive Sleep Apnea Detection from Raw ECG and SpO2 Signal Using Convolutional Neural Network.

Obstructive sleep apnea is a sleep disorder that is linked with many health complications and severe form of apnea can even be lethal. Overnight polysomnography is the gold standard for diagnosing apnea, which is expensive, time-consuming, and requires manual analysis by a sleep expert. Recently, there have been numerous studies demonstrating the application of artificial intelligence to detect apnea in real time. But the majority of these studies apply data pre-processing and feature extraction techniques resulting in a longer inference time that makes the real-time detection system inefficient. This study proposes a single convolutional neural network architecture that can automatically extract spatial features and detect apnea from both electrocardiogram (ECG) and blood-oxygen saturation (SpO2) signals. Using segments of 10s, the network classified apnea with an accuracy of 94.2% and 96% for ECG and SpO2 respectively. Moreover, the overall performance of both models was consistent with an AUC score of 0.99.

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