Automatic prediction of obstructive sleep apnea event using deep learning algorithm based on ECG and thoracic movement signals.

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2024-01-01 Epub Date: 2024-01-19 DOI:10.1080/00016489.2024.2301732

Zufei Li, Yajie Jia, Yanru Li, Demin Han

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引用次数: 0

Abstract

Background: Obstructive sleep apnea (OSA) is a sleeping disorder that can cause multiple complications.

Aims/objective: Our aim is to build an automatic deep learning model for OSA event detection using combined signals from the electrocardiogram (ECG) and thoracic movement signals.

Materials and methods: We retrospectively obtained 420 cases of PSG data and extracted the signals of ECG, as well as the thoracic movement signal. A deep learning algorithm named ResNeSt34 was used to construct the model using ECG with or without thoracic movement signal. The model performance was assessed by parameters such as accuracy, precision, recall, F1-score, receiver operating characteristic (ROC), and area under the ROC curve (AUC).

Results: The model using combined signals of ECG and thoracic movement signal performed much better than the model using ECG alone. The former had accuracy, precision, recall, F1-score, and AUC values of 89.0%, 88.8%, 89.0%, 88.2%, and 92.9%, respectively, while the latter had values of 84.1%, 83.1%, 84.1%, 83.3%, and 82.8%, respectively.

Conclusions and significance: The automatic OSA event detection model using combined signals of ECG and thoracic movement signal with the ResNeSt34 algorithm is reliable and can be used for OSA screening.

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基于心电图和胸廓运动信号的深度学习算法自动预测阻塞性睡眠呼吸暂停事件。

背景：阻塞性睡眠呼吸暂停（OSA）是一种睡眠障碍，可引起多种并发症：阻塞性睡眠呼吸暂停（OSA）是一种可引起多种并发症的睡眠障碍：我们的目的是利用心电图（ECG）和胸廓运动信号的组合信号，建立一个用于 OSA 事件检测的自动深度学习模型：我们回顾性地获取了 420 例 PSG 数据，并提取了心电图信号和胸廓运动信号。我们使用一种名为 ResNeSt34 的深度学习算法，利用带或不带胸廓运动信号的心电图构建模型。模型性能通过准确率、精确度、召回率、F1-分数、接收者操作特征（ROC）和 ROC 曲线下面积（AUC）等参数进行评估：结果：使用心电图和胸廓运动信号组合信号的模型比单独使用心电图的模型表现要好得多。前者的准确度、精确度、召回率、F1 分数和 AUC 值分别为 89.0%、88.8%、89.0%、88.2% 和 92.9%，而后者的准确度、精确度、召回率、F1 分数和 AUC 值分别为 84.1%、83.1%、84.1%、83.3% 和 82.8%：利用心电图和胸廓运动信号的组合信号以及 ResNeSt34 算法建立的 OSA 事件自动检测模型是可靠的，可用于 OSA 筛查。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.