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

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.

Chinese Abstract

背景：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。

目的：我们的目的是利用心电图 (ECG) 和胸部运动信号的组合信号构建一种用于 OSA 事件检测的自动深度学习模型。

材料和方法：我们回顾性地获得了420个病例的PSG数据, 并提取了心电图信号以及胸部运动信号。运用一种名为ResNeSt34的深度学习算法, 根据具有或不具有胸部运动信号的ECG来构建模型。模型性能的评估是根据准确度、精密度、召回率、F1分数、接收器操作特性（ROC）和ROC曲线下面积（AUC）等参数来进行的。

结果：采用心电信号和胸部运动信号组合的模型比单独使用心电信号的模型表现更好。前者的准确度、精密度、召回率、F1评分和AUC值分别为89.0%、88.8%、89.0%、8.82%和92.9%, 而后者的分别为84.1%、83.1%、84.1%、83.3%和82.8%。

结论和意义：采用ResNeSt34算法, 结合心电图信号和胸部运动信号的OSA事件自动检测模型是可靠的, 可用于OSA筛查。

Keywords:

• Electrocardiogram
• thoracic movement
• deep learning
• obstructive sleep apnea
• artificial intelligence

Acknowledgment

The authors thank to Xiu Ding, Guoguang Li, and Dan Kang, experts in PSG, they scored the respiratory events according to the AASM standard, and to Yang Zhang, engineer, from Zhejiang University, as well as Hang Su, engineer, from Tsinghua University, for algorithmic guidance.

Ethical approval and informed consent

This research had get the ethics approval, the name of the Institutional Review Board (IRB) was ‘the institutional review board of Beijing Tongren Hospital’, the permit number is TRECKY2019-049.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research was supported by the Beijing Municipal Administration of Hospitals’ Youth Programme [QMS2019020], and the National Natural Science Foundation of China [81970866].