基于短期正常心电图的深度学习识别睡眠呼吸暂停严重程度。
Identification of Sleep Apnea Severity Based on Deep Learning from a Short-term Normal ECG.
机构信息
Institute of AI and Big Data in Medicine, Wonju College of Medicine, Yonsei University, Wonju, Korea.
Department of Biomedical Engineering, College of Health Science, Yonsei University, Wonju, Korea.
出版信息
J Korean Med Sci. 2020 Dec 7;35(47):e399. doi: 10.3346/jkms.2020.35.e399.
BACKGROUND
This paper proposes a novel method for automatically identifying sleep apnea (SA) severity based on deep learning from a short-term normal electrocardiography (ECG) signal.
METHODS
A convolutional neural network (CNN) was used as an identification model and implemented using a one-dimensional convolutional, pooling, and fully connected layer. An optimal architecture is incorporated into the CNN model for the precise identification of SA severity. A total of 144 subjects were studied. The nocturnal single-lead ECG signal was collected, and the short-term normal ECG was extracted from them. The short-term normal ECG was segmented for a duration of 30 seconds and divided into two datasets for training and evaluation. The training set consists of 82,952 segments (66,360 training set, 16,592 validation set) from 117 subjects, while the test set has 20,738 segments from 27 subjects.
RESULTS
F1-score of 98.0% was obtained from the test set. Mild and moderate SA can be identified with an accuracy of 99.0%.
CONCLUSION
The results showed the possibility of automatically identifying SA severity based on a short-term normal ECG signal.
背景
本文提出了一种基于深度学习从短期正常心电图（ECG）信号自动识别睡眠呼吸暂停（SA）严重程度的新方法。
方法
采用卷积神经网络（CNN）作为识别模型，采用一维卷积、池化和全连接层实现。将最优架构纳入 CNN 模型，以精确识别 SA 严重程度。共研究了 144 名受试者。采集夜间单导联 ECG 信号，并从中提取短期正常 ECG。将短期正常 ECG 分段，持续 30 秒，并分为两个数据集进行训练和评估。训练集由 117 名受试者的 82952 个片段（66360 个训练集，16592 个验证集）组成，而测试集则有 27 名受试者的 20738 个片段。
结果
从测试集中获得了 98.0%的 F1 分数。轻度和中度 SA 可以识别，准确率为 99.0%。
结论
结果表明，基于短期正常 ECG 信号自动识别 SA 严重程度是可能的。
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