论文标题
使用一维卷积神经网络从分段的时间序列信号中提取的特征检测阻塞性睡眠呼吸暂停
Detection of Obstructive Sleep Apnoea Using Features Extracted from Segmented Time-Series ECG Signals Using a One Dimensional Convolutional Neural Network
论文作者
论文摘要
本文中的研究提出了一个一维卷积神经网络（1DCNN）模型，该模型旨在自动检测从单通道心电图（ECG）信号捕获的阻塞性睡眠呼吸暂停（OSA）。该系统提供了临床实践中的机制，可帮助诊断患有OSA患者的患者。使用1DCNN中的最新艺术品，使用卷积，最大池层层和完全连接的多层感知器（MLP）构建模型，该模型由隐藏层和SoftMax输出组成以进行分类。 1DCNN提取的突出特征，用于训练MLP。使用分段的ECG信号对模型进行训练，该信号分组为5个设置窗口尺寸的唯一数据集。从包含70个夜间ECG记录的注释数据库中选择了35个ECG信号记录。 (Group A = a01 to a20 (Apnoea breathing), Group B = b01 to b05 (moderate), and Group C = c01 to c10 (normal). A total of 6514 minutes of Apnoea was recorded. Evaluation of the model is performed using a set of standard metrics which show the proposed model achieves high classification results in both training and validation using our windowing strategy, particularly W=500 (Sensitivity 0.9705，特异性0.9725，F1得分0.9717，Kappa得分0.9430，log损失0.0836，Rocauc 0.9945）。
The study in this paper presents a one-dimensional convolutional neural network (1DCNN) model, designed for the automated detection of obstructive Sleep Apnoea (OSA) captured from single-channel electrocardiogram (ECG) signals. The system provides mechanisms in clinical practice that help diagnose patients suffering with OSA. Using the state-of-the-art in 1DCNNs, a model is constructed using convolutional, max pooling layers and a fully connected Multilayer Perceptron (MLP) consisting of a hidden layer and SoftMax output for classification. The 1DCNN extracts prominent features, which are used to train an MLP. The model is trained using segmented ECG signals grouped into 5 unique datasets of set window sizes. 35 ECG signal recordings were selected from an annotated database containing 70 night-time ECG recordings. (Group A = a01 to a20 (Apnoea breathing), Group B = b01 to b05 (moderate), and Group C = c01 to c10 (normal). A total of 6514 minutes of Apnoea was recorded. Evaluation of the model is performed using a set of standard metrics which show the proposed model achieves high classification results in both training and validation using our windowing strategy, particularly W=500 (Sensitivity 0.9705, Specificity 0.9725, F1 Score 0.9717, Kappa Score 0.9430, Log Loss 0.0836, ROCAUC 0.9945). This demonstrates the model can identify the presence of Apnoea with a high degree of accuracy.
