多模态融合的特征提取方法在SA检测中的应用

为解决睡眠呼吸暂停(Sleep Apnea,SA)检测中使用传统的机器学习方法需花大量工作在特征工程上导致效率低下，以及模型多以单通道信号进行特征提取存在识别效果不佳的问题，提出一种基于时序卷积网络(Temporal Convolutional Network,TCN)和堆叠稀疏降噪自编码器(Stacked Sparse Denoismg Auto-Encoder,SSDAEs)的多模态特征融合模型来实现特征自动提取。该模型以心电和呼吸2种信号作为输入，首先利用TCN网络提取输入信号的时序特征，然后通过SSDAEs提取信号的浅层与深层的高维特征，对于不同特征空间的心电信号特征和呼吸信号特征采用一个小型神经网络进行特征融合，将该模型与随机森林算法结合，用于解决SA片段检测问题。实验结果表明，该方法在SA片段检测的准确率、灵敏度、特异性分别是91.5%、88.9%、90.8%。通过与以往相关研究对比，验证了该模型的SA检测性能更好，效率更高。

Application of Multimodal Fusion TCN-SSDAEs-RF Method in SA Detection

In order to solve the problem that the traditional machine learning method used in sleep apnea (SA) detection requires a lot of work on feature engineering, which leads to low efficiency, and the model uses single-channel signals to extract features and has poor recognition results, a multimodal feature fusion model based on temporal convolutional network (TCN) and stacked sparse denoising auto-encoder (SSDAEs) is proposed to realize automatic feature extraction. The model takes two signals of ECG and breathing as input. First, the TCN network is used to extract the timing features of the input signal, and then the shallow and deep high-dimensional features of the signal are extracted through the SSDAEs. The ECG and respiratory signal features in different feature spaces are fused by a small neural network, and the model is combined with the random forest algorithm to solve the SA fragment detection problem. The experimental results show that the accuracy, sensitivity, and specificity of this method in the detection of SA fragments are 91.5%, 88.9%, and 90.8%, respectively. Compared with previous related studies, it is verified that the SA detection performance of this model is better and the efficiency is higher.