Abstract: 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.

Key words: SA, TCN, DAE, multimodal fusion, random forest