A Multimodal Residual Spatial-temporal Fusion Model Based on Automatic Sleep Classification
Highly accurate sleep staging plays a crucial role in correctly assessing sleep conditions.Aiming at the problem that the existing convolutional network cannot obtain the topological characteristics of physiological signals,a sleep staging algorithm based on multi-modal residual spatio-temporal fusion is proposed.Time-frequency images and spatio-temporal images are obtained using short-time Fourier transform and adaptive map convolution,which are converted into high-dimensional feature vectors;lightweight interaction of feature information flow is realized through time-frequency feature and spatio-temporal feature extraction modules;the feature enhancement fusion module fuses feature information to outputs sleep staging results.The results show that the model has a high accuracy.On the ISRUC-S3 data set,the overall accuracy is 85.3％,the F1 score is 83.8％,Cohen's kappa is 81％,and the N1 stage accuracy reaches 69.81％.Experiments on the ISRUC-S1 dataset demonstrate the generality of the model.
万方数据
维普
