A Multimodal Residual Spatial-temporal Fusion Model Based on Automatic Sleep Classification

Authors

Yecai Guo, School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China; School of Electronics and Information Engineering, Wuxi University, Wuxi 214105, China
Shuang Tong, School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China

Abstract

Abstract: 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 spatiotemporal 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.

Recommended Citation

Guo, Yecai and Tong, Shuang (2024) "A Multimodal Residual Spatial-temporal Fusion Model Based on Automatic Sleep Classification," Journal of System Simulation: Vol. 36: Iss. 9, Article 8.
DOI: 10.16182/j.issn1004731x.joss.23-0588
Available at: https://dc-china-simulation.researchcommons.org/journal/vol36/iss9/8

First Page

2065

Last Page

2074

CLC

TP391

Recommended Citation

Guo Yecai, Tong Shuang. A Multimodal Residual Spatial-temporal Fusion Model Based on Automatic Sleep Classification[J]. Journal of System Simulation, 2024, 36(9): 2065-2074.

DOI

10.16182/j.issn1004731x.joss.23-0588