MCAF-Net: Multi-Channel Temporal Cross-Attention Network with Dynamic Gating for Sleep Stage Classification
Automated sleep stage classification is essential for objective sleep evaluation and clinical diagnosis. While numerous algorithms have been developed, the predominant existing methods utilize single-channel electroencephalogram (EEG) signals, neglecting the complementary physiological information a...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/14/4251 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849733143003660288 |
|---|---|
| author | Xuegang Xu Quan Wang Changyuan Wang Yaxin Zhang |
| author_facet | Xuegang Xu Quan Wang Changyuan Wang Yaxin Zhang |
| author_sort | Xuegang Xu |
| collection | DOAJ |
| description | Automated sleep stage classification is essential for objective sleep evaluation and clinical diagnosis. While numerous algorithms have been developed, the predominant existing methods utilize single-channel electroencephalogram (EEG) signals, neglecting the complementary physiological information available from other channels. Standard polysomnography (PSG) recordings capture multiple concurrent biosignals, where sophisticated integration of these multi-channel data represents a critical factor for enhanced classification accuracy. Conventional multi-channel fusion techniques typically employ elementary concatenation approaches that insufficiently model the intricate cross-channel correlations, consequently limiting classification performance. To overcome these shortcomings, we present MCAF-Net, a novel network architecture that employs temporal convolution modules to extract channel-specific features from each input signal and introduces a dynamic gated multi-head cross-channel attention mechanism (MCAF) to effectively model the interdependencies between different physiological channels. Experimental results show that our proposed method successfully integrates information from multiple channels, achieving significant improvements in sleep stage classification compared to the vast majority of existing methods. |
| format | Article |
| id | doaj-art-e3933ecd5cab460087f7dba7052ba8f4 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-e3933ecd5cab460087f7dba7052ba8f42025-08-20T03:08:06ZengMDPI AGSensors1424-82202025-07-012514425110.3390/s25144251MCAF-Net: Multi-Channel Temporal Cross-Attention Network with Dynamic Gating for Sleep Stage ClassificationXuegang Xu0Quan Wang1Changyuan Wang2Yaxin Zhang3School of Computer Science and Engineering, Xi’an Technological University, Xi’an 710021, ChinaSchool of Computer Science and Engineering, Xi’an Technological University, Xi’an 710021, ChinaSchool of Computer Science and Engineering, Xi’an Technological University, Xi’an 710021, ChinaSchool of Computer Science and Engineering, Xi’an Technological University, Xi’an 710021, ChinaAutomated sleep stage classification is essential for objective sleep evaluation and clinical diagnosis. While numerous algorithms have been developed, the predominant existing methods utilize single-channel electroencephalogram (EEG) signals, neglecting the complementary physiological information available from other channels. Standard polysomnography (PSG) recordings capture multiple concurrent biosignals, where sophisticated integration of these multi-channel data represents a critical factor for enhanced classification accuracy. Conventional multi-channel fusion techniques typically employ elementary concatenation approaches that insufficiently model the intricate cross-channel correlations, consequently limiting classification performance. To overcome these shortcomings, we present MCAF-Net, a novel network architecture that employs temporal convolution modules to extract channel-specific features from each input signal and introduces a dynamic gated multi-head cross-channel attention mechanism (MCAF) to effectively model the interdependencies between different physiological channels. Experimental results show that our proposed method successfully integrates information from multiple channels, achieving significant improvements in sleep stage classification compared to the vast majority of existing methods.https://www.mdpi.com/1424-8220/25/14/4251automatic sleep stage classificationmulti-channel signal fusiontemporal convolutiondynamic gatedmulti-head cross-channel attention mechanism |
| spellingShingle | Xuegang Xu Quan Wang Changyuan Wang Yaxin Zhang MCAF-Net: Multi-Channel Temporal Cross-Attention Network with Dynamic Gating for Sleep Stage Classification Sensors automatic sleep stage classification multi-channel signal fusion temporal convolution dynamic gated multi-head cross-channel attention mechanism |
| title | MCAF-Net: Multi-Channel Temporal Cross-Attention Network with Dynamic Gating for Sleep Stage Classification |
| title_full | MCAF-Net: Multi-Channel Temporal Cross-Attention Network with Dynamic Gating for Sleep Stage Classification |
| title_fullStr | MCAF-Net: Multi-Channel Temporal Cross-Attention Network with Dynamic Gating for Sleep Stage Classification |
| title_full_unstemmed | MCAF-Net: Multi-Channel Temporal Cross-Attention Network with Dynamic Gating for Sleep Stage Classification |
| title_short | MCAF-Net: Multi-Channel Temporal Cross-Attention Network with Dynamic Gating for Sleep Stage Classification |
| title_sort | mcaf net multi channel temporal cross attention network with dynamic gating for sleep stage classification |
| topic | automatic sleep stage classification multi-channel signal fusion temporal convolution dynamic gated multi-head cross-channel attention mechanism |
| url | https://www.mdpi.com/1424-8220/25/14/4251 |
| work_keys_str_mv | AT xuegangxu mcafnetmultichanneltemporalcrossattentionnetworkwithdynamicgatingforsleepstageclassification AT quanwang mcafnetmultichanneltemporalcrossattentionnetworkwithdynamicgatingforsleepstageclassification AT changyuanwang mcafnetmultichanneltemporalcrossattentionnetworkwithdynamicgatingforsleepstageclassification AT yaxinzhang mcafnetmultichanneltemporalcrossattentionnetworkwithdynamicgatingforsleepstageclassification |