Xu Xuegang, Wang Quan, Wang Changyuan, Zhang Yaxin
School of Computer Science and Engineering, Xi'an Technological University, Xi'an 710021, China.
Sensors (Basel). 2025 Jul 8;25(14):4251. doi: 10.3390/s25144251.
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.
自动睡眠阶段分类对于客观的睡眠评估和临床诊断至关重要。虽然已经开发了许多算法，但现有的主要方法利用单通道脑电图（EEG）信号，而忽略了其他通道中可用的补充生理信息。标准多导睡眠图（PSG）记录可捕获多个并发生物信号，其中这些多通道数据的复杂整合是提高分类准确性的关键因素。传统的多通道融合技术通常采用基本的拼接方法，这些方法对复杂的跨通道相关性建模不足，从而限制了分类性能。为了克服这些缺点，我们提出了MCAF-Net，这是一种新颖的网络架构，它采用时间卷积模块从每个输入信号中提取特定通道的特征，并引入动态门控多头跨通道注意力机制（MCAF）来有效地建模不同生理通道之间的相互依赖关系。实验结果表明，我们提出的方法成功地整合了来自多个通道的信息，与绝大多数现有方法相比，在睡眠阶段分类方面取得了显著改进。
