MIASS: A multi-interactive attention model for sleep staging via EEG and EOG signals

IF 4 3区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE
Xuhui Wang, Yuanyuan Zhu, Wenxin Lai
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引用次数: 0

Abstract

Sleep staging is essential for sleep analysis. Recent studies have attempted to integrate multi-modal signals such as electroencephalogram (EEG) and electrooculogram (EOG) to enhance model sensitivity. However, these attempts still face limitations in effectively fusing multi-modal signals, particularly in capturing both global and fine-grained interaction information in sleep epochs simultaneously. To address this, we propose a multi-interactive model (MIASS) that integrates two core modules, the global information interaction (GII) module and the fine-grained information interaction (FII) module. The GII module can effectively capture the global correlation paradigm in EEG and EOG at the epoch level by combining the global channel and spatial attentions with a residual network. The FII module explores the fine-grained correlation paradigm between small EEG and EOG segments within epochs using the cross-attention mechanism to achieve more fine-grained interaction information. The combination of these modules increased the accuracy of the model up to 89.2%, 86.6% and 89.7% on the SleepEDF-20, SleepEDF-78 and SHHS datasets, respectively, which outperforms the comparison models by 0.2–5.7%. The ablation study confirmed the benefits of integrating global and fine-grained correlation paradigms to enhance sleep staging performance, and the model input study demonstrated that MIASS maintains good performance under various input conditions.
MIASS:通过脑电图和眼电图信号进行睡眠分期的多交互式注意力模型
睡眠分期对睡眠分析至关重要。最近的研究尝试整合脑电图(EEG)和脑电图(EOG)等多模态信号,以提高模型灵敏度。然而,这些尝试在有效融合多模态信号方面仍存在局限性,尤其是在同时捕捉睡眠时序中的全局和细粒度交互信息方面。为此,我们提出了一种多交互模型(MIASS),该模型集成了两个核心模块,即全局信息交互(GII)模块和细粒度信息交互(FII)模块。全局信息交互(GII)模块通过将全局信道和空间注意力与残差网络相结合,可有效捕捉 EEG 和 EOG 在历时水平上的全局相关范例。FII 模块则利用交叉注意机制,探索历时内小段脑电图和脑电图之间的细粒度相关范式,以获得更细粒度的交互信息。这些模块的组合使模型在 SleepEDF-20、SleepEDF-78 和 SHHS 数据集上的准确率分别提高到 89.2%、86.6% 和 89.7%,比对比模型高出 0.2-5.7%。消融研究证实了整合全局和细粒度相关范式对提高睡眠分期性能的益处,而模型输入研究则表明 MIASS 在各种输入条件下都能保持良好的性能。
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来源期刊
Computers & Electrical Engineering
Computers & Electrical Engineering 工程技术-工程:电子与电气
CiteScore
9.20
自引率
7.00%
发文量
661
审稿时长
47 days
期刊介绍: The impact of computers has nowhere been more revolutionary than in electrical engineering. The design, analysis, and operation of electrical and electronic systems are now dominated by computers, a transformation that has been motivated by the natural ease of interface between computers and electrical systems, and the promise of spectacular improvements in speed and efficiency. Published since 1973, Computers & Electrical Engineering provides rapid publication of topical research into the integration of computer technology and computational techniques with electrical and electronic systems. The journal publishes papers featuring novel implementations of computers and computational techniques in areas like signal and image processing, high-performance computing, parallel processing, and communications. Special attention will be paid to papers describing innovative architectures, algorithms, and software tools.
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