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An electroencephalography sleep staging model based on bidirectional Mamba combining synergistic attention with interactive convolution 认领 引用
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作者 Ya-mei Xu Ding-Yuan An 《Sleep Research》 2026年第2期135-149,共15页
Background:This study presents SIMSleepSM,a novel single-channel electroencephalography(EEG)sleep staging model.It addresses two primary challenges:insufficient modeling of long-range temporal dependencies combined wi... Background:This study presents SIMSleepSM,a novel single-channel electroencephalography(EEG)sleep staging model.It addresses two primary challenges:insufficient modeling of long-range temporal dependencies combined with limited multi-scale feature extraction,and poor accuracy in identifying the N1 stage.Methods:SIMSleepSM extends the SleePyCo architecture through three principal innovations.First,the spatial-channel synergistic attention(SCSA)module is adapted into a 1D variant,SCSA_1D,tailored for EEG signals and inserted into every feature layer of the backbone network.The spatial attention extracts local temporal dependencies across various time scales,whereas the channel attention captures relationships among feature channels.Together these attentions strengthen temporal dependency modeling and emphasize N1-specific features.Second,an interactive convolution block(ICB)is integrated into the feature pyramid.The ICB employs a two-branch interactive convolution to refine multi-scale feature extraction.Finally,a bidirectional Mamba-based classifier is designed.Its bidirectional state space mechanism captures long-range temporal dependencies in the EEG and thereby strengthens representation of sleep-stage dynamics.Results:On the Sleep-EDF-20,Sleep-EDF-78,and Sleep Heart Health Study(SHHS)datasets,SIMSleepSM achieves accuracy values of 88.1%,86.2%,and 84.1%;records macro F1 scores of 82.7%,81.0%,and 77.9%;obtains Cohen's Kappa coefficients of 0.839,0.810,and 0.791;and attains F1-scores on the N1 stage of 53.7%,54.4%,and 50.9%for Sleep-EDF-20,Sleep-EDF-78,and SHHS,surpassing the second-best models by 1.3%,4.0%,and 4.8%,respectively.Conclusion:Experimental results demonstrate that SIMSleepSM outperforms thirteen state-of-the-art baseline models,with particularly notable improvements in N1-stage identification.These results indicate that SIMSleepSM provides an effective and reliable solution for automatic sleep staging using single-channel EEG,highlighting it as a robust and high-performing model. 展开更多
关键词 bidirectional Mamba computer application technology interactive convolution single-channel EEG sleep staging spatial-channel synergistic attention
Interactive Dynamic Graph Convolution with Temporal Attention for Traffic Flow Forecasting 认领 引用
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作者 Zitong Zhao Zixuan Zhang Zhenxing Niu 《Computers, Materials & Continua》 SCIE EI 2026年第1期1049-1064,共16页
Reliable traffic flow prediction is crucial for mitigating urban congestion.This paper proposes Attentionbased spatiotemporal Interactive Dynamic Graph Convolutional Network(AIDGCN),a novel architecture integrating In... Reliable traffic flow prediction is crucial for mitigating urban congestion.This paper proposes Attentionbased spatiotemporal Interactive Dynamic Graph Convolutional Network(AIDGCN),a novel architecture integrating Interactive Dynamic Graph Convolution Network(IDGCN)with Temporal Multi-Head Trend-Aware Attention.Its core innovation lies in IDGCN,which uniquely splits sequences into symmetric intervals for interactive feature sharing via dynamic graphs,and a novel attention mechanism incorporating convolutional operations to capture essential local traffic trends—addressing a critical gap in standard attention for continuous data.For 15-and 60-min forecasting on METR-LA,AIDGCN achieves MAEs of 0.75%and 0.39%,and RMSEs of 1.32%and 0.14%,respectively.In the 60-min long-term forecasting of the PEMS-BAY dataset,the AIDGCN out-performs the MRA-BGCN method by 6.28%,4.93%,and 7.17%in terms of MAE,RMSE,and MAPE,respectively.Experimental results demonstrate the superiority of our pro-posed model over state-of-the-art methods. 展开更多
关键词 Traffic flow prediction interactive dynamic graph convolution graph convolution temporal multi-head trend-aware attention self-attention mechanism
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