论文标题
EEG信号的自适应尖峰表代表睡眠阶段得分
Adaptive Spike-Like Representation of EEG Signals for Sleep Stages Scoring
论文作者
论文摘要
最近，通过从脑电图（EEG）中提取时空特征（EEG），在自动阶段评分上看到了令人鼓舞的结果。这些方法需要艰苦的手动特征工程和领域知识。在这项研究中，我们提出了一种自适应方案，以通过信号强度的半高斯概率进行概率地编码，过滤和积累输入信号和权重。随后，自适应表示形式被送入变压器模型，以自动挖掘功能和相应阶段之间的相关性。针对最先进方法的最大公共数据集进行了广泛的实验，证明了我们提出的方法的有效性，并揭示了有希望的未来方向。
Recently there has seen promising results on automatic stage scoring by extracting spatio-temporal features from electroencephalogram (EEG). Such methods entail laborious manual feature engineering and domain knowledge. In this study, we propose an adaptive scheme to probabilistically encode, filter and accumulate the input signals and weight the resultant features by the half-Gaussian probabilities of signal intensities. The adaptive representations are subsequently fed into a transformer model to automatically mine the relevance between features and corresponding stages. Extensive experiments on the largest public dataset against state-of-the-art methods validate the effectiveness of our proposed method and reveal promising future directions.
