Adaptive Spike-Like Representation of EEG Signals for Sleep Stages Scoring

• URL: http://arxiv.org/abs/2204.03565v1
• Date: Sat, 2 Apr 2022 11:21:49 GMT
• Title: Adaptive Spike-Like Representation of EEG Signals for Sleep Stages Scoring
• Authors: Lingwei Zhu, Koki Odani, Ziwei Yang, Guang Shi, Yirong Kan, Zheng Chen, Renyuan Zhang
• Abstract summary: We propose an adaptive scheme to encode, filter and accumulate the input signals and the weight features by the half-Gaussian probabilities of signal intensities. Experiments on the largest public dataset against state-of-the-art methods validate the effectiveness of our proposed method and reveal promising future directions.
• Score: 6.644008481573341
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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