1 research outputs found
Data-efficient Deep Learning Approach for Single-Channel EEG-Based Sleep Stage Classification with Model Interpretability
Sleep, a fundamental physiological process, occupies a significant portion of
our lives. Accurate classification of sleep stages serves as a crucial tool for
evaluating sleep quality and identifying probable sleep disorders. Our work
introduces a novel methodology that utilizes a SE-Resnet-Bi-LSTM architecture
to classify sleep into five separate stages. The classification process is
based on the analysis of single-channel electroencephalograms (EEGs). The
suggested framework consists of two fundamental elements: a feature extractor
that utilizes SE-ResNet, and a temporal context encoder that uses stacks of
Bi-LSTM units. The effectiveness of our approach is substantiated by thorough
assessments conducted on three different datasets, namely SleepEDF-20,
SleepEDF-78, and SHHS. The proposed methodology achieves significant model
performance, with Macro-F1 scores of 82.5, 78.9, and 81.9 for the respective
datasets. We employ 1D-GradCAM visualization as a methodology to elucidate the
decision-making process inherent in our model in the realm of sleep stage
classification. This visualization method not only provides valuable insights
into the model's classification rationale but also aligns its outcomes with the
annotations made by sleep experts. One notable feature of our research lies in
the incorporation of an efficient training approach, which adeptly upholds the
model's resilience in terms of performance. The experimental evaluations
provide a comprehensive evaluation of the effectiveness of our proposed model
in comparison to the existing approaches, highlighting its potential for
practical applications