Automatic Sleep Stage Scoring with Single-Channel EEG Using Convolutional Neural Networks

TL;DR

This study demonstrates that convolutional neural networks can automatically learn to classify sleep stages from single-channel EEG data without prior domain knowledge, achieving performance comparable to state-of-the-art methods.

Contribution

The paper introduces a CNN-based approach for sleep stage scoring that learns task-specific filters directly from raw EEG data, eliminating the need for hand-engineered features.

Findings

Achieved high mean F1-score of 81% across sleep stages.

Filters learned by CNN correspond to established sleep scoring criteria.

Performance is balanced across classes and comparable to existing methods.

Abstract

We used convolutional neural networks (CNNs) for automatic sleep stage scoring based on single-channel electroencephalography (EEG) to learn task-specific filters for classification without using prior domain knowledge. We used an openly available dataset from 20 healthy young adults for evaluation and applied 20-fold cross-validation. We used class-balanced random sampling within the stochastic gradient descent (SGD) optimization of the CNN to avoid skewed performance in favor of the most represented sleep stages. We achieved high mean F1-score (81%, range 79-83%), mean accuracy across individual sleep stages (82%, range 80-84%) and overall accuracy (74%, range 71-76%) over all subjects. By analyzing and visualizing the filters that our CNN learns, we found that rules learned by the filters correspond to sleep scoring criteria in the American Academy of Sleep Medicine (AASM) manual that human experts follow. Our method's performance is balanced across classes and our results are comparable to state-of-the-art methods with hand-engineered features. We show that, without using prior domain knowledge, a CNN can automatically learn to distinguish among different normal sleep stages.