Accurate sleep stage identification through machine learning

This study compared different biosignals, biosignal length, and window length to evaluate the performance of machine learning algorithms in sleep stage classification.
The combination of electroencephalogram, electromyogram, and electrocardiogram with a 40 s window and 120 s signal length resulted in the best classification performance (precision: 0.853, recall: 0.855, F1-score: 0.853, and accuracy: 0.853).
Authors concluded that electroencephalogram signals showed a relatively higher importance for classification.