Classifying Sleep Stages Automatically in Single-channel Against Multi-channel EEG: A Performance Analysis

Abstract

Sleep plays a vital part in humans health. The correct classification of sleep stages provides clinical information that can be used to diagnose sleep disorders in patients. The gold standard for sleep evaluation is polysomnography. On the other hand, polysomnography has several drawbacks, such as expense, inconvenience, and a long wait time at sleep laboratories. Advancements in technology enable the adoption and use of single-channel EEG at in-home sleep monitoring; it provides better sleep-stage accuracy levels. Industry and academic researchers continuously improve automatic sleep-stage classification using machine and deep learning in multi-channel EEG and single-channel EEG. However, the performance evaluation of single-channel against multi-channel EEG was not established. The performance of sleep staging using XGBoost, LGBM and voting classifiers models was verified in multi-channel and single-channel EEG. A similar number of features and hyperparameters were employed for classification in multi-channel and single-channel EEG for sleep-stage classification. Each model used five healthy subjects data from the Sleep-EDFx database. K-fold validation was used for training and testing. XGBoost, LGBM, and voting classifiers classified the sleep stages and achieved an accuracy of 87.96%, 86.24%, 88.25% in multi-channel and 86.81%, 86.81%, 87.1% in single-channel, respectively. The sleep-stage classification results demonstrate that single-channel EEG gives almost similar accuracy to multi-channel EEG.