Effective Diagnosis of Various Sleep Disorders by LEE Classifier: LightGBM-EOG-EEG.

Abstract

A novel method is proposed for diagnosing the sleep disorders of insomnia, narcolepsy, periodic leg movement syndrome, nocturnal frontal lobe epilepsy, rapid eye movement behavior disorder, and sleep-disordered breathing. We use the light gradient boosting decision tree model (LightGBM) for the classification of healthy controls and different sleep disorders. The proposed approach is evaluated on the publicly available CAP dataset of 108 subjects. The LightGBM classifier using only an electrooculogram (EOG) channel (L-EOG) achieves a seven-class classification accuracy of 83.3%. This performance improves to 93.3% with the LightGBM-EOG-EEG (LEE) classifier, which harnesses the combined strengths of EOG and electroencephalogram (EEG) channels. LEE classifier employs four binary classifiers that disambiguate the classes confused by the L-EOG classifier. An additional 1% improvement is achieved by applying a simple thresholding decision rule to the results of the LEE classifier, resulting in an overall accuracy of 94.4%, the best in the literature for the seven-class classification of sleep disorders.