Investigating the contribution of distance-based features to automatic sleep stage classification.

Objective: In this paper, the contribution of distance-based features to automatic sleep stage classification is investigated. The potency of these features is analyzed individually and in combination with 48 conventionally used features.

Methods: The distance-based set consists of 32 features extracted by calculating Itakura, Itakura-Saito and COSH distances of autoregressive and spectral coefficients of Electrocardiography (EEG) (C-A), Left EOG, Chin EMG and ECG signals.

Feature ranking and rank aggregation for automatic sleep stage classification: a comparative study.

Background: Nowadays, sleep quality is one of the most important measures of healthy life, especially considering the huge number of sleep-related disorders. Identifying sleep stages using polysomnographic (PSG) signals is the traditional way of assessing sleep quality. However, the manual process of sleep stage classification is time-consuming, subjective and costly.