Classification of cyclic alternating patterns of sleep using EEG signals

Abstract

Cyclic alternating patterns (CAP) occur in electroencephalogram (EEG) signals during non-rapid eye movement sleep. The analysis of CAP can offer insights into various sleep disorders. The first step is the identification of phases A and B for the CAP cycles. In this work, we develop an easy-to-implement accurate system to differentiate between CAP A and CAP B. Small segments of the EEG signal are processed using Gaussian filters to obtain sub-band components. Features are extracted using some statistical characteristics of these signal components. Minimum redundancy maximum relevance test is employed to identify the more significant features. Three different machine learning classifiers are considered and their performance is compared. The results are analyzed for both the balanced and unbalanced datasets. The k-nearest neighbour (kNN) classifier achieves 79.14 % accuracy and F-1 score of 79.24 % for the balanced dataset. The proposed method outperforms the existing methods for CAP classification. It is easy-to-implement and can be considered as a candidate for real-time deployment.