EEG based classification of sleep cyclic alternating patterns using frequency driven forward ternary encoding

Cyclic alternating patterns (CAP) of sleep can be observed through electroencephalogram (EEG) signals. Analyzing CAP can provide valuable insights into different abnormalities relating to sleep. CAP comprises of two phases: A and B, characterized by the brain response to different types of stimuli.

In this study, we propose an efficient and accurate system to segregate the CAP phases by considering the EEG signals from two different categories, i.e., healthy individuals and those suffering from insomnia. The input signal is divided into short sequences, which are analyzed using Gaussian filters to generate the frequency band (FB) components. Forward ternary encoding (FTE) is applied to each of the FB components and the encoded values are represented using histograms to capture the intrinsic signal patterns. The feature vector is constructed by combining the histograms obtained from all FB components, while the feature selection is achieved using the Kruskal-Wallis test.

We evaluate the performance of four different machine learning classifiers and compare their results. The bagged tree (BT) classifier yields accuracy of 80.16% and 81.12% for the healthy and insomnia datasets, respectively.

The proposed method performs better than the existing studies on CAP classification for two different datasets. It is accurate and easy to implement, and hence, it holds promise for efficient real-time deployment.