Classification of healthy and insomnia subjects based on wake-to-sleep transition

This study is carried out with the aim of classifying healthy and insomniac subjects based on their wake-to sleep transition (sleep onset process) features. The features were extracted from those signals using nonparametric and parametric methods in frequency domain. Wavelet transform was used to calculate nonparametric features: relative power of EEG sub bands (delta, theta, alpha, beta and gamma). After that Sleep onset reference epochs were determined using first and last intersection of delta and alpha respectively. In the next step statistical analysis was carried out on the data set which was determined by the earlier step. The data was divided into two groups: training data and testing data. Classification tree model was executed on training data to predict the healthy and insomniac groups in test data. K-fold cross-validation method was used for this estimation.