Sleep spindles recognition system based on time and frequency domain features

Abstract

Sleep spindle is the one of important components determining N-REM (Non-Rapid Eye Movement) stage 2 in the sleep stages. The symptoms of N-REM stage 2 are sleep spindle and K-complex and here sleep spindles are automatically recognized by using time and frequency domain features belonging to EEG (Electroencephalograph) signals obtained from three patient subjects. In this study, the proposed method consists of two steps. In the first step, six time domain features have been extracted from raw EEG signals. As for the extraction of frequency domain features from raw EEG signals, Welch spectral analysis has been used and applied to raw EEG signals. By this way, 65 frequency domain features have been extracted and reduced from 65 to 4 features by using statistical measures including minimum, maximum, standard deviation, and mean values. Three feature sets including only time domain, only frequency domain, and both time and frequency domain features have been used and the numbers of these feature sets are 6, 4, and 10, respectively. In the second step, artificial neural network (ANN) with LM (Levenberg–Marquardt) has been used to classify the sleep spindles evaluated beforehand by sleep expert physicians. The obtained classification accuracies for three features sets in the classification of sleep spindles are 100%, 56.86%, and 93.84% by using LM-ANN (for ten node in hidden layer). The obtained results have presented that the proposed recognition system could be confidently used in the automatic classification of sleep spindles.