K-means Clustering for Sleep Spindles Classification

Changes in EEG sleep spindles constitute a promising indicator of sleep disorders. In this paper SleepSpindles are extracted from real EEG data from patients suffering from any kind of brain illness. In this paper a triple (STFT, WT and WMSD) algorithm for sleep spindle detection is used. Its performance is studied and quantified. After the detection and isolation, an ARMA model is applied to each spindle. The mean of the parameters of the ARMA model corresponding to all the detected spindles for each patient is computed and finally, these parameters are used in a k-means clustering classification algorithm to assign a given illness to each patient.