International Journal of Information Technology & Computer Science ( IJITCS ) (ISSN No : 2091-1610 ) Volume 10 : Issue No : 3 : Issue on : July / August , 2013 This Paper was Presented on : 2nd International Conference on Computer Science, Information System & Communication Technologies ( ICCSISCT 2013 )- Sydney , Australia on June 18 – 19 , 2013 …… Page...77 K-means Clustering for Sleep Spindles Classification Joao Caldas da Costa EIM Training and UNINOVA, University Nova of Lisbon Gold Coast, Australia joao.caldas.costa@gmail.com Manuel Duarte Ortigueira UNINOVA and Department of Electrical Engineering, University Nova of Lisbon Lisbon, Portugal mdo@fct.unl.pt ArnaldoGuimarães Batista UNINOVA and Department of Electrical Engineering, University Nova of Lisbon Lisbon, Portugal agb@fct.unl.pt Abstract : 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. Keywords - ARMA; Sleep Spindles; EEG; k-means clustering I. INTRODUCTION Sleep spindles (SS) are particular EEG patterns which occur during the sleep cycle with center frequency in the band 11.5 to 15 Hz. They are used as one of the features to classify the sleep stages [1]. Sleep spindles are promising objective indicators in sleep disorders. In order to interpret then, their structure needs to be clarified or a suitable model needs to be found. The correct detection of human SS and posterior characterization can lead to early detection of changes in brain and prevent or, at least, mitigate the influence of certain diseases [2]. Three methods have been used in the SS detection. The Short Time Fourier Transform (STFT) method relies in the fact that after the transform has been applied to a signal containing a SS, a peak will occur in the SS frequency range. The Wavelet Transform (WT) uses the normalized wavelet power to detect sleep spindles. Wave Morphology for Spindle Detection (WMSD) directly mimics manual visual scoring. The methods are combined using an AND algorithm. In this work, ARMA model for sleep spindles is used to detect meaningful differences when applied to spindles from people with pathologies. After a SS is correctly identified and isolated, an ARMA model is