Sleep Spindle Detection in EEG Signal for Investigating SleepDisturbances

The sleep spindles are discriminant patterns of the sleep stage ۲, whose detection is of significant importancefor studying memory consolidation and sleep disorders. Because of the non-linear nature of the EEG signal, sleepspindles detection by visual inspection is time-consuming and prone to human error. For this purpose, we proposed anew automatic method for sleep spindles detection. The EEG signal was first divided into one-second segments using asliding window with an overlapping of ۰.۸s; as an effective time-frequency method, the Empirical Wavelet Transform(EWT) was used to extract Intrinsic Mode Function (IMF). In the next step, some non-linear features such as ShannonEntropy, Renyi Entropy, Tsallis Entropy, Katz's and Petrosian Fractal Dimension extracted for the first three IMFs.Finally, to classify the extracted features, Support Vector Machines, K-Nearest Neighbor, Probabilistic NeuralNetwork, and AdaBoost were employed. The results of this research show that the proposed method for sleep spindlesdetection has a better performance than the existing methods.

کلیدواژه ها:

EEG signals ، sleep spindle ، Empirical Wavelet Transform ، non-linear features ، classifiers.