چکیده

The sleep spindles are discriminant patterns of the sleep stage 2 whose detection is of significant importance for studying memory consolidation and sleep disorders. Because of the non-linear nature of the EEG signal sleep spindles detection by visual inspection is time-consuming and prone to human error. For this purpose we proposed a new automatic method for sleep spindles detection. The EEG signal was first divided into one-second segments using a sliding window with an overlapping of 0.8s 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 Shannon Entropy 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 Neural Network and AdaBoost were employed. The results of this research show that the proposed method for sleep spindles detection has a better performance than the existing methods.

کليدواژه ها

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

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کریم انصاری اصل , 1400 , آشکارسازی دوک خواب در سیگنال های مغزی برای بررسی اختلالات خواب , دومین کنفرانس پژوهش های کاربردی در مهندسی برق