An efficient approach for EEG sleep spindles detection based on fractal dimension coupled with time frequency image

Publication date: March 2018 Source:Biomedical Signal Processing and Control, Volume 41 Author(s): Wessam Al-salman, Yan Li, Peng Wen, Mohammed Diykh Detection of the characteristics of the sleep stages, such as sleep spindles and K-complexes in EEG signals, is a challenging task in sleep research as visually detecting them requires high skills and efforts from sleep experts. In this paper, we propose a robust method based on time frequency image (TFI) and fractal dimension (FD) to detect sleep spindles in EEG signals. The EEG signals are divided into segments using a sliding window technique. The window size is set to 0.5 s with an overlapping of 0.4 s. A short time Fourier transform (STFT) is applied to obtain a TFI from each EEG segment. Each TFI is converted into an 8-bit binary image. Then, a box counting method is applied to estimate and discover the FDs of EEG signals. Different sets of features are extracted from each TFI after applying a statistical model to the FD of each TFI. The extracted statistical features are fed to a least square support vector machine (LS_SVM) to figure out the best combination of the features. As a result, the proposed method is found to have a high classification rate with the eight features sets. To verify the effectiveness of the proposed method, different classifiers, including a K-means, Naive Bayes and a neural network, are also employed. In this paper, the proposed method is evaluated using two publically available datasets...
Source: Biomedical Signal Processing and Control - Category: Biomedical Science Source Type: research