Abstract
K-complexes identification is a challenging task in sleep research. The detection of k-complexes in electroencephalogram (EEG) signals based on visual inspection is time consuming, prone to errors, and requires well-trained knowledge. Many existing methods for k-complexes detection rely mainly on analyzing EEG signals in time and frequency domains. In this study, an efficient method is proposed to detect k-complexes from EEG signals based on fractal dimension (FD) of time frequency (T-F) images coupled with undirected graph features. Firstly, an EEG signal is partitioned into smaller segments using a sliding window technique. Each EEG segment is passed through a spectrogram of short time Fourier transform (STFT) to obtain the T-F images. Secondly, the box counting method is applied to each T-F image to discover the FDs in EEG signals. A vector of FD features are extracted from each T-F image and then mapped into an undirected graph. The structural properties of the graphs are used as the representative features of the original EEG signals for the input of a least square support vector machine (LS-SVM) classifier. Key graphic features are extracted from the undirected graphs. The extracted graph features are forwarded to the LS-SVM for classification. To investigate the classification ability of the proposed feature extraction combined with the LS-SVM classifier, the extracted features are also forwarded to a k-means classifier for comparison. The proposed method is compared with several existing k-complexes detection methods in which the same datasets were used. The findings of this study shows that the proposed method yields better classification results than other existing methods in the literature. An average accuracy of 97% for the detection of the k-complexes is obtained using the proposed method. The proposed method could lead to an efficient tool for the scoring of automatic sleep stages which could be useful for doctors and neurologists in the diagnosis and treatment of sleep disorders and for sleep research.
Highlights
Sleep can be divided into different sleep stages that include mainly non-rapid eyes movements (NREM) sleep, rapid eyes movements (REM) sleep etc
The NREM sleep were reduced by American academy of sleep medicine (AASM) into three stages in which S3 and S4 were combined into one stage as slow waves stages (SWS) (Rechtschaffen and Kales, 1968; Iber et al, 2007; Ranjan et al, 2018)
All the experiments were conducted with the database discussed in section “EEG Data Description” and three structural graph features were extracted from each fractal dimension (FD) of the time frequency (T-F) images in this study
Summary
Sleep can be divided into different sleep stages that include mainly non-rapid eyes movements (NREM) sleep, rapid eyes movements (REM) sleep etc. NREM sleep can be further divided into four stages of drowsiness (S1), light sleep (S2), deep sleep (S3) and very deep sleep (S4). Analysis of these sleep waveforms based on their characteristic features of different stages is an important phase in sleep studies as each sleep stage has different characteristic waveforms. One of those important waveforms occurred in electroencephalogram (EEG) signals and changed over a short time are sleep spindles and k-complexes waves. K-complexes and sleep spindles patterns are the key characteristics of S2, and they are often used to identify S2
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