Journal ArticleDOI
Complex networks approach for EEG signal sleep stages classification
Mohammed Diykh,Yan Li +1 more
TLDR
The research results indicate that the proposed method can assist neurologists and sleep specialists in diagnosing and monitoring sleep disorders and provide better EEG sleep signals classification compared with the existing approaches reported.Abstract:
Developing a new EEG sleep stages classification method based on the statistical features in time domain and complex networks properties.The method provides better EEG sleep signals classification compared with the existing approaches reported.Finding of not all sleep stages can be classified with the same number of the statistical features.Stage Awake can be classified with a fewer statistical features due to including high activity compared with other sleep stages. Sleep stage scoring is a challenging task. Most of existing sleep stage classification approaches rely on analysing electroencephalography (EEG) signals in time or frequency domain. A novel technique for EEG sleep stages classification is proposed in this paper. The statistical features and the similarities of complex networks are used to classify single channel EEG signals into six sleep stages. Firstly, each EEG segment of 30s is divided into 75 sub-segments, and then different statistical features are extracted from each sub-segment. In this paper, feature extraction is important to reduce dimensionality of EEG data and the processing time in classification stage. Secondly, each vector of the extracted features, which represents one EEG segment, is transferred into a complex network. Thirdly, the similarity properties of the complex networks are extracted and classified into one of the six sleep stages using a k-means classifier. For further investigation, in the statistical features extraction phase two statistical features sets are tested and ranked based on the performance of the complex networks. To investigate the classification ability of complex networks combined with k-means, the extracted statistical features were also forwarded to a k-means and a support vector machine (SVM) for comparison. We also compare the proposed method with other existing methods in the literature. The experimental results show that the proposed method attains better classification results and a reasonable execution time compared with the SVM, k-means and the other existing methods. The research results in this paper indicate that the proposed method can assist neurologists and sleep specialists in diagnosing and monitoring sleep disorders.read more
Citations
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Journal ArticleDOI
Robust sleep stage classification with single-channel EEG signals using multimodal decomposition and HMM-based refinement
TL;DR: A novel multimodal signal decomposition and feature extraction strategy is presented to obtain effective features for sub-band signals and a rule-free refinement process based on hidden Markov model (HMM) is proposed to optimize the classification results automatically.
Journal ArticleDOI
Automatic sleep stage classification using time–frequency images of CWT and transfer learning using convolution neural network
Pankaj Jadhav,Gaurav Rajguru,Debabrata Datta,Debabrata Datta,Siddhartha Mukhopadhyay,Siddhartha Mukhopadhyay +5 more
TL;DR: A deep learning-based method by using single channel electroencephalogram (EEG) that automatically exploits the time–frequency spectrum of EEG signal, removing the need for manual feature extraction is developed.
Journal ArticleDOI
Automatic sleep staging based on SVD, VMD, HHT and morphological features of single-lead ECG signal
TL;DR: Electroencephalogram (EEG) signals were used to identify the wake, non-rapid eye movement (NREM) and rapidEye movement (REM) stages of the sleep data from two different databases with 17,758 epochs of 28 subjects (21 healthy subjects and 7 obstructive sleep apnea patients) in total.
Journal ArticleDOI
Classify epileptic EEG signals using weighted complex networks based community structure detection
TL;DR: The proposed method was efficient in detecting epileptic seizures in EEG signals using a least support vector machine, k -means, Naive Bayes, and K -nearest classifiers.
Journal ArticleDOI
A feature extraction technique based on tunable Q-factor wavelet transform for brain signal classification.
TL;DR: The results prove that the proposed TQWT based feature extraction method has great potential to extract discriminative information from brain signals and can assist doctors and other health experts to identify diversified EEG categories.
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