Abstract
This paper analyses the complexity of multivariate electroencephalogram (EEG) signals in different frequency scales for the analysis and classification of focal and non-focal EEG signals. The proposed multivariate sub-band entropy measure has been built based on tunable-Q wavelet transform (TQWT). In the field of multivariate entropy analysis, recent studies have performed analysis of biomedical signals with a multi-level filtering approach. This approach has become a useful tool for measuring inherent complexity of the biomedical signals. However, these methods may not be well suited for quantifying the complexity of the individual multivariate sub-bands of the analysed signal. In this present study, we have tried to resolve this difficulty by employing TQWT for analysing the sub-band signals of the analysed multivariate signal. It should be noted that higher value of Q factor is suitable for analysing signals with oscillatory nature, whereas the lower value of Q factor is suitable for analysing signals with non-oscillatory transients in nature. Moreover, with an increased number of sub-bands and a higher value of Q-factor, a reasonably good resolution can be achieved simultaneously in high and low frequency regions of the considered signals. Finally, we have employed multivariate fuzzy entropy (mvFE) to the multivariate sub-band signals obtained from the analysed signal. The proposed Q-based multivariate sub-band entropy has been studied on the publicly available bivariate Bern Barcelona focal and non-focal EEG signals database to investigate the statistical significance of the proposed features in different time segmented signals. Finally, the features are fed to random forest and least squares support vector machine (LS-SVM) classifiers to select the best classifier. Our method has achieved the highest classification accuracy of 84.67% in classifying focal and non-focal EEG signals with LS-SVM classifier. The proposed multivariate sub-band fuzzy entropy can also be applied to measure complexity of other multivariate biomedical signals.
Highlights
60 million people worldwide are affected by a neurological disorder known as epilepsy [1]
The Q-based multivariate sub-band fuzzy entropy described in the previous section has been applied to 50 F and NF types of bivariate EEG signals
We have studied four different segments of EEG signals of durations corresponding to 20 s, 10 s, 5 s, and 2 s, respectively, to find out the statistical significance of the computed features over different time spans
Summary
60 million people worldwide are affected by a neurological disorder known as epilepsy [1]. In [17], the authors decomposed EEG signals into numbers of intrinsic mode functions (IMFs) using the empirical mode decomposition (EMD) method [22] They extracted average sample entropy (ASE) as well as average variance of instantaneous frequency (AVIF) features from the obtained IMFs in order to classify F and NF types of EEG signals. In [18], the authors have extracted several entropy features from the individual channel IMFs—namely, approximate entropy, Shannon entropy, sample entropy, Renyi’s entropy, phase entropy 1 and phase entropy 2 from the IMFs of EEG signals They obtained the average of those entropy values of the same index IMFs of both of the channels in order to find final feature vectors. We propose tunable-Q wavelet transform (TQWT) [36] based multivariate sub-band fuzzy entropy measure and studied the effectiveness of the proposed technique for the discrimination of bivariate F and NF types of EEG signals.
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