A NOVEL APPROACH TO DETECT EPILEPTIC SEIZURES USING A COMBINATION OF TUNABLE-Q WAVELET TRANSFORM AND FRACTAL DIMENSION

The detection and quantification of seizures can be achieved through the analysis of nonstationary electroencephalogram (EEG) signals. The detection of these intractable seizures involving human beings is a challenging and difficult task. The analysis of EEG through human inspection is prone to errors and may lead to false conclusions. The computer-aided systems have been developed to assist neurophysiologists in the identification of seizure activities accurately. We propose a new machine learning and signal processing-based automated system that can detect epileptic episodes accurately. The proposed algorithm employs a promising time-frequency tool called tunable-Q wavelet transform (TQWT) to decompose EEG signals into various sub-bands (SBs). The fractal dimensions (FDs) of the SBs have been used as the discriminating features. The TQWT has many attractive features, such as tunable oscillatory attribute and time-invariance property, which are favorable for the analysis of nonstationary and transient signals. Fractal dimension is a nonlinear chaotic trait that has been proven to be very useful in the analysis and classifications of nonstationary signals including EEG. First, we decompose EEG signals into the desired SBs. Then, we compute FD for each SB. These FDs of the SBs have been applied to the least-squares support vector machine (LS-SVM) classifier with radial basis function (RBF) kernel. We have used 10-fold cross-validation to ensure reliable performance and avoid the possible over-fitting of the model. In the proposed study, we investigate the following four popular classification tasks (CTs) related to different classes of EEG signals: (i) normal versus seizure (ii) seizure-free versus seizure (iii) nonseizure versus Seizure (iv) normal versus seizure-free. The proposed model surpassed existing models in the area under the receiver operating characteristics (ROC) curve, Matthew's correlation coefficient (MCC), average classification accuracy (ACA), and average classification sensitivity (ACS). The proposed system attained perfect 100% ACS for all CTs considered in this study. The method achieved the highest classification accuracy as well as the largest area under ROC curve (AUC) for all classes. The salient feature of our proposed model is that, though many models exist in the literature, which gave high ACA, however, their performance has not been evaluated using MCC and AUC along with ACA simultaneously. The evaluation of the performance in terms of only ACA which may be misleading. Hence, the performance of the proposed model has been assessed not only in terms of ACA but also in terms AUC and MCC. Moreover, the performance of the model has been found to be almost equivalent to a perfect model, and the performance of the proposed model surpasses the existing models for the CTs investigated by us. Therefore, the proposed model is expected to assist clinicians in analyzing seizures accurately in less time without any error.