## 6 Discussion  
The detection and location of epileptogenic zones in epilepsy patients is a vital step for drug-resistant cases requiring surgery. We proposed various methods that help classify focal and non-focal EEG signals in order to develop an efficient CAD system by extracting relevant and significant features that are suitable for use in portable devices such as IoMT devices. These features are then passed on to various proposed classifiers, such as SVM, KNN, Random Forest, 1D-CNN, and 2D-DCNN. We also explored the advantages of an improved version of the synchrosqueezing transform called WT-SST that provides a tight energy distribution in the time-frequency domain features when compared to conventional DWT. This property of WT-SST is capable of characterizing the pathological signs of focal and non-focal EEG signals. Several researchers have exploited the efficacy of various features from different domains such as the time-domain, frequency-domain, time-frequency-domain, nonlinear entropy, and statistical features. Our proposed approach was evaluated using the accuracy, sensitivity, specificity, precision, and F1-score as performance parameters. From the results presented in Section 4. The time-domain raw EEG signals were decomposed using the DWT domain; the extracted features were classified using the LS-SVM and 1D-CNN networks. The performance of the method using 1D-CNN was 98.8%, 97.3%, 97.7%, 98.8%, and 98.0% for accuracy, sensitivity, specificity, precision, and F1-score, respectively. Combined hybrid features from different domains were experimented on with SVM and 2D-DCNN, and the proposed techniques showed improved performance when compared to the DWT-CNN technique. The highest score was obtained using the proposed WTSST-DCNN structure with an accuracy, sensitivity, specificity, precision, and F1-score of 99.7%, 99.5%, 99.7%, 99.7%, and 99.6%, respectively. We used average accuracy to compare the performance results of our proposed techniques with other related works that employed the same Bern-Barcelona database in their study, as tabulated in Table 5. The authors in [53] classified focal and non-focal EEG signals using the features extracted from multivariate EEG signals using TQWT. Multivariate fuzzy entropy was computed and extracted from these sub-bands features, LS-SVM was deployed in the classification stage, and the performance accuracy was recorded as 84.67%. Figure 12: Execution time of the proposed models. The authors in [54] utilized the benefit of wavelet transform for the classification of focal and non-focal EEG signals; they proposed a feature extraction technique by calculating entropies from the orthogonal wavelet filter banks coefficients that are localized in time-frequency. They obtained an accuracy of 94.25% after classifying the EEG signals with an LS-SVM classifier. Entropy features such as approximate entropy, fuzzy entropy, and sample entropy were computed in [18] as features for focal and non-focal EEG signal classification. They experimented with the features using various classifiers, and the best accuracy of 99.0% was achieved with the Non-Nested Generalized Exemplars classifier. Recently, authors in [58] computed IMFs from the EMD technique, and the entropy features were calculated from IMFs as features for the LS-SVM classifier; a classification accuracy of 83.18% was successfully achieved. Another work that used the same database is that of [PERSON] et al. [59]. In their study, the performance of features from different domains such as EMD, WPD, and DWT with multi-scale PCA and random forest classifier was conducted, and an accuracy of 99.2% was achieved with a reduced version of the dataset. Recently, some authors explored the deep learning application in the detection and classification of focal and non-focal EEG signals, as reported in [60]. A deep convolutional autoencoder was proposed with a k-means clustering algorithm, and an accuracy of 93.2% was recorded. Other recent work includes [PERSON] et al.