HIGHLIGHTS
SUMMARY
The concept of synchrosqueezing transforms was utilized with standard machine_learning techniques reporting a classification accuracy of 95.1% (Cura and Akan, 2021). A comprehensive review of the different machine_learning techniques for epilepsy classification was reported in Sharmila and Geethanjali, and the latest deep learning techniques utilized for epilepsy classification from EEG signals were analyzed thoroughly in Shoeibi et_al. As far as the SI-based HMM along with the conventional machine_learning is considered, the 2,300 samples are reduced by means of sparse feature extraction eliminating the redundant ones. Only the essential sparse features are . . .
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