Modified shuffle Net-DCNN architecture with cepstral and spectral feature for epileptic seizure detection using EEG...

Epileptic Seizure Detection plays a crucial role in identifying irregularities in brain activity patterns, such as seizures, focal onset, and generalized seizures, which often go unnoticed until they escalate into more severe conditions like prolonged seizures or cognitive impairments. Therefore, early prediction of epileptic seizure activities is vital for enabling timely medical responses, optimizing treatment strategies, and improving overall patient care and management. The difficulty of accurately and promptly detecting epileptic seizures from EEG data is addressed in this work. This effort is made more difficult by noise, signal pattern fluctuation, and the requirement for quick analysis. The limitations of current detection techniques, which frequently struggle with accuracy and dependability in clinical settings, define the issue. Addressing this critical need, the research introduces a novel hybrid model that combines Modified Shuffle Net V2 (MSNetV2) and Deep Convolutional Neural Network (DCNN) architectures through Electroencephalogram (EEG) signal data. The novelty of the research is to decrease false positives and increase detection accuracy by combining an advanced hybrid architecture model along with robust feature extraction techniques and sophisticated preprocessing techniques. This innovative model is designed specifically for early epileptic seizure detection and employs a comprehensive methodology that includes several key processes. Initially, the EEG signal data are preprocessed using the Modified Wiener Filtering (MWF) technique to remove noise and improve signal clarity. The preprocessed signals are then subjected to feature extraction to identify the most pertinent features, which are further enhanced to improve the dataset. Here, the min-max normalization procedure is used to carry out the data augmentation process. To identify epileptic episodes, the two network architectures independently process the augmented features before feeding them into the hybrid MSNetV2-DCNN model. The study uses detailed simulations and experimental evaluations to verify the efficacy of the suggested paradigm. The study comes to the conclusion that the MSNetV2-DCNN model exhibits a reliable and effective technique for epileptic seizure identification, underscoring its potential for practical use in traffic management situations as well as medical diagnostics. The proposed work achieves the highest accuracy of approximately 94% in 90% of the training data, which is the highest among the other conventional models.