Epilepsy is a prevalent neurological disorder characterized by recurrent epileptic seizures. After a stroke, it is a highly common neurological condition. For identifying seizures, the Electroencephalogram (EEG) is the gold-standard modality for capturing and recording the brain's electrical activity with high temporal resolution. The existing machine learning approaches have been leveraged before the emergence of deep learning. However, these models limited the performance as they included handcrafted features. The deep learning models perform the feature extraction automatically, which improves the classification accuracy compared with the conventional strategies. Therefore, in this article, an epileptic seizure detection approach on the basis of deep learning methods is developed to identify the abnormal brain functionalities in the initial stage. Initially, the EEG signal collection is carried out through standard benchmark databases. After that, the spectral and statistical features are directly extracted from the acquired EEG signals. Subsequently, the Short-Time Fourier Transform (STFT) is applied to transform the EEG signals into spectrograms, and then the features from those spectrograms are extracted via Vision Transformer (ViT). Furthermore, the Wave-based features are also extracted, which provide a comprehensive view of the brain activities. After that, a Coordinate Attention-based Feature Fusion mechanism is employed to fuse these diverse feature types, which captures complementary details from the EEG signals. The fused features are subjected to the Adaptive Dilated dense Recurrent neural Network with a Novel Activation Function (ADRNet-NAF) for the Epileptic seizure detection. The detection accuracy of the proposed model is improved by optimizing the hyperparameters of the ADRNet-NAF model using Modernized Exploration of Magnificent Frigatebird Optimization (MEMFO) during network training. The empirical analysis over the traditional methods is conducted to validate the Epileptic seizure detection performance of the proposed model using various measures. The proposed model achieves 94.98% accuracy, 89.30% sensitivity and 99.08% specificity.

Epilepsy is a neurological condition that affects the emotional and psychological well-being of individuals. Managing this disorder is challenging, particularly because focal seizures, which begin in specific brain regions, can sometimes evolve into generalized forms. The fundamental method for seizure identification is the analysis of the electroencephalogram (EEG), yet its manual interpretation is prone to error. In addition, the process of automated seizure detection using EEG data suffers from variations between subjects and dataset distributions as they might result in inconsistencies. Moreover, the unequal proportion of seizure to non-seizure samples increases the detection challenge. Hence, developing classification approaches capable of distinguishing and predicting the focal and generalized seizures is important for effective treatment planning. Therefore, an efficient deep learning-based focal and generalized epilepsy classification is designed in this research by considering the multimodal data. Initially, essential signals used for the validation are sourced from publicly available EEG datasets and they are converted into Short-Time Fourier Transform (STFT) images, which are designated as Feature Set 1. Next, the Sensor data used for the validation are gathered from Kaggle (https://www.kaggle.com/datasets/datasetengineer/epilepsy-dataset) and it is designated as Feature Set 2. Next, the two acquired sets of features are input to the Multilevel Spatio-Temporal Attention Fusion Network (MSTAFN) to execute the feature fusion process. Once the feature fusion procedure is completed, the fused features are given as the input to the focal and generalized epilepsy classification phase. In this phase, Adaptive Variational Autoencoders with Dense Bidirectional Gated Recurrent Units (AVDBiGRU) are employed to perform the classification process. Moreover, the focal and generalized epilepsy classification process is improved by optimizing the hyperparameters of AVDBiGRU through Fitness-based Football Optimization Algorithm (FFbOA). Finally, the focal and generalized epilepsy classified outcome is obtained from AVDBiGRU. Further, various experiments are carried out in the developed focal and generalized epilepsy classification model over the widely adopted deep learning architectures like LSTM, DCNN, InceptionV3 and BiGRU to verify their efficiency over different classes. The proposed model is evaluated on an EEG dataset containing the high-frequency oscillation (HFO) annotations from 30 pediatric patients with epilepsy and model performance is assessed by using the standard evaluation metrics that includes accuracy, sensitivity, specificity, and F1-score. The proposed model achieved 95.54% accuracy, 96.66% specificity, 93.32% F1-score and 88.53 AUC.