Enhancing Performance of Deep Learning Models for Epilepsy Seizure Detection

Abstract

Epilepsy is a neurological condition marked by recurring seizures, leading to notable effects on the well-being of individuals experiencing it. Deep learning models have shown promising results in detecting and classifying epilepsy based on electroencephalogram (EEG) data and Magnetic Resonance imaging (MRI). However, achieving high performance in epilepsy detection requires continuous efforts to enhance the accuracy and reliability of these models. This study introduces multiple approaches for improving the effectiveness of deep learning models designed for detecting epilepsy. Initially, we employ data preprocessing methods to cleanse and prepare the input data, including noise removal, data normalization, and handling missing values. Additionally, data augmentation methods, such as random rotations, translations, and scaling are employed to increase the diversity and generalizability of the training data. Secondly, various model architectures are explored to improve the model's ability to detect epilepsy. CNNs and RNNs are commonly employed, and their configurations are experimented with by adjusting the depth, and width, and adding additional layers such as residual connections or attention mechanisms. Furthermore, hyperparameter tuning techniques are employed to enhance the deep learning model's efficiency. Thoughtful choices are made regarding hyperparameters like learning rate, batch size, and regularization methods and are carefully selected through approaches like grid search or random exploration conducted to discover the best possible setup that maximizes the model's effectiveness. By implementing these strategies, the performance of deep learning models for epilepsy detection has been significantly enhanced. The improved accuracy and reliability of these models offer great potential for early detection and intervention, leading to better management and treatment outcomes for individuals living with epilepsy.