HCLA_CBiGRU: Hybrid convolutional bidirectional GRU based model for epileptic seizure detection

Abstract

Seizure detection from EEG signals is crucial for diagnosing and treating neurological disorders. However, accurately detecting seizures is challenging due to the complexity and variability of EEG signals. This paper proposes a deep learning model, called Hybrid Cross Layer Attention Based Convolutional Bidirectional Gated Recurrent Unit (HCLA_CBiGRU), which combines convolutional neural networks and recurrent neural networks to capture spatial and temporal features in EEG signals. A combinational EEG dataset was created by merging publicly available datasets and applying a preprocessing pipeline to remove noise and artifacts. The dataset was then segmented and split into training and testing sets. The HCLA_CBiGRU model was trained on the training set and evaluated on the testing set, achieving an impressive accuracy of 98.5%, surpassing existing state-of-the-art methods. Sensitivity and specificity, critical metrics in clinical practice, were also assessed, with the model demonstrating a sensitivity of 98.5% and a specificity of 98.9%, highlighting its effectiveness in seizure detection. Visualization techniques were used to analyze the learned features, showing the model's ability to capture distinguishing seizure-related characteristics. In conclusion, the proposed CBiGRU model outperforms existing methods in terms of accuracy, sensitivity, and specificity for seizure detection from EEG signals. Its integration with EEG signal analysis has significant implications for improving the diagnosis and treatment of neurological disorders, potentially leading to better patient outcomes.