Mallick, S.; Baths, V. Novel Deep Learning Framework for Detection of Epileptic Seizures Using EEG Signals. Frontiers in Computational Neuroscience 2024, 18, doi:10.3389/fncom.2024.1340251.
Mallick, S.; Baths, V. Novel Deep Learning Framework for Detection of Epileptic Seizures Using EEG Signals. Frontiers in Computational Neuroscience 2024, 18, doi:10.3389/fncom.2024.1340251.
Mallick, S.; Baths, V. Novel Deep Learning Framework for Detection of Epileptic Seizures Using EEG Signals. Frontiers in Computational Neuroscience 2024, 18, doi:10.3389/fncom.2024.1340251.
Mallick, S.; Baths, V. Novel Deep Learning Framework for Detection of Epileptic Seizures Using EEG Signals. Frontiers in Computational Neuroscience 2024, 18, doi:10.3389/fncom.2024.1340251.
Abstract
Epilepsy is a chronic neurological disorder characterized by abnormal electrical activity in the brain, often leading to recurrent seizures. With approximately 50 million people worldwide affected by epilepsy, there is a pressing need for efficient and accurate methods to detect and diagnose seizures. Electroencephalogram (EEG) signals, which record the brain’s electrical activity, have emerged as a valuable tool in detecting epilepsy and other neurological disorders. Traditionally, the process of analyzing EEG signals for seizure detection has relied on manual inspection by experts, which is time-consuming, labor-intensive, and susceptible to human error. To address these limitations, researchers have turned to machine learning and deep learning techniques to automate the seizure detection process. In this work, we propose a novel method for epileptic seizure detection, leveraging the power of 1-D Convolutional layers in combination with Bidirectional Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) and Average pooling Layer as a unit. The performance of the proposed model is verified on the Bonn dataset. Our proposed model offers significant advancements over existing approaches for the detection of epileptic seizures, particularly for 2, 3, 4, and 5-class classification tasks. To assess the robustness and generalizability of our proposed architecture, we employ 5-fold cross-validation. By dividing the dataset into five subsets and iteratively training and testing the model on different combinations of these subsets, we obtain robust performance measures, including accuracy, sensitivity, and specificity. We demonstrate the effectiveness of the proposed architecture in accurately detecting epileptic seizures from EEG signals by using EEG signals of varying lengths. The results indicate its potential as a reliable and efficient tool for automated seizure detection, paving the way for improved diagnosis and management of epilepsy.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
