Deep Learning Approaches for Seizure Detection and Prediction Using EEG Signals: A Comprehensive Review and Proposed CNN Framework

C V Keerthi Latha; M Kezia Joseph

doi:10.25163/angiotherapy.869616

Angiogenesis, Inflammation & Therapeutics | Online ISSN 2207-872X

RESEARCH ARTICLE (Open Access)

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Deep Learning Approaches for Seizure Detection and Prediction Using EEG Signals: A Comprehensive Review and Proposed CNN Framework

C V Keerthi Latha ^1*, M Kezia Joseph ^1*

+ Author Affiliations

Journal of Angiotherapy 8 (6) 1-11 https://doi.org/10.25163/angiotherapy.869616

Submitted: 14 April 2024 Revised: 03 June 2024 Published: 10 June 2024

Abstract

Background: Epilepsy, a neurological disorder characterized by recurring seizures, affects millions globally and presents significant medical challenges. The unpredictable nature of seizures necessitates advancements in their detection and prediction. This study introduces a novel approach for classifying and identifying epileptic seizures through the analysis of EEG (Electroencephalogram) data using Convolutional Neural Networks (CNNs). Methods: We employed CNNs to analyze EEG signals, identifying recognizable patterns in temporal and spatial information, thereby enhancing the accuracy of seizure detection. Our proposed CNN framework incorporates Batch Normalization (BN), dropout layers, and dense layers specifically designed for EEG signal analysis. This novel approach improves the model’s capacity for extracting and detecting complex spatial-temporal patterns in EEG data, supporting effective seizure prediction and detection. The implementation of this Deep Learning (DL) methodology allows for continuous epilepsy monitoring, significantly advancing seizure prediction accuracy. Results: Extensive validation of the framework on a publicly accessible dataset demonstrated its superiority over traditional Machine Learning (ML) techniques, achieving an accuracy rate of 98.52%. This CNN-based approach successfully distinguished between abnormal brain activity due to seizures and normal brain function. Conclusion: The developed DL framework represents a significant advancement in epileptic seizure detection and prediction. By leveraging CNNs for EEG signal analysis, this study provides a robust and accurate tool for continuous epilepsy monitoring, offering improved patient outcomes and contributing to the broader field of neurological disorder management.

Keywords: Epilepsy Seizure Detection (ESD), Convolutional Neural Networks (CNNs), EEG Signal Analysis, (DL) Deep Learning, (SD)Seizure Detection, Epilepsy Detection, SP (Seizure Prediction).

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