Advancing Epilepsy Diagnosis: Insights from EEG Signal Analysis with SVM and CNN

Epilepsy is a common neurological disorder in elderly populations, often linked to age-related conditions such as stroke and neurodegeneration. Traditional EEG signal analysis for epilepsy diagnosis is time-consuming, subjective, and unsuitable for clinical use, emphasizing the necessity for automated approaches. This study evaluates the performance of Support Vector Machine (SVM) and Convolutional Neural Network (CNN) in classifying EEG signals for epileptic seizure detection, utilizing the Bonn University EEG dataset. Time- and frequency-domain features were extracted, and 10-fold cross-validation was employed to validate the results. The findings reveal that SVM achieved 100% accuracy in distinguishing simple EEG states, such as healthy versus seizure conditions. Meanwhile, CNN outperformed SVM in processing more complex signals, achieving an average accuracy of over 98%. The results highlight the potential of integrating traditional machine learning with deep learning methods to enhance diagnostic accuracy and efficiency. These findings lay a strong foundation for developing advanced EEG-based diagnostic tools tailored to elderly epilepsy patients, facilitating more timely and effective clinical interventions

For more information, visit the full study through below link.

Content Details:-

Corresponding author: Ting Wang*

Full Length Article: Advancing Epilepsy Diagnosis: Insights from EEG Signal Analysis with SVM and CNN

Journal: Gerontology & Geriatrics: Research

Disclaimer: This content is not owned or created by us. It has been sourced from the respective site, Austin Publishing Group, and is intended purely for study and educational purposes. All rights belong to the original authors and publishers.