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Trends in Opto-electro & Optical Communication Original Research

Advancing EEG Technology for Affordable and Effective Epilepsy Detection

  1. Bhargavi Marathe Department of Information Technology Xavier Institute of Engineering Mumbai
  2. Jaya Jeswani Department of Information Technology Xavier Institute of Engineering Mumbai
  3. Sarthak Patil Department of Information Technology Xavier Institute of Engineering Mumbai
  4. Rushikesh Redij Department of Information Technology Xavier Institute of Engineering Mumbai

Abstract

For a proper diagnosis and prompt treatment, epilepsy, a neurological condition marked by recurring seizures, needs to be continuously monitored. Manual interpretation is frequently used in traditional approaches for identifying epileptic seizures from electroencephalogram (EEG) signals, which can be laborious and error-prone. In this research, a novel method for automatically detecting epilepsy from EEG data using deep learning algorithms is presented. According to centers for disease control and prevention (CDC) research, over 3 million Americans suffer from epilepsy. This study investigated the use of deep learning techniques for both EEG data analysis and seizure diagnosis. Here, a method known as 1D CNN-Long Short-Term Memory Networks (LSTM) is used, which combines long short-term memory with a dimensional convolution neural network. Whereas the lengthy short-term memory component concentrates on extracting temporal details, CNN mainly pulls spatial features from the standardized EEG sequence analysis. The CHB-MIT dataset is used in the study, with 80% of it being randomly divided into training and 20% into testing. Accuracy rates of up to 98% have been attained with the use of the CNN architecture, with CNN-LSTM achieving an average accuracy of 85%. A very effective and reasonably priced EEG identification tool designed for epilepsy has been developed because of advances in electroencephalogram (EEG) technology.

Keywords

References (12)

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