Automated Human Mind Reading Using EEG Signals for Seizure Detection

TL;DR

This paper proposes an automated EEG-based system using neural networks for seizure detection, achieving 98.33% accuracy to assist neurologists and improve diagnosis efficiency.

Contribution

It introduces a neural network model for seizure detection from EEG signals with high accuracy, aiding automation in neurological diagnosis.

Findings

Achieved 98.33% accuracy in seizure detection

Automated system reduces reliance on expert visual analysis

Potential to assist neurologists in clinical settings

Abstract

Epilepsy is one of the most occurring neurological disease globally emerged back in 4000 BC. It is affecting around 50 million people of all ages these days. The trait of this disease is recurrent seizures. In the past few decades, the treatments available for seizure control have improved a lot with the advancements in the field of medical science and technology. Electroencephalogram (EEG) is a widely used technique for monitoring the brain activity and widely popular for seizure region detection. It is performed before surgery and also to predict seizure at the time operation which is useful in neuro stimulation device. But in most of cases visual examination is done by neurologist in order to detect and classify patterns of the disease but this requires a lot of pre-domain knowledge and experience. This all in turns put a pressure on neurosurgeons and leads to time wastage and also reduce their accuracy and efficiency. There is a need of some automated systems in arena of information technology like use of neural networks in deep learning which can assist neurologists. In the present paper, a model is proposed to give an accuracy of 98.33% which can be used for development of automated systems. The developed system will significantly help neurologists in their performance.