An Automated System for Epilepsy Detection using EEG Brain Signals based on Deep Learning Approach

TL;DR

This paper introduces a deep learning ensemble system using P-1D-CNNs for automated epilepsy detection from EEG signals, achieving higher accuracy than existing methods, especially in ternary classification.

Contribution

It proposes a novel P-1D-CNN ensemble model with fewer parameters and data augmentation techniques for improved epilepsy detection accuracy.

Findings

Achieved 99.1% accuracy on the Bonn dataset.

Reduced model parameters by 60% compared to traditional CNNs.

Outperformed state-of-the-art methods in ternary classification.

Abstract

Epilepsy is a neurological disorder and for its detection, encephalography (EEG) is a commonly used clinical approach. Manual inspection of EEG brain signals is a time-consuming and laborious process, which puts heavy burden on neurologists and affects their performance. Several automatic techniques have been proposed using traditional approaches to assist neurologists in detecting binary epilepsy scenarios e.g. seizure vs. non-seizure or normal vs. ictal. These methods do not perform well when classifying ternary case e.g. ictal vs. normal vs. inter-ictal; the maximum accuracy for this case by the state-of-the-art-methods is 97+-1%. To overcome this problem, we propose a system based on deep learning, which is an ensemble of pyramidal one-dimensional convolutional neural network (P-1D-CNN) models. In a CNN model, the bottleneck is the large number of learnable parameters. P-1D-CNN works on the concept of refinement approach and it results in 60% fewer parameters compared to traditional CNN models. Further to overcome the limitations of small amount of data, we proposed augmentation schemes for learning P-1D-CNN model. In almost all the cases concerning epilepsy detection, the proposed system gives an accuracy of 99.1+-0.9% on the University of Bonn dataset.