An automated system for epilepsy detection using EEG brain signals based on deep learning approach

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.

A Novel Deep Neural Network for Robust Detection of Seizures Using EEG Signals

“…Seizures are transient neurological dysfunctions caused by abnormal brain neurons and excessive supersynchronized discharges. e visual inspection of EEG for seizure detection by expert neurologists is a time-consuming and laborious process, and the diagnosis may not be accurate because of the massive amounts of EEG data and the discrepant clinical judgment standards of different neurologists [1,2]. erefore, scientific research on EEG-based automatic detection of epilepsy has attracted much attention.…”

Multiband entropy-based feature-extraction method for automatic identification of epileptic focus based on high-frequency components in interictal iEEG

“…Recently, Ullah et al has proposed an automated system for epilepsy detection for Bonn dataset based on deep learning approach, yielding 99.1% accuracy 56 . A similar dataset was used to design a deep convolutional neural network (CNN) with 13 layer for categorizing the normal, preictal, and seizure class and obtained an average accuracy of 88.7%, a specificity of 90% and a sensitivity of 95% 57 .…”

In Search of Consciousness with Artificial Intellegence

“…Automatic feature learning thus has major advantages over conventional machine learning approaches based on the extraction and selection of manual features [8]. It can be accomplished by integrating deep learning, which automatically discovers and learns the discriminative features necessary for classification of inputs.…”