Research Area:  Machine Learning

Abstract:

An encephalogram (EEG) is a commonly used ancillary test to aide in the diagnosis of epilepsy. The EEG signal contains information about the electrical activity of the brain. Traditionally, neurologists employ direct visual inspection to identify epileptiform abnormalities. This technique can be time-consuming, limited by technical artifact, provides variable results secondary to reader expertise level, and is limited in identifying abnormalities. Therefore, it is essential to develop a computer-aided diagnosis (CAD) system to automatically distinguish the class of these EEG signals using machine learning techniques. This is the first study to employ the convolutional neural network (CNN) for analysis of EEG signals. In this work, a 13-layer deep convolutional neural network (CNN) algorithm is implemented to detect normal, preictal, and seizure classes. The proposed technique achieved an accuracy, specificity, and sensitivity of 88.67%, 90.00% and 95.00%, respectively.

Keywords:  
Deep Convolutional Neural Network
Automated Detection
Diagnosis
Seizure
Eeg Signals
computer-aided diagnosis
Machine Learning
Deep Learning

Author(s) Name:  U. Rajendra Acharya, Shu Lih Oh, Yuki Hagiwara, Jen Hong Tan, Hojjat Adeli

Publisher name:  Elsevier

DOI:  10.1016/j.compbiomed.2017.09.017

Volume Information:  Volume 100, 1 September 2018, Pages 270-278

Paper Link:   https://www.sciencedirect.com/science/article/abs/pii/S0010482517303153