Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals

• Published in: Computers in biology and medicine Vol. 100; pp. 270 - 278
• Main Authors: Acharya, U. Rajendra, Oh, Shu Lih, Hagiwara, Yuki, Tan, Jen Hong, Adeli, Hojjat
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01-09-2018; Elsevier Limited
• Subjects: Abnormalities; Accuracy; Algorithms; Artificial neural networks; Automation; Brain; Brain research; Convolutional neural network; Decomposition; Deep learning; Diagnosis; EEG; Electroencephalography; Encephalogram signals; Entropy; Epilepsy; Inspection; Learning algorithms; Machine learning; Medical diagnosis; Neural networks; Patients; Principal components analysis; Researchers; Seizure; Seizures; Wavelet transforms; Encephalogram signals; Deep learning; Seizure; Epilepsy; Convolutional neural network
• ISSN: 0010-4825; 1879-0534
• DOI: 10.1016/j.compbiomed.2017.09.017

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.
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•Classification of normal, preictal, and seizure EEG signals.•Performed 13-layer deep convolutional neural network.•Implemented ten-fold cross-validation strategy.•Obtained accuracy of 88.7%, sensitivity of 95% and specificity of 90%.