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; Diagnosis, Computer-Assisted; EEG; Electroencephalography; Encephalogram signals; Entropy; Epilepsy; Epilepsy - physiopathology; Female; Humans; Learning algorithms; Machine Learning; Male; Medical diagnosis; Neural networks; Neural Networks (Computer); Patients; Principal components analysis; Researchers; Seizure; Seizures; Seizures - physiopathology; Signal Processing, Computer-Assisted; Wavelet transforms; Encephalogram signals; Deep learning; Seizure; Epilepsy; Convolutional neural network

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 tim...