Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals
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...
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| Published in | Computers in biology and medicine Vol. 100; pp. 270 - 278 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Elsevier Ltd
01.09.2018
Elsevier Limited |
| Subjects | |
| Online Access | Get full text |
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| 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.
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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%. |
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| AbstractList | 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.
[Display omitted]
•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%. 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. 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.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. |
| Author | Oh, Shu Lih Hagiwara, Yuki Acharya, U. Rajendra Adeli, Hojjat Tan, Jen Hong |
| Author_xml | – sequence: 1 givenname: U. Rajendra surname: Acharya fullname: Acharya, U. Rajendra email: aru@np.edu.sg organization: Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore – sequence: 2 givenname: Shu Lih surname: Oh fullname: Oh, Shu Lih organization: Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore – sequence: 3 givenname: Yuki surname: Hagiwara fullname: Hagiwara, Yuki organization: Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore – sequence: 4 givenname: Jen Hong surname: Tan fullname: Tan, Jen Hong organization: Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore – sequence: 5 givenname: Hojjat surname: Adeli fullname: Adeli, Hojjat organization: Departments of Neuroscience, Neurology, Biomedical Engineering, Biomedical Informatics, and Civil, Environmental, and Geodetic Engineering, The Ohio State University, 470 Hitchcock Hall, 2070 Neil Avenue, Columbus, OH, 43210, United States |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/28974302$$D View this record in MEDLINE/PubMed |
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| SubjectTerms | 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 |
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| Title | Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals |
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