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

By Usha R. Acharya, Shu Lih Oh, Yuki Hagiwara, Jen Hong Tan, Hojjat Adeli. Comput. Biol. Medicine, 2017

1,449 citations recorded by the source corpus.

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.

Subfields: Cardiology and Cardiovascular Medicine, Cognitive Neuroscience, Signal Processing

View Paper: https://www.semanticscholar.org/paper/6827b7abcdc6f9c0f91e239b4da394251aff35bd

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In Cardiology and Cardiovascular Medicine

In Cognitive Neuroscience

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Citations in the corpus

EEG based multi-class seizure type classification using convolutional neural network and transfer learning (2020, methodology): “The application of CNN towards the classification of epileptic seizures have been implemented in recent studies (Acharya, Oh, Hagiwara, Tan, & Adeli, 2018; Achilles, et al., 2018; Birjandtalab, Heydarzadeh, & Nourani, 2017; Truong, et al., 2017; Ullah, ul Haq Qazi, & Aboalsamh, 2018; Zhou et al.,…”
Deep learning for electroencephalogram (EEG) classification tasks: a review (2019, background): “[78] CNN ReLU (conv) N SV (1) N/A MR SD 88.”
Deep learning-based electroencephalography analysis: a systematic review (2019, neutral): “[2, 38, 183, 132]), while others employed grid search (e.”
Deep learning for healthcare applications based on physiological signals: A review (2018, background): “, 2017 [103] Seizure detection CNN Freiburg EEG DB [104, 105, 106, 107, 108] accuracy: 88.”
Epileptic Seizures Detection Using Deep Learning Techniques: A Review (2020, methodology): “10 [69] Bonn MATLAB Z-Score Normalization 1D-CNN 10 softmax 86.”
Epileptic Seizure Detection Based on EEG Signals and CNN (2018, methodology): “The EEG signal is obviously nonlinear and nonstationary in nature, while the signal is highly complex, and a visual interpretation of the signals is diﬃcult (Acharya et al., 2017).”
SleepEEGNet: Automated sleep stage scoring with sequence to sequence deep learning approach (2019, background): “In an overnight polysomnography (PSG) (also called as sleep study), usually EEG is mainly used to monitor the brain activities to diagnose sleep disorders [1] and other common disorders such as epilepsy [2].”
Detection of Coronavirus Disease (COVID-19) Based on Deep Features (2020, methodology): “The deep learning is one of the recent techniques applicable in the field of medicine for diagnosis purpose [16-28].”
A review of feature extraction and performance evaluation in epileptic seizure detection using EEG (2019, methodology): “Using the raw signal with ANN [CCS + 18] and one-dimensional CNN following the z -score normalization process [AOH + 18] provided promising results.”
A Case Study on EEG Signal Correlation Towards Potential Epileptic Foci Triangulation (2024, unknown): “These results indicate that neuronal functional structures are involved in the formation of EEG signals and that transmission does not only occur through the white matter [52,55].”