Epileptic seizure detection using a hybrid 1D CNN-machine learning approach from EEG data

Fatima Hassan, Syed Fawad Hussain, Saeed Mian Qaisar*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Abstract

Electroencephalography (EEG) is a widely used technique for the detection of epileptic seizures. It can be recorded in a noninvasive manner to present the electrical activity of the brain. The visual inspection of nonlinear and highly complex EEG signals is both costly and time-consuming. Therefore, an effective automatic detection system is needed to assist in the long-term evaluation and treatment of patients. Traditional approaches based on machine learning require feature extraction, while deep learning approaches are time-consuming and require more layers for effective feature learning and processing of complex EEG waveforms. Deep learning-based approaches also have weak generalization ability. This paper proposes a solution based on the combination of convolution neural networks (CNN) and machine learning classifiers. It preprocesses the EEG signal using the Butterworth filter and performs feature extraction using CNN. From the extracted set of features, the approach selects only the relevant features using mutual information-based estimators to reduce the curse of dimensionality and improve classification accuracy. The selected features are then passed as input to different machine learning classifiers. The suggested solution is evaluated on the University of Bonn dataset and CHB-MIT datasets. Our model effectively predicts 2, 3, 4, and 5 classes with accuracy of 100%, 99%, 94.6%, and 94%, respectively, for the Bonn dataset and 98% for CHB-MIT datasets.

Original languageEnglish
Article number9579422
Number of pages16
JournalJournal of Healthcare Engineering
Volume2022
DOIs
Publication statusPublished - 29 Nov 2022

Bibliographical note

Publisher Copyright:
Copyright © 2022 Fatima Hassan et al.

ASJC Scopus subject areas

  • Biotechnology
  • Surgery
  • Biomedical Engineering
  • Health Informatics

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