Epilepsy Diagnosis Using Directed Acyclic Graph SVM Technique in EEG Signals

Epilepsy is a complicated neurological disorder that causes rapid and frequent seizures in both adults and children. EEG signals are emerging as non-invasive methods for analyzing epilepsy. However, processing and analyzing large volumes of EEG data requires significant time and expertise from neuro...

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Bibliographic Details
Published in:Traitement du signal 2024-12, Vol.41 (6), p.3163-3172
Main Authors: Babu, Shyam, Wadhwani, Arun Kumar
Format: Article
Language:eng ; fre
Subjects:
Accuracy
Algorithms
Autism
Automation
Brain research
Classification
Convulsions & seizures
Data analysis
Datasets
Diagnosis
Electroencephalography
Epilepsy
Fourier transforms
Investigations
Machine learning
Neural networks
Neurological diseases
Signal processing
Support vector machines
Wavelet transforms
Online Access:Get full text
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Summary:Epilepsy is a complicated neurological disorder that causes rapid and frequent seizures in both adults and children. EEG signals are emerging as non-invasive methods for analyzing epilepsy. However, processing and analyzing large volumes of EEG data requires significant time and expertise from neurophysiologists. This research presents a novel method to effectively differentiate between focal EEG and non-focal EEG data using the DAGSVM classifier. The suggested method utilizes the Bern Barcelona dataset and applies the Discrete Fourier Transform to EEG signals to identify time-frequency characteristics. We use 7,000 EEG signals, with 700 for testing and 6,800 for training. Results suggest that the DAGSVM classifier significantly exceeds existing approaches, achieving an accuracy of 99.71%. High accuracy improves patient results by facilitating the early diagnosis and care of epilepsy.
ISSN:0765-0019
1958-5608
DOI:10.18280/ts.410632