Using Support Vector Machines as an Intelligent Algorithm for Detecting Seizures from EEG Signals

Mohammadpoor, Mojtaba; Alizadeh, Atefe

doi:10.52547/shefa.9.2.1

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Volume 9, Issue 2 (Spring 2021)

Shefaye Khatam 2021, 9(2): 1-9

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Using Support Vector Machines as an Intelligent Algorithm for Detecting Seizures from EEG Signals

Mojtaba Mohammadpoor ^*

, Atefe Alizadeh

Department of Electrical and Computer Engineering, Gonabad Higher Education Complex, Gonabad, Iran , m.mohammadpoor@gmail.com

Abstract: (2521 Views)

Introduction: Electroencephalography (EEG) is the most commonly used method to study the function of the brain. This study represents a computerized model for distinguishing between epileptic and healthy subjects using EEG signals with relatively high accuracy. Materials and Methods: The EEG database used in this study was obtained from the data available in Andrzejak. This dataset consists of 5 EEG sets (designated as A to E), each containing 100 EEG sections. Collections A and B comprised EEG signals that have been taken from 5 healthy volunteers. The C and D sets referred to EEGs from patients with focal epilepsy (without ictal recordings) and the E set was derived from a patient with ictal recording. Support vector machines were used after applying principal components analysis or linear discriminant analysis over the features of the signals. MATLAB has been used to implement and test the proposed classification algorithm. To evaluate the proposed method, the confusion matrix, overall success rate, ROC, and the AUC of each class were extracted. K-fold cross-validation technique was used to validate the results. Results: The overall success rate achieved in this study was above 82%. Dimension reduction algorithms can improve its accuracy and speed. Conclusion: It is helpful to be able to predict the occurrence of a seizure early and accurately. Using the computerized model represented in this study could accomplish this goal.

Keywords: Seizures, Electroencephalography, Passive Cutaneous Anaphylaxis

Full-Text [PDF 863 kb] (893 Downloads)

Type of Study: Research --- Open Access, CC-BY-NC | Subject: Bioinformatics in Neuroscience

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‎ 10.52547/shefa.9.2.1

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Mohammadpoor M, Alizadeh A. Using Support Vector Machines as an Intelligent Algorithm for Detecting Seizures from EEG Signals. Shefaye Khatam 2021; 9 (2) :1-9
URL: http://shefayekhatam.ir/article-1-2125-en.html

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Volume 9, Issue 2 (Spring 2021)

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