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Detection of epileptic seizures in EEG by using machine learning techniques

AL-Huseiny Muayed S., Sajit Ahmed S.

Diagnostyka

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2023

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Vol. 24, No. 1

art. no. 2023108

EN

In this research a public dataset of recordings of EEG signals of healthy subjects and epileptic patients was used to build three simple classifiers with low time complexity, these are decision tree, random forest and AdaBoost algorithm. The data was initially preprocessed to extract short waves of electrical signals representing brain activity. The signals are then used for the selected models. Experimental results showed that random forest achieved the best accuracy of detection of the presence/absence of epileptic seizure in the EEG signals at 97.23% followed by decision tree with accuracy of 96.93%. The least performing algorithm was the AdaBoost scoring accuracy of 87.23%. Further, the AUC scores were 99% for decision tree, 99.9% for random forest and 95.6% for AdaBoost. These results are comparable to state-of-the-art classifiers which have higher time complexity.

2

Channel based epilepsy seizure type detection from electroencephalography (EEG) signals with machine learning techniques

Tuncer Erdem, Bolat Emine Doğru

Biocybernetics and Biomedical Engineering

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2022

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Vol. 42, no. 2

575--595

EN

Epileptic seizures result from disturbances in the electrical activity of the brain, classified as focal, generalized, or unknown. Failure to correctly classify epileptic seizures may result in inappropriate treatment and continuation of seizures. Therefore, automatic detection of generalized, focal, and other epileptic seizures from EEG signals is important. In this research article, Focal-Generalized classification method is proposed that compares traditional classification algorithms and deep learning methods. Two different classifications: four-class (Case (I) Complex Partial Seizure (CPSZ) (C4-T4 Onset)-CPSZ (FP2-F8 Onset)-CPSZ (T5-O1 Onset)- Absence Seizure (ABSZ)) and two-class (Case (II) CPSZ-ABSZ) problems are considered. This study includes preprocessing of scalp Electroencephalogram (EEG) data, feature extraction with discrete wavelet method, feature selection using Correlation-based Feature Selection (CFS) method, and classification of data with classifier algorithms (K-Nearest Neighbors (Knn), Support Vector Machine (SVM), Random Forest (RF) and Long Short-Term Memory (LSTM). The proposed method was applied on 23 subjects in the Temple University Hospital (TUH) scalp EEG data set, and a classification success rate of 95,92% for case (I) and 98,08% for case (II) was successfully achieved with deep learning architecture LSTM.

3

Modified Atom Search Optimization-based Deep Recurrent Neural Network for epileptic seizure prediction using electroencephalogram signals

Borhade Ratnaprabha Ravindra, Nagmode Manoj S.

Biocybernetics and Biomedical Engineering

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2020

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Vol. 40, no. 4

1638--1653

EN

Context and background: Epilepsy is considered as the common neurological disease in the world. Early prediction of epileptic seizure gained great influence on the epileptic patient's life. Epileptic patients suffer from unpredictable conditions that may occur at any moment. Motivation: Various epileptic seizure prediction methods are introduced for accurately predicting the pre-ictal state of human brain, but to determine the discriminative features poses a major challenge in the medical sector. Hypothesis: Develop a technique for epileptic seizure prediction using electroencephalogram signals that detects the epileptic seizure automatically. Method: In this research, an effective optimization algorithm, named Modified Atom Search Optimization-based Deep Recurrent Neural Network is proposed to perform accurate seizure prediction with less computation time. Here, the Deep Recurrent Neural Network classifier per-forms the seizure prediction using various hidden layers associated in the hierarchy layer based on the optimally selected features. The proposed Modified Atom Search Optimization algorithm is designed using the Squirrel Search Algorithm and Atom Search Optimization. It is worth interesting to note that the proposed Modified Atom Search Optimization-based Deep Recurrent Neural Network performed early and accurate seizure prediction using electroencephalogram signals. Result: The analysis of the proposed SASO-based Deep RNN is carried out using CHB-MIT Scalp EEG dataset using the metrics, namely accuracy, sensitivity, and specificity. The proposed algorithm obtained better performance in terms of specificity, accuracy, and sensitivity with the values of 97.536%, 96.545%, and 96.520% by varying training percentage, and 93.736%, 94.128%, and 96.520% by varying K-fold value. Conclusion: The proposed method has significant benefits like, faster convergence rate, easy to implement, low complexity, high speed, and robustness. The weights of the classifier are optimally trained using the proposed algorithm in order to reveal the effectiveness of prediction performance.

4

A machine learning approach to epileptic seizure prediction using Electroencephalogram (EEG) Signal

Savadkoohi Marzieh, Oladunni Timothy, Thompson Lara

Biocybernetics and Biomedical Engineering

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2020

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Vol. 40, no. 3

1328--1341

EN

This study investigates the properties of the brain electrical activity from different recording regions and physiological states for seizure detection. Neurophysiologists will find the work useful in the timely and accurate detection of epileptic seizures of their patients. We explored the best way to detect meaningful patterns from an epileptic Electroencephalogram (EEG). Signals used in this work are 23.6 s segments of 100 single channel surface EEG recordings collected with the sampling rate of 173.61 Hz. The recorded signals are from five healthy volunteers with eyes closed and eyes open, and intracranial EEG recordings from five epilepsy patients during the seizure-free interval as well as epileptic seizures. Feature engineering was done using; i) feature extraction of each EEG wave in time, frequency and time-frequency domains via Butterworth filter, Fourier Transform and Wavelet Transform respectively and, ii) feature selection with T-test, and Sequential Forward Floating Selection (SFFS). SVM and KNN learning algorithms were applied to classify preprocessed EEG signal. Performance comparison was based on Accuracy, Sensitivity and Specificity. Our experiments showed that SVM has a slight edge over KNN.

5

Gray-level co-occurrence matrix of Fourier synchrosqueezed transform for epileptic seizure detection

Mamli Shamzin, Kalbkhani Hashem

Biocybernetics and Biomedical Engineering

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2019

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Vol. 39, no. 1

87--99

EN

Epilepsy is a brain disorder that many persons of different ages in the world suffer from it. According to the world health organization, epilepsy is characterized by repetitive seizures and more electrical discharge in a group of brain neurons results in sudden physical actions. The aim of this paper is to introduce a new method to classify epileptic phases based on Fourier synchro-squeezed transform (FSST) of electroencephalogram (EEG) signals. FSST is a time-frequency (TF) analysis and provides sharper TF estimates than the conventional short-time Fourier transform (STFT). Absolute of FSST of EEG signal is computed and segmented into five non-overlapping frequency sub-bands as delta (d), theta (u), alpha (a), beta (b), and gamma (g). Each sub-band is considered as a gray-scale image and then we propose to obtain the gray-level co-occurrence matrix (GLCM) of each sub-band as features. We concatenate the features of different sub-bands to obtain the final feature vector. After selecting informative features by infinite latent feature selection (ILFS) method, the support vector machine (SVM) and K-nearest neighbor (KNN) classifiers are used separately to classify EEG signals. We use the EEG signals from Bonn University database and different combinations of its sets are considered. Simulation results show that the proposed method efficiently classifies the EEG signals and can be used to determine the phase of epilepsy.