Classification of Epileptic and Non-Epileptic Electroencephalogram (EEG) Signals Using Fractal Analysis and Support Vector Regression

Seizures are a common symptom of this neurological condition, which is caused by the discharge of brain nerve cells at an excessively fast rate. Chaos, nonlinearity, and other nonlinearities are common features of scalp and intracranial Electroencephalogram (EEG) data recorded in clinics. EEG signals that aren't immediately evident are challenging to categories because of their complexity. The Gradient Boost Decision Tree (GBDT) classifier was used to classify the majority of the EEG signal segments automatically. According to this study, the Hurst exponent, in combination with AFA, is an efficient way to identify epileptic signals. As with any fractal analysis approach, there are problems and factors to keep in mind, such as identifying whether or not linear scaling areas are present. These signals were classified as either epileptic or non-epileptic by using a combination of GBDT and a Support Vector Regression (SVR). The combined method's identification accuracy was 98.23%. This study sheds light on the effectiveness of AFA feature extraction and GBDT classifiers in EEG classification. The findings can be utilized to develop theoretical guidance for the clinical identification and prediction of epileptic EEG signals. Doi: 10.28991/ESJ-2022-06-01-011 Full Text: PD

A Modified Gated Recurrent Unit Approach for Epileptic Electroencephalography Classification

Epilepsy is one of the most severe non-communicable brain disorders associated with sudden attacks. Electroencephalography (EEG), a non-invasive technique, records brain activities, and these recordings are routinely used for the clinical evaluation of epilepsy. EEG signal analysis for seizure identification relies on expert manual examination, which is labour-intensive, time-consuming, and prone to human error. To overcome these limitations, researchers have proposed machine learning and deep learning approaches. Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) have shown significant results in automating seizure prediction, but due to complex gated mechanisms and the storage of excessive redundant information, these approaches face slow convergence and a low learning rate. The proposed modified GRU approach includes an improved update gate unit that adjusts the update gate based on the output of the reset gate. By decreasing the amount of superfluous data in the reset gate, convergence is speeded, which improves both learning efficiency and the accuracy of epilepsy seizure prediction. The performance of the proposed approach is verified on a publicly available epileptic EEG dataset collected from the University of California, Irvine machine learning repository (UCI) in terms of performance metrics such as accuracy, precision, recall, and F1 score when it comes to diagnosing epileptic seizures. The proposed modified GRU has obtained 98.84% accuracy, 96.9% precision, 97.1 recall, and 97% F1 score. The performance results are significant because they could enhance the diagnosis and treatment of neurological disorders, leading to better patient outcomes

Classificação de sinais eletroencefalográficos utilizando Transformada Wavelet Discreta e Máquina de Vetores de Suporte: uma aplicação na diferenciação entre crises epilépticas e crises não epilépticas psicogênicas

O presente trabalho aborda o estudo e aplicação da Transformada Wavelet Discreta (DWT) em conjunto com o classificador do tipo Máquina de Vetores de Suporte (SVM) na diferenciação entre crises epilépticas e crises não epilépticas psicogênicas (CNEP). Um banco de dados com exames de eletroencefalograma (EEG) contendo crises epilépticas e crises não epilépticas psicogênicas foi coletado na Unidade de Videoeletroencefalografia do Instituto de Psiquiatria do Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo (IPq-HCFMUSP). No processamento do sinal EEG, foi utilizada a Transformada Wavelet Discreta (DWT) baseada nas famílias Coiflet 1 e Daubechies 4 e a extração direta do sinal (sem usar DWT). A partir desses processamentos, foram gerados vetores de características para o treinamento e avaliação do classificador SVM. Na análise do desempenho do classificador, foram realizados testes modificando-se o número de vetores de características para o treinamento do classificador, a origem do vetor de características (Coiflet 1, Daubechies 4 e extração direta) e o tipo de kernel (Linear, Polinomial, Função de Base Radial - RBF - e Sigmoide). Como resultado, no caso do emprego de janelas de 1 segundo no processamento do sinal EEG, o classificador foi capaz de atingir uma taxa de acerto (acurácia) de até 100% usando o kernel Linear e as famílias Coiflet 1 e Daubechies 4. No caso da utilização do tempo total de cada crise, o classificador obteve uma taxa de acerto de até 100% nos quatro tipo de kernel usando a família Coiflet 1. Desse modo, com base nos vetores de características utilizados, foi possível concluir que o classificador SVM é eficiente e o seu uso é viável na diferenciação entre crise epiléptica e CNEP.The present work deals with the study and application of the Discrete Wavelet Transform (DWT) in conjunction with the Supporting Vector Machine (SVM) classifier in the differentiation between epileptic seizures and psychogenic non-epileptic seizures (PNES). A database with electroencephalogram (EEG) tests containing epileptic seizures and psychogenic non-epileptic seizures was collected at the Videoelectroencephalography Unit of the Institute of Psychiatry of the Hospital das Clínicas of the Medical School of the University of São Paulo (IPq-HCFMUSP). In the EEG signal processing, the Wavelet Discrete Transform (DWT) based on the Coiflet 1 and Daubechies 4 families and the direct signal extraction (without DWT) were used. From these processing, characteristic vectors were generated for the training and evaluation of the SVM classifier. In the analysis of the performance of the classifier, tests were performed by modifying the number of characteristics vectors for the classifier training, the origin of the characteristic vector (Coiflet 1, Daubechies 4 and direct extraction) and the kernel type (Linear, Polynomial , Radial Base Function - RBF - and Sigmoid). As a result, in the case of the use of 1-second windows in the EEG signal processing, the classifier was able to achieve a hit rate (accuracy) of up to 100% using the Linear kernel and the Coiflet 1 and Daubechies 4 families. In the case of the use of the total time of each crisis, the classifier obtained a hit rate of up to 100% in the four kernel types using the Coiflet 1 family. Thus, based on the feature vectors used, it was possible to conclude that the classifier SVM is efficient and its use is feasible in the differentiation between epileptic seizures and PNES