What is it about?
In this research, a Principal Component Analysis (PCA) with Genetic Algorithm based Machine Learning (ML) approach is developed for the binary classification of epileptic seizures from the EEG dataset. The proposed approach utilizes PCA to reduce the number of features for binary classification of epileptic seizures and is applied to the existing machine learning models to evaluate the model performance in comparison to the higher number of features. Here, Genetic Algorithm (GA) is employed to tune the hyperparameters of the machine learning models for identifying the best ML model. The proposed approach is applied to the UCI epileptic seizure recognition dataset, which is originated from the EEG dataset of Bonn University. As a preliminary analysis of the proposed approach, the data analysis result shows a significant reduction in the number of features but has minimal impact on the ML performance parameters in comparison to the existing ML method.
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Why is it important?
Epilepsy is a central nervous system (CNS) disorder. About 1.2% (3.4 million people) in the US and more than 65 million people globally are experiencing this. Remarkably, it is true that about 1 in 26 people develop epilepsy during their lifetime. Though the symptoms of seizures diverge from person to person, a few common of them are jerking movements, losing consciousness, blank staring for a brief period of time, and confusion. People who are suffering from epileptic seizures can injure themselves by sudden falling, biting of the tongue, and so on. Eventually, the situation can lead to an unexpected phase of losing control of urine or stool. The symptom disparity in the exposition of epileptic seizures makes it hard to detect visually even with advanced technology. For this reason, researchers have tried to discover epileptic seizures and showed their motives over the past few years.
Perspectives
My perspective is, therefore, to develop a machine learning approach for the detection of epileptic seizures from the EEG dataset. Although an increasing number of features develop the model capable enough to provide a higher detection rate, the drawback of this approach is the lack of efficiency and performance in real-time. Hence, in the proposed ML approach Principal Component Analysis (PCA) is performed to reduce the number of features in the EEG dataset for binary classification of an epileptic seizure. The proposed approach is applied to available machine learning models such as K-Nearest Neighbors (KNN), Logistic Regression (LR), Stochastic Gradient Descent (SGD), Naive Bayes (NB), Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), Extra Trees Classifier (ETC), and eXtreme Gradient Boosting (XGBoost). The performance parameters such as AUC, accuracy, precision, recall, specificity, and Prevalence Rate (PR) are calculated to analyze the performance of the proposed ML approach in comparison to the existing ML method. Further, GA is applied to tune the hyperparameters for identifying the best ML model. The proposed approach is applied in the EEG dataset from Bonn University and results show that the performance of the proposed ML approach with the reduced feature is close to the existing ML model with a higher number of features.
Md Khurram Monir Rabby
Read the Original
This page is a summary of: Epileptic seizures classification in EEG using PCA based genetic algorithm through machine learning, April 2021, ACM (Association for Computing Machinery),
DOI: 10.1145/3409334.3452065.
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Resources
Epileptic Seizures Classification in EEG Using PCA Based Genetic Algorithm Through Machine Learning
In this research, a Principal Component Analysis (PCA) with Genetic Algorithm based Machine Learning (ML) approach is developed for the binary classification of epileptic seizures from the EEG dataset. The proposed approach utilizes PCA to reduce the number of features for binary classification of epileptic seizures and is applied to the existing machine learning models to evaluate the model performance in comparison to the higher number of features. Here, Genetic Algorithm (GA) is employed to tune the hyperparameters of the machine learning models for identifying the best ML model. The proposed approach is applied to the UCI epileptic seizure recognition dataset, which is originated from the EEG dataset of Bonn University. As a preliminary analysis of the proposed approach, the data analysis result shows a significant reduction in the number of features but has minimal impact on the ML performance parameters in comparison to the existing ML method.
Wavelet Transform-based Feature Extraction Approach for Epileptic Seizure Classification
In this research, a wavelet transform-based feature extraction approach is proposed for the detection of epileptic seizures from the EEG raw dataset. The proposed approach uses the Wavelet Transform (WT) method to divide the seizure and non-seizure classes of signals into multiple sub-bands and extracts the features of the dataset following Petrosian Fractal Dimension (PFD), Higuchi Fractal Dimension (HFD), and Singular Value Decomposition Entropy (SVDE). The Kruskal-Wallis test is performed to determine the difference in the random sampling and the extracted features are leveraged to divide the dataset into the training and testing sets for developing the model in order to train the network. The proposed approach is applied to the EEG dataset of Bonn University. Hence, the Neural Network (NN), Artificial Neural Network (ANN), Support Vector Machine (SVM), and Convolutional Neural Network (CNN) are used as preliminary models in the proposed approach for training the networks. As a preliminary analysis of the proposed approach, the training and testing Area Under the Curve (AUC) is calculated in the Receiver Operating Characteristic (ROC) curve to measure the performances of the existing models. The primary results show that, in the proposed approach, the performance of ANN is better than NN, SVM, and CNN.
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