Analysis of EEG signals for the detection of epileptic seizures using feature extraction

SciEnggJ Pub Date : 2024-02-13 DOI:10.54645/202417supcqp-72
Ravikumar Kandagatla, V. J. Naidu, P.S. Sreenivasa Reddy, Veera Kavya Pandilla, Marriwada Joshitha, Chanamala Rakesh
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Abstract

The electroencephalogram, which tracks electrical signals in the central nervous system, has been extensively used to diagnose epilepsy, which represents a particular sort of brain abnormality. However, developing seizure classification techniques with significantly better precision and reduced complexity remains challenging. The Epileptic Seizure Recognition dataset, which is publicly accessible in the Kaagle and in the machine learning repository, was used to identify seizures. To identify the seizure, we compared six classification methods to determine which one had the highest success rate. The dataset is subsequently divided, trained, and tested in order to categorize it further using six machine learning algorithms: Stochastic Gradient Descent, Logistic Regression, Naïve Bayes, K-Nearest Neighbors Algorithm, Extra Tree Classifier and Decision Tree. When contrasted with alternative techniques, Extra Trees Classifier possesses the highest accuracy results. The algorithm attained a 96 percent success rate.
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利用特征提取分析脑电信号以检测癫痫发作
脑电图可跟踪中枢神经系统中的电信号,已被广泛用于诊断癫痫,癫痫是一种特殊的脑部异常。然而,开发具有更高精度和更低复杂度的癫痫发作分类技术仍具有挑战性。癫痫发作识别数据集用于识别癫痫发作,该数据集可在 Kaagle 和机器学习资料库中公开访问。为了识别癫痫发作,我们比较了六种分类方法,以确定哪种方法的成功率最高。随后对数据集进行划分、训练和测试,以便使用六种机器学习算法进一步分类:随机梯度下降、逻辑回归、奈夫贝叶斯、K-近邻算法、额外树分类器和决策树。与其他技术相比,额外树分类器的准确率最高。该算法的成功率高达 96%。
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