Prabhakar Sunil Kumar, Lee Jae Jun, Won Dong-Ok
Department of Artificial Intelligence Convergence, Hallym University, Chuncheon 24252, Republic of Korea.
Department of Anesthesiology and Pain Medicine, Hallym University College of Medicine, Chuncheon 24252, Republic of Korea.
Bioengineering (Basel). 2024 Sep 29;11(10):986. doi: 10.3390/bioengineering11100986.
Electroencephalography (EEG) helps to assess the electrical activities of the brain so that the neuronal activities of the brain are captured effectively. EEG is used to analyze many neurological disorders, as it serves as a low-cost equipment. To diagnose and treat every neurological disorder, lengthy EEG signals are needed, and different machine learning and deep learning techniques have been developed so that the EEG signals could be classified automatically. In this work, five ensemble models are proposed for EEG signal classification, and the main neurological disorder analyzed in this paper is epilepsy. The first proposed ensemble technique utilizes an equidistant assessment and ranking determination mode with the proposed Enhance the Sum of Connection and Distance (ESCD)-based feature selection technique for the classification of EEG signals; the second proposed ensemble technique utilizes the concept of Infinite Independent Component Analysis (I-ICA) and multiple classifiers with majority voting concept; the third proposed ensemble technique utilizes the concept of Genetic Algorithm (GA)-based feature selection technique and bagging Support Vector Machine (SVM)-based classification model. The fourth proposed ensemble technique utilizes the concept of Hilbert Huang Transform (HHT) and multiple classifiers with GA-based multiparameter optimization, and the fifth proposed ensemble technique utilizes the concept of Factor analysis with Ensemble layer K nearest neighbor (KNN) classifier. The best results are obtained when the Ensemble hybrid model using the equidistant assessment and ranking determination method with the proposed ESCD-based feature selection technique and Support Vector Machine (SVM) classifier is utilized, achieving a classification accuracy of 89.98%.
脑电图（EEG）有助于评估大脑的电活动，从而有效地捕捉大脑的神经元活动。EEG作为一种低成本设备，用于分析多种神经系统疾病。为了诊断和治疗各种神经系统疾病，需要长时间的EEG信号，并且已经开发了不同的机器学习和深度学习技术，以便能够自动对EEG信号进行分类。在这项工作中，提出了五种集成模型用于EEG信号分类，本文分析的主要神经系统疾病是癫痫。第一种提出的集成技术利用等距评估和排名确定模式以及基于增强连接和距离之和（ESCD）的特征选择技术对EEG信号进行分类；第二种提出的集成技术利用无限独立成分分析（I-ICA）的概念和具有多数投票概念的多个分类器；第三种提出的集成技术利用基于遗传算法（GA）的特征选择技术和基于袋装支持向量机（SVM）的分类模型的概念。第四种提出的集成技术利用希尔伯特黄变换（HHT）的概念和具有基于GA的多参数优化的多个分类器，第五种提出的集成技术利用具有集成层K近邻（KNN）分类器的因子分析概念。当使用基于等距评估和排名确定方法以及基于ESCD的特征选择技术和支持向量机（SVM）分类器的集成混合模型时，可获得最佳结果，分类准确率达到89.98%。
