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Alcoholic EEG signal classification with Correlation Dimension based distance metrics approach and Modified Adaboost classification

机译：含酒精EEG信号分类基于相关维度距离指标方法和修改的Adaboost分类

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摘要

The basic function of the brain is severely affected by alcoholism. For the easy depiction and assessment of the mental condition of a human brain, Electroencephalography (EEG) signals are highly useful as it can record and measure the electrical activities of the brain much to the satisfaction of doctors and researchers. Utilizing the standard conventional techniques is quite hectic to derive the useful information as these signals are highly non-linear and non-stationary in nature. While recording the EEG signals, the activities of the neurons are recorded from various scalp regions which has varied characteristics and has a very low magnitude. Therefore, human interpretation of such signals is very difficult and consumes a lot of time. Hence, with the advent of Computer Aided Diagnosis (CAD) Techniques, identifying the normal versus alcoholic EEG signals has been of great utility in the medical field. In this work, we perform the initial clustering of the alcoholic EEG signals by means of using Correlation Dimension (CD) for easy feature extraction and then the suitable features are selected in it by means of employing various distance metrics like correlation distance, city block distance, cosine distance and chebyshev distance. Proceeding in such a methodology aids and assures that a good discrimination could be achieved between normal and alcoholic EEG signals using non-linear features. Finally, classification is then carried out with the suitable classifiers chosen such as Adaboost.RT classifier, the proposed Modified Adaboost.RT classifier by means of introducing Ridge and Lasso based soft thresholding technique, Random Forest with bootstrap resampling technique, Artificial Neural Networks (ANN) such as Radial Basis Functions (RBF) and Multi-Layer Perceptron (MLP), Support Vector Machine (SVM) with Linear, Polynomial and RBF Kernel, Naïve Bayesian Classifier (NBC), K-means classifier, and K Nearest Neighbor (KNN) Classifier and the results are analyzed. Results report a comparatively high classification accuracy of about 98.99% when correlation distance metrics are utilized with CD and the proposed Modified Adaboost.RT classifier using Ridge based soft thresholding technique.
机译：大脑的基本功能受到酗酒的严重影响。对于易于描述和评估人脑的精神状况，脑电图（EEG）信号非常有用，因为它可以记录和测量大脑的电气活动，以满足医生和研究人员的满足。利用标准的传统技术非常忙于导出有用的信息，因为这些信号是高度非线性和非静止的。在记录EEG信号时，从各种头皮区域记录神经元的活动，其具有多种特性并且具有非常低的幅度。因此，对这种信号的人类解释非常困难并且消耗很多时间。因此，随着计算机辅助诊断（CAD）技术的出现，鉴定正常与酒精脑电图信号在医学领域已经存在很大的效用。在这项工作中，我们通过使用相关尺寸（CD）来执行醇类EEG信号的初始聚类，以便于特征提取，然后通过采用相关距离等各种距离度量，城市块距离选择合适的特征，余弦距离和Chebyshev距离。在这种方法中进行并确保可以使用非线性特征在正常和酒精EEG信号之间实现良好的歧视。最后，使用诸如Adaboost.rt分类器的合适分类器进行分类，所提出的修改的adaboost.rt分类器通过引入脊和套索的软阈值技术，随机森林，具有自动射门重采采样技术，人工神经网络（ANN ）如径向基函数（RBF）和多层的Perceptron（MLP），带有线性，多项式和RBF内核，Naïve贝叶斯分类器（NBC），K-Means分类器和K最近邻居（KNN ）分类器和结果进行了分析。结果报告使用脊基软阈值技术的相关距离度量和建议的修改adaboost.rt分类器，报告相对高的分类精度约为98.99％。

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