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

Abstract

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.

Machine learning with neuroimaging biomarkers: Application in the diagnosis and prediction of drug addiction

“…Adaptive Boosting (AdaBoost), decision tree and linear discriminant function (LDF) are also exploited to recognize drug addicts. [57][58][59][60] Among these classifying models, ML should be adaptive to data properties according to research requirements. For example, relative to RF, decision tree is better at handling small and low-dimensional brain imaging data with higher processing speed.…”

“…58 As an emerging machine, diverse EEG features should be entered into modified Adaboost to compare classifying abilities between each other for improving the capacity to diagnose addiction. 58 As a part of RF, decision tree with an intelligible and simple logic structure has no strict restriction on data. 107 In one fMRI study, decision tree regarded the number of subjects in each functional network cluster generated by k-means as features to pick out MA abstainers.…”

“…Other machines of supervised learning like k‐nearest neighbor (k‐NN), Adaptive Boosting (AdaBoost), decision tree and linear discriminant function (LDF) are also exploited to recognize drug addicts 57–60 . Among these classifying models, ML should be adaptive to data properties according to research requirements.…”

“…AdaBoost is an efficient and practical boosting algorithm for classification and gives a new way to design learning machines through ensembling feeble classifiers 106 . Ridge/Lasso‐modified Adaboost classifiers relying on soft thresholding took advantage of EEG features obtained by the correlation dimension‐based distance metrics method and acquired the highest accuracy in the identification of alcohol risk level relative to kinds of classifiers such as RF and SVM 58 . As an emerging machine, diverse EEG features should be entered into modified Adaboost to compare classifying abilities between each other for improving the capacity to diagnose addiction 58 …”

Performance Analysis of Ovarian Cancer Detection and Classification for Microarray Gene Data

“…Choosing features is thus an important stage in developing a machine learning model. Its aim is to find the optimal features for a machine learning model [60].…”