Improvement in the automatic classification of Alzheimer’s disease using EEG after feature selection

Tavares, Guilherme; San-Martin, Rodrigo; Ianof, Jéssica Natuline; Anghinah, Renato; Fraga, Francisco J.

doi:10.1109/smc.2019.8914006

Cited by 9 publications

(6 citation statements)

References 27 publications

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“…The SVM has been employed widely in different classification and regression problems. The performance of the SVM is affected by the kernel function, which may be a linear, radial basis, sigmoid, or polynomial function [25] [45]. A library for support vector machines was used for classification, using the SVM-SVC (support vector classification, SVC) model with a linear function, C-SVC of cost 1.…”

Section: Machine Learning and Assessment Methodsmentioning

confidence: 99%

Enhancing EEG-Based Classification of Depression Patients Using Spatial Information

Jiang

Tang

et al. 2021

IEEE Trans. Neural Syst. Rehabil. Eng.

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Section: Machine Learning and Assessment Methodsmentioning

confidence: 99%

Enhancing EEG-Based Classification of Depression Patients Using Spatial Information

Jiang

Tang

et al. 2021

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…The confusion matrix is composed of two rows and two columns reporting the number of true positives (TP), false positives (FP), true negatives (TN), and false negatives (FN). True (T) and false (F) stand for the predicted result, while positives (P) and negatives (N) indicate the actual condition ( Tavares et al, 2019 ).…”

Section: Methodsmentioning

confidence: 99%

Functional Brain Networks in Mild Cognitive Impairment Based on Resting Electroencephalography Signals

Youssef

Xiao

Liu

et al. 2021

Front. Comput. Neurosci.

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The oscillatory patterns of electroencephalography (EEG), during resting states, are informative and helpful in understanding the functional states of brain network and their contribution to behavioral performances. The aim of this study is to characterize the functional brain network alterations in patients with amnestic mild cognitive impairment (aMCI). To this end, rsEEG signals were recorded before and after a cognitive task. Functional connectivity metrics were calculated using debiased weighted phase lag index (DWPLI). Topological features of the functional connectivity network were analyzed using both the classical graph approach and minimum spanning tree (MST) algorithm. Subsequently, the network and connectivity values together with Mini-Mental State Examination cognitive test were used as features to classify the participants. Results showed that: (1) across the pre-task condition, in the theta band, the aMCI group had a significantly lower global mean DWPLI than the control group; the functional connectivity patterns were different in the left hemisphere between two groups; the aMCI group showed significantly higher average clustering coefficient and the remarkably lower global efficiency than the control. (2) Analysis of graph measures under post-task resting state, unveiled that for the percentage change of post-task vs. pre-task in beta EEG, a significant increase in tree hierarchy was observed in aMCI group (2.41%) than in normal control (−3.89%); (3) Furthermore, the classification analysis of combined measures of functional connectivity, brain topology, and MMSE test showed improved accuracy compared to the single method, for which the connectivity patterns and graph metrics were used as separate inputs. The classification accuracy obtained for the case of post-task resting state was 87.2%, while the one achieved under pre-task resting state was found to be 77.7%. Therefore, the functional network alterations in aMCI patients were more prominent during the post-task resting state. This study suggests that the disintegration observed in MCI functional network during the resting states, preceding and following a task, might be possible biomarkers of cognitive dysfunction in aMCI patients.

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“…Some reports also suggest random forest classifier. According to it, subsets of training data set are randomly selected, decision trees are built with it, and to find out the class of an object, we have to aggregate the votes from the all decision tree (Tavares et al, 2019). Thus, in a random forest classifier, the training data are randomly subdivided, and with each sample data, the decision tree is constructed.…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

Epileptic seizure detection: a comparative study between deep and traditional machine learning techniques

2020

Journal of Integrative Neuroscience

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Electroencephalography is the recording of brain electrical activities that can be used to diagnose brain seizure disorders. By identifying brain activity patterns and their correspondence between symptoms and diseases, it is possible to give an accurate diagnosis and appropriate drug therapy to patients. This work aims to categorize electroencephalography signals on different channels' recordings for classifying and predicting epileptic seizures. The collection of the electroencephalography recordings contained in the dataset attributes 179 information and 11,500 instances. Instances are of five categories, where one is the symptoms of epilepsy seizure. We have used traditional, ensemble methods and deep machine learning techniques highlighting their performance for the epilepsy seizure detection task. One dimensional convolutional neural network, ensemble machine learning techniques like bagging, boosting (AdaBoost, gradient boosting, and XG boosting), and stacking is implemented. Traditional machine learning techniques such as decision tree, random forest, extra tree, ridge classifier, logistic regression, K-Nearest Neighbor, Naive Bayes (gaussian), and Kernel Support Vector Machine (polynomial, gaussian) are used for classifying and predicting epilepsy seizure. Before using ensemble and traditional techniques, we have preprocessed the data set using the Karl Pearson coefficient of correlation to eliminate irrelevant attributes. Further accuracy of classification and prediction of the classifiers are manipulated using k-fold crossvalidation methods and represent the Receiver Operating Characteristic Area Under the Curve for each classifier. After sorting and comparing algorithms, we have found the convolutional neural network and extra tree bagging classifiers to have better performance than all other ensemble and traditional classifiers.

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Improvement in the automatic classification of Alzheimer’s disease using EEG after feature selection

Cited by 9 publications

References 27 publications

Enhancing EEG-Based Classification of Depression Patients Using Spatial Information

Enhancing EEG-Based Classification of Depression Patients Using Spatial Information

Functional Brain Networks in Mild Cognitive Impairment Based on Resting Electroencephalography Signals

Epileptic seizure detection: a comparative study between deep and traditional machine learning techniques

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