A Big-Data-Analytics Framework for Supporting Classification of ADHD and Healthy Children via Principal Component Analysis of EEG Sleep Spindles Power Spectra

Dea, Federica De; Ajčević, Miloš; Stecca, Matteo; Zanus, Caterina; Carrozzi, Marco; Cuzzocrea, Alfredo; Accardo, Agostino

doi:10.1016/j.procs.2019.09.329

Cited by 12 publications

(6 citation statements)

References 22 publications

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“…To remove artifacts caused by involuntary eye movements of the subject from a multi-channel EEG, a wide analysis of the main components is used. Siuly et al (2015) and Artoni et al (2018) and Dea et al (2019) and Polat et al (2008) and Putilov et al (2015) described this process in detail. The method of principal components consists of finding for the initial data such orthogonal transformation into a new coordinate system for which the following conditions are satisfied:  selective dispersion of data along the first coordinate is maximum;  sample dispersion of data along the kth coordinate is maximum under the condition of orthogonality to the first (k -1) coordinates.…”

Section: Principal Component Methodsmentioning

confidence: 99%

Review of EEG Feature Selection by Neural Networks

Rakhmatulin

2020

SSRN Journal

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The basis of the work of electroencephalography (EEG) is the registration of electrical impulses from the brain using a special sensor or electrode. This method is used to treat and diagnose various diseases. In the past few years, due to the development of neural network technologies, the interest of researchers in EEG has noticeably increased. Neural networks for training the model require obtaining data with minimal noise distortion. In the processing of EEG signals to eliminate noise (artifacts), signal filtering and various methods for extracting signs are used. The presented manuscript provides a detailed analysis of modern methods for extracting the signs of an EEG signal used in studies of the last decade. The information presented in this paper will allow researchers to understand how to more carefully process the data of EEG signals before using neural networks to classify the signal. Due to the absence of any standards in the method of extracting EEG signs, the most important moment of this manuscript is a detailed description of the necessary steps for recognizing artifacts, which will allow researchers to maximize the potential of neural networks in the tasks of classifying EEG signal.

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Section: Principal Component Methodsmentioning

confidence: 99%

Review of EEG Feature Selection by Neural Networks

Rakhmatulin

2020

SSRN Journal

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“…Using SVM and LASSO, 97% accuracy was attained with saccade length, frequency of fast forward movements, and several saccades method of repeatedly focused phrases. Using Reading-eye movements(𝑛 = 61), [17] an ambitious study in higher education was presented to distinguish between highly competent university students and underqualified pupils. Instead of using general eye movement metrics like mean fixation duration, the identification was predicated on subtle eye movement patterns connected to reading processes [18].…”

Section: Related Workmentioning

confidence: 99%

A Linear Regularized Normalized Model for Dyslexia and ADHD Prediction Using Learning Approaches

Pavan Kumar Varma Kothapalli

2023

IJRITCC

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A learning disability called dyslexia typically affects school-age kids. Children have trouble spelling, reading, and writing words. Children who experience this problem often struggle with negative emotions, rage, frustration, and low self-esteem. Consequently, a dyslexia predictor system is required to assist children in overcoming the risk. There are many current ways of predicting dyslexia. However, they need to provide higher prediction accuracy. Also, this work concentrates on another disorder known as Attention-Deficit Hyperactivity Disorder (ADHD). The prediction process is more challenging as the prediction process shows some negative consequences. The data is typically gathered from online resources for the prediction process. This study examines how the predictor model predicts dyslexia and ADHD using learning strategies. Here, the most important features for accurately classifying dyslexia, non-dyslexia and ADHD are extracted using a new Support Vector Machine (SVM) for feature selection based on the norm and norm. Based on the weighted values, the predicted model provides improved subset features from the internet-accessible dataset. The accuracy, precision, F1-score, specificity, sensitivity, and execution time are all examined here using 10-fold cross-validation. The maximum accuracy reached with this feature subset during the prediction process is carefully reviewed. The experiment results imply that the anticipated model is used to accurately predict the defect and as a tool for CDSS. Recently, dyslexia and ADHD prediction has been greatly aided by computer-based predictor systems. The expected model also effectively fits the experimental design, bridging the gap between feature selection and classification.

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“…In this case, the maximum amplitude of artifacts is observed in the frontal leads and decreases towards the occipital leads. In the next researches for [18,19,20] to remove artifacts caused by involuntary eye movements of the subject from a multi-channel EEG, a wide analysis of the main components is used. For these artifacts, it is very difficult to visually find regularity in the presented figures (Fig.…”

Section: Table 1 the Structure Of The Data File For Training -Data1 Testmentioning

confidence: 99%

Deep learning and machine learning for EEG signal processing on the example of recognizing the disease of alcoholism

Rakhmatulin

2021

Preprint

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Alcoholism is one of the most common diseases in the world. This type of substance abuse leads to mental and physical dependence on ethanol-containing drinks. Alcoholism is accompanied by progressive degradation of the personality and damage to the internal organs. Today still not exists a quick diagnosis method to detect this disease. This article presents the method for the quick and anonymous alcoholism diagnosis by neural networks. For this method, don't need any private information about the subject. For the implementation, we considered various algorithms of machine learning and deep neural networks. In detail analyzed the correlation of the signals from electrodes by neural networks. The wavelet transforms and the fast Fourier transform was considered. The manuscript demonstrates that the deep neural network which operates only with a dataset of EEG correlation signals can anonymously classify the alcoholic and control groups with high accuracy. On the one hand, this method will allow subjects to be tested for alcoholism without any personal data, which will not cause inconvenience or shame in the subject, and on the other hand, the subject will not be able to deceive specialists who diagnose the subject for the presence of the disease.

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A Big-Data-Analytics Framework for Supporting Classification of ADHD and Healthy Children via Principal Component Analysis of EEG Sleep Spindles Power Spectra

Cited by 12 publications

References 22 publications

Review of EEG Feature Selection by Neural Networks

Review of EEG Feature Selection by Neural Networks

A Linear Regularized Normalized Model for Dyslexia and ADHD Prediction Using Learning Approaches

Deep learning and machine learning for EEG signal processing on the example of recognizing the disease of alcoholism

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