Improving Alcoholism Diagnosis: Comparing Instance-Based Classifiers Against Neural Networks for Classifying EEG Signal

Rahman, Shelia; Sharma, Tanusree; Mahmud, Mufti

doi:10.1007/978-3-030-59277-6_22

Cited by 24 publications

(7 citation statements)

References 17 publications

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“…A Principal Component Analysis (PCA)-based dimensionality reduction was performed on the feature vector and then the reduced vector was fed to a fuzzy inference system for the classification of alcoholic and nonalcoholic cases. Rahman et al (2020) assessed the effects of dimension reduction techniques on the classification performances of both traditional ML and Deep Learning (DL) methods in identifying alcoholic cases. The authors concluded that the PCA technique achieves interesting results when used with a DL method.…”

Section: Related Workmentioning

confidence: 99%

Discrete to Continuous Algorithm for Optimal Channel Selection to Detect Alcoholism through EEG Recordings

Sedrati¹,

Rhalem²,

Aqili³

et al. 2023

Journal of Computer Science

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Alcoholism is a serious public health issue, and early diagnosis of this brain disease can be performed by analyzing Electroencephalogram (EEG) signals. However, the high dimensionality of EEG datasets requires significant computational time and resources for the automatic processing of EEG signals. This study proposes a novel method to reduce EEG dataset dimensionality using a Discrete to Continuous algorithm (DtC) by selecting optimal EEG channels. The DtC approach compares alcoholic and nonalcoholic EEG signals as two-time series in a two-dimensional space based on a distance measurement between the two-time series. The Dynamic Time Warping (DTW) algorithm is used to compare the performance of the DtC approach. Classification performance metrics were evaluated for both the DtC and DTW algorithms. The optimal selected channels by our approach are the C3, CP5, PO7, and F8 channels with accuracy values of 100, 100, 94 and 81%, respectively. These findings are consistent with previous research on statistical analysis and machine learning methods and with the DTW algorithm results. Our findings are also in line with scientific evidence from clinical research. The DtC approach was efficient in selecting the best channels to reduce the EEG dataset dimensionality, allowing us to select four out of the 64 EEG channels (C3, CP5, PO7, and F8) that retain essential information related to alcoholism, which is useful in reducing computational time and resources during the classification task of alcoholic EEG.

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Section: Related Workmentioning

confidence: 99%

Discrete to Continuous Algorithm for Optimal Channel Selection to Detect Alcoholism through EEG Recordings

Sedrati¹,

Rhalem²,

Aqili³

et al. 2023

Journal of Computer Science

View full text Add to dashboard Cite

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“…The classification was then performed using ensemble extreme learning machines based on linear discriminant analysis (LDA). Rahman et al [ 32 ] have shown that ICA performed better in the instance-based learning method, KNN, while PCA had better results when used with a deep learning (bidirectional long short-term memory) model. Thus, one must carefully choose a feature selection method with the type of classification method adapted.…”

Section: Background and Related Workmentioning

confidence: 99%

Deep Convolutional Neural Network Regularization for Alcoholism Detection Using EEG Signals

Mukhtar

Qaisar

Zaguia

2021

Sensors

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Alcoholism is attributed to regular or excessive drinking of alcohol and leads to the disturbance of the neuronal system in the human brain. This results in certain malfunctioning of neurons that can be detected by an electroencephalogram (EEG) using several electrodes on a human skull at appropriate positions. It is of great interest to be able to classify an EEG activity as that of a normal person or an alcoholic person using data from the minimum possible electrodes (or channels). Due to the complex nature of EEG signals, accurate classification of alcoholism using only a small dataset is a challenging task. Artificial neural networks, specifically convolutional neural networks (CNNs), provide efficient and accurate results in various pattern-based classification problems. In this work, we apply CNN on raw EEG data and demonstrate how we achieved 98% average accuracy by optimizing a baseline CNN model and outperforming its results in a range of performance evaluation metrics on the University of California at Irvine Machine Learning (UCI-ML) EEG dataset. This article explains the stepwise improvement of the baseline model using the dropout, batch normalization, and kernel regularization techniques and provides a comparison of the two models that can be beneficial for aspiring practitioners who aim to develop similar classification models in CNN. A performance comparison is also provided with other approaches using the same dataset.

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“…With the recent advancement of computing and better understanding of artificial intelligence (AI), various types of rule base [12]- [15], bio/brain-inspired [16] and machine learning (ML) approaches [17], [18] have acquired unrivalled concentration of the researchers in the last decade for the biological and healthcare big data mining [17], disease prediction and detection [19]- [23], anomaly detection [24]- [27], personalized treatment planning for risk prediction [28]- [30], clinical decision support system [31], [32], text processing [33], [34], disease management [14], [35] and mobile health based app [36]- [38]. During this COVID-19 outbreak, AI and ML have also been used in infection detection, self-testing and spread prevention in home, clinic and office settings.…”

Section: Introductionmentioning

confidence: 99%

iWorksafe: Towards Healthy Workplaces During COVID-19 With an Intelligent Phealth App for Industrial Settings

et al. 2021

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The recent outbreak of the novel Coronavirus Disease (COVID-19) has given rise to diverse health issues due to its high transmission rate and limited treatment options. Almost the whole world, at some point of time, was placed in lock-down in an attempt to stop the spread of the virus, with resulting psychological and economic sequela. As countries start to ease lock-down measures and reopen industries, ensuring a healthy workplace for employees has become imperative. Thus, this paper presents a mobile app-based intelligent portable healthcare (pHealth) tool, called iWorkSafe, to assist industries in detecting possible suspects for COVID-19 infection among their employees who may need primary care. Developed mainly for low-end Android devices, the iWorkSafe app hosts a fuzzy neural network model that integrates data of employees' health status from the industry's database, proximity and contact tracing data from the mobile devices, and user-reported COVID-19 self-test data. Using the built-in Bluetooth low energy sensing technology and K Nearest Neighbor and K-means techniques, the app is capable of tracking users' proximity and trace contact with other employees. Additionally, it uses a logistic regression model to calculate the COVID-19 self-test score and a Bayesian Decision Tree model for checking real-time health condition from intelligent e-health platform for further clinical attention of the employees. Rolled out in an apparel factory on 12 employees as a test case, the pHealth tool generates an alert to maintain social distancing among employees inside the industry. In addition, the app helps employees to estimate risk with possible COVID-19 infection based on the collected data and found that the score is effective in estimating personal health condition of the app user.INDEX TERMS Industry 4.0, artificial intelligence, machine learning, mobile app, digital health, safe workplace, worker safety, Coronavirus.

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Improving Alcoholism Diagnosis: Comparing Instance-Based Classifiers Against Neural Networks for Classifying EEG Signal

Cited by 24 publications

References 17 publications

Discrete to Continuous Algorithm for Optimal Channel Selection to Detect Alcoholism through EEG Recordings

Discrete to Continuous Algorithm for Optimal Channel Selection to Detect Alcoholism through EEG Recordings

Deep Convolutional Neural Network Regularization for Alcoholism Detection Using EEG Signals

iWorksafe: Towards Healthy Workplaces During COVID-19 With an Intelligent Phealth App for Industrial Settings

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