What is the clinical evidence from this study?

Study design: Other. Population: Alcoholism (n=122). Intervention: LASSO regression with BDA-based EANN classifier vs. Conventional machine learning models (SVM, Random Forest, ANN, LSTM). Primary outcome: Classification accuracy.

September 13, 2024Scientific Reports15 citationsOpen Access

Detection of Alcoholic EEG signal using LASSO regression with metaheuristics algorithms based LSTM and enhanced artificial neural network classification algorithms

GMGowri Shankar ManivannanShri Dharmasthala Manjunatheswara College of Ayurveda and Hospital KMKalaiyarasi ManiSathyabama Institute of Science and Technology HRHarikumar RajaguruLNM Institute of Information Technology

Key Result

The proposed LASSO regression with BDA-based EANN classifier achieved a classification accuracy of 99.59% in detecting alcoholic EEG signals.

Structured PICO

Population

122 subjects (normal and alcoholic) whose EEG recordings were analyzed to detect alcoholism using deep learning models.

Intervention

LASSO regression for feature extraction, metaheuristic algorithms (PSO, BCHS, BDA) for feature selection, and LSTM/EANN for classification.

Comparator

Traditional machine learning methods and other deep neural network-based solutions (SVM, random forests, ANN).

Outcome

Classification accuracy of alcoholic EEG signals.

The proposed LASSO regression with BDA-based EANN model achieved 99.59% accuracy in classifying alcoholic EEG signals, offering a highly accurate automated detection method.

Abstract

The world has a higher count of death rates as a result of Alcohol consumption. Identification is possible because Alcoholic EEG waves have a certain behavior that is totally different compared to the non-alcoholic individual. The available approaches take longer to provide the feedback because they analyze the data manually. For this reason, in the present paper we propose a novel approach applied to detect alcoholic EEG signals automatically by using deep learning methods. Our strategy has advantages as far as fast detection is concerned; hence people can help immediately when there is a need. The potential for a significant decrease in deaths from alcohol poisoning and improvement to public health is presented by this advancement. In order to create clusters and classify the alcoholic EEG signals, this research uses a cascaded process. To begin with, an initial clustering and feature extraction is done by LASSO regression. After that, a variety of meta-heuristics algorithms like Particle Swarm Optimization (PSO), Binary Coding Harmony Search (BCHS) as well as Binary Dragonfly Algorithm (BDA) are employed for feature minimization. When this method is used, normal and alcoholic EEG signals may be differentiated using non-linear features. PSO, BCHS, and BDA features allow for estimation of statistical parameters through t-test, Friedman statistic test, Mann-Whitney U test, and Z-Score with corresponding p-values for alcoholic EEG signals. Lastly, classification is done by the use of support vector machines (SVM) (including linear, polynomial, and Gaussian kernels), random forests, artificial neural networks (ANN), enhanced artificial neural networks (EANN), and LSTM models. Results showed that LASSO regression with BDA-based EANN proposed classifier have a classification accuracy of 99.59%, indicating that our method is highly accurate at classifying alcoholic EEG signals.

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