Analyzing physical activity impact based on ubiquitous nonlinear dynamics and electroencephalography data

Article type: Research Article

Authors: Shukla, Prashant Kumar^{a; *} | Maheshwary, Priti^b | Kundu, Shakti^c | Mondal, Dipannita^d | Kumar, Ankit^e | Joshi, Shubham^f | Pareek, Piyush Kumar^g

Affiliations: [a] Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India | [b] Rabindranath Tagore University, Bhopal, Madhya Pradesh, India | [c] Directorate of Online Education, Manipal University Jaipur, Jaipur, Rajasthan, India | [d] Department of Artificial Intelligence and Data Science, Dr. D.Y. Patil College of Engineering and Innovation, Talegaon Dabhade, Maharashtra, India | [e] Department of Computer Engineering and Applications, GLA University Mathura, Uttar Pradesh, India | [f] Department of Computer Science Engineering, Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune, India | [g] Department of Artificial Intelligence and Machine Learning and Head, IPR Cell Nitte Meenakshi Institute of Technology, Bengaluru, Karnataka, India

Correspondence: [*] Corresponding author: Prashant Kumar Shukla, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur-522302, Andhra Pradesh, India. E-mail: prashantshukla2005@kluniversity.in.

Abstract: BACKGROUND: Understanding complex systems is made easier with the tools provided by the theory of nonlinear dynamic systems. It provides novel ideas, algorithms, and techniques for signal processing, analysis, and classification. Presently, these ideas are being applied to the investigation of how physiological signals evolve. OBJECTIVE: The study applies nonlinear dynamics theory to electroencephalogram (EEG) signals to better comprehend the range of alcoholic mental states. One of the main contributions of this paper is an algorithm for automatically distinguishing between sober and drunken EEG signals based on their salient features. METHODS: The study utilized various entropy-based features, including ApEn, SampEn, Shannon and Renyi entropies, PE, TS, FE, WE, and KSE, to extract information from EEG signals. To identify the most relevant features, the study employed ranking methods like T-test, Wilcoxon, and Bhattacharyya, and trained SVM classifiers with the selected features. The Bhattacharyya ranking method was found to be the most effective in achieving high classification accuracy, sensitivity, and specificity. RESULTS: Classification accuracy of 95.89%, the sensitivity of 94.43%, and specificity of 96.67% are achieved by the SVM classifier with radial basis function (RBF) for polynomial Kernel using the Bhattacharyya ranking method. CONCLUSION: From the result, it is clear that the model serves as a cost-effective and accurate decision-support tool for doctors in diagnosing alcoholism and for rehabilitation centres to monitor the effectiveness of interventions aimed at mitigating or reversing brain damage caused by alcoholism.

Keywords: EEG, ApEn, SampEn, PE, TS, FE, WE, KSE, SVM

DOI: 10.3233/THC-220771

Journal: Technology and Health Care, vol. 32, no. 3, pp. 1301-1312, 2024