INVESTIGATING THE VARIATIONS IN THE BRAIN ACTIVITY BETWEEN HEALTHY SUBJECTS AND MILD COGNITIVE IMPAIRMENT (MCI) PATIENTS-Reference-Cited by-同舟云学术

INVESTIGATING THE VARIATIONS IN THE BRAIN ACTIVITY BETWEEN HEALTHY SUBJECTS AND MILD COGNITIVE IMPAIRMENT (MCI) PATIENTS

Published:2023-01 Issue:09 Volume:31 Page:
ISSN:0218-348X
Container-title:Fractals
language:en
Short-container-title:Fractals

Author:

PAKNIYAT NAJMEH¹^ORCID,RAMAKRISHNAN BALAMURALI²^ORCID,PALLAVI V.³^ORCID,KREJCAR ONDREJ⁴⁵⁶^ORCID,FRISCHER ROBERT⁵^ORCID,NAMAZI HAMIDREZA⁵⁷^ORCID

Affiliation:

1. 30 Shore Breeze Drive, Toronto ON, Canada M8V 0J1, Canada

2. Centre for Nonlinear Systems, Chennai Institute of Technology, Chennai 600069, Tamil Nadu, India

3. Computer Science and Engineering, Vemu Institute of Technology, Tirupati-Chittoor Highway, P. Kothakota, Chittoor 517112, Andhra Pradesh, India

4. Center for Basic and Applied Research, Faculty of Informatics and Management, University of Hradec Kralove, Hradec Kralove, Czechia

5. Institute of Technology and Business in Ceske Budejovice, Ceske Budejovice, Czechia

6. Department of Biomedical Engineering and Measurement, Faculty of Mechanical Engineering Technical, University of Kosice, Slovakia

7. School of Engineering, Monash University, Selangor, Malaysia

Abstract

Analysis of brain activity for patients with brain disorders is an important research area. Mild cognitive impairment (MCI) is a condition in which patients have more memory or thinking problems compared to healthy people of the same age. In this work, we studied the alterations in brain activity among control subjects and patients with MCI. Three complexity techniques, namely sample entropy, approximate entropy, and fractal dimension, were employed to study electroencephalogram (EEG) signals recorded from 102 control (healthy) subjects, and seven subjects with MCI in a comfortable position, on a bed, with their eyes closed. The results showed that the EEG signals of patients with MCI show greater complexity than the EEG signals of healthy subjects. This analysis method can be applied to compare brain activity among healthy subjects and patients with other brain diseases.

Funder

Centre for Nonlinear Systems, Chennai Institute of Technology, India vide Funding

Publisher

World Scientific Pub Co Pte Ltd

Subject

Applied Mathematics,Geometry and Topology,Modeling and Simulation

Link

https://www.worldscientific.com/doi/pdf/10.1142/S0218348X23501244

Reference55 articles.

1. Epileptic Seizure Prediction Based on Hybrid Seek Optimization Tuned Ensemble Classifier Using EEG Signals

2. Combined EEG and immersive virtual reality unveil dopaminergic modulation of error monitoring in Parkinson’s Disease

3. Exploring high-density corticomuscular networks after stroke to enable a hybrid Brain-Computer Interface for hand motor rehabilitation

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