AGE-BASED ANALYSIS OF THE BRAIN ACTIVITY DURING SLEEP INDUCED BY MEDICATION

Najmeh Pakniyat, Gayathri Vivekanandhan, Karthikeyan Rajagopal, Ondrej Krejcar, Kamil Kuca and Hamidreza Namazi
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Najmeh Pakniyat: 30 Shore Breeze Drive, Toronto, ON, Canada M8V 0J1, Canada
Gayathri Vivekanandhan: ��Centre for Artificial Intelligence, Chennai Institute of Technology, Chennai, India
Karthikeyan Rajagopal: ��Centre for Nonlinear Systems, Chennai Institute of Technology, Chennai, India
Ondrej Krejcar: �Center for Basic and Applied Research, Faculty of Informatics and Management, University of Hradec, Kralove, Hradec Kralove 50003, Czechia¶Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia Kuala Lumpur, Jalan Sultan, Yahya Petra, Kuala Lumpur 54100, Malaysia
Kamil Kuca: ��Department of Chemistry, Faculty of Science, University of Hradec Kralove, 50003 Hradec Kralove, Czechia**Biomedical Research Center, University, Hospital Hradec Kralove, Hradec Kralove, Czech Republic
Hamidreza Namazi: �Center for Basic and Applied Research, Faculty of Informatics and Management, University of Hradec, Kralove, Hradec Kralove 50003, Czechia††School of Engineering, Monash University, Selangor, Malaysia

FRACTALS (fractals), 2023, vol. 31, issue 01, 1-8

Abstract: One of the important areas of research in neuroscience is to investigate how brain activity changes during aging. In this research, we employ complexity techniques to analyze how brain activity changes based on the age of subjects during sleep. For this purpose, we analyze the Electroencephalogram (EEG) signals of 22 subjects induced by sleep medication using fractal theory and sample entropy. The analysis showed that the fractal dimension and sample entropy of EEG signals decrease due to aging. Therefore, we concluded that aging causes lower complexity in EEG signals during sleep. The employed method of analysis could be applied to analyze the effect of aging on the variations of the activity of other organs (e.g. heart, muscle) during aging by studying their related physiological signals (e.g. ECG, EMG).

Keywords: Age; Electroencephalogram (EEG) Signals; Complexity; Fractal Theory; Sample Entropy; Sleep (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1142/S0218348X23500111

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