Multiscale Fluctuation Dispersion Entropy of EEG as a Physiological Biomarker of Schizotypy

Ahmad Zandbagleh; Hamed Azami; Sattar Mirzakuchaki; Mohammad Reza Daliri; Saeid Sanei; Preethi Premkumar

doi:10.1109/ACCESS.2023.3321868

IEEE Access (Jan 2023)

Multiscale Fluctuation Dispersion Entropy of EEG as a Physiological Biomarker of Schizotypy

Ahmad Zandbagleh,
Hamed Azami,
Sattar Mirzakuchaki,
Mohammad Reza Daliri,
Saeid Sanei,
Preethi Premkumar

Affiliations

Ahmad Zandbagleh: ORCiD; School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
Hamed Azami: Centre for Addiction and Mental Health, University of Toronto, Toronto, ON, Canada
Sattar Mirzakuchaki: ORCiD; School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
Mohammad Reza Daliri: ORCiD; School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
Saeid Sanei: Electrical and Electronic Engineering Department, Imperial College London, London, U.K
Preethi Premkumar: Division of Psychology, School of Applied Sciences, London Southbank University, London, U.K

DOI: https://doi.org/10.1109/ACCESS.2023.3321868
Journal volume & issue: Vol. 11
pp. 110124 – 110135

Abstract

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Altered electroencephalography (EEG) activity in schizotypal individuals is a powerful indicator of proneness towards psychosis. This alteration is beyond decreased alpha power often measured in resting state EEG. Multiscale fluctuation dispersion entropy (MFDE) measures the non-linear complexity of the fluctuations of EEGs and is a more effective approach compared to the traditional linear power spectral density (PSD) measures of EEG activity in patients with neurodegenerative disorders. In this study, we applied MFDE to EEG signals to distinguish high schizotypy (HS) and low schizotypy (LS) individuals. The study includes several trials from 29 participants psychometrically classified as HS (n=19) and LS (n=10). After preprocessing, MFDE was computed in frontal, parietal, central, temporal and occipital regions for each participant at multiple time scales. Statistical analysis and machine learning algorithms were used to calculate the differences in MFDE measures between the HS and LS groups. Our findings revealed significant differences in MFDE measures between LS and HS individuals in the delta frequency band (at time scale 100 ms). HS individuals exhibited increased complexity and irregularity compared to LS individuals in the delta frequency band particularly in the occipital region. Furthermore, the MFDE measures resulted in high accuracy (96.55%) in discriminating between HS and LS individuals and outperformed the models based on power spectrum, demonstrating the potential of MFDE as a neurophysiological marker for schizotypy traits. The increased non-linear fluctuation in delta frequency band in the occipital region of HS individuals implies the changes in cognitive functions, such as memory and attention, and has significant potential as a biomarker for schizotypy and proneness towards psychosis.

Published in IEEE Access

ISSN: 2169-3536 (Online)
Publisher: IEEE
Country of publisher: United States
LCC subjects: Technology: Electrical engineering. Electronics. Nuclear engineering
Website: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639

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