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ORIGINAL RESEARCH article

Front. Psychiatry
Sec. Autism
Volume 16 - 2025 | doi: 10.3389/fpsyt.2025.1505297

Entropy, Complexity and Spectral Features of EEG Signals in Autism and Typical Development: A Quantitative Approach

Provisionally accepted
Aleksandar Tenev Aleksandar Tenev 1*Silvana Markovska-Simoska Silvana Markovska-Simoska 2Andreas Müller Andreas Müller 3Igor Mishkovski Igor Mishkovski 1
  • 1 Faculty of Computer Science and Engineering, Saints Cyril and Methodius University of Skopje, Skopje, North Macedonia
  • 2 Macedonian Academy of Sciences and Arts, Skopje, Skopje, North Macedonia
  • 3 Brain and Trauma Foundation Grison/Switzerland, Chur, Switzerland

The final, formatted version of the article will be published soon.

    Autism spectrum disorder (ASD) is a neurodevelopmental condition that affects the brain's function. Electroencephalography (EEG) is a non-invasive technique that measures the electrical activity of the brain and can reveal its dynamics and information processing.This study explores an eyes-opened resting state quantitative EEG analysis of 49 children with ASD and 39 typically developing (TD or Control) children, using various features of entropy and complexity. Time and frequency domain features are applied for all EEG channels, such as the power spectra, brain rate, sample entropy, permutation entropy, spectral entropy, Tsallis entropy, Renyi entropy, Lempel-Ziv complexity, and Higuchi fractal dimension. The features are compared between the ASD and TD groups and tested for statistical significance. The results showed that the ASD group had a lower brain rate, higher Tsallis entropy and Renyi entropy and lower Lempel-Ziv complexity than the TD group. The entropy results show impaired neural synchronization, increased randomness and noise in ASD. Lempel-Ziv complexity results showed that it can be a potential indicator of focal spikes existence in the EEG signals of ASD. The brain-rate results show a low level of arousal in ASD. The findings suggest that entropy and complexity measures can be useful tools for characterizing the EEG features of ASD and provide insights into the neurophysiological mechanisms of the disorder.

    Keywords: entropy, Complexity, Brain-rate, autism, quantitative EEG

    Received: 02 Oct 2024; Accepted: 14 Jan 2025.

    Copyright: © 2025 Tenev, Markovska-Simoska, Müller and Mishkovski. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Aleksandar Tenev, Faculty of Computer Science and Engineering, Saints Cyril and Methodius University of Skopje, Skopje, North Macedonia

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.