Entropy, complexity, and spectral features of EEG signals in autism and typical development: a quantitative approach
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 r...
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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Psychiatry |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyt.2025.1505297/full |
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| author | Aleksandar Tenev Silvana Markovska-Simoska Andreas Müller Igor Mishkovski |
| author_facet | Aleksandar Tenev Silvana Markovska-Simoska Andreas Müller Igor Mishkovski |
| author_sort | Aleksandar Tenev |
| collection | DOAJ |
| description | 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 were applied for all EEG channels, such as the power spectra, brain rate, sample entropy, permutation entropy, spectral entropy, Tsallis entropy, Rényi entropy, Lempel–Ziv complexity, and Higuchi fractal dimension. The features were 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 Rényi entropy, and lower Lempel–Ziv complexity than the TD group. The entropy results show impaired neural synchronization, increased randomness, and noise in ASD. The Lempel–Ziv complexity results showed that it is a potential indicator of the existence of focal spikes 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. |
| format | Article |
| id | doaj-art-a0f619b9789142cb8342a306e81879b7 |
| institution | Kabale University |
| issn | 1664-0640 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychiatry |
| spelling | doaj-art-a0f619b9789142cb8342a306e81879b72025-02-04T06:31:49ZengFrontiers Media S.A.Frontiers in Psychiatry1664-06402025-02-011610.3389/fpsyt.2025.15052971505297Entropy, complexity, and spectral features of EEG signals in autism and typical development: a quantitative approachAleksandar Tenev0Silvana Markovska-Simoska1Andreas Müller2Igor Mishkovski3Faculty of Computer Science and Engineering, St Cyril and Methodius University of Skopje, Skopje, North MacedoniaDepartment of Neurophysiology, Macedonian Academy of Sciences and Arts, Skopje, North MacedoniaBrain and Trauma Foundation Grison/Switzerland, Chur, SwitzerlandFaculty of Computer Science and Engineering, St Cyril and Methodius University of Skopje, Skopje, North MacedoniaAutism 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 were applied for all EEG channels, such as the power spectra, brain rate, sample entropy, permutation entropy, spectral entropy, Tsallis entropy, Rényi entropy, Lempel–Ziv complexity, and Higuchi fractal dimension. The features were 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 Rényi entropy, and lower Lempel–Ziv complexity than the TD group. The entropy results show impaired neural synchronization, increased randomness, and noise in ASD. The Lempel–Ziv complexity results showed that it is a potential indicator of the existence of focal spikes 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.https://www.frontiersin.org/articles/10.3389/fpsyt.2025.1505297/fullentropycomplexitybrain-rateautismquantitative EEG |
| spellingShingle | Aleksandar Tenev Silvana Markovska-Simoska Andreas Müller Igor Mishkovski Entropy, complexity, and spectral features of EEG signals in autism and typical development: a quantitative approach Frontiers in Psychiatry entropy complexity brain-rate autism quantitative EEG |
| title | Entropy, complexity, and spectral features of EEG signals in autism and typical development: a quantitative approach |
| title_full | Entropy, complexity, and spectral features of EEG signals in autism and typical development: a quantitative approach |
| title_fullStr | Entropy, complexity, and spectral features of EEG signals in autism and typical development: a quantitative approach |
| title_full_unstemmed | Entropy, complexity, and spectral features of EEG signals in autism and typical development: a quantitative approach |
| title_short | Entropy, complexity, and spectral features of EEG signals in autism and typical development: a quantitative approach |
| title_sort | entropy complexity and spectral features of eeg signals in autism and typical development a quantitative approach |
| topic | entropy complexity brain-rate autism quantitative EEG |
| url | https://www.frontiersin.org/articles/10.3389/fpsyt.2025.1505297/full |
| work_keys_str_mv | AT aleksandartenev entropycomplexityandspectralfeaturesofeegsignalsinautismandtypicaldevelopmentaquantitativeapproach AT silvanamarkovskasimoska entropycomplexityandspectralfeaturesofeegsignalsinautismandtypicaldevelopmentaquantitativeapproach AT andreasmuller entropycomplexityandspectralfeaturesofeegsignalsinautismandtypicaldevelopmentaquantitativeapproach AT igormishkovski entropycomplexityandspectralfeaturesofeegsignalsinautismandtypicaldevelopmentaquantitativeapproach |