Autism Detection in Children using Common Spatial Patterns of MEG Signals

Kasturi Barik; Katsumi Watanabe; Tetsu Hirosawa; Yuko Yoshimura; Mitsuru Kikuchi; Joydeep Bhattacharya; Goutam Saha

doi:10.1109/EMBC40787.2023.10340449

Autism Detection in Children using Common Spatial Patterns of MEG Signals

Kasturi Barik
, Katsumi Watanabe
, Tetsu Hirosawa
, Yuko Yoshimura
, Mitsuru Kikuchi
, Joydeep Bhattacharya
, Goutam Saha

Academy of Music

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

Abstract

Autism exhibits a wide range of developmental disabilities and is associated with aberrant anatomical and functional neural patterns. To detect autism in young children (4-7 years) in an automatic and non-invasive fashion, we have recorded magnetoencephalogram (MEG) signals from 30 autistic and 30 age-matched typically developing (TD) children. We have used a machine learning classification framework with common spatial pattern (CSP)-based logarithmic band power (LBP) features. When comparing the LBP feature to the conventional logarithmic variance (LV) spatial pattern, CSP + LBP (92.77%) has performed better than CSP + LV (90.66%) in the 1-100 Hz frequency range for distinguishing autistic children from TD children. In frequency band-wise analysis using our proposed method, the high gamma frequency band (50-100 Hz) has shown the highest classification accuracy (97.14%). Our findings reveal that the occipital lobe exhibits the most distinct spatial pattern in autistic children over the whole frequency range. This study shows that spatial brain activation patterns can be utilized as potential biomarkers of autism in young children. The improved performance signifies the clinical relevance of the work for autism detection using MEG signals.

Original language	English
Title of host publication	2023 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023 - Proceedings
Publisher	IEEE
Pages	1-4
Number of pages	4
ISBN (Electronic)	9798350324471
ISBN (Print)	9798350324488
DOIs	https://doi.org/10.1109/EMBC40787.2023.10340449
Publication status	Published - 24 Jul 2023
Event	45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023 - Sydney, Australia Duration: 24 Jul 2023 → 27 Jul 2023

Publication series

Name	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)	2375-7477
ISSN (Electronic)	2694-0604

Conference

Conference	45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023
Country/Territory	Australia
City	Sydney
Period	24/07/23 → 27/07/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Access to Document

10.1109/EMBC40787.2023.10340449

Cite this

Barik, K., Watanabe, K., Hirosawa, T., Yoshimura, Y., Kikuchi, M., Bhattacharya, J., & Saha, G. (2023). Autism Detection in Children using Common Spatial Patterns of MEG Signals. In 2023 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023 - Proceedings (pp. 1-4). (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). IEEE. https://doi.org/10.1109/EMBC40787.2023.10340449

Barik, Kasturi ; Watanabe, Katsumi ; Hirosawa, Tetsu et al. / Autism Detection in Children using Common Spatial Patterns of MEG Signals. 2023 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023 - Proceedings. IEEE, 2023. pp. 1-4 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

@inproceedings{d49db9c2e603431d825cd78ec79c46e4,

title = "Autism Detection in Children using Common Spatial Patterns of MEG Signals",

abstract = "Autism exhibits a wide range of developmental disabilities and is associated with aberrant anatomical and functional neural patterns. To detect autism in young children (4-7 years) in an automatic and non-invasive fashion, we have recorded magnetoencephalogram (MEG) signals from 30 autistic and 30 age-matched typically developing (TD) children. We have used a machine learning classification framework with common spatial pattern (CSP)-based logarithmic band power (LBP) features. When comparing the LBP feature to the conventional logarithmic variance (LV) spatial pattern, CSP + LBP (92.77\%) has performed better than CSP + LV (90.66\%) in the 1-100 Hz frequency range for distinguishing autistic children from TD children. In frequency band-wise analysis using our proposed method, the high gamma frequency band (50-100 Hz) has shown the highest classification accuracy (97.14\%). Our findings reveal that the occipital lobe exhibits the most distinct spatial pattern in autistic children over the whole frequency range. This study shows that spatial brain activation patterns can be utilized as potential biomarkers of autism in young children. The improved performance signifies the clinical relevance of the work for autism detection using MEG signals.",

author = "Kasturi Barik and Katsumi Watanabe and Tetsu Hirosawa and Yuko Yoshimura and Mitsuru Kikuchi and Joydeep Bhattacharya and Goutam Saha",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023 ; Conference date: 24-07-2023 Through 27-07-2023",

year = "2023",

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series = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",

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Barik, K, Watanabe, K, Hirosawa, T, Yoshimura, Y, Kikuchi, M, Bhattacharya, J & Saha, G 2023, Autism Detection in Children using Common Spatial Patterns of MEG Signals. in 2023 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023 - Proceedings. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, IEEE, pp. 1-4, 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023, Sydney, Australia, 24/07/23. https://doi.org/10.1109/EMBC40787.2023.10340449

Autism Detection in Children using Common Spatial Patterns of MEG Signals. / Barik, Kasturi; Watanabe, Katsumi; Hirosawa, Tetsu et al.
2023 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023 - Proceedings. IEEE, 2023. p. 1-4 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

TY - GEN

T1 - Autism Detection in Children using Common Spatial Patterns of MEG Signals

AU - Barik, Kasturi

AU - Watanabe, Katsumi

AU - Hirosawa, Tetsu

AU - Yoshimura, Yuko

AU - Kikuchi, Mitsuru

AU - Bhattacharya, Joydeep

AU - Saha, Goutam

PY - 2023/7/24

Y1 - 2023/7/24

N2 - Autism exhibits a wide range of developmental disabilities and is associated with aberrant anatomical and functional neural patterns. To detect autism in young children (4-7 years) in an automatic and non-invasive fashion, we have recorded magnetoencephalogram (MEG) signals from 30 autistic and 30 age-matched typically developing (TD) children. We have used a machine learning classification framework with common spatial pattern (CSP)-based logarithmic band power (LBP) features. When comparing the LBP feature to the conventional logarithmic variance (LV) spatial pattern, CSP + LBP (92.77%) has performed better than CSP + LV (90.66%) in the 1-100 Hz frequency range for distinguishing autistic children from TD children. In frequency band-wise analysis using our proposed method, the high gamma frequency band (50-100 Hz) has shown the highest classification accuracy (97.14%). Our findings reveal that the occipital lobe exhibits the most distinct spatial pattern in autistic children over the whole frequency range. This study shows that spatial brain activation patterns can be utilized as potential biomarkers of autism in young children. The improved performance signifies the clinical relevance of the work for autism detection using MEG signals.

AB - Autism exhibits a wide range of developmental disabilities and is associated with aberrant anatomical and functional neural patterns. To detect autism in young children (4-7 years) in an automatic and non-invasive fashion, we have recorded magnetoencephalogram (MEG) signals from 30 autistic and 30 age-matched typically developing (TD) children. We have used a machine learning classification framework with common spatial pattern (CSP)-based logarithmic band power (LBP) features. When comparing the LBP feature to the conventional logarithmic variance (LV) spatial pattern, CSP + LBP (92.77%) has performed better than CSP + LV (90.66%) in the 1-100 Hz frequency range for distinguishing autistic children from TD children. In frequency band-wise analysis using our proposed method, the high gamma frequency band (50-100 Hz) has shown the highest classification accuracy (97.14%). Our findings reveal that the occipital lobe exhibits the most distinct spatial pattern in autistic children over the whole frequency range. This study shows that spatial brain activation patterns can be utilized as potential biomarkers of autism in young children. The improved performance signifies the clinical relevance of the work for autism detection using MEG signals.

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DO - 10.1109/EMBC40787.2023.10340449

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SN - 9798350324488

T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

SP - 1

EP - 4

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PB - IEEE

T2 - 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023

Y2 - 24 July 2023 through 27 July 2023

ER -

Barik K, Watanabe K, Hirosawa T, Yoshimura Y, Kikuchi M, Bhattacharya J et al. Autism Detection in Children using Common Spatial Patterns of MEG Signals. In 2023 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Conference, EMBC 2023 - Proceedings. IEEE. 2023. p. 1-4. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). doi: 10.1109/EMBC40787.2023.10340449

Autism Detection in Children using Common Spatial Patterns of MEG Signals

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