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Coherence Matrix Based Early Infantile Epileptic Encephalopathy Analysis with ResNet

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Cognitive Systems and Information Processing (ICCSIP 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1787))

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Abstract

EIEE syndrome, known as early infantile epileptic encephalopathy, is considered to be the earliest onset form of age-dependent epileptic encephalopathy. The main manifestations are tonic-spasmodic seizures in early infancy, accompanied by burst suppressive electroencephalogram (EEG) patterns and severe psychomotor disturbances, with structural brain lesions in some cases. Specific to EIEE syndrome, this paper presents a comprehensive analysis of EEG features at three different periods: pre-seizure, seizure and post-seizure. Coherent features are extracted to characterize EEG signals in EIEE syndrome, and Kruskal-Wallis H Test and Gradient-weighted Class Activation Map** (Grad-CAM) are used to investigate and visualize the significance of features in different frequency band for distinguishing the three stages. The study found that activity synchrony between temporal and central regions decreased significantly in the \(\gamma \) band during seizures. And the coherence feature in the \(\gamma \) band combined with the ResNet18-based seizure detection model achieved an accuracy of 91.86%. It is believed that changes in the \(\gamma \) band can be considered as a biomarker of seizure cycle changes in EIEE syndrome.

This work was supported by the National Natural Science Foundation of China (U1909209), the National Key Research and Development Program of China (2021YFE0100100, 2021YFE0205400), the Natural Science Key Foundation of Zhejiang Province (LZ22F030002), and the Research Funding of Education of Zhejiang Province (GK228810299201).

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Authors and Affiliations

  1. Machine Learning and I-Health International Cooperation Base of Zhejiang Province, Hangzhou Dianzi University, Hangzhou, China

    Yaohui Chen, ** Wang & Jiuwen Cao

  2. Research Center for Intelligent Sensing, Zhejiang Lab, Hangzhou, China

    Jiuwen Cao

  3. Department of Neurology, The Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, 310003, China

    Tiejia Jiang & Feng Gao

  4. Plateforme d’Etude de la Sensorimotricité (PES), BioMedTech Facilities, Université Paris Cité, 75270, Paris, France

    Dan** Wang

Authors
  1. Yaohui Chen
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  2. **aonan Cui
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  3. Runze Zheng
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  4. Yuanmeng Feng
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  5. Tiejia Jiang
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  6. Feng Gao
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  7. Dan** Wang
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  8. Jiuwen Cao
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Correspondence to Jiuwen Cao .

Editor information

Editors and Affiliations

  1. Tsinghua University, Bei**g, China

    Fuchun Sun

  2. University of Manchester, Manchester, UK

    Angelo Cangelosi

  3. Universität Hamburg, Hamburg, Germany

    Jianwei Zhang

  4. Fuzhou University, Fuzhou, China

    Yuanlong Yu

  5. Tsinghua University, Bei**g, China

    Hua** Liu

  6. Tsinghua University, Bei**g, China

    Bin Fang

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This study has been approved by the Second Affiliated Hospital of Zhejiang University and registered in Chinese Clinical Trail Registry (ChiCTR1900020726). All patients gave their informed consent prior to their inclusion in the study.

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Chen, Y. et al. (2023). Coherence Matrix Based Early Infantile Epileptic Encephalopathy Analysis with ResNet. In: Sun, F., Cangelosi, A., Zhang, J., Yu, Y., Liu, H., Fang, B. (eds) Cognitive Systems and Information Processing. ICCSIP 2022. Communications in Computer and Information Science, vol 1787. Springer, Singapore. https://doi.org/10.1007/978-981-99-0617-8_7

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  • DOI: https://doi.org/10.1007/978-981-99-0617-8_7

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-0616-1

  • Online ISBN: 978-981-99-0617-8

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