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International Conference on Intelligent Decision Technologies

IDT 2017: Intelligent Decision Technologies 2017 pp 13–21Cite as

Patients’ EEG Data Analysis via Spectrogram Image with a Convolution Neural Network

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Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 72))

Abstract

Electroencephalogram (EEG) recording is relatively safe for the patients who are in deep coma or quasi brain death, so it is often used to verify the diagnosis of brain death in clinical practice. The objective of this paper is to apply deep learning method to EEG signal analysis in order to confirm clinical brain death diagnosis. A novel approach using spectrogram images produced from EEG signals as the input dataset of Convolution Neural Network (CNN) is proposed in this paper. A deep CNN was trained to obtain the similarity degree of the patients’ EEG signals with the clinical diagnosed symptoms. This method can evaluate the condition of the brain damage patients and can be a reliable reference of quasi brain death diagnosis.

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Author information

Authors and Affiliations

  1. Graduate School of Engineering, Saitama Institute of Technology, Fusaiji 1690, Fukaya-shi, Saitama, 3690293, Japan

    Longhao Yuan & Jianting Cao

Authors
  1. Longhao Yuan
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  2. Jianting Cao
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Correspondence to Jianting Cao .

Editor information

Editors and Affiliations

  1. Maritime University , Gdynia, Poland

    Ireneusz Czarnowski

  2. Bournemouth University and KES International, Poole, Dorset, United Kingdom

    Robert J. Howlett

  3. University of Canberra, Canberra, Aust Capital Terr, Australia

    Lakhmi C. Jain

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Yuan, L., Cao, J. (2018). Patients’ EEG Data Analysis via Spectrogram Image with a Convolution Neural Network. In: Czarnowski, I., Howlett, R., Jain, L. (eds) Intelligent Decision Technologies 2017. IDT 2017. Smart Innovation, Systems and Technologies, vol 72. Springer, Cham. https://doi.org/10.1007/978-3-319-59421-7_2

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  • DOI: https://doi.org/10.1007/978-3-319-59421-7_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59420-0

  • Online ISBN: 978-3-319-59421-7

  • eBook Packages: EngineeringEngineering (R0)

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