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Deep Learning in Classifying Depth of Anesthesia (DoA)

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Intelligent Systems and Applications (IntelliSys 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 868))

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Abstract

This present study is what we think is one of the first studies to apply Deep Learning to learn depth of anesthesia (DoA) levels based solely on the raw EEG signal from a single channel (electrode) originated from many subjects under full anesthesia. The application of Deep Neural Networks to detect levels of Anesthesia from Electroencephalogram (EEG) is relatively new field and has not been addressed extensively in current researches as done with other fields. The peculiarities of the study emerges from not using any type of pre-processing at all which is usually done to the EEG signal in order to filter it or have it in better shape, but rather accept the signal in its raw nature. This could make the study a peculiar, especially with using new development tool that seldom has been used in deep learning which is the DeepLEarning4J (DL4J), the java programming environment platform made easy and tailored for deep neural network learning purposes. Results up to 97% in detecting two levels of Anesthesia have been reported successfully.

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Author information

Authors and Affiliations

  1. Computer Science and Engineering Department, College of Engineering, Qatar University, POB 2713, Doha, Qatar

    Mohamed H. AlMeer

  2. Department of Electronic and Computer Engineering, Brunel University London, Uxbridge, UB8 3PH, UK

    Maysam F. Abbod

Authors
  1. Mohamed H. AlMeer
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  2. Maysam F. Abbod
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Corresponding author

Correspondence to Mohamed H. AlMeer .

Editor information

Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, UK

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, Bradford, UK

    Rahul Bhatia

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AlMeer, M.H., Abbod, M.F. (2019). Deep Learning in Classifying Depth of Anesthesia (DoA). In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2018. Advances in Intelligent Systems and Computing, vol 868. Springer, Cham. https://doi.org/10.1007/978-3-030-01054-6_11

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