Please use this identifier to cite or link to this item:
http://bura.brunel.ac.uk/handle/2438/17334
Title: | Deep learning in classifying depth of anesthesia (DoA) |
Authors: | AlMeer, MH Abbod, MF |
Keywords: | Deep learning;DeepLearning4J;Depth of anesthesia;DoA;Neural networks |
Issue Date: | 2018 |
Publisher: | Springer |
Citation: | Advances in Intelligent Systems and Computing, 2019, 868 pp. 160 - 169 |
Abstract: | © Springer Nature Switzerland AG 2019. 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. |
URI: | http://bura.brunel.ac.uk/handle/2438/17334 |
DOI: | http://dx.doi.org/10.1007/978-3-030-01054-6_11 |
ISBN: | 9783030010539 |
ISSN: | 2194-5357 http://dx.doi.org/10.1007/978-3-030-01054-6_11 |
Appears in Collections: | Dept of Electronic and Electrical Engineering Embargoed Research Papers |
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Fulltext.pdf | Access allowed from09/11/2019 | 374.79 kB | Adobe PDF | View/Open |
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