Deep learning for healthcare applications based on physiological signals: A review

FAUST, Oliver, HAGIWARA, Yuki, HONG, Tan Jen, LIN, Oh Shu and ACHARYA, U Rajendra (2018). Deep learning for healthcare applications based on physiological signals: A review. Computer Methods and Programs in Biomedicine, 161, 1-13.

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Official URL: https://www.sciencedirect.com/science/article/pii/...
Link to published version:: https://doi.org/10.1016/j.cmpb.2018.04.005
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    Abstract

    Background and objective: We have cast the net into the ocean of knowledge to retrieve the latest scientific research on deep learning methods for physiological signals. We found 53 research papers on this topic, published from 01.01.2008 to 31.12.2017. Methods: An initial bibliometric analysis shows that the reviewed papers focused on Electromyogram(EMG), Electroencephalogram(EEG), Electrocardiogram(ECG), and Electrooculogram(EOG). These four categories were used to structure the subsequent content review. Results: During the content review, we understood that deep learning performs better for big and varied datasets than classic analysis and machine classification methods. Deep learning algorithms try to develop the model by using all the available input. Conclusions: This review paper depicts the application of various deep learning algorithms used till recently, but in future it will be used for more healthcare areas to improve the quality of diagnosis

    Item Type: Article
    Departments - Does NOT include content added after October 2018: Faculty of Science, Technology and Arts > Department of Engineering and Mathematics
    Identification Number: https://doi.org/10.1016/j.cmpb.2018.04.005
    Page Range: 1-13
    Depositing User: Carmel House
    Date Deposited: 11 May 2018 13:45
    Last Modified: 11 Apr 2019 01:18
    URI: http://shura.shu.ac.uk/id/eprint/21073

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