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

Faust O 1 , Hagiwara Y 2 , Hong TJ 2 , Lih OS 2 , Acharya UR 3

Affiliations 

  • 1 Department of Engineering and Mathematics, Sheffield Hallam University, United Kingdom. Electronic address: o.faust@shu.ac.uk
  • 2 Department of Electronic & Computer Engineering, Ngee Ann Polytechnic, Singapore
  • 3 Department of Electronic & Computer Engineering, Ngee Ann Polytechnic, Singapore; Department of Biomedical Engineering, School of Science and Technology, SIM University, Singapore; Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
Comput Methods Programs Biomed, 2018 Jul;161:1-13.
PMID: 29852952 DOI: 10.1016/j.cmpb.2018.04.005

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.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.

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