Deep learning for healthcare applications

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

Faust O1, Hagiwara Y2, Hong TJ2, Lih OS2, Acharya UR3.

Author information

Abstract

BACKGROUND AND OBJECTIVE:

We have cast the net into the ocean of knowledge to retrieve the latest scientific research on deeplearning 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.

Copyright © 2018 Elsevier B.V. All rights reserved.

KEYWORDS:

Deep learning; Electrocardiogram; Electroencephalogram; Electromyogram; Electrooculogram; Physiological signals

PMID:

 

29852952

 

DOI:

 

10.1016/j.cmpb.2018.04.005