Deep learning for healthcare applications based on physiological signals: A review. (July 2018)

• Record Type: Journal Article
• Authors: Faust, Oliver; Hagiwara, Yuki; Hong, Tan Jen; Lih, Oh Shu; Acharya, U Rajendra
• Is Part Of: Computer methods and programs in biomedicine. Volume 161(2018)
• Page Start: 1
• Page End: 13
• Publication Date: 2018-07
• Subjects: Deep learning -- Physiological signals -- Electrocardiogram -- Electroencephalogram -- Electromyogram -- Electrooculogram; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2018.04.005
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Importance: In 2017 the number of publications on deep learning for physiological signal analysis increased significantly. Indeed, 2017 saw more papers published on that topic than in all the years prior - combined. Thesis: Deep learning works well with large and varied datasets. The current body of research does not reflect the depth and breadth of healthcare applications. Conclusion: There is much scope for research in the area of physiological signal analysis with deep learning. 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.