The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Deep learning for healthcare applications based on physiological signals: A review
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.
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“Finally, a recent review of deep learning for physiological signals classification revealed that CNNs were the most popular architecture (Faust et al., 2018) for the considered task.”
BackgroundBig data and machine learning algorithms for health-care delivery
Ngiam, K. Y., & Khor, I. W. (2019) · The Lancet Oncology · doi:10.1016/s1470-2045(19)30149-4 · Cited by 1,424
Artificial intelligence for mental health and mental illnesses: an overview
Graham, S., Depp, C., Lee, E. E., Nebeker, C., Tu, X., Kim, H.-C., & Jeste, D. V. (2019) · Current Psychiatry Reports · doi:10.1007/s11920-019-1094-0 · Cited by 970
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Classification of myocardial infarction with multi-lead ECG signals and deep CNN
Baloglu, U. B., Talo, M., Yildirim, O., Tan, R. S., & Acharya, U. R. (2019) · Pattern Recognition Letters · doi:10.1016/j.patrec.2019.02.016 · Cited by 473
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Metadata
- Authors
- Faust, O., Hagiwara, Y., Hong, T. J., Lih, O. S., & Acharya, U. R.
- Venue
- Computer Methods and Programs in Biomedicine
- Publisher
- Elsevier BV
- Published
- 2018-07
- Volume
- 161
- Pages
- 1-13
- ISSN
- 0169-2607 (ISSN-L)
- OpenAlex
- W2797694788
- Cited by
- 1,026
- References
- 15
- FWCI
- 80.17
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
