Deep Learning on 1-D Biosignals: a Taxonomy-based Survey

Yearbook of Medical Informatics
Nagarajan GanapathyThomas M Deserno

Abstract

Deep learning models such as convolutional neural networks (CNNs) have been applied successfully to medical imaging, but biomedical signal analysis has yet to fully benefit from this novel approach. Our survey aims at (i) reviewing deep learning techniques for biosignal analysis in computer- aided diagnosis; and (ii) deriving a taxonomy for organizing the growing number of applications in the field. A comprehensive literature research was performed using PubMed, Scopus, and ACM. Deep learning models were classified with respect to the (i) origin, (ii) dimension, and (iii) type of the biosignal as input to the deep learning model; (iv) the goal of the application; (v) the size and (vi) type of ground truth data; (vii) the type and (viii) schedule of learning the network; and (ix) the topology of the model. Between January 2010 and December 2017, a total 71 papers were published on the topic. The majority (n = 36) of papers are on electrocariography (ECG) signals. Most applications (n = 25) aim at detection of patterns, while only a few (n = 6) at predection of events. Out of 36 ECG-based works, many (n = 17) relate to multi-lead ECG. Other biosignals that have been identified in the survey are electromyography, phonocardiography...Continue Reading

Citations

Feb 15, 2020·Sensors·Beanbonyka RimMin Hong
Oct 20, 2019·Sensors·Patrick ThiamFriedhelm Schwenker
Apr 30, 2020·Sensors·Ju WangThomas M Deserno
Apr 25, 2020·Sensors·Vaggelis NtalianisKonstantinos Moustakas
Mar 5, 2021·Journal of Medical Systems·Nagarajan GanapathyRamakrishnan Swaminathan
Jul 2, 2021·Physiological Measurement·Samuel M PröllKarl D Fritscher
Jun 25, 2020·Computer Methods and Programs in Biomedicine·Choon-Hian GohEinly Lim

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BETA
feature extraction

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Scopus

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