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Deep Learning in Physiological Signal Data: A Survey

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Department of Computer Science, Soonchunhyang University, Asan 31538, Korea
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Department of Medical IT Engineering, Soonchunhyang University, Asan 31538, Korea
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Department of Computer Software Engineering, Soonchunhyang University, Asan 31538, Korea
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Author to whom correspondence should be addressed.
Sensors 2020, 20(4), 969; https://doi.org/10.3390/s20040969
Received: 7 January 2020 / Revised: 31 January 2020 / Accepted: 9 February 2020 / Published: 11 February 2020
(This article belongs to the Special Issue Internet of Medical Things in Healthcare Applications)
Deep Learning (DL), a successful promising approach for discriminative and generative tasks, has recently proved its high potential in 2D medical imaging analysis; however, physiological data in the form of 1D signals have yet to be beneficially exploited from this novel approach to fulfil the desired medical tasks. Therefore, in this paper we survey the latest scientific research on deep learning in physiological signal data such as electromyogram (EMG), electrocardiogram (ECG), electroencephalogram (EEG), and electrooculogram (EOG). We found 147 papers published between January 2018 and October 2019 inclusive from various journals and publishers. The objective of this paper is to conduct a detailed study to comprehend, categorize, and compare the key parameters of the deep-learning approaches that have been used in physiological signal analysis for various medical applications. The key parameters of deep-learning approach that we review are the input data type, deep-learning task, deep-learning model, training architecture, and dataset sources. Those are the main key parameters that affect system performance. We taxonomize the research works using deep-learning method in physiological signal analysis based on: (1) physiological signal data perspective, such as data modality and medical application; and (2) deep-learning concept perspective such as training architecture and dataset sources. View Full-Text
Keywords: deep-learning; machine learning; physiological signals; 1D signal data analysis deep-learning; machine learning; physiological signals; 1D signal data analysis
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Rim, B.; Sung, N.-J.; Min, S.; Hong, M. Deep Learning in Physiological Signal Data: A Survey. Sensors 2020, 20, 969.

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