Deep Learning in Physiological Signal Data: A Survey
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 desir...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-02-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/20/4/969 |
| _version_ | 1797999614989697024 |
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| author | Beanbonyka Rim Nak-Jun Sung Sedong Min Min Hong |
| author_facet | Beanbonyka Rim Nak-Jun Sung Sedong Min Min Hong |
| author_sort | Beanbonyka Rim |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T11:07:32Z |
| format | Article |
| id | doaj.art-77625a084045499da53b6a280a5ad27c |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T11:07:32Z |
| publishDate | 2020-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-77625a084045499da53b6a280a5ad27c2022-12-22T04:28:14ZengMDPI AGSensors1424-82202020-02-0120496910.3390/s20040969s20040969Deep Learning in Physiological Signal Data: A SurveyBeanbonyka Rim0Nak-Jun Sung1Sedong Min2Min Hong3Department of Computer Science, Soonchunhyang University, Asan 31538, KoreaDepartment of Computer Science, Soonchunhyang University, Asan 31538, KoreaDepartment of Medical IT Engineering, Soonchunhyang University, Asan 31538, KoreaDepartment of Computer Software Engineering, Soonchunhyang University, Asan 31538, KoreaDeep 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.https://www.mdpi.com/1424-8220/20/4/969deep-learningmachine learningphysiological signals1d signal data analysis |
| spellingShingle | Beanbonyka Rim Nak-Jun Sung Sedong Min Min Hong Deep Learning in Physiological Signal Data: A Survey Sensors deep-learning machine learning physiological signals 1d signal data analysis |
| title | Deep Learning in Physiological Signal Data: A Survey |
| title_full | Deep Learning in Physiological Signal Data: A Survey |
| title_fullStr | Deep Learning in Physiological Signal Data: A Survey |
| title_full_unstemmed | Deep Learning in Physiological Signal Data: A Survey |
| title_short | Deep Learning in Physiological Signal Data: A Survey |
| title_sort | deep learning in physiological signal data a survey |
| topic | deep-learning machine learning physiological signals 1d signal data analysis |
| url | https://www.mdpi.com/1424-8220/20/4/969 |
| work_keys_str_mv | AT beanbonykarim deeplearninginphysiologicalsignaldataasurvey AT nakjunsung deeplearninginphysiologicalsignaldataasurvey AT sedongmin deeplearninginphysiologicalsignaldataasurvey AT minhong deeplearninginphysiologicalsignaldataasurvey |