Deep Learning in Physiological Signal Data: A Survey

Overview

Journal Sensors (Basel)

Publisher MDPI

Specialty Biotechnology

Date 2020 Feb 15

PMID 32054042

Citations 53

Authors

Beanbonyka Rim

Nak-Jun Sung

Sedong Min

Min Hong

Affiliations

Soon will be listed here.

Abstract

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.

Citing Articles

Real-Time Driver Drowsiness Detection Using Facial Analysis and Machine Learning Techniques.

Essahraui S, Lamaakal I, El Hamly I, Maleh Y, Ouahbi I, El Makkaoui K Sensors (Basel). 2025; 25(3).

PMID: 39943451 PMC: 11819803. DOI: 10.3390/s25030812.

Fractal Analysis of Electrodermal Activity for Emotion Recognition: A Novel Approach Using Detrended Fluctuation Analysis and Wavelet Entropy.

Mercado-Diaz L, Veeranki Y, Large E, Posada-Quintero H Sensors (Basel). 2025; 24(24.

PMID: 39771865 PMC: 11679127. DOI: 10.3390/s24248130.

Distilling the knowledge from large-language model for health event prediction.

Ding S, Ye J, Hu X, Zou N Sci Rep. 2024; 14(1):30675.

PMID: 39730390 PMC: 11680798. DOI: 10.1038/s41598-024-75331-2.

Deep learning model using continuous skin temperature data predicts labor onset.

Basavaraj C, Grant A, Aras S, Erickson E BMC Pregnancy Childbirth. 2024; 24(1):777.

PMID: 39587525 PMC: 11587739. DOI: 10.1186/s12884-024-06862-9.

Aiming for Relevance.

Eini-Porat B, Eytan D, Shalit U AMIA Jt Summits Transl Sci Proc. 2024; 2024:145-154.

PMID: 38827113 PMC: 11141809.

References

Mousavi S, Afghah F, Rajendra Acharya U . SleepEEGNet: Automated sleep stage scoring with sequence to sequence deep learning approach. PLoS One. 2019; 14(5):e0216456. PMC: 6504038. DOI: 10.1371/journal.pone.0216456. View

Chambon S, Galtier M, Arnal P, Wainrib G, Gramfort A . A Deep Learning Architecture for Temporal Sleep Stage Classification Using Multivariate and Multimodal Time Series. IEEE Trans Neural Syst Rehabil Eng. 2018; 26(4):758-769. DOI: 10.1109/TNSRE.2018.2813138. View

Zhang P, Wang X, Zhang W, Chen J . Learning Spatial-Spectral-Temporal EEG Features With Recurrent 3D Convolutional Neural Networks for Cross-Task Mental Workload Assessment. IEEE Trans Neural Syst Rehabil Eng. 2018; 27(1):31-42. DOI: 10.1109/TNSRE.2018.2884641. View

Zhang T, Zheng W, Cui Z, Zong Y, Li Y . Spatial-Temporal Recurrent Neural Network for Emotion Recognition. IEEE Trans Cybern. 2018; 49(3):839-847. DOI: 10.1109/TCYB.2017.2788081. View

Hwang B, You J, Vaessen T, Myin-Germeys I, Park C, Zhang B . Deep ECGNet: An Optimal Deep Learning Framework for Monitoring Mental Stress Using Ultra Short-Term ECG Signals. Telemed J E Health. 2018; 24(10):753-772. DOI: 10.1089/tmj.2017.0250. View

Brito C, Machado A, Sousa A . Electrocardiogram Beat-Classification Based on a ResNet Network. Stud Health Technol Inform. 2019; 264:55-59. DOI: 10.3233/SHTI190182. View

Wang L, Lin Y, Wang J . A RR interval based automated apnea detection approach using residual network. Comput Methods Programs Biomed. 2019; 176:93-104. DOI: 10.1016/j.cmpb.2019.05.002. View

Yoon D, Lim H, Jung K, Kim T, Lee S . Deep Learning-Based Electrocardiogram Signal Noise Detection and Screening Model. Healthc Inform Res. 2019; 25(3):201-211. PMC: 6689506. DOI: 10.4258/hir.2019.25.3.201. View

Sokolovsky M, Guerrero F, Paisarnsrisomsuk S, Ruiz C, Alvarez S . Deep Learning for Automated Feature Discovery and Classification of Sleep Stages. IEEE/ACM Trans Comput Biol Bioinform. 2019; 17(6):1835-1845. DOI: 10.1109/TCBB.2019.2912955. View

10.

Phang C, Noman F, Hussain H, Ting C, Ombao H . A Multi-Domain Connectome Convolutional Neural Network for Identifying Schizophrenia From EEG Connectivity Patterns. IEEE J Biomed Health Inform. 2019; 24(5):1333-1343. DOI: 10.1109/JBHI.2019.2941222. View

11.

Wei X, Zhou L, Zhang Z, Chen Z, Zhou Y . Early prediction of epileptic seizures using a long-term recurrent convolutional network. J Neurosci Methods. 2019; 327:108395. DOI: 10.1016/j.jneumeth.2019.108395. View

12.

Chao H, Zhi H, Dong L, Liu Y . Recognition of Emotions Using Multichannel EEG Data and DBN-GC-Based Ensemble Deep Learning Framework. Comput Intell Neurosci. 2019; 2018:9750904. PMC: 6311795. DOI: 10.1155/2018/9750904. View

13.

Hussein R, Palangi H, Ward R, Jane Wang Z . Optimized deep neural network architecture for robust detection of epileptic seizures using EEG signals. Clin Neurophysiol. 2018; 130(1):25-37. DOI: 10.1016/j.clinph.2018.10.010. View

14.

Wei W, Dai Q, Wong Y, Hu Y, Kankanhalli M, Geng W . Surface-Electromyography-Based Gesture Recognition by Multi-View Deep Learning. IEEE Trans Biomed Eng. 2019; 66(10):2964-2973. DOI: 10.1109/TBME.2019.2899222. View

15.

Dey D, Chaudhuri S, Munshi S . Obstructive sleep apnoea detection using convolutional neural network based deep learning framework. Biomed Eng Lett. 2019; 8(1):95-100. PMC: 6208553. DOI: 10.1007/s13534-017-0055-y. View

16.

Tian X, Deng Z, Ying W, Choi K, Wu D, Qin B . Deep Multi-View Feature Learning for EEG-Based Epileptic Seizure Detection. IEEE Trans Neural Syst Rehabil Eng. 2019; 27(10):1962-1972. DOI: 10.1109/TNSRE.2019.2940485. View

17.

Chen M, Wang G, Xie P, Sang Z, Lv T, Zhang P . Region Aggregation Network: Improving Convolutional Neural Network for ECG Characteristic Detection. Annu Int Conf IEEE Eng Med Biol Soc. 2018; 2018:2559-2562. DOI: 10.1109/EMBC.2018.8512789. View

18.

Dantas H, Warren D, Wendelken S, Davis T, Clark G, Mathews V . Deep Learning Movement Intent Decoders Trained With Dataset Aggregation for Prosthetic Limb Control. IEEE Trans Biomed Eng. 2019; 66(11):3192-3203. DOI: 10.1109/TBME.2019.2901882. View

19.

Savalia S, Emamian V . Cardiac Arrhythmia Classification by Multi-Layer Perceptron and Convolution Neural Networks. Bioengineering (Basel). 2018; 5(2). PMC: 6027502. DOI: 10.3390/bioengineering5020035. View

20.

Zhao D, Tang F, Si B, Feng X . Learning joint space-time-frequency features for EEG decoding on small labeled data. Neural Netw. 2019; 114:67-77. DOI: 10.1016/j.neunet.2019.02.009. View