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Deep Learning in Physiological Signal Data: A Survey
2020
Sensors
Preserved Fulltext
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. ...
R3DCNN
3D convolutional neural networks
RA
Region aggregation
RASS
Richmond agitation-sedation scale
RBM
Restricted Boltzmann machine
RCNN
Recurrent convolutional neural network
RNN
Recurrent ...
doi:10.3390/s20040969
pmid:32054042
pmcid:PMC7071412
fatcat:5ga4um5zsfddtpp47csw2dkyce
Opportunities and Challenges of Deep Learning Methods for Electrocardiogram Data: A Systematic Review
[article]
2020
arXiv
pre-print
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Specifically, we found that a hybrid architecture of a convolutional neural network and recurrent neural network ensemble using expert features yields the best results. ...
Different deep learning architectures have been used in various ECG analytics tasks, such as disease detection/classification, annotation/localization, sleep staging, biometric human identification, and ...
learning" OR "deep neural network" OR "deep neural networks" OR "convolutional neural network" OR "cnn" OR "recurrent neural network" OR "rnn" OR "long short term memory" OR "lstm" OR "autoencoder" OR ...
arXiv:2001.01550v3
fatcat:ho7qhyqivzgn3hmitmg45stali
2020 Index IEEE Journal of Biomedical and Health Informatics Vol. 24
2020
IEEE journal of biomedical and health informatics
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., A Globalized Model for Mapping Wearable Seismocardiogram Signals to Whole-Body Ballistocardiogram Signals Based on Deep Learning; JBHI May 2020 1296-1309 Herskovic, V., see Saint-Pierre, C., JBHI Jan ...
, see 2833-2843 Hong, H., see Xue, B., JBHI Feb. 2020 614-625 Hoog Antink, C., Mai, Y., Aalto, R., Bruser, C., Leonhardt, S., Oksala, N., and Vehkaoja, A., Ballistocardiography Can Estimate Beat-to-Beat ...
., +, TSE-CNN: A Two-Stage End-to-End CNN for Human Activity Recognition. Huang, J., +, JBHI Jan. 2020 292-299 Unsupervised 3D End-to-End Medical Image Registration With Volume Tweening Network. ...
doi:10.1109/jbhi.2020.3048808
fatcat:iifrkwtzazdmboabdqii7x5ukm
Radar Sensing in Assisted Living: an Overview
2019
2019 IEEE MTT-S International Microwave Biomedical Conference (IMBioC)
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This paper gives an overview of trends in radar sensing for assisted living. ...
The last section shows examples of classification in human activity recognition and medical applications, e.g. breathing disorder and sleep stages recognition. ...
Memory, CNN: Convolutional Neural Network), ANN: Artificial Neural Network. ...
doi:10.1109/imbioc.2019.8777748
fatcat:x5cpk3mf7bevrcgi75b6kosxne
Sense and Learn: Self-Supervision for Omnipresent Sensors
[article]
2021
arXiv
pre-print
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Existing purely supervised end-to-end deep learning techniques depend on the availability of a massive amount of well-curated data, acquiring which is notoriously difficult but required to achieve a sufficient ...
In particular, we show that the self-supervised network can be utilized as initialization to significantly boost the performance in a low-data regime with as few as 5 labeled instances per class, which ...
ACKNOWLEDGEMENTS The authors would like to thank Félix de Chaumont Quitry, Marco Tagliasacchi and Richard F. Lyon for their valuable feedback and help with this work. ...
arXiv:2009.13233v2
fatcat:ver2i7o5zvgv3boterps4tqxcu
Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey
[article]
2023
arXiv
pre-print
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2023 pre-print arXiv:2302.02515v1 fatcat:5ubhbkqrtffbbh5y42hepyynku
We review different network architectures and training methods used for these tasks and discuss the challenges and opportunities when applying deep learning to time series data. ...
This paper surveys the current state of the art in the fast-moving field of deep learning for time series classification and extrinsic regression. ...
ACKNOWLEDGMENTS This work was supported by an Australian Government Research Training Program (RTP) scholarship. ...
arXiv:2302.02515v2
fatcat:t77vp2c2gnbrjdyl43paefxqgm
Electroencephalogram Emotion Recognition Based on 3D Feature Fusion and Convolutional Autoencoder
2021
Frontiers in Computational Neuroscience
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based on 3D feature fusion and convolutional autoencoder (CAE). ...
In order to overcome the disadvantage that traditional machine learning based emotion recognition technology relies too much on a manual feature extraction, we propose an EEG emotion recognition algorithm ...
In an autoencoder, an encoder can turn source data to a hidden layer, while a decoder can map the hidden layer to source data. ...
doi:10.3389/fncom.2021.743426
pmid:34733148
pmcid:PMC8558247
fatcat:ktlvaplxsrf3tjzpgb4di2pmqu
Self-Supervised Learning from Multi-Sensor Data for Sleep Recognition
2020
IEEE Access
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Most of sleep recognition methods are limited to single-task recognition, which only involves single-modal sleep data, and there is no generalized model for multi-task recognition on multi-sensor sleep ...
Moreover, the shortage and imbalance of sleep samples also limits the expansion of the existing machine learning methods like support vector machine, decision tree and convolutional neural network, which ...
The bidirectional recurrent neural network proposes that each training sequence is two recurrent neural networks, one is forward and the other is backward, and both of them are connected with an output ...
doi:10.1109/access.2020.2994593
fatcat:ywchbx4szjhdnixe33z2jxptpe
Influence of Channel Selection and Subject's Age on the Performance of the Single Channel EEG-Based Automatic Sleep Staging Algorithms
2023
Sensors
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The electroencephalogram (EEG) signal is a key parameter used to identify the different sleep stages present in an overnight sleep recording. ...
Automatic sleep staging algorithms offer a practical and cost-effective alternative to manual sleep staging. ...
Acknowledgments: The authors extend their appreciation to the Deputyship for Research and Innovation, Ministry of Education in Saudi Arabia for funding this research work through project number (IF-PSAU ...
doi:10.3390/s23020899
pmid:36679711
pmcid:PMC9866121
fatcat:4dtglriqwjgitooiefuzy5lhey
Image Recognition and Analysis of Intrauterine Residues Based on Deep Learning and Semi-Supervised Learning
2020
IEEE Access
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The multi-scale cyclic convolutional network is an end-toend image recognition network. Its input is an image of any size, and the output prediction result uses a codec structure. ...
In order to alleviate the impact of using the network structure of image classification as a base network, the Feature Fusion (FF) module is designed in the multiscale recurrent network. ...
doi:10.1109/access.2020.3020322
fatcat:mgkpfn7oozbp5actzq42bx5mtm
A Systematic Review of Detecting Sleep Apnea Using Deep Learning
2019
Sensors
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The goal of this work is to analyze the published research in the last decade, providing an answer to the research questions such as how to implement the different deep networks, what kind of pre-processing ...
Polysomnography, the gold standard, is expensive, inaccessible, uncomfortable and an expert technician is needed to score. ...
The multiclass classification overall accuracy was 79.61%.
Recurrent Neural Network (RNN) SpO 2 and IHR signals were tested by Pathinarupothi et al. [33] as an input to as LSTM. ...
doi:10.3390/s19224934
pmid:31726771
pmcid:PMC6891618
fatcat:4molitaojnhtzpgx43ebzz7rwq
Autonomous Driving with Deep Learning: A Survey of State-of-Art Technologies
[article]
2020
arXiv
pre-print
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Due to the limited space, we focus the analysis on several key areas, i.e. 2D and 3D object detection in perception, depth estimation from cameras, multiple sensor fusion on the data, feature and task ...
This is a survey of autonomous driving technologies with deep learning methods. ...
DeepVO [250] (shown in Fig. 11 ) is an end-to-end framework for monocular VO by using Recurrent Convolutional Neural Networks (RCNNs), which not only automatically learns effective feature representation ...
arXiv:2006.06091v3
fatcat:nhdgivmtrzcarp463xzqvnxlwq
A Survey on Deep Learning for Multimodal Data Fusion
2020
Neural Computation
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Thus, this review presents a survey on deep learning for multimodal data fusion to provide readers, regardless of their original community, with the fundamentals of multimodal deep learning fusion method ...
With the wide deployments of heterogeneous networks, huge amounts of data with characteristics of high volume, high variety, high velocity, and high veracity are generated. ...
sleep multimodal recurrent neural network. ...
doi:10.1162/neco_a_01273
pmid:32186998
fatcat:ls27tbkldrbx7n4h7nlc73qyte
Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification
2018
IEEE Transactions on Biomedical Engineering
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To our knowledge, this is the first work going beyond the standard single-output classification to consider multitask neural networks for automatic sleep staging. ...
This paper proposes a joint classification-and-prediction framework based on convolutional neural networks (CNNs) for automatic sleep staging, and, subsequently, introduces a simple yet efficient CNN architecture ...
Other network variants, such as Deep Belief Networks (DBNs) [28] , Auto-encoder [21] , Deep Neural Networks (DNNs) [23] , have also been explored. Moreover, Recurrent Neural Networks (RNNs), e.g. ...
doi:10.1109/tbme.2018.2872652
pmid:30346277
pmcid:PMC6487915
fatcat:cldhfe23gfadxc5736ojx4qhna
Deep Learning in EEG: Advance of the Last Ten-Year Critical Period
2021
IEEE Transactions on Cognitive and Developmental Systems
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Due to the lack of a comprehensive and topic widely covered survey for deep learning in EEG, we attempt to summarize recent progress to provide an overview, as well as perspectives for future developments ...
We first briefly mention the artifacts removal for EEG signal and then introduce deep learning models that have been utilized in EEG processing and classification. ...
To this end, a few attempts were done. For example, Supratak et al. inputted raw EEG data into a CNN for the classification of sleep stages. ...
doi:10.1109/tcds.2021.3079712
fatcat:5rck4hvysfhe5o2tfjywytr5o4
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