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Deep Learning in Physiological Signal Data: A Survey

Rim, Sung, Min, Hong
2020 Sensors  
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)  ...  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  ...  Boltzmann Machine DBLSTM-WS Bi-directional LSTM network-based wavelet sequences DBM Deep Boltzmann Machine DBN Deep belief network DBN-GC Deep belief networks with glia chains DCNN Deep convolution neural  ... 
doi:10.3390/s20040969 pmid:32054042 pmcid:PMC7071412 fatcat:5ga4um5zsfddtpp47csw2dkyce

A Novel Sleep Staging Network Based on Data Adaptation and Multimodal Fusion

Lijuan Duan, Mengying Li, Changming Wang, Yuanhua Qiao, Zeyu Wang, Sha Sha, Mingai Li
2021 Frontiers in Human Neuroscience  
To tackle the above two problems, we propose an automatic sleep staging network model based on data adaptation and multimodal feature fusion using EEG and electrooculogram (EOG) signals. 3D-CNN is used  ...  Most of the existing sleep staging researches using hand-engineered features rely on prior knowledges of sleep analysis, and usually single channel electroencephalogram (EEG) is used for sleep staging  ...  To construct a network that learns complex joint feature representations, we use multimodal fusion network based on Deep belief network (DBN) to learn the nonlinear relationship between EEG and EOG.  ... 
doi:10.3389/fnhum.2021.727139 pmid:34690720 pmcid:PMC8531206 fatcat:7bncjgccijbsrkvozhxb5z7qsy

A hybrid self-attention deep learning framework for multivariate sleep stage classification

Ye Yuan, Kebin Jia, Fenglong Ma, Guangxu Xun, Yaqing Wang, Lu Su, Aidong Zhang
2019 BMC Bioinformatics  
To alleviate the time consumption caused by manual visual inspection of PSG, automatic multivariate sleep stage classification has become an important research topic in medical and bioinformatics.  ...  We present a unified hybrid self-attention deep learning framework, namely HybridAtt, to automatically classify sleep stages by capturing channel and temporal correlations from multivariate PSG records  ...  ; DBN: Deep belief networks; DNN: Deep neural networks; ECG: Electrocardiogram; EEG: Electroencephalogram; EMG: Electromyogram; EOG: Electrooculogram; NN: Neural networks; NREM: Non-rapid eye movement;  ... 
doi:10.1186/s12859-019-3075-z pmid:31787093 pmcid:PMC6886163 fatcat:hjqobi27azec7an4nzzz64ltdm

Deep Belief Networks for Electroencephalography: A Review of Recent Contributions and Future Outlooks

Faezeh Movahedi, James L. Coyle, Ervin Sejdic
2018 IEEE journal of biomedical and health informatics  
We covered various applications of electroencephalography in medicine, including emotion recognition, sleep stage classification, and seizure detection, in order to understand how deep learning algorithms  ...  In this manuscript, we provide an overview of deep learning approaches with a focus on deep belief networks in electroencephalography applications.  ...  In the last step, a voting principle based on classification entropy is used to classify sleep stages. Movahedi et al.  ... 
doi:10.1109/jbhi.2017.2727218 pmid:28715343 pmcid:PMC5967386 fatcat:p4ylvq3rcfcwlj22rl2zl6uijm

Deep learning for healthcare applications based on physiological signals: A review

Oliver Faust, Yuki Hagiwara, Tan Jen Hong, Oh Shu Lih, U Rajendra Acharya
2018 Computer Methods and Programs in Biomedicine  
Deep learning algorithms try to develop the model by using all the available input.  ...  Results: During the content review, we understood that deep learning performs better for big and varied datasets than classic analysis and machine classification methods.  ...  apnea detection, sleep apnea, sleep stage classification, slow eye- EMG Electromyogram movements EOG classification Electrooculogram classification, emotion classification, patient-specific ecg classification  ... 
doi:10.1016/j.cmpb.2018.04.005 pmid:29852952 fatcat:3tn4ookjyjgafgnbb2tyzznhz4

A Convolutional Network for Sleep Stages Classification [article]

Isaac Fernández-Varela, Elena Hernández-Pereira, Diego Alvarez-Estevez, Vicente Moret-Bonillo
2019 arXiv   pre-print
Sleep stages classification is a crucial task in the context of sleep studies. It involves the simultaneous analysis of multiple signals recorded during sleep.  ...  In this work, we avoid this bias using a deep learning model that learns relevant features without human intervention.  ...  In this work we use deep learning to classify sleep stages with a convolutional neural network that learns the relevant features for each stage.  ... 
arXiv:1902.05748v1 fatcat:4wqsavaed5bgdi2t6vh7iyzhxi

EEG-based emotion classification using deep belief networks

Wei-Long Zheng, Jia-Yi Zhu, Yong Peng, Bao-Liang Lu
2014 2014 IEEE International Conference on Multimedia and Expo (ICME)  
We train a deep belief network (DBN) with differential entropy features extracted from multichannel EEG as input.  ...  In recent years, there are many great successes in using deep architectures for unsupervised feature learning from data, especially for images and speech.  ...  Martin et al. applied deep belief nets (DBN) and hidden Markov model to detect sleep stage using multimodal clinical sleep datasets.  ... 
doi:10.1109/icme.2014.6890166 dblp:conf/icmcs/ZhengZPL14 fatcat:pqtcjomnfrgmzcjyfdieu32wym

A Deep Learning Approach with an Attention Mechanism for Automatic Sleep Stage Classification [article]

Martin Längkvist, Amy Loutfi
2018 arXiv   pre-print
The performance of the auto-encoder with selective attention is compared with a regular auto-encoder and previous works using a deep belief network (DBN).  ...  Automatic sleep staging is a challenging problem and state-of-the-art algorithms have not yet reached satisfactory performance to be used instead of manual scoring by a sleep technician.  ...  We would also like to thank senior physician Lena Leissner and sleep technician Meeri  ... 
arXiv:1805.05036v1 fatcat:rz2zojrcjvavhlutybk3mghpfq

Eye State Identification Utilizing EEG Signals: A Combined Method Using Self-Organizing Map and Deep Belief Network

Neda Ahmadi, Mehrbakhsh Nilashi, Behrouz Minaei-Bidgoli, Murtaza Farooque, Sarminah Samad, Nojood O. Aljehane, Waleed Abdu Zogaan, Hossein Ahmadi, Sheng Bin
2022 Scientific Programming  
The method is developed using SOM clustering and DBN, which is a deep layer neural network with multiple layers of Restricted Boltzmann Machines (RBMs).  ...  This paper accordingly proposes a new method for EEG signal analysis through Self-Organizing Map (SOM) clustering and Deep Belief Network (DBN) approaches to efficiently improve the computation and accuracy  ...  We use the Wake-Sleep algorithm [63] to avoid GD in this stage and to accelerate the feature extraction convergence in Deep Belief Network. is study used deep belief network with 2, 4, and 6 RBM layers  ... 
doi:10.1155/2022/4439189 fatcat:3hvwxfryzzaftjz4ap6zip7u2q

GACNN SleepTuneNet: A Genetic Algorithm Designing the Convolutional Neural Network Architecture for Optimal Classification of Sleep Stages from a Single EEG Channel

2019 Turkish Journal of Electrical Engineering and Computer Sciences  
This study presents a method for designing-by a genetic algorithm, without manual intervention-the feature learning architecture for classification of sleep stages from a single EEG channel, when using  ...  Based on the results, our model not only achieved the highest classification accuracy, but it also distinguished the sleep stages based on either of the two EEG electrode signals, in both datasets.  ...  sleep stages based on AASM standard.  ... 
doi:10.3906/elk-1903-186 fatcat:2x424rciwreuxj4m5hrxjxekgu

Deep Learning in EEG: Advance of the Last Ten-Year Critical Period

Shu Gong, Kaibo Xing, Andrzej Cichocki, Junhua Li
2021 IEEE Transactions on Cognitive and Developmental Systems  
We hope that this paper could serve as a summary of past work for deep learning in EEG and the beginning of further developments and achievements of EEG studies based on deep learning.  ...  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.  ...  For instance, LSTM model was used for sleep stage classification based on a single channel EEG [119] .  ... 
doi:10.1109/tcds.2021.3079712 fatcat:5rck4hvysfhe5o2tfjywytr5o4

Investigating the use of uni-directional and bi-directional long short-term memory models for automatic sleep stage scoring

Luay Fraiwan, Mohanad Alkhodari
2020 Informatics in Medicine Unlocked  
Prior to the training and classification process, the LSTM network architecture is built using Uni-and Bi-directional structures to utilize both the forward and backward chains of data sequences.  ...  The database comes with annotation files that include expert manual stage scoring based on the Rechtschaffen & Kales (R&K) scoring manual.  ...  Most automated sleep stages classifications rely on either feature extraction algorithms or deep learning networks [19, 3, 12] .  ... 
doi:10.1016/j.imu.2020.100370 fatcat:uygqatlktbhlrfcpncfeyeufly

EOGNET: A Novel Deep Learning Model for Sleep Stage Classification Based on Single-Channel EOG Signal

Jiahao Fan, Chenglu Sun, Meng Long, Chen Chen, Wei Chen
2021 Frontiers in Neuroscience  
Therefore, we propose a novel sleep staging approach using electrooculogram (EOG) signals, which are more convenient to acquire than the EEG.  ...  In recent years, automatic sleep staging methods have achieved competitive performance using electroencephalography (EEG) signals.  ...  Method 3 Längkvist et al. (2012) extracted 28 features from multimodal sleep data to train a deep belief network (DBN). A 2-layer DBN combined with a softmax classifier was used.  ... 
doi:10.3389/fnins.2021.573194 fatcat:vmxyypxwpzapzpqepkc4eid2me

Modeling Physiological Data with Deep Belief Networks

Dan Wang, Yi Shang
2013 International Journal of Information and Education Technology  
In this work, we present a system based on Deep Belief Networks (DBNs) that can automatically extract features from raw physiological data of 4 channels in an unsupervised fashion and then build 3 classifiers  ...  Traditionally hand-crafted features are chosen based on expert knowledge and then used for classification or regression.  ...  Deep belief networks (DBNs) [17] , as a semi-supervised learning algorithm, is promising for this problem.  ... 
doi:10.7763/ijiet.2013.v3.326 pmid:25165501 pmcid:PMC4142685 fatcat:35kauwqdazdc7nrryqanjgok6q

Developmental Strategies in Diagnosing Obstructive Sleep Apnea

2020 International Journal of Engineering and Advanced Technology  
Obstructive Sleep Apnea (OSA) syndrome is the most widespread sleep disorder characterized by chronic episodes of reduction in the airflow or stoppage in airflow during sleep, being caused by blockage  ...  literature research value from 2003 to 2019 and setting a roadmap for bio-engineers and medical doctors thereby reducing research period and improving medical service efficiency concerning obstructive sleep  ...  Unbalanced data due to prevalence of non apnea events and fixed Deep Belief Network structure.  ... 
doi:10.35940/ijeat.b3175.029320 fatcat:ap6om3n7cjg7jh26y4rjsff7ta
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