scholarly journals Confidence-Based Framework Using Deep Learning for Automated Sleep Stage Scoring

2021 ◽  
Vol Volume 13 ◽  
pp. 2239-2250
Author(s):  
Jung Kyung Hong ◽  
Taeyoung Lee ◽  
Roben Deocampo Delos Reyes ◽  
Joonki Hong ◽  
Hai Hong Tran ◽  
...  
Keyword(s):  
Deep Learning ◽  
Sleep Stage ◽  
Sleep Stage Scoring

scholarly journals Automated sleep stage scoring of the Sleep Heart Health Study using deep neural networks

SLEEP ◽  
2019 ◽  
Vol 42 (11) ◽  
Author(s):  
Linda Zhang ◽  
Daniel Fabbri ◽  
Raghu Upender ◽  
David Kent
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Sleep Stage ◽  
Health Study ◽  
Sleep Staging ◽  
Heart Health ◽  
Training Set ◽  
Cohen's Kappa ◽  
Sleep Stage Scoring

Abstract Study Objectives Polysomnography (PSG) scoring is labor intensive and suffers from variability in inter- and intra-rater reliability. Automated PSG scoring has the potential to reduce the human labor costs and the variability inherent to this task. Deep learning is a form of machine learning that uses neural networks to recognize data patterns by inspecting many examples rather than by following explicit programming. Methods A sleep staging classifier trained using deep learning methods scored PSG data from the Sleep Heart Health Study (SHHS). The training set was composed of 42 560 hours of PSG data from 5213 patients. To capture higher-order data, spectrograms were generated from electroencephalography, electrooculography, and electromyography data and then passed to the neural network. A holdout set of 580 PSGs not included in the training set was used to assess model accuracy and discrimination via weighted F1-score, per-stage accuracy, and Cohen’s kappa (K). Results The optimal neural network model was composed of spectrograms in the input layer feeding into convolutional neural network layers and a long short-term memory layer to achieve a weighted F1-score of 0.87 and K = 0.82. Conclusions The deep learning sleep stage classifier demonstrates excellent accuracy and agreement with expert sleep stage scoring, outperforming human agreement on sleep staging. It achieves comparable or better F1-scores, accuracy, and Cohen’s kappa compared to literature for automated sleep stage scoring of PSG epochs. Accurate automated scoring of other PSG events may eventually allow for fully automated PSG scoring.

scholarly journals SleepEEGNet: Automated sleep stage scoring with sequence to sequence deep learning approach

PLoS ONE ◽  
2019 ◽  
Vol 14 (5) ◽  
pp. e0216456 ◽  
Author(s):  
Sajad Mousavi ◽  
Fatemeh Afghah ◽  
U. Rajendra Acharya
Keyword(s):  
Deep Learning ◽  
Sleep Stage ◽  
Learning Approach ◽  
Sleep Stage Scoring

A Deep Learning Approach with Conditional Random Field for Automatic Sleep Stage Scoring

2021 ◽  
Author(s):  
Yuanyuan Wu ◽  
Xiangzhen Fang ◽  
Jin Li ◽  
Lin Zhang ◽  
Zhiwei Chen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Random Field ◽  
Sleep Stage ◽  
Conditional Random Field ◽  
Learning Approach ◽  
Sleep Stage Scoring

scholarly journals 0307 Automated Sleep Stage Scoring Using Deep Learning

SLEEP ◽  
2018 ◽  
Vol 41 (suppl_1) ◽  
pp. A118-A118 ◽  
Author(s):  
L Zhang ◽  
D Fabbri ◽  
R Upender ◽  
D Kent
Keyword(s):  
Deep Learning ◽  
Sleep Stage ◽  
Sleep Stage Scoring

Combining decision trees classifiers: a case study of automatic sleep stage scoring

2012 ◽  
Vol 2 (1) ◽  
pp. 60 ◽  
Author(s):  
Natheer Khasawneh ◽  
Stefan Conrad ◽  
Luay Fraiwan ◽  
Eyad Taqieddin ◽  
Basheer Khasawneh
Keyword(s):  
Decision Trees ◽  
Sleep Stage ◽  
Sleep Stage Scoring

scholarly journals CCRRSleepNet: A Hybrid Relational Inductive Biases Network for Automatic Sleep Stage Classification on Raw Single-Channel EEG

2021 ◽  
Vol 11 (4) ◽  
pp. 456
Author(s):  
Wenpeng Neng ◽  
Jun Lu ◽  
Lei Xu
Keyword(s):  
Deep Learning ◽  
Single Channel ◽  
Sleep Stage ◽  
Learning Model ◽  
Actual Performance ◽  
Feature Extraction Method ◽  
Inductive Bias ◽  
Stage Classification ◽  
Sleep Stage Classification ◽  
Deep Learning Model

In the inference process of existing deep learning models, it is usually necessary to process the input data level-wise, and impose a corresponding relational inductive bias on each level. This kind of relational inductive bias determines the theoretical performance upper limit of the deep learning method. In the field of sleep stage classification, only a single relational inductive bias is adopted at the same level in the mainstream methods based on deep learning. This will make the feature extraction method of deep learning incomplete and limit the performance of the method. In view of the above problems, a novel deep learning model based on hybrid relational inductive biases is proposed in this paper. It is called CCRRSleepNet. The model divides the single channel Electroencephalogram (EEG) data into three levels: frame, epoch, and sequence. It applies hybrid relational inductive biases from many aspects based on three levels. Meanwhile, multiscale atrous convolution block (MSACB) is adopted in CCRRSleepNet to learn the features of different attributes. However, in practice, the actual performance of the deep learning model depends on the nonrelational inductive biases, so a variety of matching nonrelational inductive biases are adopted in this paper to optimize CCRRSleepNet. The CCRRSleepNet is tested on the Fpz-Cz and Pz-Oz channel data of the Sleep-EDF dataset. The experimental results show that the method proposed in this paper is superior to many existing methods.

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