scholarly journals A Convolutional Network for the Classification of Sleep Stages

Proceedings ◽  
2018 ◽  
Vol 2 (18) ◽  
pp. 1174 ◽  
Author(s):  
Isaac Fernández-Varela ◽  
Elena Hernández-Pereira ◽  
Vicente Moret-Bonillo
Keyword(s):  
Sleep Medicine ◽  
Sleep Stages ◽  
Kappa Index ◽  
Night Sleep ◽  
Convolutional Network ◽  
Sleep Study ◽  
Convolutional Networks ◽  
Sleep Studies ◽  
Automatic Methods

The classification of sleep stages is a crucial task in the context of sleep medicine. It involves the analysis of multiple signals thus being tedious and complex. Even for a trained physician scoring a whole night sleep study can take several hours. Most of the automatic methods trying to solve this problem use human engineered features biased for a specific dataset. In this work we use deep learning to avoid human bias. We propose an ensemble of 5 convolutional networks achieving a kappa index of 0.83 when classifying 500 sleep studies.

scholarly journals 0447 ResTNet: A Robust End-to-End Deep Learning Approach to Sleep Staging of Self Applied Somnography Studies

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A171-A171
Author(s):  
S Æ Jónsson ◽  
E Gunnlaugsson ◽  
E Finssonn ◽  
D L Loftsdóttir ◽  
G H Ólafsdóttir ◽  
...  
Keyword(s):  
Deep Learning ◽  
Sleep Stage ◽  
Quality Data ◽  
Sleep Stages ◽  
Learning Approach ◽  
Sleep Staging ◽  
Self Administration ◽  
Sleep Study ◽  
Sleep Studies ◽  
End To End

Abstract Introduction Sleep stage classifications are of central importance when diagnosing various sleep-related diseases. Performing a full PSG recording can be time-consuming and expensive, and often requires an overnight stay at a sleep clinic. Furthermore, the manual sleep staging process is tedious and subject to scorer variability. Here we present an end-to-end deep learning approach to robustly classify sleep stages from Self Applied Somnography (SAS) studies with frontal EEG and EOG signals. This setup allows patients to self-administer EEG and EOG leads in a home sleep study, which reduces cost and is more convenient for the patients. However, self-administration of the leads increases the risk of loose electrodes, which the algorithm must be robust to. The model structure was inspired by ResNet (He, Zhang, Ren, Sun, 2015), which has been highly successful in image recognition tasks. The ResTNet is comprised of the characteristic Residual blocks with an added Temporal component. Methods The ResTNet classifies sleep stages from the raw signals using convolutional neural network (CNN) layers, which avoids manual feature extraction, residual blocks, and a gated recurrent unit (GRU). This significantly reduces sleep stage prediction time and allows the model to learn more complex relations as the size of the training data increases. The model was developed and validated on over 400 manually scored sleep studies using the novel SAS setup. In developing the model, we used data augmentation techniques to simulate loose electrodes and distorted signals to increase model robustness with regards to missing signals and low quality data. Results The study shows that applying the robust ResTNet model to SAS studies gives accuracy > 0.80 and F1-score > 0.80. It outperforms our previous model which used hand-crafted features and achieves similar performance to a human scorer. Conclusion The ResTNet is fast, gives accurate predictions, and is robust to loose electrodes. The end-to-end model furthermore promises better performance with more data. Combined with the simplicity of the SAS setup, it is an attractive option for large-scale sleep studies. Support This work was supported by the Icelandic Centre for Research RANNÍS (175256-0611).

RECOGNITION OF SLEEP STAGES BASED ON A COMBINED NEURAL NETWORK AND FUZZY SYSTEM USING WAVELET TRANSFORM FEATURES

2014 ◽  
Vol 26 (02) ◽  
pp. 1450029 ◽  
Author(s):  
Chuang-Chien Chiu ◽  
Bui Huy Hai ◽  
Shoou-Jeng Yeh
Keyword(s):  
Neural Network ◽  
Fuzzy System ◽  
Single Channel ◽  
Sleep Stages ◽  
Elman Neural Network ◽  
Sleep Studies ◽  
Average Accuracy ◽  
Wavelet Decompositions

Recognition of sleep stages is an important task in the assessment of the quality of sleep. Several biomedical signals, such as EEG, ECG, EMG and EOG are used extensively to classify the stages of sleep, which is very important for the diagnosis of sleep disorders. Many sleep studies have been conducted that focused on the automatic classification of sleep stages. In this research, a new classification method is presented that uses an Elman neural network combined with fuzzy rules to extract sleep features based on wavelet decompositions. The nine subjects who participated in this study were recruited from Cheng-Ching General Hospital in Taichung, Taiwan. The sampling frequency was 250 Hz, and a single-channel (C3-A1) EEG signal was acquired for each subject. The system consisted of a combined neural network and fuzzy system that was used to recognize sleep stages based on epochs (10-second segments of data). The classification results relied on the strong points of combined neural network and fuzzy system, which achieved an average specificity of approximately 96% and an average accuracy of approximately 94%.

scholarly journals GCIceNet: a graph convolutional network for accurate classification of water phases

2020 ◽  
Vol 22 (45) ◽  
pp. 26340-26350
Author(s):  
QHwan Kim ◽  
Joon-Hyuk Ko ◽  
Sunghoon Kim ◽  
Wonho Jhe
Keyword(s):  
Unsupervised Learning ◽  
Order Parameters ◽  
Water Molecules ◽  
Convolutional Network ◽  
Convolutional Networks ◽  
Supervised And Unsupervised Learning

We develop GCIceNet, which automatically generates machine-based order parameters for classifying the phases of water molecules via supervised and unsupervised learning with graph convolutional networks.

scholarly journals 0491 Is a Single Night Sleep Study Sufficient for the Accurate Diagnosis of Sleep Apnea? An Exploration of Multi-Night Sleep Studies

SLEEP ◽  
2018 ◽  
Vol 41 (suppl_1) ◽  
pp. A185-A185
Author(s):  
J M Dzierzewski ◽  
N D Dautovich ◽  
B Rybarczyk ◽  
M Alattar ◽  
S A Taylor
Keyword(s):  
Sleep Apnea ◽  
Accurate Diagnosis ◽  
Night Sleep ◽  
Sleep Study ◽  
Sleep Studies ◽  
Single Night

CME/Answers: Obstructive Sleep Apnea: How to Deal with Night- To-Night Variability of Respiratory Events?

Praxis ◽  
2021 ◽  
Vol 110 (1) ◽  
pp. 16-18
Author(s):  
Maurice Roeder ◽  
Esther I. Schwarz ◽  
Thomas Gaisl ◽  
Malcolm Kohler
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Apnea Hypopnea Index ◽  
Night Sleep ◽  
Sleep Study ◽  
Diagnostic Uncertainty ◽  
Sleep Studies ◽  
Respiratory Events ◽  
Obstructive Sleep ◽  
Single Night

Abstract.According to current recommendations, the diagnosis of obstructive sleep apnea (OSA) is established by a single-night sleep study. However, recent reports suggest a remarkable night-to-night variability of OSA severity. We report on a 76-year-old man with suspected OSA who underwent six sleep studies within 13 months. Sleep studies demonstrated a remarkable variability of respiratory events based on an apnea-hypopnea index (AHI) varying between 1.1 and 43.1/h. There were no changes in body weight, alcohol intake, medication or comorbidities during the evaluation period. Due to diagnostic uncertainty and missing subjective benefit, the initially implemented CPAP therapy was stopped after one year of therapy. Considering night-to-night variability of OSA severity, single-night sleep studies might not be accurate enough in order to reliably diagnose or exclude OSA.

scholarly journals Classification of Brainwaves for Sleep Stages by High-Dimensional FFT Features from EEG Signals

2020 ◽  
Vol 10 (5) ◽  
pp. 1797 ◽  
Author(s):  
Mera Kartika Delimayanti ◽  
Bedy Purnama ◽  
Ngoc Giang Nguyen ◽  
Mohammad Reza Faisal ◽  
Kunti Robiatul Mahmudah ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sleep Stage ◽  
Machine Learning Algorithms ◽  
High Dimensional ◽  
Sleep Stages ◽  
Eeg Signals ◽  
Stage Classification ◽  
Sleep Stage Classification ◽  
Low Dimensional

Manual classification of sleep stage is a time-consuming but necessary step in the diagnosis and treatment of sleep disorders, and its automation has been an area of active study. The previous works have shown that low dimensional fast Fourier transform (FFT) features and many machine learning algorithms have been applied. In this paper, we demonstrate utilization of features extracted from EEG signals via FFT to improve the performance of automated sleep stage classification through machine learning methods. Unlike previous works using FFT, we incorporated thousands of FFT features in order to classify the sleep stages into 2–6 classes. Using the expanded version of Sleep-EDF dataset with 61 recordings, our method outperformed other state-of-the art methods. This result indicates that high dimensional FFT features in combination with a simple feature selection is effective for the improvement of automated sleep stage classification.

scholarly journals Efficient End-to-End Sentence-Level Lipreading with Temporal Convolutional Networks

2021 ◽  
Vol 11 (15) ◽  
pp. 6975
Author(s):  
Tao Zhang ◽  
Lun He ◽  
Xudong Li ◽  
Guoqing Feng
Keyword(s):  
Performance Improvement ◽  
State Of The Art ◽  
Error Rates ◽  
Convolutional Network ◽  
Convolutional Networks ◽  
Sentence Level ◽  
End To End ◽  
High Level ◽  
Improved Accuracy ◽  
Talking Face

Lipreading aims to recognize sentences being spoken by a talking face. In recent years, the lipreading method has achieved a high level of accuracy on large datasets and made breakthrough progress. However, lipreading is still far from being solved, and existing methods tend to have high error rates on the wild data and have the defects of disappearing training gradient and slow convergence. To overcome these problems, we proposed an efficient end-to-end sentence-level lipreading model, using an encoder based on a 3D convolutional network, ResNet50, Temporal Convolutional Network (TCN), and a CTC objective function as the decoder. More importantly, the proposed architecture incorporates TCN as a feature learner to decode feature. It can partly eliminate the defects of RNN (LSTM, GRU) gradient disappearance and insufficient performance, and this yields notable performance improvement as well as faster convergence. Experiments show that the training and convergence speed are 50% faster than the state-of-the-art method, and improved accuracy by 2.4% on the GRID dataset.

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