scholarly journals A New Approach for Automatic Sleep Staging: Siamese Neural Networks

2021 ◽  
Vol 38 (5) ◽  
pp. 1423-1430
Author(s):  
Enes Efe ◽  
Seral Özşen
Keyword(s):  
Neural Networks ◽  
Sleep Quality ◽  
Network Design ◽  
Classification Performance ◽  
Measurement Methods ◽  
Sleep Stages ◽  
Sleep Staging ◽  
New Approach ◽  
Leibler Divergence ◽  
Staging Systems

Sleep staging aims to gather biological signals during sleep, and categorize them by sleep stages: waking (W), non-REM-1 (N1), non-REM-2 (N2), non-REM-3 (N3), and REM (R). These stages are distributed irregularly, and their number varies with sleep quality. These features adversely affect the performance of automatic sleep staging systems. This paper adopts Siamese neural networks (SNNs) to solve the problem. During the network design, seven distance measurement methods, namely, Euclidean, Manhattan, Jaccard, Cosine, Canberra, Bray-Curtis, and Kullback Leibler divergence (KLD), were compared, revealing that Bray-Curtis (83.52%) and Cosine (84.94%) methods boast the best classification performance. The results of our approach are promising compared to traditional methods.

scholarly journals An automatic sleep staging method based on CRNN-HMM model

2021 ◽  
Vol 2078 (1) ◽  
pp. 012054
Author(s):  
Mengran Wu ◽  
Hong Xie ◽  
Huiping Shi
Keyword(s):  
Sleep Quality ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Short Term Memory ◽  
Single Channel ◽  
Hidden Markov ◽  
Classification Performance ◽  
Sleep Stages ◽  
Sleep Staging ◽  
Data Set

Abstract Sleep staging is an important process for detecting sleep quality and diagnosing sleep disorders. However, traditional sleep staging is a labor-intensive task, and it is prone to subjective errors. Therefore, this paper innovatively proposes an automatic sleep staging model based on single-channel EOG—CRNN-HMM. The CRNN-HMM in this paper combines Convolutional recursive neural networks(CRNN) and hidden Markov model(HMM). The main idea of this model is to use CRNN to automatically extract features from EOG, and send the feature signals to a variant of RNN, Bi-directional Long Short-Term Memory(BiLSTM), to mine the dependencies between sleep stages and realize automatic staging of sleep data. Finally, a Hidden Markov Model is used to convert the prior information of the sleep phase of the adjacent EOG cycle in order to improve the classification performance of S1, thereby improving the classification performance of CRNN. The simulation results show that the overall accuracy of the model on the CAP-Sleep data set reaches 95.0%, which proves that the model can provide a way for the evaluation of sleep quality.

scholarly journals U-Sleep: resilient high-frequency sleep staging

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Mathias Perslev ◽  
Sune Darkner ◽  
Lykke Kempfner ◽  
Miki Nikolic ◽  
Poul Jørgen Jennum ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Frequency ◽  
State Of The Art ◽  
Sleep Medicine ◽  
Sleep Stages ◽  
Sleep Staging ◽  
Wide Range ◽  
Staging Systems ◽  
All Cause Mortality ◽  
Global Population

AbstractSleep disorders affect a large portion of the global population and are strong predictors of morbidity and all-cause mortality. Sleep staging segments a period of sleep into a sequence of phases providing the basis for most clinical decisions in sleep medicine. Manual sleep staging is difficult and time-consuming as experts must evaluate hours of polysomnography (PSG) recordings with electroencephalography (EEG) and electrooculography (EOG) data for each patient. Here, we present U-Sleep, a publicly available, ready-to-use deep-learning-based system for automated sleep staging (sleep.ai.ku.dk). U-Sleep is a fully convolutional neural network, which was trained and evaluated on PSG recordings from 15,660 participants of 16 clinical studies. It provides accurate segmentations across a wide range of patient cohorts and PSG protocols not considered when building the system. U-Sleep works for arbitrary combinations of typical EEG and EOG channels, and its special deep learning architecture can label sleep stages at shorter intervals than the typical 30 s periods used during training. We show that these labels can provide additional diagnostic information and lead to new ways of analyzing sleep. U-Sleep performs on par with state-of-the-art automatic sleep staging systems on multiple clinical datasets, even if the other systems were built specifically for the particular data. A comparison with consensus-scores from a previously unseen clinic shows that U-Sleep performs as accurately as the best of the human experts. U-Sleep can support the sleep staging workflow of medical experts, which decreases healthcare costs, and can provide highly accurate segmentations when human expertize is lacking.

scholarly journals Expert-level sleep scoring with deep neural networks

2018 ◽  
Vol 25 (12) ◽  
pp. 1643-1650 ◽  
Author(s):  
Siddharth Biswal ◽  
Haoqi Sun ◽  
Balaji Goparaju ◽  
M Brandon Westover ◽  
Jimeng Sun ◽  
...  
Keyword(s):  
Neural Networks ◽  
Sleep Apnea ◽  
Deep Neural Networks ◽  
Training Data ◽  
Sleep Stages ◽  
Apnea Hypopnea Index ◽  
Sleep Staging ◽  
Limb Movements ◽  
Sleep Scoring ◽  
At Home

Abstract Objectives Scoring laboratory polysomnography (PSG) data remains a manual task of visually annotating 3 primary categories: sleep stages, sleep disordered breathing, and limb movements. Attempts to automate this process have been hampered by the complexity of PSG signals and physiological heterogeneity between patients. Deep neural networks, which have recently achieved expert-level performance for other complex medical tasks, are ideally suited to PSG scoring, given sufficient training data. Methods We used a combination of deep recurrent and convolutional neural networks (RCNN) for supervised learning of clinical labels designating sleep stages, sleep apnea events, and limb movements. The data for testing and training were derived from 10 000 clinical PSGs and 5804 research PSGs. Results When trained on the clinical dataset, the RCNN reproduces PSG diagnostic scoring for sleep staging, sleep apnea, and limb movements with accuracies of 87.6%, 88.2% and 84.7% on held-out test data, a level of performance comparable to human experts. The RCNN model performs equally well when tested on the independent research PSG database. Only small reductions in accuracy were noted when training on limited channels to mimic at-home monitoring devices: frontal leads only for sleep staging, and thoracic belt signals only for the apnea-hypopnea index. Conclusions By creating accurate deep learning models for sleep scoring, our work opens the path toward broader and more timely access to sleep diagnostics. Accurate scoring automation can improve the utility and efficiency of in-lab and at-home approaches to sleep diagnostics, potentially extending the reach of sleep expertise beyond specialty clinics.

Comprehensive Spectral Analysis of HRV during Sleep and their Application in Sleep Staging

2013 ◽  
Vol 765-767 ◽  
pp. 2668-2672
Author(s):  
Meng Xiao ◽  
Hong Yan ◽  
Zhi Jun Xiao ◽  
Xiang Lin Yang ◽  
Yu Zhou Yang
Keyword(s):  
Low Frequency ◽  
Classification Performance ◽  
Sleep Stages ◽  
Spectral Features ◽  
Sleep Staging ◽  
Power Mean ◽  
Spectral Bands ◽  
Random Forest Method ◽  
Almost All ◽  
Light Sleep

Most studies considering spectral features of HRV during sleep divided total frequency band into low frequency (LF, 0.04~0.15Hz) and high frequency (HF, 0.15~0.4 Hz) roughly, and were limited to a few measures like the power in LF and HF, or the ratio of them. To make full use of HRV, more comprehensive spectral features were evaluated in this paper. LF was further divided into true LF (0.04~0.1Hz) and medium frequency (0.1~0.15Hz). Spectrum power, mean frequency and spectral entropy of different spectral bands, fractal dimension and peak in HF (20 measures in total) were calculated for wake, REM, light sleep and deep sleep. The significance between sleep stages of each feature was evaluated. The random forest method was adopted for sleep staging and features importance rank. The results suggested that almost all the new proposed features showed significant differences during different sleep stages. They can improve sleep stages classification performance notably. Our study provided new features for sleep stages classification based on ECG.

scholarly journals Capturing the Development of Internal Representations in a High-Performing Deep Network for Sleep Stage Classification

2021 ◽  
Vol 2 (4) ◽  
Author(s):  
Sarun Paisarnsrisomsuk ◽  
Carolina Ruiz ◽  
Sergio A. Alvarez
Keyword(s):  
Input Data ◽  
Single Channel ◽  
Sleep Stage ◽  
Quantitative Measure ◽  
Classification Performance ◽  
Sleep Stages ◽  
Layer Depth ◽  
Human Sleep ◽  
Stage Classification ◽  
Sleep Stage Classification

AbstractDeep neural networks can provide accurate automated classification of human sleep signals into sleep stages that enables more effective diagnosis and treatment of sleep disorders. We develop a deep convolutional neural network (CNN) that attains state-of-the-art sleep stage classification performance on input data consisting of human sleep EEG and EOG signals. Nested cross-validation is used for optimal model selection and reliable estimation of out-of-sample classification performance. The resulting network attains a classification accuracy of $$84.50 \pm 0.13\%$$ 84.50 ± 0.13 % ; its performance exceeds human expert inter-scorer agreement, even on single-channel EEG input data, therefore providing more objective and consistent labeling than human experts demonstrate as a group. We focus on analyzing the learned internal data representations of our network, with the aim of understanding the development of class differentiation ability across the layers of processing units, as a function of layer depth. We approach this problem visually, using t-Stochastic Neighbor Embedding (t-SNE), and propose a pooling variant of Centered Kernel Alignment (CKA) that provides an objective quantitative measure of the development of sleep stage specialization and differentiation with layer depth. The results reveal a monotonic progression of both of these sleep stage modeling abilities as layer depth increases.

scholarly journals A Systematic Review of Sensing Technologies for Wearable Sleep Staging

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1562
Author(s):  
Syed Anas Imtiaz
Keyword(s):  
Systematic Review ◽  
Classification Accuracy ◽  
Sleep Stages ◽  
Sleep Staging ◽  
Regulatory Requirements ◽  
Wearable System ◽  
Wearable Systems

Designing wearable systems for sleep detection and staging is extremely challenging due to the numerous constraints associated with sensing, usability, accuracy, and regulatory requirements. Several researchers have explored the use of signals from a subset of sensors that are used in polysomnography (PSG), whereas others have demonstrated the feasibility of using alternative sensing modalities. In this paper, a systematic review of the different sensing modalities that have been used for wearable sleep staging is presented. Based on a review of 90 papers, 13 different sensing modalities are identified. Each sensing modality is explored to identify signals that can be obtained from it, the sleep stages that can be reliably identified, the classification accuracy of systems and methods using the sensing modality, as well as the usability constraints of the sensor in a wearable system. It concludes that the two most common sensing modalities in use are those based on electroencephalography (EEG) and photoplethysmography (PPG). EEG-based systems are the most accurate, with EEG being the only sensing modality capable of identifying all the stages of sleep. PPG-based systems are much simpler to use and better suited for wearable monitoring but are unable to identify all the sleep stages.

scholarly journals Analysis and visualization of sleep stages based on deep neural networks

2021 ◽  
Vol 10 ◽  
pp. 100064
Author(s):  
Patrick Krauss ◽  
Claus Metzner ◽  
Nidhi Joshi ◽  
Holger Schulze ◽  
Maximilian Traxdorf ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Sleep Stages

scholarly journals A Survey on Bias in Deep NLP

2021 ◽  
Vol 11 (7) ◽  
pp. 3184
Author(s):  
Ismael Garrido-Muñoz  ◽  
Arturo Montejo-Ráez  ◽  
Fernando Martínez-Santiago  ◽  
L. Alfonso Ureña-López 
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Natural Language Processing ◽  
Probability Distribution ◽  
Natural Language ◽  
Network Design ◽  
Language Processing ◽  
Deep Neural Networks ◽  
Learning Processes ◽  
Relevant Issue

Deep neural networks are hegemonic approaches to many machine learning areas, including natural language processing (NLP). Thanks to the availability of large corpora collections and the capability of deep architectures to shape internal language mechanisms in self-supervised learning processes (also known as “pre-training”), versatile and performing models are released continuously for every new network design. These networks, somehow, learn a probability distribution of words and relations across the training collection used, inheriting the potential flaws, inconsistencies and biases contained in such a collection. As pre-trained models have been found to be very useful approaches to transfer learning, dealing with bias has become a relevant issue in this new scenario. We introduce bias in a formal way and explore how it has been treated in several networks, in terms of detection and correction. In addition, available resources are identified and a strategy to deal with bias in deep NLP is proposed.

scholarly journals Structure Extension of Tree-Augmented Naive Bayes

Entropy ◽  
2019 ◽  
Vol 21 (8) ◽  
pp. 721 ◽  
Author(s):  
YuGuang Long ◽  
LiMin Wang ◽  
MingHui Sun
Keyword(s):  
Naive Bayes ◽  
Classification Performance ◽  
Naïve Bayes ◽  
Training Data ◽  
Conditional Probability Distribution ◽  
Independence Assumption ◽  
Bayesian Network Classifiers ◽  
Leibler Divergence ◽  
The Difference ◽  
Structure Extension

Due to the simplicity and competitive classification performance of the naive Bayes (NB), researchers have proposed many approaches to improve NB by weakening its attribute independence assumption. Through the theoretical analysis of Kullback–Leibler divergence, the difference between NB and its variations lies in different orders of conditional mutual information represented by these augmenting edges in the tree-shaped network structure. In this paper, we propose to relax the independence assumption by further generalizing tree-augmented naive Bayes (TAN) from 1-dependence Bayesian network classifiers (BNC) to arbitrary k-dependence. Sub-models of TAN that are built to respectively represent specific conditional dependence relationships may “best match” the conditional probability distribution over the training data. Extensive experimental results reveal that the proposed algorithm achieves bias-variance trade-off and substantially better generalization performance than state-of-the-art classifiers such as logistic regression.

Detection of the Sleep Stages Throughout Non-Obtrusive Measures of Inter-Beat Fluctuations and Motion: Night and Day Sleep of Female Shift Workers

2017 ◽  
Vol 16 (04) ◽  
pp. 1750033 ◽  
Author(s):  
Martin O. Mendez ◽  
Elvia R. Palacios-Hernandez ◽  
Alfonso Alba ◽  
Juha M. Kortelainen ◽  
Mirja L. Tenhunen ◽  
...  
Keyword(s):  
Eye Movement ◽  
Performance Measures ◽  
Night Shift ◽  
Sleep Stages ◽  
Support Vector ◽  
Rapid Eye Movement ◽  
Sleep Staging ◽  
Kappa Index ◽  
Extensive Evaluation ◽  
Night And Day

Automatic sleep staging based on inter-beat fluctuations and motion signals recorded through a pressure bed sensor during sleep is presented. The analysis of the sleep was based on the three major divisions of the sleep time: Wake, non-rapid eye movement (nREM) and rapid eye movement (REM) sleep stages. Twelve sleep recordings, from six females working alternate shift, with their respective annotations were used in the study. Six recordings were acquired during the night and six during the day after a night shift. A Time-Variant Autoregressive Model was used to extract features from inter-beat fluctuations which later were fed to a Support Vector Machine classifier. Accuracy, Kappa index, and percentage in wake, REM and nREM were used as performance measures. Comparison between the automatic sleep staging detection and the standard clinical annotations, shows mean values of [Formula: see text]% for accuracy [Formula: see text] for kappa index, and mean errors of 5% for sleep stages. The performance measures were similar for night and day sleep recordings. In this sample of recordings, the results suggest that inter-beat fluctuations and motions acquired in non-obtrusive way carried valuable information related to the sleep macrostructure and could be used to support to the experts in extensive evaluation and monitoring of sleep.

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