A State Space and Density Estimation Framework for Sleep Staging in Obstructive Sleep Apnea

Kang, Dae Young; DeYoung, Pamela; Malhotra, Atul; Owens, Robert L.; Coleman, Todd P.

doi:10.1109/tbme.2017.2702123

Cited by 44 publications

(24 citation statements)

References 51 publications

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“…Therefore, the model design of this study considers the possibility of abnormal signal acquisition during overnight sleep PSG. Second, since there are transitional rules associated with the sleep staging, Markov models, CNNs, and RNNs have been used in recognition of sleep EEG in recent years [13][14][15][16][17]. This research innovatively applied the method of three-epoch splicing to simulate the technician recognition of EEG, so that if there is an epoch with atypical or severe interference, technicians could refer to the previous and following epochs of the EEG.…”

Section: Discussionmentioning

confidence: 99%

Automated multi-model deep neural network for sleep stage scoring with unfiltered clinical data

Zhang

et al. 2020

Sleep Breath

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Purpose To develop an automated framework for sleep stage scoring from PSG via a deep neural network. Methods An automated deep neural network was proposed by using a multi-model integration strategy with multiple signal channels as input. All of the data were collected from one single medical center from July 2017 to April 2019. Model performance was evaluated by overall classification accuracy, precision, recall, weighted F1 score, and Cohen's Kappa.Results Two hundred ninety-four sleep studies were included in this study; 122 composed the training dataset, 20 composed the validation dataset, and 152 were used in the testing dataset. The network achieved human-level annotation performance with an average accuracy of 0.8181, weighted F1 score of 0.8150, and Cohen's Kappa of 0.7276. Top-2 accuracy (the proportion of test samples for which the true label is among the two most probable labels given by the model) was significantly improved compared to the overall classification accuracy, with the average being 0.9602. The number of arousals affected the model's performance. Conclusion This research provides a robust and reliable model with the inter-rater agreement nearing that of human experts. Determining the most appropriate evaluation parameters for sleep staging is a direction for future research.

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Section: Discussionmentioning

confidence: 99%

Automated multi-model deep neural network for sleep stage scoring with unfiltered clinical data

Zhang

et al. 2020

Sleep Breath

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“…The ISRUC3 database was scored by two experts and the label made by the second expert was used in this paper. The Pz-Oz channel of the S-EDF database is used according to the recommendations of various studies [ 3 , 4 , 5 , 6 , 7 ]. At the same time, for the DRMS database, as the researches [ 9 , 10 , 11 , 12 ] recommended, the Cz-A1 channel was adopted in this work.…”

Section: Methodsmentioning

confidence: 99%

“…Diykh et al adopted different structural and spectral attributes extracted from weighted undirected networks to automatically classify the sleep stages [ 4 ]. Kang et al present a statistical framework to estimate whole-night sleep states in patients with obstructive sleep apnea (OSA)—the most common sleep disorder [ 5 ]. In this framework, they extracted 11 spectral features from 60903 epochs to estimate per-night sleep stages with a 5-state hidden Markov model.…”

Section: Introductionmentioning

confidence: 99%

An Automatic Sleep Stage Classification Algorithm Using Improved Model Based Essence Features

Shen

Ran

et al. 2020

Sensors

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The automatic sleep stage classification technique can facilitate the diagnosis of sleep disorders and release the medical expert from labor-consumption work. In this paper, novel improved model based essence features (IMBEFs) were proposed combining locality energy (LE) and dual state space models (DSSMs) for automatic sleep stage detection on single-channel electroencephalograph (EEG) signals. Firstly, each EEG epoch is decomposed into low-level sub-bands (LSBs) and high-level sub-bands (HSBs) by wavelet packet decomposition (WPD), separately. Then, the DSSMs are estimated by the LSBs and the LE calculation is carried out on HSBs. Thirdly, the IMBEFs extracted from the DSSM and LE are fed into the appropriate classifier for sleep stage classification. The performance of the proposed method was evaluated on three public sleep databases. The experimental results show that under the Rechtschaffen’s and Kale’s (R&K) standard, the sleep stage classification accuracies of six classes on the Sleep EDF database and the Dreams Subjects database are 92.04% and 78.92%, respectively. Under the American Academy of Sleep Medicine (AASM) standard, the classification accuracies of five classes in the Dreams Subjects database and the ISRUC database reached 79.90% and 81.65%. The proposed method can be used for reliable sleep stage classification with high accuracy compared with state-of-the-art methods.

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“…According to the AASM rule, as sleep deepens, high-frequency content of the EEG decreases and the signal waves become slow. PSD is used as a popular handcrafted feature in sleep studies [41], [42]. The Welch method [43] is used to estimate the power spectrum of a time series.…”

Section: ) Power Spectral Density (Psd)mentioning

confidence: 99%

Transfer Learning Convolutional Neural Network for Sleep Stage Classification Using Two-Stage Data Fusion Framework

et al. 2020

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The most important part of sleep quality assessment is the classification of sleep stages, which helps to diagnose sleep-related disease. In the traditional sleep staging method, subjects have to spend a night in the sleep clinic for recording polysomnogram. Sleep expert classifies the sleep stages by monitoring the signals, which is time consuming and frustrating task and can be affected by human error. New studies propose fully automated techniques for classifying sleep stages that makes sleep scoring possible at home. Despite comprehensive studies have been presented in this field the classification results have not yet reached the gold standard due to the concentration on the use of a limited source of information such as single channel EEG. Therefore, this article introduces a new method for fusing two sources of information, including electroencephalogram (EEG) and electrooculogram (EOG), to achieve promising results in the classification of sleep stages. In the proposed method, extracted features from the EEG and EOG signals, are divided into two feature sets consisting of the EEG features and fused features of EEG and EOG. Then, each feature set transformed into a horizontal visibility graph (HVG). The images of the HVG are produced in a novel framework and classified by proposed transfer learning convolutional neural network for data fusion (TLCNN-DF). Employing transfer learning at the training stage of the model has accelerated the training process of the CNN and improved the performance of the model. The proposed algorithm is used to classify the Sleep-EDF and Sleep-EDFx benchmark datasets. The algorithm can classify the Sleep-EDF dataset with an accuracy of 93.58% and Cohen's kappa coefficient of 0.899. The results show proposed method can achieve superior performance compared to state-of-the-art studies on classification of sleep stages. Furthermore, it can attain reliable results as an alternative to conventional sleep staging. INDEX TERMS Convolutional neural network, data fusion, horizontal visibility graph, Sleep stage classification, transfer learning.

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A State Space and Density Estimation Framework for Sleep Staging in Obstructive Sleep Apnea

Cited by 44 publications

References 51 publications

Automated multi-model deep neural network for sleep stage scoring with unfiltered clinical data

Automated multi-model deep neural network for sleep stage scoring with unfiltered clinical data

An Automatic Sleep Stage Classification Algorithm Using Improved Model Based Essence Features

Transfer Learning Convolutional Neural Network for Sleep Stage Classification Using Two-Stage Data Fusion Framework

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