Electrocardiogram-based sleep analysis for sleep apnea screening and diagnosis

Ma, Yan; Sun, Shibo; Zhang, Ming; Guo, Dan; Liu, Arron Runzhou; Wei, Yu-Lin

doi:10.1007/s11325-019-01874-8

Cited by 24 publications

(18 citation statements)

References 57 publications

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“…Therefore, in this part, we present them for each proposed method in our references along with the proposed detection results to facilitate the comparison. Some of the proposed methods (as for example in[ 4 ]) have obviously weak detection results while having less computational complexity. Other methods (like the ones associated with the DNNs) have satisfactory results but also have extracomputational complexity.…”

Section: Resultsmentioning

confidence: 99%

“…Several separated OSA detection methods have been proposed up until now. [ 1 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 ] Most of these methods have consisted of feature extraction, feature selection, and classifier parts. In Figure 1 , we can see the collective flowchart of an OSA detection approach based on the ECG signal processing:…”

Section: Introductionmentioning

confidence: 99%

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Enhancing obstructive apnea disease detection using dual-tree complex wavelet transform-based features and the hybrid “K-means, recursive least-squares” learning for the radial basis function network

2020

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Enhancing obstructive apnea disease detection using dual-tree complex wavelet transform-based features and the hybrid “K-means, recursive least-squares” learning for the radial basis function network

2020

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“…Conclusion. -Traditionally, obstructive sleep apnea is diagnosed using polysomnography in combination with respiratory and/or cardiovascular data (heart rate [46] and blood pressure [47]), and conventional hypnograms alone are often considered ineffective in determining OSA severity [46]. In this letter, we introduce a method to estimate the OSA severity through a physiological parameter L that is derived solely from standard hypnograms (fig.…”

Section: Estimating the Characteristic Time Constant δTmentioning

confidence: 99%

Random walk model simulates the increased drowsiness of children with obstructive sleep apnea

Guo

Dvir

Havlin

et al. 2020

EPL

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Obstructive sleep apnea (OSA) is a common sleep disorder, which is particularly harmful to children as it may lead to learning deficits, attention deficit hyperactivity disorder (ADHD) and growth retardation. Furthermore, OSA alters the dynamics of sleep-stage transitions and in particular increases the transition time from being awake to falling asleep ("drowsiness"). In this letter, we show that sleep bout durations during this transient state can be described by an exponential distribution with a longer characteristic time scale for OSA compared to healthy children. This finding can be simulated and better understood by using a random walk model of the integrated neuronal voltage of wake-promoting neurons, and by introducing a new concept of a light sleep threshold parameter L that distinguishes between drowsiness and deeper forms of light sleep. Our analysis also shows that the value of L correlates well with OSA severity. Moreover, we find that after OSA treatment, the parameter L returns to normal values similar to those we detected for healthy children. We anticipate that our methodology can help in better understanding and modeling sleep dynamics, and may improve diagnostics and treatment monitoring of OSA.

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“…The improvement in detecting the OSA can improve the daily life of many patients and save the lives of many others. The OSA detection has been the investigation topic of many researchers [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ]. As the ECG signal acquisition is much faster and less computationally demanding, the OSA can be detected from analysis of short durations of the ECG signals and this helps the designing of the home setting of handled devices that can easily be used by the patients.…”

Section: Introductionmentioning

confidence: 99%

“…Reference [ 10 ] is based on cardiopulmonary coupling (CPC) and Respiratory event index (REI) features and statistical analysis for classification.…”

Section: Introductionmentioning

confidence: 99%

Introducing the hybrid “K-means, RLS” learning for the RBF network in obstructive apnea disease detection using Dual-tree complex wavelet transform based features

Ostadieh

Amirani

2020

Journal of Electrical Bioimpedance

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Apnea is one of the deadliest diseases that can be prevented and cured if it is detected in time. In this paper, we propose a precise method for early detection of the obstructive sleep apnea (OSA) disease using the latest feature selection and extraction methods. The feature selection in this paper is based on the Dual tree complex wavelet (DT-CWT) coefficients of the ECG signals of several patients. The feature extraction from these coefficients is done using frequency and time techniques. The Feature selection is done using the spectral regression discriminant analysis (SRDA) algorithm and the classification is performed using the hybrid RBF network. A hybrid RBF neural network is introduced in this paper for detecting apnea that is much less computationally demanding than the previously presented SVM networks. Our findings showed a 3 percent improvement in the detection and at least a 30 percent reduction in the computational complexity in comparison with methods that have been presented recently.

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Electrocardiogram-based sleep analysis for sleep apnea screening and diagnosis

Cited by 24 publications

References 57 publications

Enhancing obstructive apnea disease detection using dual-tree complex wavelet transform-based features and the hybrid “K-means, recursive least-squares” learning for the radial basis function network

Enhancing obstructive apnea disease detection using dual-tree complex wavelet transform-based features and the hybrid “K-means, recursive least-squares” learning for the radial basis function network

Random walk model simulates the increased drowsiness of children with obstructive sleep apnea

Introducing the hybrid “K-means, RLS” learning for the RBF network in obstructive apnea disease detection using Dual-tree complex wavelet transform based features

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