Sleep Disordered Breathing Detection Using Heart Rate Variability and R-Peak Envelope Spectrogram

Department of Bioengineering, the University of Texas at Arlington, Arlington, TX 76010, USA.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 01/2009; 2009:7106-9. DOI: 10.1109/IEMBS.2009.5332897
Source: IEEE Xplore


We report that combining the interbeat heart rate as measured by the RR interval (RR) and R-peak envelope (RPE) derived from R-peak of ECG waveform may significantly improve the detection of sleep disordered breathing (SDB) from single lead ECG recording. The method uses textural features extracted from normalized gray-level cooccurrence matrices of the time frequency plots of HRV or RPE sequences. An optimum subset of textural features is selected for classification of the records. A multi-layer perceptron (MLP) serves as a classifier. To evaluate the performance of the proposed method, single Lead ECG recordings from 7 normal subjects and 7 obstructive sleep apnea patients were used. With 500 randomized Monte-Carlo simulations, the average training sensitivity, specificity and accuracy were 100.0%, 99.9%, and 99.9%, respectively. The mean testing sensitivity, specificity and accuracy were 99.0%, 96.7%, and 97.8%, respectively.

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Available from: Mohammad A Al-Abed, Nov 17, 2014
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    • "HRV analysis is a popular noninvasive tool for detecting ANS function [18]. The detection of OSA can be performed and significantly improved through the HRV analysis, since fluctuations in the SaO 2 value of the blood, accompanied by apneic episodes, cause variations in the HR [19] [20]. The SB was used as a criterion for the detection of OSA in many studies [13] [21]. "
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    ABSTRACT: Many articles that appeared in the literature agreed upon the feasibility of diagnosing obstructive sleep apnea (OSA) with a single-lead electrocardiogram (ECG). Although high accuracies have been achieved on detection apneic episodes and classification into apnea/hypopnea, there has not been a consensus on the best method of selecting the feature parameters. This study presents a classification scheme for OSA using common features belonging to time domain (TD), frequency domain (FD) and non-linear calculations of the heart rate variability (HRV) analysis and then proposes a method of feature selection based on the correlation matrices (CMs). The results show that the CMs can be utilized on minimizing the feature sets used for any type of diagnosis.
    Turkish Journal of Electrical Engineering and Computer Sciences 01/2013; 22(2). DOI:10.3906/elk-1207-132 · 0.41 Impact Factor
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    ABSTRACT: There is a need for developing simple signal processing algorithms for less costly, reliable and noninvasive Obstructive Sleep Apnoea (OSA) diagnosing. One of the promising directions is to provide the OSA analysis based on the heart rate variability (HRV), which clearly shows a non-stationary behavior. So, a feature extraction approach, being capable of capturing the dynamic heart rate information and suitable for OSA detection, remains an open issue. Grounded on discriminating capability of frequency bands of HRV activity between normal and OSA patients, features can be extracted. However, some HRV normal spectrograms resemble like pathological ones, and vice versa; so, prior to extract the feature set, the energy spatial contribution contained in each subŨband should be clarified. This paper presents a methodology for OSA detection based on a set of short-time feature banked features that are extracted from the spectrogram of the HRV time series. The methodology introduces the spectral splitting scheme, which searches for spectral components with alike stochastic behavior improving the OSA detection accuracy. Two different splitting approaches are considered (heuristic and relevance-based); both of them performing minute-by-minute classification comparable with other outcomes that are reported in literature, but avoiding more complex methods or more computed features. For validation purposes, the methodology is tested on 1-min HRV-segments estimated from 50 Physionet database recordings. Using a parallel combining k-nn classifier, the assessed dynamic feature set reaches as much as 80% value of accuracy, for both considered approaches of spectral splitting. Attained results can be oriented in research focused on finding alternative methods used for less costly and noninvasive OSA diagnosing with the additional benefit of easier clinical interpretation of HRV-derived parameters.
    Expert Systems with Applications 08/2012; 39(10):9118–9128. DOI:10.1016/j.eswa.2012.02.043 · 2.24 Impact Factor
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    ABSTRACT: This article is based on a combination of time-frequency domain functions, and nonlinear techniques in the analysis of heart rate variability (HRV) for diagnosing obstructive sleep apnea (OSA) using only single-lead electrocardiography (ECG) signals. The contribution of the presented study to earlier ones is that it enables numerically determining what type of HRV features better represent the aforementioned target by using correlation matrices and neural networks (NNs).
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