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

ABSTRACT 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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    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; · 0.57 Impact Factor

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