Article

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.
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