Abstract:In this paper the permutation entropy (PE) obtained from heart rate variability (HRV) is analyzed in a statistical model. In this model we also integrate other feature extraction techniques, the cepstrum coefficients derived from the same HRV and a set of band powers obtained from the electrocardiogram derived respiratory (EDR) signal. The aim of the model is detecting obstructive sleep apnea (OSA) events. For this purpose, we apply two statistical classification methods: Logistic Regression (LR) and Quadratic Discriminant Analysis (QDA). For testing the models we use seventy ECG recordings from the Physionet database which are divided into equal-size learning and testing sets. Both sets consist of 35 recordings, each containing a single ECG signal. In our experiments we have found that the features extracted from the EDR signal present a sensitivity of 65.6% and specificity of 87.7% (auc = 85) in the LR classifier, and sensitivity of 59.4% and specificity of 90.3% (auc = 83.9) in the QDA classifier. The HRV-based cepstrum coefficients present a sensitivity of 63.8% and specificity of 89.2% (auc = 86) in the LR OPEN ACCESSEntropy 2015, 17 915 classifier, and sensitivity of 67.2% and specificity of 86.8% (auc = 86.9) in the QDA. Subsequent tests show that the contribution of the permutation entropy increases the performance of the classifiers, implying that the complexity of RR interval time series play an important role in the breathing pauses detection. Particularly, when all features are jointly used, the quantification task reaches a sensitivity of 71.9% and specificity of 92.1% (auc = 90.3) for LR. Similarly, for QDA the sensitivity is 75.1% and the specificity is 90.5% (auc = 91.7).
Permutation entropy obtained from heart rate variability (HRV) is analyzed in a statistical model integrating electrocardiogram derived respiratory (EDR) features and cepstrum coefficients in order to detect obstructive sleep apnea (OSA) events. 70 ECG recordings from Physionet database are divided into a learning set and a test set of equal size. Each set consists of 35 recordings, containing a single ECG signal. Each recording includes a set of reference annotations, one for each minute, which indicates the presence or absence of apnea during that minute. Statistical classification methods based on Logistic Regression (LR) is applied to the classification of sleep apnea epochs. EDR presents a sensitivity of 64.3% and specificity of 86.5% (auc=83.9). Cepstrum presents a sensitivity of 63.8% and specificity of 89.2% (auc=86). Contribution of the permutation entropy increases the performance of the LR model, playing an important role in the OSA quantification task. In particular, when all features are analyzed, classifier reaches a sensitivity of 70.2% and specificity of 91.8% (auc=89.8).
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