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Entropy 2015, 17(3), 914-927; doi:10.3390/e17030914

Application of the Permutation Entropy over the Heart Rate Variability for the Improvement of Electrocardiogram-based Sleep Breathing Pause Detection

1
Institute for Technological Development and Innovation in Communications, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria 35017, Spain
2
Institute of Oceanography and Global Change, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria 35017, Spain
*
Author to whom correspondence should be addressed.
Academic Editors: Carlos M. Travieso-González and Jesús B. Alonso-Hernández
Received: 30 November 2014 / Revised: 12 February 2015 / Accepted: 15 February 2015 / Published: 20 February 2015
(This article belongs to the Special Issue Entropy in Bioinspired Intelligence)
View Full-Text   |   Download PDF [375 KB, uploaded 24 February 2015]   |  

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 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). View Full-Text
Keywords: permutation entropy; sleep apnea; logistic regression; electrocardiogram derived respiratory; cepstrum permutation entropy; sleep apnea; logistic regression; electrocardiogram derived respiratory; cepstrum
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Ravelo-García, A.G.; Navarro-Mesa, J.L.; Casanova-Blancas, U.; Martin-Gonzalez, S.; Quintana-Morales, P.; Guerra-Moreno, I.; Canino-Rodríguez, J.M.; Hernández-Pérez, E. Application of the Permutation Entropy over the Heart Rate Variability for the Improvement of Electrocardiogram-based Sleep Breathing Pause Detection. Entropy 2015, 17, 914-927.

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