Utility of approximate entropy from overnight pulse oximetry data in the diagnosis of the obstructive sleep apnea syndrome.
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Hornero, R, Alvarez, D, Abásolo, D, del Campo, F and Zamarrón, C (2007) Utility of approximate entropy from overnight pulse oximetry data in the diagnosis of the obstructive sleep apnea syndrome. IEEE Trans Biomed Eng, 54 (1). 107 - 113. ISSN 0018-9294
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Official URL: http://dx.doi.org/10.1109/TBME.2006.883821
Abstract
Approximate entropy (ApEn) is a family of statistics introduced as a quantification of regularity in time series without any a priori knowledge about the system generating them. The aim of this preliminary study was to assess whether a time series analysis of arterial oxygen saturation (SaO2) signals from overnight pulse oximetry by means of ApEn could yield essential information on the diagnosis of obstructive sleep apnea (OSA) syndrome. We analyzed SaO2 signals from 187 subjects: 111 with a positive diagnosis of OSA and 76 with a negative diagnosis of OSA. We divided our data in a training set (44 patients with OSA Positive and 30 patients with OSA Negative) and a test set (67 patients with OSA Positive and 46 patients with OSA Negative). The training set was used for algorithm development and optimum threshold selection. Results showed that recurrence of apnea events in patients with OSA determined a significant increase in ApEn values. This method was assessed prospectively using the test dataset, where we obtained 82.09% sensitivity and 86.96% specificity. We conclude that ApEn analysis of SaO2 from pulse oximetric recording could be useful in the study of OSA
| Item Type: | Article |
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| Additional Information: | Copyright 2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. |
| Uncontrolled Keywords: | Algorithms, Artificial Intelligence, Diagnosis, Computer-Assisted, Entropy, Female, Humans, Male, Middle Aged, Monitoring, Physiologic, Oximetry, Pattern Recognition, Automated, Reproducibility of Results, Sensitivity and Specificity, Sleep Apnea, Obstructive |
| Divisions: | Faculty of Engineering and Physical Sciences > Mechanical Engineering Sciences |
| ID Code: | 58445 |
| Deposited By: | Symplectic Elements |
| Deposited On: | 03 Jan 2012 16:22 |
| Last Modified: | 13 Jun 2013 02:54 |
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