@inbook{ae70ccca2d1746a4a37283275ac24d0d,
title = "Analysis of nocturnal oxygen saturation recordings using kernel entropy to assist in sleep apnea-hypopnea diagnosis",
abstract = "In this study, a new entropy measure known as kernel entropy (KerEnt), which quantifies the irregularity in a series, was applied to nocturnal oxygen saturation (SaO 2) recordings. A total of 96 subjects suspected of suffering from sleep apnea-hypopnea syndrome (SAHS) took part in the study: 32 SAHS-negative and 64 SAHS-positive subjects. Their SaO 2 signals were separately processed by means of KerEnt. Our results show that a higher degree of irregularity is associated to SAHS-positive subjects. Statistical analysis revealed significant differences between the KerEnt values of SAHS-negative and SAHS-positive groups. The diagnostic utility of this parameter was studied by means of receiver operating characteristic (ROC) analysis. A classification accuracy of 81.25% (81.25% sensitivity and 81.25% specificity) was achieved. Repeated apneas during sleep increase irregularity in SaO 2 data. This effect can be measured by KerEnt in order to detect SAHS. This non-linear measure can provide useful information for the development of alternative diagnostic techniques in order to reduce the demand for conventional polysomnography (PSG).",
keywords = "kernel entropy, bayesian",
author = "Marcos, {J. Victor} and Roberto Hornero and Nabney, {Ian T.} and Daniel {\'A}lvarez and {del Campo}, F{\'e}lix",
note = "MEDLINE{\textregistered} is the source for the citation and abstract of this record.",
year = "2011",
doi = "10.1109/IEMBS.2011.6090499",
language = "English",
isbn = "978-1-4244-4121-1",
series = "Conference proceedings IEEE Engineering in Medicine and Biology Society",
publisher = "IEEE Computer Society",
pages = "1745--1748",
booktitle = "Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC, 2011",
address = "United States",
}