Analysis of nocturnal oxygen saturation recordings using kernel entropy to assist in sleep apnea-hypopnea diagnosis

J. Victor Marcos, Roberto Hornero, Ian T. Nabney, Daniel Álvarez, Félix del Campo

Research output: Chapter in Book/Report/Conference proceedingChapter in a book

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).
Original languageEnglish
Title of host publicationAnnual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC, 2011
Place of PublicationUnited States
PublisherIEEE Computer Society
Pages1745-1748
Number of pages4
ISBN (Print)978-1-4244-4121-1
DOIs
Publication statusPublished - 2011

Publication series

NameConference proceedings IEEE Engineering in Medicine and Biology Society
PublisherIEEE

Bibliographical note

MEDLINE® is the source for the citation and abstract of this record.

Keywords

  • kernel entropy, bayesian

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