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

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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).

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Publication date2011
Publication titleAnnual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC, 2011
PublisherIEEE
Pages1745-1748
Number of pages4
ISBN (Electronic)978-1-4244-4122-8
ISBN (Print)978-1-4244-4121-1
Original languageEnglish
Event33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Boston (MA), United States

Publication series

NameConference proceedings IEEE Engineering in Medicine and Biology Society
PublisherIEEE
ISSN (Print)1557-170X

Conference

Conference33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Abbreviated titleEMBC ’11
CountryUnited States
CityBoston (MA)
Period30/08/113/09/11

    Keywords

  • kernel entropy, bayesian

DOI

Employable Graduates; Exploitable Research

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