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).

Details

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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