Literature DB >> 18853941

A portable automated assessment tool for sleep apnea using a combined Holter-oximeter.

Conor Heneghan1, Chern-Pin Chua, John F Garvey, Philip de Chazal, Redmond Shouldice, Patricia Boyle, Walter T McNicholas.   

Abstract

STUDY
OBJECTIVES: Resource limitations have raised interest in portable monitoring systems that can be used by specialist sleep physicians as part of an overall strategy to improve access to the diagnosis of sleep apnea. This study validates a combined electrocardiogram and oximetry recorder (Holter-oximeter) against simultaneous polysomnography for detection of sleep apnea.
DESIGN: Prospective study.
SETTING: A dedicated sleep disorders unit. PARTICIPANTS: 59 adults presenting for evaluation of suspected sleep apnea.
INTERVENTIONS: NA. MEASUREMENTS AND
RESULTS: An automated algorithm previously developed for sleep apnea detection was applied to the electrocardiogram and oximetry measurements. The algorithm provides (a) epoch-by-epoch estimates of apnea occurrence and (b) estimates of overall per-subject AHI. Using separate thresholds of AHI > or =15 and AHI <5 for defining clinically significant and insignificant sleep apnea, sensitivity, specificity, and likelihood ratios, conditional on positive or negative (but not indeterminate) test results were used to assess agreement between the proposed system and polysomnography. Sensitivity of 95.8% and specificity of 100% was achieved. Positive and negative likelihood ratios were >20 and 0.04 respectively, with 16.7% of subjects having intermediate test results (AHI 5-14/h). Regardless ofAHI, 85.3% of respiratory events were correctly annotated on an epoch-by-epoch basis. AHI underestimation bias was 0.9/h, and the antilogs of log-transformed limits of agreement were 0.3 and 2.7. Correlation between estimated and reference AHI was 0.95 (P <0.001).
CONCLUSION: Combined Holter-oximeter monitoring compares well with polysomnography for identifying sleep apnea in an attended setting and is potentially suitable for home-based automated assessment of sleep apnea in a population suspected of having sleep apnea.

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Mesh:

Year:  2008        PMID: 18853941      PMCID: PMC2572749     

Source DB:  PubMed          Journal:  Sleep        ISSN: 0161-8105            Impact factor:   5.849


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