Sleep apnea detection using time-delayed heart rate variability

Sleep apnea is a sleep disorder distinguished by repetitive absence of breathing. Compared with the traditional expensive and cumbersome methods, sleep apnea diagnosis or screening with physiological information that can be easily acquired is needed. This paper describes algorithms using heart rate...

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Published in2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Vol. 2015; pp. 7679 - 7682
Main Authors Nano, Marina-Marinela, Xi Long, Werth, Jan, Aarts, Ronald M., Heusdens, Richard
Format Conference Proceeding Journal Article
LanguageEnglish
Published United States IEEE 01.01.2015
Subjects
Accuracy
Adult
Algorithms
Delay effects
Electrocardiography - methods
Feature extraction
Heart Rate - physiology
Heart rate variability
Humans
Middle Aged
Rail to rail inputs
Signal Processing, Computer-Assisted
Sleep - physiology
Sleep apnea
Sleep Apnea Syndromes - diagnosis
Sleep Apnea Syndromes - physiopathology
Support Vector Machine
Support vector machines
Online AccessGet full text
ISSN1094-687X
1557-170X
DOI10.1109/EMBC.2015.7320171

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Summary:Sleep apnea is a sleep disorder distinguished by repetitive absence of breathing. Compared with the traditional expensive and cumbersome methods, sleep apnea diagnosis or screening with physiological information that can be easily acquired is needed. This paper describes algorithms using heart rate variability (HRV) to automatically detect sleep apneas as long as it can be easily acquired with unobtrusive sensors. Because the changes in cardiac activity are usually hysteretic than the presence of apneas with a few minutes, we propose to use the delayed HRV features to identify the episodes with sleep apneic events. This is expected to help improve the apnea detection performance. Experiments were conducted with a data set of 23 sleep apnea patients using support vector machine (SVM) classifiers and cross validations. Results show that using eleven HRV features with a time delay of 1.5 minutes rather than the features without time delay for SA detection, the overall accuracy increased from 74.9% to 76.2% and the Cohen's Kappa coefficient increased from 0.49 to 0.52. Further, an accuracy of 94.5% and a Kappa of 0.89 were achieved when applying subject-specific classifiers.
ISSN:1094-687X
1557-170X
DOI:10.1109/EMBC.2015.7320171