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Chaos. 2014 Jun;24(2):024404. doi: 10.1063/1.4869825.

Symbolic dynamics marker of heart rate variability combined with clinical variables enhance obstructive sleep apnea screening.

Author information

1
Institute for Technological Development and Innovation in Communications (IDeTIC), Universidad de Las Palmas de Gran Canaria, Las Palmas de G.C. 35017, Spain.
2
Department of Mathematics. Universidad de Las Palmas de Gran Canaria, Las Palmas de G.C. 35017, Spain.
3
Pulmonary Medicine Department. Hospital Universitario de Gran Canaria Dr. Negrín, Las Palmas de G.C. 35010, Spain.
4
Department of Physics, Humboldt-Universität zu Berlin, Berlin 10115, Germany.
5
Sleep Center, Charité Universitätsmedizin, Berlin 10117, Germany.

Abstract

Many sleep centres try to perform a reduced portable test in order to decrease the number of overnight polysomnographies that are expensive, time-consuming, and disturbing. With some limitations, heart rate variability (HRV) has been useful in this task. The aim of this investigation was to evaluate if inclusion of symbolic dynamics variables to a logistic regression model integrating clinical and physical variables, can improve the detection of subjects for further polysomnographies. To our knowledge, this is the first contribution that innovates in that strategy. A group of 133 patients has been referred to the sleep center for suspected sleep apnea. Clinical assessment of the patients consisted of a sleep related questionnaire and a physical examination. The clinical variables related to apnea and selected in the statistical model were age (p < 10(-3)), neck circumference (p < 10(-3)), score on a questionnaire scale intended to quantify daytime sleepiness (p < 10(-3)), and intensity of snoring (p < 10(-3)). The validation of this model demonstrated an increase in classification performance when a variable based on non-linear dynamics of HRV (p < 0.01) was used additionally to the other variables. For diagnostic rule based only on clinical and physical variables, the corresponding area under the receiver operating characteristic (ROC) curve was 0.907 (95% confidence interval (CI) = 0.848, 0.967), (sensitivity 87.10% and specificity 80%). For the model including the average of a symbolic dynamic variable, the area under the ROC curve was increased to 0.941 (95% = 0.897, 0.985), (sensitivity 88.71% and specificity 82.86%). In conclusion, symbolic dynamics, coupled with significant clinical and physical variables can help to prioritize polysomnographies in patients with a high probability of apnea. In addition, the processing of the HRV is a well established low cost and robust technique.

PMID:
24985458
DOI:
10.1063/1.4869825
[Indexed for MEDLINE]

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