Analysis of heart rate variability for predicting cardiorespiratory events in infants
Cardiorespiratory events (CREs), including bradycardia and apnea, in infants are a major concern for physicians and families. Our hypothesis was that there is a difference in the heart rate variability (HRV) of infants who have CREs when compared to normal control infants. The purpose of this study was to develop CRE prediction models based on HRV measured during a polysomnographic (PSG) recording. ANCOVA analysis accounting for factors such as age and sleep state show a relationship between HRV variables and CRE. Prediction models, including neural networks and support vector machines, were developed to predict CRE within either (a) 1-week or (b) 1-month after the PSG. The support vector machine prediction accuracy, for CRE susceptibility one month after the PSG on an independent testing dataset, was 50.0% sensitivity and 82.6% specificity. Although the developed prediction models were not sufficiently accurate for clinical decision making, these results support the potential role of abnormalities in autonomic control of heart rate among infants at risk for CREs. © 2011 Elsevier Ltd. All rights reserved.
Biomedical Signal Processing and Control
Analysis of heart rate variability for predicting cardiorespiratory events in infants.
Biomedical Signal Processing and Control,
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