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Query: UMLS:C0037315 (sleep apnea)
 8,000 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

An analysis of the HRV during decreases in the amplitude fluctuations of PPG (DAP) events, and their utility in OSAS screening is presented. The overall data set used in the study includes the polysomnographic records of 21 children. DAP events were automatically detected by an algorithm based on the envelope attenuations of the PPG. DAP events were classified as apneic or non apneic by a linear discriminant analysis. The features used by the linear discriminant come from the temporal and spectral parameters of the heart rate obtained by Smooth Pseudo Wigner Ville Distribution. Two indexes were defined: the number of DAP events per hour ratio r(DAP) and the number of apneic DAP events per hour ratio r(DAP)(alpha). Results show a 12% increase in accuracy for r(DAP)(alpha) with respect to r(DAP) in classifying 1 hour polysomnographic segments, reaching values of 72.7% and 80% for sensitivity and specificity, respectively. As for subject classification, the improvement in accuracy is 6.7% obtaining values of 87.5% and 71.4% for sensitivity and specificity respectively. These results suggest that the combination of DAP and HRV could be an alternative for sleep apnea screening with the added benefit of low cost and simplicity.
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PMID:Detection of obstructive sleep apnea in children using decreases in the amplitude fluctuations of PPG signal and HRV. 1916 58

Sleep apnea, characterized by frequent pauses in breathing during sleep, poses a serious threat to the healthy growth and development of children. Polysomnography (PSG), the gold standard for sleep apnea diagnosis, is resource intensive and confined to sleep laboratories, thus reducing its accessibility. Pulse oximetry alone, providing blood oxygen saturation (SpO2) and blood volume changes in tissue (PPG), has the potential to identify children with sleep apnea. Thus, we aim to develop a tool for at-home sleep apnea screening that provides a detailed and automated 30 sec epoch-by-epoch sleep apnea analysis. We propose to extract features characterizing pulse oximetry (SpO2 and pulse rate variability [PRV], a surrogate measure of heart rate variability) to create a multivariate logistic regression model that identifies epochs containing apnea/hypoapnea events. Overnight pulse oximetry was collected using a smartphone-based pulse oximeter, simultaneously with standard PSG from 160 children at the British Columbia Children's hospital. The sleep technician manually scored all apnea/hypoapnea events during the PSG study. Based on these scores we labeled each epoch as containing or not containing apnea/hypoapnea. We randomly divided the subjects into training data (40%), used to develop the model applying the LASSO method, and testing data (60%), used to validate the model. The developed model was assessed epoch-by-epoch for each subject. The test dataset had a median area under the receiver operating characteristic (ROC) curve of 81%; the model provided a median accuracy of 74% sensitivity of 75%, and specificity of 73% when using a risk threshold similar to the percentage of apnea/hypopnea epochs. Thus, providing a detailed epoch-by-epoch analysis with at-home pulse oximetry alone is feasible with accuracy, sensitivity and specificity values above 73% However, the performance might decrease when analyzing subjects with a low number of apnea/hypoapnea events.
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PMID:Identifying individual sleep apnea/hypoapnea epochs using smartphone-based pulse oximetry. 2826 87