Discrimination of sleep-apnea-related decreases in the amplitude fluctuations of PPG signal in children by HRV analysis. Gil, E., Mendez, M., Vergara, J., M., Cerutti, S., Bianchi, A., M., & Laguna, P. IEEE transactions on bio-medical engineering, 56(4):1005-14, 4, 2009.
Paper abstract bibtex In this paper, an analysis of heart rate variability (HRV) during decreases in the amplitude fluctuations of photopletysmography (PPG) [decreases in the amplitude fluctuations of photopletysmography (DAP)] events for obstructive sleep apnea syndrome (OSAS) screening is presented. Two hundred and sixty-eight selected signal segments around the DAP event were extracted and classified in five groups depending on SaO (2) and respiratory behavior. Four windows around each DAP are defined and temporal evolution of time-frequency HRV parameters was analyzed for OSAS screening. Results show a significant increase in sympathetic activity during DAP events, which is higher in cases associated with apnea. DAP events were classified as apneic or nonapneic using a linear discriminant analysis from the HRV indexes. The ratio of DAP events per hour r(DAP) and the ratio of apneic DAP events per hour r(DAP)(a) were computed. Results show an accuracy of 79% for r(DAP)(a) (12% increase with respect to r(DAP)), a sensitivity of 87.5%, and a specificity of 71.4% when classifying 1-h polysomnographic excerpts. As for clinical subject classification, an accuracy of 80% (improvement of 6.7% ), a sensitivity of 87.5%, and a specificity of 71.4% are reached. These results suggest that the combination of DAP and HRV could be an improved alternative for sleep apnea screening from PPG with the added benefit of its low cost and simplicity.
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abstract = {In this paper, an analysis of heart rate variability (HRV) during decreases in the amplitude fluctuations of photopletysmography (PPG) [decreases in the amplitude fluctuations of photopletysmography (DAP)] events for obstructive sleep apnea syndrome (OSAS) screening is presented. Two hundred and sixty-eight selected signal segments around the DAP event were extracted and classified in five groups depending on SaO (2) and respiratory behavior. Four windows around each DAP are defined and temporal evolution of time-frequency HRV parameters was analyzed for OSAS screening. Results show a significant increase in sympathetic activity during DAP events, which is higher in cases associated with apnea. DAP events were classified as apneic or nonapneic using a linear discriminant analysis from the HRV indexes. The ratio of DAP events per hour r(DAP) and the ratio of apneic DAP events per hour r(DAP)(a) were computed. Results show an accuracy of 79% for r(DAP)(a) (12% increase with respect to r(DAP)), a sensitivity of 87.5%, and a specificity of 71.4% when classifying 1-h polysomnographic excerpts. As for clinical subject classification, an accuracy of 80% (improvement of 6.7% ), a sensitivity of 87.5%, and a specificity of 71.4% are reached. These results suggest that the combination of DAP and HRV could be an improved alternative for sleep apnea screening from PPG with the added benefit of its low cost and simplicity.},
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author = {Gil, Eduardo and Mendez, Martín and Vergara, José María and Cerutti, Sergio and Bianchi, Anna Maria and Laguna, Pablo},
journal = {IEEE transactions on bio-medical engineering},
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Two hundred and sixty-eight selected signal segments around the DAP event were extracted and classified in five groups depending on SaO (2) and respiratory behavior. Four windows around each DAP are defined and temporal evolution of time-frequency HRV parameters was analyzed for OSAS screening. Results show a significant increase in sympathetic activity during DAP events, which is higher in cases associated with apnea. DAP events were classified as apneic or nonapneic using a linear discriminant analysis from the HRV indexes. The ratio of DAP events per hour r(DAP) and the ratio of apneic DAP events per hour r(DAP)(a) were computed. Results show an accuracy of 79% for r(DAP)(a) (12% increase with respect to r(DAP)), a sensitivity of 87.5%, and a specificity of 71.4% when classifying 1-h polysomnographic excerpts. As for clinical subject classification, an accuracy of 80% (improvement of 6.7% ), a sensitivity of 87.5%, and a specificity of 71.4% are reached. 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