A snoring classifier based on heart rate variability analysis

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Ieong, Chio-In
Dong, Cheng
Nan, Wenya
Rosa, Agostinho
Guimarães, Ronaldo [UNESP]
Vai, Mang-I.
Mak, Pui-In
Wan, Feng
Mak, Peng-Un

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The effect of snoring on the cardiovascular system is not well-known. In this study we analyzed the Heart Rate Variability (HRV) differences between light and heavy snorers. The experiments are done on the full-whole-night polysomnography (PSG) with ECG and audio channels from patient group (heavy snorer) and control group (light snorer), which are gender- and age-paired, totally 30 subjects. A feature Snoring Density (SND) of audio signal as classification criterion and HRV features are computed. Mann-Whitney statistical test and Support Vector Machine (SVM) classification are done to see the correlation. The result of this study shows that snoring has close impact on the HRV features. This result can provide a deeper insight into the physiological understand of snoring. © 2011 CCAL.



Audio channels, Audio signal, Classification criterion, Control groups, Heart rate variability, Mann-Whitney, Polysomnography, Support vector machine (SVM), Cardiology, Heart, Statistical tests, Support vector machines

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Computing in Cardiology, v. 38, p. 345-348.