This paper proposed a method that can automatically classify sleep apnea by using features extracted from pulse rate variability(PRV) signals induced from piezo snoring sensor for patients with obstructive sleep apnea(OSA). We have extracted eight features(NN, SDNN, RMSSD, NN10, NN50, LF, HF and LF/HF ratio) based on time and frequency analyses of PRV. Sleep apnea was classified by a linear discriminant analysis(LDA). A performance was evaluated using snore recordings from 13 patients with OSA (ages: $54.5{pm}10.5$ years, body mass index: $26.3{pm}2.5kg/m^2$, apnea-hypopnea index: $19.2{pm}6.0/h$). The sensitivity and specificity were $78.9{pm}0.9%$ and $78.9{pm}0.9%$ for training set and $77.7{pm}10.9%$ and $79.0{pm}2.8%$ for test set, respectively. Our study demonstrated the feasibility of implementing a piezo snoring sensor based on a portable device as a simple and cost-effective solution for contributing to the OSA screening.