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We apply the maximal overlap discrete wavelet transform (MODWT)-based spectral density estimation method to measure heart rate variability (HRV) from short-duration pulse wave signals produced by an automated oscillometric blood pressure (BP) monitor during routine measurements. To test the accuracy of this wavelet HRV metric, we study the linear correlations that it achieves with chronological age and BP in a healthy population of 85 subjects. We define accuracy as the quality of the linear regression of HRV with age and BP. Results are compared with a number of traditional HRV metrics and earlier published work. The MODWT HRV metric achieves higher (and more significant) correlations with age and BP compared to other metrics. Moreover, these correlations are in agreement with earlier published work on correlations of HRV (measured from much longer duration electrocardiogram signals) with age and BP. As a further enhancement, we combine the MODWT HRV metric with other HRV metrics inside a multiple-linear-regression model and show an improvement in the correlations between the predicted and actual ages and the predicted and actual BP. Our work thus indicates the suitability of the MODWT metric either as a stand alone or in combination with other metrics for characterizing HRV from short-duration oscillometric pulse wave signals. Based on our results, we conclude that oscillometric BP monitors can be used to measure HRV in addition to measuring BP.