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This paper presents a new measure of heart rate variability (HRV) that can be estimated using Doppler ultrasound techniques and is robust to variations in the angle of incidence of the ultrasound beam and the measurement noise. This measure employs the multiple signal characterization (MUSIC) algorithm which is a high-resolution method for estimating the frequencies of sinusoidal signals embedded in white noise from short-duration measurements. We show that the product of the square-root of the estimated signal-to-noise ratio (SNR) and the mean-square error of the frequency estimates is independent of the noise level in the signal. Since varying angles of incidence effectively changes the input SNR, this measure of HRV is robust to the input noise as well as the angle of incidence. This paper includes the results of analyzing synthetic and real Doppler ultrasound data that demonstrates the usefulness of the new measure in HRV analysis.

This letter shows that the single frequency approximation for a narrowband lowpass signal embedded in white noise using the Pisarenko harmonic decomposition algorithm is approximately the power-weighted mean frequency of the signal. Experimental results indicate that this method is superior to a commonly used Fourier transform based mean frequency estimation method.Index Terms-Pisarenko harmonic decomposition, powerweighted mean frequency.

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