Does Method II improve the estimation of low and high frequency powers in heart rate data with ectopic beats compared to Method I?
Using linear splines to fill in missing heart rate data during bad intervals improves the estimation of low and high frequency powers compared to computing autocorrelation without explicit assumptions.
The authors examine two different methods for characterizing the heart rate (HR) power spectrum of data with bad intervals containing ectopic or missing beats. Both methods estimate the spectrum using the fast Fourier transform of the HR autocorrelation. Method I computes the autocorrelation without making explicit assumptions about the specific value of the HR in the bad intervals. The only assumption is that the bad intervals are relatively independent of the HR. Method II uses linear splines to fill in the HR during bad intervals. Comparing the performance of the methods for the specific problem of determining the low (0.02-0.1 Hz) and high (0.15-0.4 Hz) frequency powers, it is found that method II performs substantially better than method I. This finding, however, applies only to the specific spectral measures tested, and for other measures method I may yield better results.>
Albrecht et al. (2003) studied this question.