APPROXIMATE MINIMUM BIAS MULTICHANNEL SPECTRAL ESTIMATION FOR HEART-RATE-VARIABILITY

Spectral decomposition of variations in heart rate permits noninvasive measurement of autonomic nervous activity in humans and animals. Auto nomic metrics based on spectral analysis are useful in monitoring clin ical conditions such as diabetic neuropathy and reinnervation in heart transplant patients. A persistent problem in deriving such autonomic measures is the prerequisite of an accurate and unbiased power spectru m of heart rate variability (HRV). Numerous parametric and nonparametr ic power spectrum estimators have been introduced, each with its own a dvantages and drawbacks. Estimator bias has received little attention, despite the fact that at least one common HRV spectrum estimator, the autoregressive method, is known to exhibit bias even in idealized cir cumstances. We introduce an approximately minimum bias, nonparametric, multichannel spectrum estimation procedure for HRV and contemporaneou s signals. The procedure, which is designed specifically for irregular sampling, does not require data segmentation and provides statistical ly consistent, low variance multichannel spectrum estimates. Estimator performance on simulated and clinical data is presented and compared with results from autoregressive models and Welch periodograms with an d without compensation for irregular sampling. Results indicate that t he proposed method exhibits advantages over conventional HRV spectrum estimators. Relative computational complexity of the proposed method i s also considered.