Does a forecasting linear method using heart rate variability spectral variables identify individual autonomic profiles related to posture in normal subjects?
A forecasting linear method using spectral variables from short ECG segments can accurately identify individual autonomic profiles related to body posture.
BACKGROUND: Power spectrum analysis of heart rate variability (HRV) can estimate the state of sympathovagal balance modulating sinus node activity. In view of the large distribution of spectral variables, a recognition of well-defined physiological conditions has never been attempted on an individual basis. METHODS AND RESULTS: We considered 10 spectral variables extracted from short segments (200 to 500 cardiac cycles) of 350 ECG tracings recorded in normal subjects in both supine and upright positions (700 patterns). The tracings were first ordered consecutively and subsequently assigned alternatively to a training or to a test set (each consisting of 175 cases, providing 350 patterns considered to be independent). A forecasting linear method estimated a normalized activation index (ranging from -1 for supine to +1 for upright) that concentrated the information derived from spectral variables and that identified, in the test set, individual by individual, approximately 84% of corresponding body postures. CONCLUSIONS: The combined use of spectral methodology and forecasting analysis has revealed an information content embedded, per se, in a short series of RR intervals capable of recognizing, individual by individual, two different autonomic profiles related to posture.
Malliani et al. (Tue,) studied this question.
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