(Circulation. 1997;96:4143-4145.)
© 1997 American Heart Association, Inc.
Articles |
From the Centro Ricerche Cardiovascolari, CNR, L.I.T.A. Vialba, Medicina Interna II, Ospedale L. Sacco, Università degli Studi di Milano (A.M., M.P., R.F., S.G., D.L., N.M.); the Dipartimento di Bioingegneria, Politecnico di Milano (S.C.); and the Dipartimento di Medicina Interna, Università degli Studi di Genova (G.S.M.).
Correspondence to Prof Alberto Malliani, Università di Milano, Ospedale L. Sacco, Medicina Interna II, Via G.B. Grassi 74, 20157 Milano, Italy. E-mail albertom{at}fisiopat.sacco.unimi.it
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,
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.
Key Words: electrocardiography nervous system circulation heart rate
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