Abstract
A pattern recognition technique based on approximate estimation of power spectral densities (PSD) of sub-bands resulted from wavelet decomposition of R-R interval (RRI) data for identification of patients with Congestive Heart Failure (CHF) is investigated. Both trial and test data used in this work are drawn from MIT databases. Two standard patterns of the base-2 logarithmic values of the reciprocal of the probability measure of the approximated PSD of CHF patients and normal subjects are derived by averaging all corresponding values of all sub-bands of 12 CHF data and 12 normal subjects in the trial set. The computed pattern of each data under test is then compared band-by-band with both standard patterns of CHF and normal subjects to find the closest pattern. The new technique resulted in an identification accuracy of about 90% by applying it on the test data.
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