Abstract
In this work we address the problem to classify individuals into patterns of 24-hour heart rate (HR) variability, and systolic (SBP) and diastolic (DBF) blood pressure variability on the basis of up to 96 parameter values per subject, per 24-hours. Pattern recognition findings could probably lead to estimate the effect of the 24h BP variability on ventricular structure in essential hypertension. A parallel waveform pattern recognition algorithm applying measurement vector methods for the analysis of BP and HR variability is presented.
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