Statistical signal characterization for congestive heart failure patient's classification

Abstract

This paper aims at investigating a new technique of time-domain analysis of heart variability (R-R interval (RRI)) for the screening of patients with Congestive Heart Failure (CHF). This method is based on the Statistical Signal Characterization (SSC) of the analytical signal that is generated using Hilbert transformation of the RRI data. The four SSC parameters are: amplitude mean, period mean, amplitude deviation and period deviation. These parameters and their maximum and minimum values are determined over sliding segments of 300-samples, 32-samples and 16-samples for both the instantaneous amplitudes and the instantaneous frequencies derived from the analytical signal of the RRI data. Data used in this work are drawn from MIT database. Threshold values used in the identification of CHF patients from normal records are selected using the Receiver Operating Characteristics (ROC) curves on trial data. This new technique correctly classifies 31/33 of trial data and 65/70 of test data.