Congestive heart failure: Different non-invasive diagnosis techniques

Heart rate variability is analyzed in time-domain or in frequency-domain. Three different novel non-invasive techniques for analysis of heart rate variability (R-R interval (RRI)) for the screening of patients with Congestive Heart Failure (CHF) are investigated. The first method, which is a time-domain 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. The trial data used for estimating of the classification factor consists of 15 CHF (patient) subjects and 18 Normal Sinus Rhythm (NSR) or simply normal subjects. The performance of the algorithm is then evaluated on test data set consists of 17 CHF subjects and 53 NSR subjects. This new technique correctly classifies 31/33 of trial data and 65/70 of test dataThe second and third techniques, which are frequency-domain methods, are based on the soft-decision wavelet-decomposition algorithm for estimating an approximate power spectral density (PSD) of (RRI) of ECG data for screening of congestive heart failure (CHF) from normal subjects. In the second method, the ratio of the power in the low-frequency (LF) band to the power in the high-frequency (HF) band of the RRI signal is used as the classification factor. Results are shown for 9 different wavelets filters. This new technique shows a classification efficiency of 93.93% on trial data and 88.57% on test data. An FFT-based frequency domain screening technique is also implemented and included in this chapter for the purpose of comparison with the wavelet-based technique. The FFT-based technique shows an efficiency of classification of 93.93% on trial data and 81.42% on test data.In the third technique, which is a pattern recognition technique, two standard patterns of the base-2 logarithmic values of the reciprocal of the approximate PSD of sub-bands resulted from wavelet decomposition of RRI data 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. This new simple technique results in 90% identification accuracy by applying it on the test data.