A Classification System to Detect Congestive Heart Failure Using Second-Order Difference Plot of RR Intervals

Abstract

A classification system to detect congestive heart failure (CHF) patients from normal (N) patients is described. The classification procedure uses the k-nearest neighbor algorithm and uses features from the second-order difference plot (SODP) obtained from Holter monitor cardiac RR intervals. The classification system which employs a statistical procedure to obtain the final result gave a success rate of 100% to distinguish CHF patients from normal patients. For this study the Holter monitor data of 36 normal and 36 CHF patients were used. The classification system using standard deviation of RR intervals also performed well, although it did not match the 100% success rate using the features from SODP. However, the success rate for classification using this procedure for SDRR was many fold higher compared to using a threshold. The classification system in this paper will be a valuable asset to the clinician, in the detection congestive heart failure.

Alteration of the P-wave non-linear dynamics near the onset of paroxysmal atrial fibrillation

“…Hence, it is usually chosen depending upon the character of the data [16]. In the present study an approach similar to the developed in previous works [22,23] was used to select the optimal ρ for each single Pwave feature. Thus, CTM was computed for radius of 0.1, 0.2, .…”

The Performance of Short-Term Heart Rate Variability in the Detection of Congestive Heart Failure

“…The diagnoses reliability, however, might be increased if the screening test of heart failure could be assisted by signal processing techniques and biomedical analysis. In the past years, several works [11–15] have shown the possibility of classifying subjects with heart failure. For instance, Işler and Kuntalp (2007) using short-term heart rate variability (HRV) intervals have shown that normalizing classical HRV and entropy measures can lead to high levels of sensitivity (82.76%) and specificity (100%).…”

Patient Specific Congestive Heart Failure Detection From Raw ECG signal

“…2009;Engin 2007;Maurice E.Cohen 1996;Dabanloo et al 2010;Işler Y n.d.). Some of them considered Heart Rate Variability (HRV) of ECG signals (Işler & Kuntalp 2007;Kamath 2012b;Işler Y n.d.;Kannathal et al 2006;Thuraisingham 2010). In both cases, hidden important information (such as wavelet, eigenvector methods, Poincare, RR interval, etc.)…”

“…have been used as classifier. Poincare plot and the SODP are commonly used methods as non-linear analysis of the components in biomedical signals (Işler Y n.d.;Thuraisingham 2010;Karmakar et al 2009;Maurice E.Cohen 1996;Dabanloo et al 2010). It is also known that poincare plot is an example of chaotic system (Maurice E. Cohen 1996).…”