The challenge of prediction and prevention of sudden cardiac death in congestive heart failure

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

Congestive heart failure (CHF) is a cardiac disease associated with the decreasing capacity of the cardiac output. It has been shown that the CHF is the main cause of the cardiac death around the world. Some works proposed to discriminate CHF subjects from healthy subjects using either electrocardiogram (ECG) or heart rate variability (HRV) from long-term recordings. In this work, we propose an alternative framework to discriminate CHF from healthy subjects by using HRV short-term intervals based on 256 RR continuous samples. Our framework uses a matching pursuit algorithm based on Gabor functions. From the selected Gabor functions, we derived a set of features that are inputted into a hybrid framework which uses a genetic algorithm and k-nearest neighbour classifier to select a subset of features that has the best classification performance. The performance of the framework is analyzed using both Fantasia and CHF database from Physionet archives which are, respectively, composed of 40 healthy volunteers and 29 subjects. From a set of nonstandard 16 features, the proposed framework reaches an overall accuracy of 100% with five features. Our results suggest that the application of hybrid frameworks whose classifier algorithms are based on genetic algorithms has outperformed well-known classifier methods.

Ventricular Dysrhythmias in Heart Failure

Left-ventricular systolic dysfunction, or heart failure (HF), is a chronic, progressive condition with a poor prognosis. Approximately 50% of deaths, especially in mild to moderate cases, are sudden. Most sudden deaths are thought to be due to ventricular tachycardia; however, premature ventricular contractions and couplets parallel severity of HF and have been associated with increased mortality risk as opposed to dysrhythmic death. Ventricular arrhythmogenesis results from many mechanisms (afterdepolarizations, reentry, and enhanced automaticity) and preconditions (electrophysiologic abnormalities, neuroendocrine activation, electrolyte imbalances, scar from an ischemic event in ischemic cardiomyopathy, fibrosis in dilated cardiomyopathy, hemodynamic abnormalities, and HF medical management). Nurses are key caregivers in optimally managing HF, either by direct actions or by using advocacy, communication, and collaboration skills to promote positive outcomes. Ventricular dysrhythmia management consists of facilitating core HF pharmacologic and nonpharmacologic medical therapies, using amiodarone to improve symptoms, as needed, and utilizing implantable cardioverter-defibrillator therapy to reduce the risk of sudden cardiac death.