Influence of physiological factors on heart rate variability in predicting cardiovascular diseases

Diagnosis of Heart disease is an important and critical task which can provide prediction about the heart disease so that treatment made easy. The Electrocardiogram of a person gives the total electrical and muscular functions of a human heart and information about cardiac health. Here, the focused on exploring advanced techniques of HRV analysis and indices to develop robust methods for diagnosing patients with CHF from an ECG records. Majority of studies on HRV report several differences between patients with congestive heart failure (CHF), Coronary Artery Disease (CAD) and healthy subjects, such as time-domain, frequency domain and nonlinear HRV measure. In this study classification is performed using clustering method and by finding an optimal k-Nearest Neighbours to discriminate the patients with CHF from the normal. The wavelet entropy, which has been used in the other biomedical signal classification schemes like EEG spike detection, is also used as an HRV measure to enhance the performance of the classifier. The nonlinear and dynamic properties of signals are to be understood well by Poincare plot measures. The stress has a psychological origin but affects several physiological processes in the human body: increased muscle tension in the neck, change in concentration of several hormones and a change in heart rate (HR) and heart rate variability. Here, paper is able to give physiological parameters and its influence to predict diseases while calculating other effect on HRV.