Design of Mathematical Model for Prediction of Heart Rate & Deficiency of Nitric Oxide in Blood Vessels

Abstract

This research work presents an effective application of artificial neural network for cardiac arrhythmia classification. Design of a mathematical model and unique cardiac chamber which help the clinician for the selection of appropriate therapy and prediction of heart rate variability after its successful implementation. Depending upon the heart rate variation after effective therapy implementation it also helps to predicts the deficiency of nitric oxide in blood vessel which is indirect method. The diseases that affect the cardiovascular system are the main cause of deaths in developed countries. Most of these deaths are due to sudden cardiac arrest and severe cardiac arrhythmia. Therefore, the automatic detection of cardiac arrhythmias from the bedside or ambulatories ECG becomes an important tool for risk assessment. Automatic real time analysis is performed by using artificial neural network. Following three consecutive steps are required for automatic detection: 1) R-R interval detection 2) Heart Rate Calculation and 3) Classification. After heart rate classification by neural network, mathematical model will suggest appropriate therapy for the patient depends on its heart rate and blood pressure. Suggested therapy is first line emergency treatment for the patient suffering from high heart rate, low heart rate, low diastolic blood pressure and high systolic blood pressure