ANALYSIS OF HEART RATE VARIABILITY FOR THE DETECTION OF SUDDEN CARDIAC DEATH

Abstract

Sudden cardiac deatha(SCD) is a condition in which the heart stops working suddenly. According to studies, millions of people die every year as a result of it around the world. Specific medical devices, such as defibrillators, can help to prevent such accidents, but these defibrillators can't predict SCD, so there's no accurate way to detect SCD. As a result, there is a critical need for a non-invasive technique that can predict SCD so that doctors can make correct decisions and take appropriate steps to save people's lives. The aim of this thesis is to present a non-linear method of feature extraction, such as the Poincare plot, Asymmetric Entropy, and Run Entropy, for analysing the signal's heart rate variability (HRV). A boxplot is then used to compare the extracted features for each category, i.e. NSR, CAD, CHF, and SCD.