Predicting the Change in State of the Human Heart Based on Synthetic Heart Chamber Volume Data

Cardiovascular Disease (CVD) is one of the most detrimental health issues the world experiences each year. Approximately 647,000 people die from CVD each year in America according to the Centers for Disease Control and Prevention. In other words, one person every 37 seconds. Globally, 17.9 million people die from CVD each year as reported by the World Health Organization. Financially, America spends approximately $219 billion each year. The American Heart Association estimates the financial cost to reach $1.1 trillion by 2035. These metrics show the need to continue research in aiding individuals affected with CVD. In this paper, we use the theory of our previous work, a wearable ultrasound vest to create a real-time near 3D model of the heart, to create the beginnings of a heart state prediction system. That is, we create a synthetic dataset using the ranges of normal and abnormal heart chamber volumes to calculate external surface areas and generate data via statistical bootstrapping. Then, feed this system into a Machine Learning algorithm called a Constrained State Preserved Extreme Learning Machine (CSPELM). Our results show that we can differentiate between abnormal data (Atrial Fibrillation, Chronic Mitral Regurgitation, and Post-Myocardial Infarction) from normal data, where higher percentages for abnormal and low percentages for normal is the goal, with CSPELM predictions of 60.28-88.33% to 33.61%, respectively.