HeartToGo: A Personalized Medicine Technology for Cardiovascular Disease Prevention and Detection

To date, cardiovascular disease (CVD) is the first leading cause of global death. The Electrocardiogram (ECG) is the most widely adopted clinical tool that measures and records the electrical activity of the heart from the body surface. The mainstream resting ECG machines for CVD diagnosis and supervision can be ineffective in detecting abnormal transient heart activities, which may not occur during an individual's hospital visit. Common Holter-based portable solutions offer 24-hour ECG recording, containing hundreds of thousands of heart beats that not only are tedious and time-consuming to analyze manually but also miss the capability to provide any real-time feedback. In this study, we seek to establish a cell phone-based personalized medicine technology for CVD, capable of performing continuous monitoring and recording of ECG in real time, generating individualized cardiac health summary report in layman's language, automatically detecting abnormal CVD conditions and classifying them at any place and anytime. Specifically, we propose to develop an artificial neural network (ANN)-based machine learning technique, combining both individualized medical information and clinical ECG database data, to train the cell phone to learn to adapt to its user's physiological conditions to achieve better ECG feature extraction and more accurate CVD classification results.