SBIR Phase I:Trustworthy and Automated Electrocardiogram Analysis

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to improve outcomes for patients with heart problems.This project will develop an AI solution for the automatic interpretation of electrocardiograms (ECG) to detect and predict irregular heartbeats.This will benefit patients with cardiac arrhythmias and will reduce spending on cardiovascular disease.This Small Business Innovation Research Phase (SBIR) I project aims to refine existing deep learning-based detection algorithms, develop methods for the prediction of the onset of cardiac arrhythmias, develop interpretable and explainable tools for clinicians, and develop a web-based software tool for users to develop insights from ECG signals. This project leverages artificial intelligence technologies, including deep learning and natural language processing, as well as signal processing techniques for a trustworthy and automated ECG analysis tool for prediction and detectionThe techniques represent multimodal approaches derived from various data sources, including the ECG signals and electronic health record (EHR) documentation of clinician ECG interpretations.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.