SBIR Phase I: Trustworthy and Automated Electrocardiogram Analysis

这个小型企业创新研究（SBIR）第一阶段项目的更广泛的影响/商业潜力是改善心脏病患者的预后。 该项目将开发一种用于自动解释心电图（ECG）的人工智能解决方案，以检测和预测不规则心跳。 这将使心律失常患者受益，并将减少心血管疾病的支出。 这个小型企业创新研究阶段（SBIR）I项目旨在完善现有的基于深度学习的检测算法，开发预测心律失常发作的方法，为临床医生开发可解释和可解释的工具，并为用户开发基于Web的软件工具，以从ECG信号中获得见解。该项目利用人工智能技术，包括深度学习和自然语言处理，以及信号处理技术，用于预测和检测的可靠和自动化ECG分析工具。这些技术代表了从各种数据源衍生的多模态方法，包括ECG信号和电子健康记录（EHR）该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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 detection The 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.