CardioLearn: A Cloud Deep Learning Service for Cardiac Disease Detection from Electrocardiogram
This addresses the need for accessible cardiac disease detection tools for individuals without training data or computational resources, though it is incremental as it builds on existing deep learning methods.
The authors tackled the problem of cardiac disease detection from electrocardiograms by developing CardioLearn, a cloud deep learning service that provides an out-of-the-box solution, including a portable smart hardware device and mobile app for real-time detection.
Electrocardiogram (ECG) is one of the most convenient and non-invasive tools for monitoring peoples' heart condition, which can use for diagnosing a wide range of heart diseases, including Cardiac Arrhythmia, Acute Coronary Syndrome, et al. However, traditional ECG disease detection models show substantial rates of misdiagnosis due to the limitations of the abilities of extracted features. Recent deep learning methods have shown significant advantages, but they do not provide publicly available services for those who have no training data or computational resources. In this paper, we demonstrate our work on building, training, and serving such out-of-the-box cloud deep learning service for cardiac disease detection from ECG named CardioLearn. The analytic ability of any other ECG recording devices can be enhanced by connecting to the Internet and invoke our open API. As a practical example, we also design a portable smart hardware device along with an interactive mobile program, which can collect ECG and detect potential cardiac diseases anytime and anywhere.