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.

本研究呈现了我们在建立、训练和提供心脏疾病检测的深度学习云服务 CardioLearn 上的工作，该服务可提供公共服务方案，解决了传统 ECG 疾病检测模型存在的诊断率误差高的限制，我们也设计了一个便携式智能硬件设备及与之交互的移动程序，可以随时随地检测潜在的心脏疾病。