A Compatible ECG Diagnosis Cloud Computing Framework and Prototype Application - Zhang, Zhen; Li, Hongqiang; Gong, Zheng; Jin, Rize; Chung, Tae Sun (ACM International Conference Proceeding Series, 2020)
The ECG signal analysis and diagnosis algorithms have been studied for decades. There are some state of art algorithms that have been developed. In this paper, we proposed a compatible ECG automatic diagnosis Cloud Computing framework in order to integrate these exist algorithms. On the other hand, there are many studies regarding the IoT based health diagnosis system. But there are few of that aiming at the personal use health monitor and diagnose. Basing on our proposed framework, users can diagnose their heart health status by themselves conveniently anywhere and anytime through the mobile application. The ECG character automatic classification computing algorithm is compatible for Python and MATLAB by introducing the hybrid programming technic on the cloud computing side. So that, it is easy for researchers to integrate their developed algorithm into this framework to build an application quickly. We developed a prototype application as well to verify the availability of this framework.
Keywords: Automatic classification; Automatic diagnosis; Diagnosis algorithms; Health diagnosis; Health status; Hybrid programming; Mobile applications; Personal use; cloud computing framework; Compatible; ECG automatic diagnosis; prototype application; python and MATLAB hybrid programming