CardioLearn

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CardioLearn: A Cloud Deep Learning Service for Cardiac Disease Detection from Electrocardiogram (WWW 2020 Demo)

CardioLearn 최신버젼 다운로드

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# CardioLearn

You can find our online demo at [link](https://heartvoice.com.cn/diagnosis.html) .

Shenda Hong; Zhaoji Fu; Rongbo Zhou; Jie Yu; Yongkui Li; Kai Wang; Guanlin Cheng; CardioLearn: A Cloud Deep Learning Service for Cardiac Disease Detection from Electrocardiogram. In The World Wide Web Conference, WWW 2020, Taipei, Taiwan, April 20–24, 2020. https://dl.acm.org/doi/abs/10.1145/3366424.3383529

## Background

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.

## What we do

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.

CardioLearn

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