The abnormal vs normal ECG classification based on key features and statistical learning

Authors:
Jun Dong;Jia-fei Tong;Xia Liu
Affiliations:
Suzhou Institute of Nano-technology and Nano-bionics, Chinese Academy of Sciences, China;Suzhou Institute of Nano-technology and Nano-bionics, Chinese Academy of Sciences, China;Shanghai Ruijin Hospital, China
Venue:
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
Year:
2010

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Abstract

As cardiovascular diseases appear frequently in modern society, the medicine and health system should be adjusted to meet the new requirements Chinese government has planned to establish basic community medical insurance system (BCMIS) before 2020, where remote medical service is one of core issues Therefore, we have developed the “remote network hospital system” which includes data server and diagnosis terminal by the aid of wireless detector to sample ECG To improve the efficiency of ECG processing, in this paper, abnormal vs normal ECG classification approach based on key features and statistical learning is presented, and the results are analyzed Large amount of normal ECG could be filtered by computer automatically and abnormal ECG is left to be diagnosed specially by physicians.