Abstract
J wave is an ECG sign of many clinical syndrome and the accurate detection about it is conducive to the clinical diagnosis of J wave syndrome. Under the background of ECG big data, a novel J wave detection method based on massive ECG data and MapReduce is proposed, which use data mining technology to detect abnormal ECG signal, especially J wave. Firstly, the characteristic of ECG time and frequency domain signal are extracted, and the information gain of every feature is extracted; then, the decision tree is used to classify and recognize ECG signal; lastly, above process are implemented under the parallel programming model MapReduce so that the massive ECG data can be handled, to detect J wave accurately. In order to test and verify the validity of this method, all the ECG data of MIT-BIH are used to do the experiment, and the results demonstrated that, the accuracy and specificity of the proposed method are satisfactory, which provides a new research mentality for the detection of many clinical syndrome.
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Acknowledgment
This work was supported by National Natural Science Foundation of China (Grant No. 61371062); Scientific Research Project for Shanxi Scholarship Council of China (Grant No. 2013-032); International Cooperation Project of Shanxi Province (Grant No. 2014081029-01).
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Li, D., Ma, W., Zhao, J. (2016). A Novel J wave Detection Method Based on Massive ECG Data and MapReduce. In: Wang, Y., Yu, G., Zhang, Y., Han, Z., Wang, G. (eds) Big Data Computing and Communications. BigCom 2016. Lecture Notes in Computer Science(), vol 9784. Springer, Cham. https://doi.org/10.1007/978-3-319-42553-5_34
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DOI: https://doi.org/10.1007/978-3-319-42553-5_34
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