Abstract
In this paper the improved symbol entropy algorithm based on empirical mode decomposition (EMD) was proposed to detect ventricular tachycardia (VT) and ventricular fibrillation (VF). The original symbol entropy arithmetic needed longer time series to distinguish VT and VF by high accuracy while the algorithm we proposed can distinguish VT and VF in shorter time series by high accuracy. Otherwise the execution time of new arithmetic was shorter than original algorithm. The classification accuracy of original arithmetic was 93.5%, and the improved arithmetic was 97.75%. The computer time of feature was 33.32 times less than original.
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Acknowledgment
This work was supported by the National Natural Science Foundation of China (Grant No. 61671220, 61640218, 61201428), the Shandong Distinguished Middle-aged and Young Scientist Encourage and Reward Foundation, China (Grant No. ZR2016FB14), the Project of Shandong Province Higher Educational Science and Technology Program, China (Grant No. J16LN07), the Shandong Province Key Research and Development Program, China (Grant No. 2016GGX101022).
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Wei, Y., Meng, Q., Liu, H., Zhou, J., Wang, D. (2017). An Improved Symbol Entropy Algorithm Based on EMD for Detecting VT and VF. In: Cong, F., Leung, A., Wei, Q. (eds) Advances in Neural Networks - ISNN 2017. ISNN 2017. Lecture Notes in Computer Science(), vol 10262. Springer, Cham. https://doi.org/10.1007/978-3-319-59081-3_41
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DOI: https://doi.org/10.1007/978-3-319-59081-3_41
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