Abstract
This paper use the Lempel-Ziv complexity to automatically detect ventricular fibrillation (VF) and ventricular tachycardia (VT) based on Wavelet transform (WT) and empirical mode decomposition (EMD). We respectively select WT and EMD to decompose original signals into different sub-bands. Electrocardiogram (ECG) signals were first decomposed into five sub-bands based on Wavelet transform and EMD. Then the complexity of each sub-band was used as a feature to detect VF and VT. A public dataset was utilized. Experimental results show the new method can distinguish VT from VF with the accuracy up to 99.50%.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China (Grant No. 61201428, 61302090), the Natural Science Foundation of Shandong Province, China (Grant No. ZR2010FQ020, ZR2013FL002).
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Xia, D., Li, Y., Meng, Q., He, J. (2017). A Method Using the Lempel-Ziv Complexity to Detect Ventricular Tachycardia and Fibrillation. 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_19
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DOI: https://doi.org/10.1007/978-3-319-59081-3_19
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