Abstract
This paper generalizes the discrete Fourier transform matrix to an energy-preserved transform matrix as well as introduces the idea of the generalized mean frequency and the idea of the generalized frequency variance to perform the tachycardias classification. In particular, these two physical quantities are employed as the features and the random forest is employed as a classifier for performing the tachycardias classification. The computer numerical simulation results show that the performance based on both the generalized mean frequency and the generalized frequency variance is better than that based on both the conventional mean frequency and the conventional frequency variance.
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Availability of data and materials
The dataset used and analyzed during the current study is available in the MIT repository database (http://ecg.mit.edu) [3].
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Database for performing the tachycardias classification. http://ecg.mit.edu
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Acknowledgements
This paper was supported partly by the National Nature Science Foundation of China (Nos. U1701266, 61671163 and 62071128), the Team Project of the Education Ministry of the Guangdong Province (No. 2017KCXTD011), the Guangdong Higher Education Engineering Technology Research Center for Big Data on Manufacturing Knowledge Patent (No. 501130144), Hong Kong Innovation and Technology Commission, Enterprise Support Scheme (No. S/E/070/17) and the Shenzhen Science and Technology International Cooperation Research Project (No. GJHZ20180418190504612).
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Ho, C.YF., Ling, B.WK., Deng, D.X. et al. Tachycardias Classification via the Generalized Mean Frequency and Generalized Frequency Variance of Electrocardiograms. Circuits Syst Signal Process 41, 1207–1222 (2022). https://doi.org/10.1007/s00034-021-01819-1
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DOI: https://doi.org/10.1007/s00034-021-01819-1