Minimum Fuzzy Membership Function Extraction for Automatic Premature Ventricular Contraction Detection


Joon-Shik Lim, Journal of Information Processing Systems Vol. 8, No. 1, pp. 125-132, Feb. 2007  


Keywords: Premature Ventricular Contraction(PVC), Fuzzy Neural Networks, Wavelet Transforms, Weighted Fuzzy Membership Functions
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Abstract

This paper presents an approach to detect premature ventricular contractions(PVC) using the neural network with weighted fuzzy membership functions(NEWFM), NEWFM classifies normal and PVC beats by the trained weighted fuzzy membership functions using wavelet transformed coefficients extracted from the MIT-BIH PVC database. The eight most important coefficients of d3 and d4 are selected by the non-overlap area distribution measurement method. The selected 8 coefficients are used for 3 data sets showing reliable accuracy rates 99,80%, 99,21%, and 98.78%, respectively, which means the selected input features are less dependent to the data sets. The ECG signal segments and fuzzy membership functions of the 8 coefficients enable input features to interpret explicitly.


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Cite this article
[APA Style]
Lim, J. (2007). Minimum Fuzzy Membership Function Extraction for Automatic Premature Ventricular Contraction Detection. Journal of Internet Computing and Services, 8(1), 125-132.

[IEEE Style]
J. Lim, "Minimum Fuzzy Membership Function Extraction for Automatic Premature Ventricular Contraction Detection," Journal of Internet Computing and Services, vol. 8, no. 1, pp. 125-132, 2007.

[ACM Style]
Joon-Shik Lim. 2007. Minimum Fuzzy Membership Function Extraction for Automatic Premature Ventricular Contraction Detection. Journal of Internet Computing and Services, 8, 1, (2007), 125-132.