Premature Ventricular Contraction (PVC) Classifications by Probabilistic Neural Network (PNN) Using the Optimal Mother Wavelets
This paper presented our attempt to determine the reliability and accuracy of classifying Premature Ventricular Contraction (PVC) and several other arrhythmias using optimal mother wavelets and feature dataset obtained from our previous study in 1J. The proposed classifier is Probabilistic Neural Network (PNN) with less-overlapping data set between training and testing. In our previous study of |1|, we found that the most outperformed wavelets among the 35 mother wavelets tested are haar, db3 and sym3 with overall average accuracy percentage of 85.47％ for haar and 84.13％ both for db3 and sym3. The result is slightly lower (<90％) than expected, as we found that the statistical indices of the wavelet coefficients used might not be good features; instead, using the whole coefficients may give higher accuracy. However, the calculation of peak-to-peak ratio proves to be encouraging as it provides convenient differentiator and is believed to be one of the factors that contribute to high accuracy. Addition to that, the selection of inverted R peak for PVC that do not have R peak also plays important role. It is observed that the accuracy of PVC with no R peak (inverted P. peak detection) is to be 91 28％ fur l.aar, *2.I9％ Tor db3 and 92.19％ for sym3.
statistical indices wavelet coefficient ECG DWT PNN PVC
Nur Asyiqin bt Amir Hamzah Roslib Besar Noor Ziela bt Abdul Rahman
Center for Diploma Programme (CDP) Multimedia University (MMU), 75450 Jalan Ayer Keroh Lama, Melaka, Faculty of Engineering and Technology (FET), Multimedia University (MMU), 75450 Jalan Ayer Keroh Lam
国际会议
2010 International Conference on Signal and Information Processing(2010年IEEE信号与信息处理国际会议 ICSIP2010)
长沙
英文
340-345
2010-12-14（万方平台首次上网日期，不代表论文的发表时间）