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- W2107094345 abstract "This paper proposes a support vector machine (SVM) for real-time detection of premature ventricular contraction (PVC) from normal beats and others. This includes a signal feature extraction module and a statistical pattern recognition module. In feature extraction, time, frequency and morphological features are extracted, here six features are selected and made up a feature vector for input the pattern identifier. After this, an SVM is used to recognize PVC from normal beats and others; this classifier is fit for the requirements of precision and real-time at the same time. Finally, by means of testing electrocardiogram (ECG) data which from MIT-BIH arrhythmia database, the correct rating is more than 97%. Through the comparison with other methods, this achieves favorable results both in real-time and accuracy requirement." @default.
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- W2107094345 date "2011-06-01" @default.
- W2107094345 modified "2023-10-15" @default.
- W2107094345 title "Research on premature ventricular contraction real-time detection based support vector machine" @default.
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- W2107094345 doi "https://doi.org/10.1109/icinfa.2011.5949116" @default.
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