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- W3194921545 abstract "In this paper, we propose an effective electrocardiogram (ECG) signal classification method using XGBoost classifier. The ECG signals are passed through four phases of data acquisition, noise filtering, feature extraction, and classification. In first phase, dataset is collected from the MIT-BIH arrhythmia database. In second phase, noise is removed using baseline wandering removal filter. In next phase, 45 descriptors from four prominent features which showed good results in previous work, namely, wavelets, higher order statistics (HOS), morphological descriptors, and R-R intervals, are being employed. Using these features as an input to the XGBoost classifier, the signals are being classified into four classes (N, S, V, and F) as per the ANSI-AAMI standards; of all the classifiers, XGBoost shows best result with an accuracy of 99.43%." @default.
- W3194921545 created "2021-08-30" @default.
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- W3194921545 date "2021-08-13" @default.
- W3194921545 modified "2023-09-26" @default.
- W3194921545 title "Cardiac Arrhythmia Detection and Classification From ECG Signals Using XGBoost Classifier" @default.
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- W3194921545 doi "https://doi.org/10.1002/9781119769262.ch8" @default.
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