Electrodiogram Signal Classification by Using XGBoost in Different Discrete Wavelet Transform

Bibit Waluyo Aji, Sri Nur Chasanah, Fihris Aulia Sa’adah, Bambang Irawanto

Abstract


Electrocardiogram (ECG) is electrical signal from heart. ECG can use for Detection or tracking the hearth health. The one of method can use is machine learning. Machine learning is Algorithm which can learning from data and is used for classifying and predicting. Machine Learning can use for signal classification, in this case is for ECG classification. In signal processing, wavelet transform is common method for analyzing signal. Wavelet transform has many familly. The aim from this research is to find the best wavelet transform in the classification of Electrocardiogram (ECG) signals on XGBoost. The Discrete Wavelet Transform which is used for the research is daubechies, coiflets, symlets, biorthogonal, reverse biorthogonal, haar. Finally, the best wavelet transform in the classification is biorthogonal (3.1) with F1 score 1.0.


Keywords


Signal Classification; Wavelet; Electrocardiogram; XGBoost

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References


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DOI: http://dx.doi.org/10.12962/j24775401.v10i2.21954

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International Journal of Computing Science and Applied Mathematics by Pusat Publikasi Ilmiah LPPM, Institut Teknologi Sepuluh Nopember is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Based on a work at https://iptek.its.ac.id/index.php/ijcsam.