Abstract
This study attempted to enhance ECG detection and classification of arrhythmias by using ECG arrhythmia classification algorithm implemented from the Haar wavelet transform and the k-nearest neighbor (k-NN) classifier. The development of the ECG arrhythmia classification algorithm consisted of five essential phases which included pre-processing, R-peak detection, feature extraction, feature selection, and classification. The pre-processing phase involved the band-pass Butterworth filter and zero-phase digital filter. The Haar wavelet transform and thresholding process were used to detect the R-peaks of the ECG signals. The morphological features were extracted from the R-peak locations, whereas the statistical features were extracted from the wavelet decomposition of Haar wavelet transform in the feature extraction phase. The feature selection phase utilized the neighborhood component analysis (NCA) and hyper-parameter optimization to select relevant features for the classification model. The classification model was developed by using the k-nearest neighbor (k-NN) classifier. The ECG signals obtained from the MIT-BIH arrhythmia database were used to evaluate the performance of the classification algorithm as proposed in this study. The result of this study showed average accuracy (ACC) of 97.30%.
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Khairuddin, A.M., Ku Azir, K.N.F. (2021). Using the HAAR Wavelet Transform and K-nearest Neighbour Algorithm to Improve ECG Detection and Classification of Arrhythmia. In: Fujita, H., Selamat, A., Lin, J.CW., Ali, M. (eds) Advances and Trends in Artificial Intelligence. From Theory to Practice. IEA/AIE 2021. Lecture Notes in Computer Science(), vol 12799. Springer, Cham. https://doi.org/10.1007/978-3-030-79463-7_26
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