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Ku Nurul Fazira Ku Azir
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Ku Nurul Fazira Ku Azir
Official Name
Ku Azir, Ku Nurul Fazira
Alternative Name
Ku Azir, Ku Nurul Fazira
Fazira Ku Azir, Ku Nurul
Azir, Ku
Azir, Ku Nurul Fazira Ku
Azir, K. N.F.K.
Ku Azir, K. N.F.
Azir, K. N.F.Ku
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Scopus Author ID
56879016500
Researcher ID
AAY-3466-2021
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PublicationR-Peaks and Wavelet-Based Feature Extraction on K-Nearest Neighbor for ECG Arrhythmia Classification( 2024-01-01)
;Khairuddin A.M.The aim of this research is to classify 17 types of arrhythmias by applying the algorithm developed from combining the morphological and the wavelet-based statistical features. The proposed arrhythmia classification algorithm consists of four stages: pre-processing, detection of R-peaks, feature extraction, and classification. Seven morphological features (MF) that were retrieved from the R-peak locations. Following this, another nine wavelet-based statistical features (SF) were gathered by decomposing wavelets in level 4 from the Daubechies 1 wavelet (Db1). These 16 features are then applied to the k-nearest neighbor (k-NN) algorithm. The accuracy (ACC) of the suggested classification algorithm was assessed by using the MIT-BIH arrhythmia benchmark database (MIT-BIHADB). The experimental results of this work attained an average accuracy (ACC) of 99.00%.