Ku Nurul Fazira Ku Azir
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Preferred name
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
Main Affiliation
Scopus Author ID
56879016500
Researcher ID
AAY-3466-2021

Research Output

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  • Publication
    R-Peaks and Wavelet-Based Feature Extraction on K-Nearest Neighbor for ECG Arrhythmia Classification
    ( 2024-01-01)
    Khairuddin A.M.
    ;
    Ku Nurul Fazira Ku Azir
    ;
    Mohd Rashidi Che Beson
    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%.
  • Publication
    Techniques for Developing QRS Enhancement and Detection Algorithms in Electrocardiography (ECG): A Review
    ( 2024-05-10)
    Khairuddin A.M.
    ;
    Ku Nurul Fazira Ku Azir
    ;
    Mohd Rashidi Che Beson
    Algorithms are increasingly being used and recognized for their ability to improve the performance of diagnostic tools such as contemporary electrocardiogram (ECG). For instance, evidence from previous studies reveals that QRS enhancement and detection algorithms have enabled the ECG device to measure and classify heartbeat more accurately. Based on the review of the previous works on QRS detection in ECG, this paper examines the key components of the ECG, QRS detection features, the different techniques used for developing QRS enhancement and detection algorithms as well as the criteria for evaluating their performance.