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
    A general framework for improving electrocardiography monitoring system with machine learning
    ( 2019-03-01)
    Khairuddin A.M.
    ;
    Ku Nurul Fazira Ku Azir
    ;
    Eh Kan P.
    As one of the most important health monitoring systems, electrocardiography (ECG) is used to obtain information about the structure and functions of the human heart for detecting and preventing cardiovascular disease. Given its important role, it is vital that the ECG monitoring system provides relevant and accurate information about the heart. Over the years, numerous attempts were made to design and develop more effective ECG monitoring system. Nonetheless, the literature reveals not only several limitations in conventional ECG monitoring system but also emphasizes on the need to adopt new technology such as machine learning to improve the monitoring system as well as its medical applications. This paper reviews previous works on machine learning to explain its key features, capabilities as well as presents a general framework for improving ECG monitoring system.
  • 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
    Ambient Cues of Kitchen Counter in Guiding Cooking Activities for Alzheimer's Patient
    ( 2019-03-01)
    Basharudin N.W.
    ;
    Ku Nurul Fazira Ku Azir
    ;
    Khairuddin A.M.
    ;
    Ehkan P.
    Loss of memory is among the first symptoms reported by patients suffering from Alzheimer's disease (AD). The individual pattern of impaired memory functions correlates with functional brain integrity will result in the inability of the patient to complete a particular task. This research focuses on designing the ambient cues in the kitchen counter to guide Alzheimer's patient to complete cooking activities based on the concept of Stimulus-Response Compatibility (SRC) and whereas the activity is focus on the sequential workflow of cooking activities based on the Hierarchical Task Analysis (HTA). Wizard-of-Oz is used to simulate the behavior of theoretical intelligent ambient cues. The results show with the aid of ambient cues in the kitchen, patient be able to complete the task until the end even if there is confusing or disruption in the middle of the activity. The effectiveness of ambient cues show that patient can easily understood the cues of guiding the cooking flow when mistakes happened.
  • 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.