Displaying publications 21 - 22 of 22 in total

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  1. Diastolic dysfunction grading, echocardiographic and electrocardiogram findings in 50 patients with apical hypertrophic cardiomyopathy
    Aslannif R, Suraya K, Koh HB, Tey YS, Tan KL, Tham CH, et al.
    Med J Malaysia, 2019 12;74(6):521-526.
    PMID: 31929479
    INTRODUCTION: Apical Hypertrophic Cardiomyopathy (Apical HCM) is an uncommon variant of hypertrophic cardiomyopathy, but it is relatively more common in Asian countries. This is a retrospective, non-randomised, single centre study of patients with Apical HCM focusing on their diastolic dysfunction grading, echocardiographic parameters and electrocardiograms (ECG).

    METHODS: All Apical HCM patients coming for clinic visits at the Institut Jantung Negara from September 2017 to September 2018 were included. We assessed their echocardiography images, grade their diastolic function and reviewed their ECG on presentation.

    RESULTS: Fifty patient were included, 82% (n=41) were males and 18% (n=9) females. The diastolic function grading of 37 (74%) patients were able to be determined using the updated 2016 American Society of Echocardiography (ASE) diastolic guidelines. Fifty percent (n=25) had the typical ace-ofspades shape left ventricle (LV) appearance in diastole and 12% (n=6) had apical pouch. All patients had T inversion in the anterior leads of their ECG, and only 52% (n=26) fulfilled the ECG left ventricular hypertrophy (LVH) criteria. Majority of our patients presented with symptoms of chest pain (52%, n=26) and dyspnoea (42%, n=21).

    CONCLUSION: The updated 2016 ASE guideline makes it easier to evaluate LV diastolic function in most patients with Apical HCM. It also helps in elucidating the aetiology of dyspnoea, based on left atrial pressure. Clinicians should have a high index of suspicion for Apical HCM when faced with deep T inversion on ECG, in addition to a thick LV apex with an aceof- spades appearance during diastole.

    Matched MeSH terms: Electrocardiography/methods*
  2. Dynamic ECG features for atrial fibrillation recognition
    Abdul-Kadir NA, Mat Safri N, Othman MA
    Comput Methods Programs Biomed, 2016 Nov;136:143-50.
    PMID: 27686711 DOI: 10.1016/j.cmpb.2016.08.021
    BACKGROUND: Atrial fibrillation (AF) can cause the formation of blood clots in the heart. The clots may move to the brain and cause a stroke. Therefore, this study analyzed the ECG features of AF and normal sinus rhythm signals for AF recognition which were extracted by using a second-order dynamic system (SODS) concept.
    OBJECTIVE: To find the appropriate windowing length for feature extraction based on SODS and to determine a machine learning method that could provide higher accuracy in recognizing AF.
    METHOD: ECG features were extracted based on a dynamic system (DS) that uses a second-order differential equation to describe the short-term behavior of ECG signals according to the natural frequency (ω), damping coefficient, (ξ), and forcing input (u). The extracted features were windowed into 2, 3, 4, 6, 8, and 10 second episodes to find the appropriate windowing size for AF signal processing. ANOVA and t-tests were used to determine the significant features. In addition, pattern recognition machine learning methods (an artificial neural network (ANN) and a support vector machine (SVM)) with k-fold cross validation (k-CV) were used to develop the ECG recognition system.
    RESULTS: Significant differences (p 
    Matched MeSH terms: Electrocardiography/methods*
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