Displaying publications 1 - 20 of 78 in total

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  1. Cardiac arrhythmias
    Gill SS
    Family Practitioner, 1977;2:14-17.
    Matched MeSH terms: Arrhythmias, Cardiac
  2. Some disorders of cardiac rhythm and their treatment
    Keele KD
    Malayan Medical Journal, 1937;12:47-9.
    Matched MeSH terms: Arrhythmias, Cardiac
  3. Management of life threatening cardiac arrhythmias
    Ahmad Murtazam ZA
    Family Physician, 1992;4:10-13.
    Matched MeSH terms: Arrhythmias, Cardiac
  4. Fulltext ECG Parameters for Malignant Ventricular Arrhythmias: A Comprehensive Review
    Mandala S, Di TC
    J Med Biol Eng, 2017;37(4):441-453.
    PMID: 28867990 DOI: 10.1007/s40846-017-0281-x
    Many studies showed electrocardiogram (ECG) parameters are useful for predicting fatal ventricular arrhythmias (VAs). However, the studies have several shortcomings. Firstly, all studies lack of effective way to present behavior of various ECG parameters prior to the occurrence of the VAs. Secondly, they also lack of discussion on how to consider the parameters as abnormal. Thirdly, the reports do not include approaches to increase the detection accuracy for the abnormal patterns. The purpose of this study is to address the aforementioned issues. It identifies ten ECG parameters from various sources and then presents a review based on the identified parameters. From the review, it has been found that the increased risk of VAs can be represented by presence and certain abnormal range of the parameters. The variation of parameters range could be influenced by either gender or age. This study also has discovered the facts that averaging, outliers elimination and morphology detection algorithms can contribute to the detection accuracy.
    Matched MeSH terms: Arrhythmias, Cardiac
  5. Fulltext Correlation of iatrogenic mild hyperkalaemia and bradyarrhythmia: a problem of polypharmacy in elderly
    Ahmad, N. H., Tan, T. L.
    Medicine & Health, 2017;12(2):329-334.
    MyJurnal
    Mild hyperkalaemia does not typically cause cardiac symptoms. However, for an elderly patient on atrio-ventricular (AV) nodal blocker, even mild hyperkalaemia may result in disastrous outcome. We report a case of persistent bradyarrythmia caused by iatrogenic hyperkalaemia in a patient who had concomitant use of AV nodal medication. An 81-year-old lady with multiple comorbidities and a long list of medications presented with symptomatic bradyarrhythmia. She, in fact, had two AV nodal blockers in her prescription, a beta-blocker and amiodarone. Her potassium level was found to be mildly elevated due to acute renal failure. She remained bradycardic despite initial treatment and was subsequently dependant on intravenous isoproterenol until her renal function improved. This case highlights the different threshold for manifestation of hyperkalaemic symptoms in a growing group of patients: elderly patients with multiple comorbidities and polypharmacy.
    Keywords: bradyarrythmia, bradycardia, elderly, hyperkalaemia, polypharmacy
    Matched MeSH terms: Arrhythmias, Cardiac*
  6. ECG lead misplacement in the frontal and horizontal plane mimicking A myocardial infarction
    Kaisbain N, Khoo KKL, Lim WJ
    Am J Emerg Med, 2023 Dec;74:196.e1-196.e4.
    PMID: 37863804 DOI: 10.1016/j.ajem.2023.10.009
    BACKGROUND/AIMS: Electrocardiogram (ECG) is an inexpensive, fundamental screening tool used in daily clinical practice. It is essential in the diagnosis of life-threatening conditions, such as acute myocardial infarctions, ventricular arrhythmias etc. However, ECG lead misplacement is a common technical error, which may translate into wrong interpretations, unnecessary investigations, and improper treatments.

    METHODS/RESULTS: We report a case of a multiple ECG lead misplacement made across two different planes of the heart, resulting in a bizarre series of ECG, mimicking an acute high lateral myocardial infarction. Multiple ECGs were done as there were abrupt changes compared to previous ECGS. Patient was pain free and administration of potentially harmful procedures and treatments were prevented.

    CONCLUSION: Our case demonstrated the importance of high clinical suspicion in diagnosing ECG lead misplacement. It is the responsibility of both the healthcare workers who are performing and interpreting the ECG to be alert of a possible lead malposition, to prevent untoward consequences to the patient.

    Matched MeSH terms: Arrhythmias, Cardiac/diagnosis
  7. Current methods in electrocardiogram characterization
    Martis RJ, Acharya UR, Adeli H
    Comput Biol Med, 2014 May;48:133-49.
    PMID: 24681634 DOI: 10.1016/j.compbiomed.2014.02.012
    The Electrocardiogram (ECG) is the P-QRS-T wave depicting the cardiac activity of the heart. The subtle changes in the electric potential patterns of repolarization and depolarization are indicative of the disease afflicting the patient. These clinical time domain features of the ECG waveform can be used in cardiac health diagnosis. Due to the presence of noise and minute morphological parameter values, it is very difficult to identify the ECG classes accurately by the naked eye. Various computer aided cardiac diagnosis (CACD) systems, analysis methods, challenges addressed and the future of cardiovascular disease screening are reviewed in this paper. Methods developed for time domain, frequency transform domain, and time-frequency domain analysis, such as the wavelet transform, cannot by themselves represent the inherent distinguishing features accurately. Hence, nonlinear methods which can capture the small variations in the ECG signal and provide improved accuracy in the presence of noise are discussed in greater detail in this review. A CACD system exploiting these nonlinear features can help clinicians to diagnose cardiovascular disease more accurately.
    Matched MeSH terms: Arrhythmias, Cardiac/diagnosis; Arrhythmias, Cardiac/physiopathology
  8. Sodium channel haploinsufficiency and structural change in ventricular arrhythmogenesis
    Jeevaratnam K, Guzadhur L, Goh YM, Grace AA, Huang CL
    Acta Physiol (Oxf), 2016 Feb;216(2):186-202.
    PMID: 26284956 DOI: 10.1111/apha.12577
    Normal cardiac excitation involves orderly conduction of electrical activation and recovery dependent upon surface membrane, voltage-gated, sodium (Na(+) ) channel α-subunits (Nav 1.5). We summarize experimental studies of physiological and clinical consequences of loss-of-function Na(+) channel mutations. Of these conditions, Brugada syndrome (BrS) and progressive cardiac conduction defect (PCCD) are associated with sudden, often fatal, ventricular tachycardia (VT) or fibrillation. Mouse Scn5a(+/-) hearts replicate important clinical phenotypes modelling these human conditions. The arrhythmic phenotype is associated not only with the primary biophysical change but also with additional, anatomical abnormalities, in turn dependent upon age and sex, each themselves exerting arrhythmic effects. Available evidence suggests a unified binary scheme for the development of arrhythmia in both BrS and PCCD. Previous biophysical studies suggested that Nav 1.5 deficiency produces a background electrophysiological defect compromising conduction, thereby producing an arrhythmic substrate unmasked by flecainide or ajmaline challenge. More recent reports further suggest a progressive decline in conduction velocity and increase in its dispersion particularly in ageing male Nav 1.5 haploinsufficient compared to WT hearts. This appears to involve a selective appearance of slow conduction at the expense of rapidly conducting pathways with changes in their frequency distributions. These changes were related to increased cardiac fibrosis. It is thus the combination of the structural and biophysical changes both accentuating arrhythmic substrate that may produce arrhythmic tendency. This binary scheme explains the combined requirement for separate, biophysical and structural changes, particularly occurring in ageing Nav 1.5 haploinsufficient males in producing clinical arrhythmia.
    Matched MeSH terms: Arrhythmias, Cardiac/genetics*; Arrhythmias, Cardiac/physiopathology*
  9. Fulltext A Hybrid Deep CNN Model for Abnormal Arrhythmia Detection Based on Cardiac ECG Signal
    Ullah A, Rehman SU, Tu S, Mehmood RM, Fawad, Ehatisham-Ul-Haq M
    Sensors (Basel), 2021 Feb 01;21(3).
    PMID: 33535397 DOI: 10.3390/s21030951
    Electrocardiogram (ECG) signals play a vital role in diagnosing and monitoring patients suffering from various cardiovascular diseases (CVDs). This research aims to develop a robust algorithm that can accurately classify the electrocardiogram signal even in the presence of environmental noise. A one-dimensional convolutional neural network (CNN) with two convolutional layers, two down-sampling layers, and a fully connected layer is proposed in this work. The same 1D data was transformed into two-dimensional (2D) images to improve the model's classification accuracy. Then, we applied the 2D CNN model consisting of input and output layers, three 2D-convolutional layers, three down-sampling layers, and a fully connected layer. The classification accuracy of 97.38% and 99.02% is achieved with the proposed 1D and 2D model when tested on the publicly available Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database. Both proposed 1D and 2D CNN models outperformed the corresponding state-of-the-art classification algorithms for the same data, which validates the proposed models' effectiveness.
    Matched MeSH terms: Arrhythmias, Cardiac/diagnosis
  10. HARDC : A novel ECG-based heartbeat classification method to detect arrhythmia using hierarchical attention based dual structured RNN with dilated CNN
    Islam MS, Hasan KF, Sultana S, Uddin S, Lio' P, Quinn JMW, et al.
    Neural Netw, 2023 May;162:271-287.
    PMID: 36921434 DOI: 10.1016/j.neunet.2023.03.004
    Deep learning-based models have achieved significant success in detecting cardiac arrhythmia by analyzing ECG signals to categorize patient heartbeats. To improve the performance of such models, we have developed a novel hybrid hierarchical attention-based bidirectional recurrent neural network with dilated CNN (HARDC) method for arrhythmia classification. This solves problems that arise when traditional dilated convolutional neural network (CNN) models disregard the correlation between contexts and gradient dispersion. The proposed HARDC fully exploits the dilated CNN and bidirectional recurrent neural network unit (BiGRU-BiLSTM) architecture to generate fusion features. As a result of incorporating both local and global feature information and an attention mechanism, the model's performance for prediction is improved. By combining the fusion features with a dilated CNN and a hierarchical attention mechanism, the trained HARDC model showed significantly improved classification results and interpretability of feature extraction on the PhysioNet 2017 challenge dataset. Sequential Z-Score normalization, filtering, denoising, and segmentation are used to prepare the raw data for analysis. CGAN (Conditional Generative Adversarial Network) is then used to generate synthetic signals from the processed data. The experimental results demonstrate that the proposed HARDC model significantly outperforms other existing models, achieving an accuracy of 99.60%, F1 score of 98.21%, a precision of 97.66%, and recall of 99.60% using MIT-BIH generated ECG. In addition, this approach significantly reduces run time when using dilated CNN compared to normal convolution. Overall, this hybrid model demonstrates an innovative and cost-effective strategy for ECG signal compression and high-performance ECG recognition. Our results indicate that an automated and highly computed method to classify multiple types of arrhythmia signals holds considerable promise.
    Matched MeSH terms: Arrhythmias, Cardiac/diagnosis
  11. Fulltext The rationale and design of the Micra Transcatheter Pacing Study: safety and efficacy of a novel miniaturized pacemaker
    Ritter P, Duray GZ, Zhang S, Narasimhan C, Soejima K, Omar R, et al.
    Europace, 2015 May;17(5):807-13.
    PMID: 25855677 DOI: 10.1093/europace/euv026
    Recent advances in miniaturization technologies and battery chemistries have made it possible to develop a pacemaker small enough to implant within the heart while still aiming to provide similar battery longevity to conventional pacemakers. The Micra Transcatheter Pacing System is a miniaturized single-chamber pacemaker system that is delivered via catheter through the femoral vein. The pacemaker is implanted directly inside the right ventricle of the heart, eliminating the need for a device pocket and insertion of a pacing lead, thereby potentially avoiding some of the complications associated with traditional pacing systems.
    Matched MeSH terms: Arrhythmias, Cardiac/diagnosis; Arrhythmias, Cardiac/physiopathology; Arrhythmias, Cardiac/therapy*
  12. Fulltext Management of atrial fibrillation
    Citation: Clinical Practice Guideline. Management of Atrial Fibrillation. Putrajaya: Ministry of Health, Malaysia; 2012

    Keywords: CPG
    Matched MeSH terms: Arrhythmias, Cardiac
  13. Fulltext Basics of electrocardiogram (ECG) and its application in diagnosis of heart ailments: an educational series
    Chodankar, Nagesh N., May, Honey Ohn, D’Souza, Urban John Arnold
    Borneo Journal of Medical Sciences, 2018;12(1):3-22.
    MyJurnal
    Electrocardiogram (ECG) is a record of electrical activity of the heart. PQRST waves represent
    the electrical activities of atria and ventricles. A complete three-dimensional electrical activity is
    possible to be recorded using a 12-lead ECG. The normal and different routinely-met clinical ECG
    are elaborated and discussed. This routine, normal and abnormal ECG, like arrhythmias and heart
    block records as well as their clinical notes shall be educational information for the medical students.
    Matched MeSH terms: Arrhythmias, Cardiac
  14. Arrhythmia detection using deep convolutional neural network with long duration ECG signals
    Yıldırım Ö, Pławiak P, Tan RS, Acharya UR
    Comput Biol Med, 2018 11 01;102:411-420.
    PMID: 30245122 DOI: 10.1016/j.compbiomed.2018.09.009
    This article presents a new deep learning approach for cardiac arrhythmia (17 classes) detection based on long-duration electrocardiography (ECG) signal analysis. Cardiovascular disease prevention is one of the most important tasks of any health care system as about 50 million people are at risk of heart disease in the world. Although automatic analysis of ECG signal is very popular, current methods are not satisfactory. The goal of our research was to design a new method based on deep learning to efficiently and quickly classify cardiac arrhythmias. Described research are based on 1000 ECG signal fragments from the MIT - BIH Arrhythmia database for one lead (MLII) from 45 persons. Approach based on the analysis of 10-s ECG signal fragments (not a single QRS complex) is applied (on average, 13 times less classifications/analysis). A complete end-to-end structure was designed instead of the hand-crafted feature extraction and selection used in traditional methods. Our main contribution is to design a new 1D-Convolutional Neural Network model (1D-CNN). The proposed method is 1) efficient, 2) fast (real-time classification) 3) non-complex and 4) simple to use (combined feature extraction and selection, and classification in one stage). Deep 1D-CNN achieved a recognition overall accuracy of 17 cardiac arrhythmia disorders (classes) at a level of 91.33% and classification time per single sample of 0.015 s. Compared to the current research, our results are one of the best results to date, and our solution can be implemented in mobile devices and cloud computing.
    Matched MeSH terms: Arrhythmias, Cardiac
  15. Fulltext Cardiac arrhythmias and echocardiographic features in Wolff-Parkinson-White syndrome
    Ng WH, Kew ST
    Med J Malaysia, 1980 Sep;35(1):41-5.
    PMID: 7253998
    Electrocardiographic features of the Woljf-Parkinson-White syndrome may be seen in normal individuals and in those with congenital or acquired heart disease. Predisposition to tachyarrhythmias and its misinterpretation are common. In this report a case of Wolff-Parkinson-White syndrome in a 25 year old Malay male who presented with cardiac arrhythmias is described. Echocardiographic findings and the role of echocardiography are discussed.
    Matched MeSH terms: Arrhythmias, Cardiac/diagnosis*
  16. Fulltext Evaluation of conventional and computerised monitoring of cardiac function in the coronary care unit
    Ng KH
    Med J Malaysia, 1983 Dec;38(4):289-93.
    PMID: 6599984
    One of the important functions of the Coronary Care Unit (CCU) is the continuous and intensive monitoring of cardiac function. To date, many monitoring techniques have been developed and tested. In this paper, both the conventional and computerised monitoring techniques are reviewed and evaluated. It is shown that a computerised system has several defirute advantages over the conventional system, e.g. lower false alarm rate, accurate and fast data processing, retrospective studies. However one also ought to be aware of the limitations,
    Matched MeSH terms: Arrhythmias, Cardiac/diagnosis
  17. Cardiac arrhythmias in acute myocardial infarction
    Ng WH, Goh TH, Ishak E, Ahmad Z
    Med J Malaysia, 1979 Dec;34(2):131-5.
    PMID: 548713
    Matched MeSH terms: Arrhythmias, Cardiac/diagnosis*
  18. [Possibilities for predicting of cardiac arrhythmias and conductivity disorders in former athletes.]
    Krysiuk OB, Obrezan AG, Zadvorev SF, Yakovlev AA
    Adv Gerontol, 2020;33(1):131-136.
    PMID: 32362096
    In order to analyze the relationship between the athletic qualification and syndrome of cardiac rhythm and conductivity disturbances in former athletes, a retrospective analysis of medical records of 39 male former athletes with cardiovascular complaints (mean age 61,6±11,3 years, mean duration of career in sports 23,9±17,3 years, mean duration of post-athletic period 20,1±9,9 years) was carried out. The patients were screened for cardiac arrhythmias and underwent echocardiography. The overall prevalence of sustained paroxysms of atrial fibrillation was 42%, increasing with the athletic qualification. Ryan grade 4b-5 premature ventricular contractions were found in 14% of patients. 3 parameters were found to be the independent predictors of arrhythmias in former athletes, i. e. athletic qualification, multifocal atherosclerosis (as an anti-risk factor), and age. The coefficient of determinance for the created prognostic model reached 43%. Further prospective studies are needed to validate an algorithm.
    Matched MeSH terms: Arrhythmias, Cardiac/diagnosis
  19. Fulltext ECG-based prediction algorithm for imminent malignant ventricular arrhythmias using decision tree
    Mandala S, Cai Di T, Sunar MS, Adiwijaya
    PLoS One, 2020;15(5):e0231635.
    PMID: 32407335 DOI: 10.1371/journal.pone.0231635
    Spontaneous prediction of malignant ventricular arrhythmia (MVA) is useful to avoid delay in rescue operations. Recently, researchers have developed several algorithms to predict MVA using various features derived from electrocardiogram (ECG). However, there are several unresolved issues regarding MVA prediction such as the effect of number of ECG features on a prediction remaining unclear, possibility that an alert for occurring MVA may arrive very late and uncertainty in the performance of the algorithm predicting MVA minutes before onset. To overcome the aforementioned problems, this research conducts an in-depth study on the number and types of ECG features that are implemented in a decision tree classifier. In addition, this research also investigates an algorithm's execution time before the occurrence of MVA to minimize delays in warnings for MVA. Lastly, this research aims to study both the sensitivity and specificity of an algorithm to reveal the performance of MVA prediction algorithms from time to time. To strengthen the results of analysis, several classifiers such as support vector machine and naive Bayes are also examined for the purpose of comparison study. There are three phases required to achieve the objectives. The first phase is literature review on existing relevant studies. The second phase deals with design and development of four modules for predicting MVA. Rigorous experiments are performed in the feature selection and classification modules. The results show that eight ECG features with decision tree classifier achieved good prediction performance in terms of execution time and sensitivity. In addition, the results show that the highest percentage for sensitivity and specificity is 95% and 90% respectively, in the fourth 5-minute interval (15.1 minutes-20 minutes) that preceded the onset of an arrhythmia event. Such results imply that the fourth 5-minute interval would be the best time to perform prediction.
    Matched MeSH terms: Arrhythmias, Cardiac/diagnosis*
  20. Fulltext Secular trends of health care resource utilization and costs between Brugada syndrome and congenital long QT syndrome: A territory-wide study
    Lee S, Chung CTS, Radford D, Chou OHI, Lee TTL, Ng ZMW, et al.
    Clin Cardiol, 2023 Oct;46(10):1194-1201.
    PMID: 37489866 DOI: 10.1002/clc.24102
    BACKGROUND: Health care resource utilization (HCRU) and costs are important metrics of health care burden, but they have rarely been explored in the setting of cardiac ion channelopathies.

    HYPOTHESIS: This study tested the hypothesis that attendance-related HCRUs and costs differed between patients with Brugada syndrome (BrS) and congenital long QT syndrome (LQTS).

    METHODS: This was a retrospective cohort study of consecutive BrS and LQTS patients at public hospitals or clinics in Hong Kong, China. HCRUs and costs (in USD) for Accident and Emergency (A&E), inpatient, general outpatient and specialist outpatient attendances were analyzed between 2001 and 2019 at the cohort level. Comparisons were made using incidence rate ratios (IRRs [95% confidence intervals]).

    RESULTS: Over the 19-year period, 516 BrS (median age of initial presentation: 51 [interquartile range: 38-61] years, 92% male) and 134 LQTS (median age of initial presentation: 21 [9-44] years, 32% male) patients were included. Compared to LQTS patients, BrS patients had lower total costs (2 008 126 [2 007 622-2 008 629] vs. 2 343 864 [2 342 828-2 344 900]; IRR: 0.857 [0.855-0.858]), higher costs for A&E attendances (83 113 [83 048-83 177] vs. 70 604 [70 487-70 721]; IRR: 1.177 [1.165-1.189]) and general outpatient services (2,176 [2,166-2,187] vs. 921 [908-935]; IRR: 2.363 [2.187-2.552]), but lower costs for inpatient stay (1 391 624 [1 391 359-1 391 889] vs. 1 713 742 [1 713 166-1 714 319]; IRR: 0.812 [0.810-0.814]) and lower costs for specialist outpatient services (531 213 [531 049-531 376] vs. 558 597 [558268-558926]; IRR: 0.951 [0.947-0.9550]).

    CONCLUSIONS: Overall, BrS patients consume 14% less health care resources compared to LQTS patients in terms of attendance costs. BrS patients require more A&E and general outpatient services, but less inpatient and specialist outpatient services than LQTS patients.

    Matched MeSH terms: Arrhythmias, Cardiac/complications
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