Displaying publications 1 - 20 of 209 in total

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  1. The use of BLRP model for disaggregating daily rainfall affected by monsoon in Peninsular Malaysia
    Zulkifli Yusop, Harisaweni, Fadhilah Yusof
    Sains Malaysiana, 2016;45:87-97.
    Rainfall intensity is the main input variable in various hydrological analysis and modeling. Unfortunately, the quality of rainfall data is often poor and reliable data records are available at coarse intervals such as yearly, monthly and daily. Short interval rainfall records are scarce because of high cost and low reliability of the measurement and the monitoring systems. One way to solve this problem is by disaggregating the coarse intervals to generate the short one using the stochastic method. This paper describes the use of the Bartlett Lewis Rectangular Pulse (BLRP) model. The method was used to disaggregate 10 years of daily data for generating hourly data from 5 rainfall stations in Kelantan as representative area affected by monsoon period and 5 rainfall stations in Damansara affected by inter-monsoon period. The models were evaluated on their ability to reproduce standard and extreme rainfall model statistics derived from the historical record over disaggregation simulation results. The disaggregation of daily to hourly rainfall produced monthly and daily means and variances that closely match the historical records. However, for the disaggregation of daily to hourly rainfall, the standard deviation values are lower than the historical ones. Despite the marked differences in the standard deviation, both data series exhibit similar patterns and the model adequately preserve the trends of all the properties used in evaluating its performances.
    Matched MeSH terms: Electrocardiography
  2. Takotsubo cardiomyopathy in association with hyperthyroidism
    Zuhdi AS, Yaakob ZH, Sadiq MA, Ismail MD, Undok AW, Ahmad WA
    Medicina (Kaunas), 2011;47(4):219-21.
    PMID: 21829054
    Takotsubo cardiomyopathy is a rare, acute, nonischemic cardiomyopathy causing transient left ventricular dysfunction, which can mimic myocardial infarction on its presentation. While many cardiac manifestations have been associated with hyperthyroidism, we report a rare case where it has precipitated takotsubo cardiomyopathy.
    Matched MeSH terms: Electrocardiography
  3. 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: Electrocardiography
  4. Fulltext The ' widow maker': Electrocardiogram features that should not be missed
    Yusuf Muharam M, Ahmad R, Harmy M
    Malays Fam Physician, 2013;8(1):45-7.
    PMID: 25606269 MyJurnal
    Patients with Wellen's syndrome often present with chest pain and found to have specific precordial T-wave changes on the electrocardiogram (ECG). They subsequently develop a large anterior wall myocardial infarction. These specific electrocardiographic abnormalities are associated with critical stenosis of the proximal left anterior descending coronary artery (LAD). This syndrome is often under-recognised and has fatal consequences; it is, therefore, also known as the widow maker. We highlight a case of a 39-year old gentleman who had a history of coronary artery disease and typical ECG characteristics of Wellen's syndrome.
    Matched MeSH terms: Electrocardiography
  5. Conduction abnormalities in thyrotoxicosis--a report of three cases
    Yusoff K, Khalid BA
    Ann Acad Med Singap, 1993 Jul;22(4):609-12.
    PMID: 8257070
    Cardiac arrhythmias are common in patients with thyrotoxicosis. Conduction abnormalities have been seen in a few thyrotoxic patients, but these, in particular high grade atrioventricular (AV) block, often occur in the presence of other conditions. Three thyrotoxic patients with conduction abnormalities are described: two were associated with severe hypokalaemia and the third had congestive cardiac failure. Conditions predisposing to conduction abnormality should be identified when this occurs in a thyrotoxic patient as their correction may help resolve or explain the conduction abnormality.
    Matched MeSH terms: Electrocardiography
  6. Automated detection of diabetic subject using pre-trained 2D-CNN models with frequency spectrum images extracted from heart rate signals
    Yildirim O, Talo M, Ay B, Baloglu UB, Aydin G, Acharya UR
    Comput Biol Med, 2019 10;113:103387.
    PMID: 31421276 DOI: 10.1016/j.compbiomed.2019.103387
    In this study, a deep-transfer learning approach is proposed for the automated diagnosis of diabetes mellitus (DM), using heart rate (HR) signals obtained from electrocardiogram (ECG) data. Recent progress in deep learning has contributed significantly to improvement in the quality of healthcare. In order for deep learning models to perform well, large datasets are required for training. However, a difficulty in the biomedical field is the lack of clinical data with expert annotation. A recent, commonly implemented technique to train deep learning models using small datasets is to transfer the weighting, developed from a large dataset, to the current model. This deep learning transfer strategy is generally employed for two-dimensional signals. Herein, the weighting of models pre-trained using two-dimensional large image data was applied to one-dimensional HR signals. The one-dimensional HR signals were then converted into frequency spectrum images, which were utilized for application to well-known pre-trained models, specifically: AlexNet, VggNet, ResNet, and DenseNet. The DenseNet pre-trained model yielded the highest classification average accuracy of 97.62%, and sensitivity of 100%, to detect DM subjects via HR signal recordings. In the future, we intend to further test this developed model by utilizing additional data along with cloud-based storage to diagnose DM via heart signal analysis.
    Matched MeSH terms: Electrocardiography*
  7. A new approach for arrhythmia classification using deep coded features and LSTM networks
    Yildirim O, Baloglu UB, Tan RS, Ciaccio EJ, Acharya UR
    Comput Methods Programs Biomed, 2019 Jul;176:121-133.
    PMID: 31200900 DOI: 10.1016/j.cmpb.2019.05.004
    BACKGROUND AND OBJECTIVE: For diagnosis of arrhythmic heart problems, electrocardiogram (ECG) signals should be recorded and monitored. The long-term signal records obtained are analyzed by expert cardiologists. Devices such as the Holter monitor have limited hardware capabilities. For improved diagnostic capacity, it would be helpful to detect arrhythmic signals automatically. In this study, a novel approach is presented as a candidate solution for these issues.

    METHODS: A convolutional auto-encoder (CAE) based nonlinear compression structure is implemented to reduce the signal size of arrhythmic beats. Long-short term memory (LSTM) classifiers are employed to automatically recognize arrhythmias using ECG features, which are deeply coded with the CAE network.

    RESULTS: Based upon the coded ECG signals, both storage requirement and classification time were considerably reduced. In experimental studies conducted with the MIT-BIH arrhythmia database, ECG signals were compressed by an average 0.70% percentage root mean square difference (PRD) rate, and an accuracy of over 99.0% was observed.

    CONCLUSIONS: One of the significant contributions of this study is that the proposed approach can significantly reduce time duration when using LSTM networks for data analysis. Thus, a novel and effective approach was proposed for both ECG signal compression, and their high-performance automatic recognition, with very low computational cost.

    Matched MeSH terms: Electrocardiography/methods*; Electrocardiography, Ambulatory
  8. Hybrid ABSORB bioresorbable vascular scaffold and drug eluting stent or hybrid BVS-DES percutaneous coronary intervention: Method and rationale for hybrid overlapping PCI
    Yew KL
    Int J Cardiol, 2015;186:74-6.
    PMID: 25814348 DOI: 10.1016/j.ijcard.2015.02.092
    Matched MeSH terms: Electrocardiography
  9. Fulltext The comparison of QT dispersion and 24 hour ambulatory blood pressure monitoring amongst diabetic patients with and without microalbuminuria
    Yeo CK, Hapizah MN, Khalid BAK, Wan Nazainimoon WM, Khalid Y
    Med J Malaysia, 2004 Jun;59(2):185-9.
    PMID: 15559168
    Diabetes mellitus is an important coronary artery disease risk factor. The presence of microalbuminuria, which indicates renal involvement in diabetic patients, is associated with an increased cardiovascular risk. There are suggestions that diabetic patients with microalbuminuria have more adverse risk profile such as higher ambulatory blood pressure and total cholesterol levels to account for the increased cardiovascular morbidity and mortality. QT dispersion is increasingly being recognized as a prognostic factor for coronary artery disease and sudden death. Some studies have suggested that QT dispersion is an important predictor of mortality in Type II diabetic patients. Our cross sectional study was to compare the QT dispersion and 24 hour ambulatory blood pressure monitoring between diabetic patients with microalbuminuria and those without microalbuminuria. Diabetic patients with overt coronary artery disease were excluded from the study. A total of 108 patients were recruited of which 57 patients had microalbuminuria and 51 were without microalbuminuria. The mean value of QT dispersion was significantly higher in patients with microalbuminuria than in patients without microalbuminuria (58.9 +/- 27.9 ms vs. 47.1 +/- 25.0 ms, p < 0.05). The mean 24 hour systolic and diastolic blood pressures were significantly higher in patients with microalbuminuria than in patients without microalbuminuria (129.5 +/- 12.3 mm Hg vs 122.3 +/- 10.2 mm Hg, p < 0.05 and 78.4 +/- 6.9 mm Hg vs 75.3 +/- 6.8 mm Hg, p < 0.05, respectively). Our study suggests that QT dispersion prolongation, related perhaps to some autonomic dysfunction, is an early manifestation of cardiovascular aberration in diabetic patients with microalbuminuria. The higher blood pressure levels recorded during a 24-hour period min diabetics with microalbuminuria could also possibly account for the worse cardiovascular outcome in this group of patients.
    Matched MeSH terms: Electrocardiography*
  10. Fulltext Anaesthetic challenges in a patient with Wolff-Parkinson-White (WPW) syndrome for orchidectomy
    Yeap TB, Teah MK, Thevarajah S, Azerai S
    BMJ Case Rep, 2021 Mar 25;14(3).
    PMID: 33766970 DOI: 10.1136/bcr-2020-241176
    Wolff-Parkinson-White (WPW) syndrome is an extremely rare congenital cardiac conduction disorder. It is due to an aberrant pathway between the atrium and ventricle. This manuscript entails a man with an underlying WPW who was posted for an elective orchidectomy. We discussed the important perioperative precautions to prevent the precipitation of acute cardiac events.
    Matched MeSH terms: Electrocardiography
  11. The clinical benefit of cardiac resynchronization therapy for narrow QRS compared to broad QRS complex patients
    Yap LB, Qadir F, Nguyen ST, Ma SK, Koh KW, Muhammad Z, et al.
    Int J Cardiol, 2015 Mar 15;183:178-9.
    PMID: 25666128 DOI: 10.1016/j.ijcard.2015.01.042
    Matched MeSH terms: Electrocardiography/methods
  12. Fulltext A comparison of long-term outcomes between narrow and broad QRS complex patients treated with cardiac resynchronization therapy
    Yap LB, Nguyen ST, Qadir F, Ma SK, Muhammad Z, Koh KW, et al.
    Acta Cardiol, 2016 Jun;71(3):323-30.
    PMID: 27594128 DOI: 10.2143/AC.71.3.3152093
    Matched MeSH terms: Electrocardiography/methods*
  13. Fulltext A Study on Arrhythmia via ECG Signal Classification Using the Convolutional Neural Network
    Wu M, Lu Y, Yang W, Wong SY
    Front Comput Neurosci, 2020;14:564015.
    PMID: 33469423 DOI: 10.3389/fncom.2020.564015
    Cardiovascular diseases (CVDs) are the leading cause of death today. The current identification method of the diseases is analyzing the Electrocardiogram (ECG), which is a medical monitoring technology recording cardiac activity. Unfortunately, looking for experts to analyze a large amount of ECG data consumes too many medical resources. Therefore, the method of identifying ECG characteristics based on machine learning has gradually become prevalent. However, there are some drawbacks to these typical methods, requiring manual feature recognition, complex models, and long training time. This paper proposes a robust and efficient 12-layer deep one-dimensional convolutional neural network on classifying the five micro-classes of heartbeat types in the MIT- BIH Arrhythmia database. The five types of heartbeat features are classified, and wavelet self-adaptive threshold denoising method is used in the experiments. Compared with BP neural network, random forest, and other CNN networks, the results show that the model proposed in this paper has better performance in accuracy, sensitivity, robustness, and anti-noise capability. Its accurate classification effectively saves medical resources, which has a positive effect on clinical practice.
    Matched MeSH terms: Electrocardiography
  14. Fulltext Fall Detection System Using Global System for Mobile Communication
    Wong, Jackson Sing Ann, Yew, Hoe Tung
    Borneo Journal of Medical Sciences, 2018;12(101):53-54.
    MyJurnal
    In this modern and fast-moving world, elderly’s safety and security have become an important issue. According to the World Population Prospects of the United Nations 2015, there is 12.3 per cent population aged 60 and above globally and it is the fastest growing population at a rate of 3.26 per cent per year. In order to reduce the worries about the elderly living alone at home, Elderly Monitoring System is required for continuous monitoring. “Fall†is one of the critical incidents for the elderly living alone as it causes serious injuries. A fall detection system using global system for mobile communication can help to reduce the time of unaware of their next of kin.
    Matched MeSH terms: Electrocardiography
  15. Fast prototype of a wireless cardiac rhythm interpretive instrument
    Wong KI, Ho MM
    Annu Int Conf IEEE Eng Med Biol Soc, 2008;2008:502-5.
    PMID: 19162703 DOI: 10.1109/IEMBS.2008.4649200
    Extended patient monitoring has become increasingly important for detection of cardiac conditions, such as irregularities in the rhythms of the heart, while patient is practicing normal daily activity. This paper presents a design of a single lead wireless cardiac rhythm interpretive instrument that capable of capture the electrocardiogram (ECG) in digital format and transmitted to a remote base-station (i.e. PC) for storage and further interpretation. The design has achieved high quality of ECG and free of interference in the presence of motion.
    Matched MeSH terms: Electrocardiography, Ambulatory/instrumentation*
  16. Application of smartphone electrocardiogram
    Wong KC
    Med J Malaysia, 2021 07;76(4):565.
    PMID: 34305119
    No abstract provided.
    Matched MeSH terms: Electrocardiography*
  17. The nasocardiac reflex during nasoendoscopy: A commonly overlooked risk
    Wong A, Abu Bakar MZ
    Am J Otolaryngol, 2021 01 04;42(2):102869.
    PMID: 33429183 DOI: 10.1016/j.amjoto.2020.102869
    PURPOSE: The nasocardiac reflex is known but not well researched. We aimed to ascertain the electrocardiographic features of the reflex and to chronologically map the heart rhythm dynamics during nasoendoscopy. We also intended to identify variables that could potentially affect the occurrence of this reflex.

    MATERIAL AND METHODS: A prospective, quasi-experimental physiological study. Selected healthy subjects were observed electrocardiographically for 60 s continuously in three equal phases of 20 s each - baseline phase, nasoendoscopic phase, and recovery phase (post-nasoendoscopy). Heart rate fluctuations were charted, followed by identification of a positive nasocardiac reflex group of subjects and a negative group. Analyses against multiple variables were done.

    RESULTS: A total of 53 subjects were analysed. Heart rate during the baseline phase was 81.0 ± 9.9, nasoendoscopic phase was 72.7 ± 10.1, and recovery phase was 75.2 ± 9.6. Sixteen subjects (30.2%) had a positive nasocardiac reflex, and they remained in sinus rhythm with no occurrences of skipped beats, atrioventricular blocks or asystoles. One subject (1.9%) developed temporary ectopic premature ventricular contractions after nasoendoscopy. No variables were found affecting the incidence of a nasocardiac reflex in our study.

    CONCLUSIONS: The pattern of heart rate dynamics was consistent as heart rates drop rapidly upon endoscope insertion and recover in some measure after its withdrawal. Although all our subjects remained asymptomatic, clinicians should not overlook the risks of a severe nasocardiac reflex when performing nasoendoscopy. We recommend that electrical cardiac monitoring be part of the management of vasovagal responses during in-office endonasal procedures.

    Matched MeSH terms: Electrocardiography*
  18. Fulltext Evoked K-complexes and altered interaction between the central and autonomic nervous systems during sleep in alcohol use disorder
    Willoughby AR, de Zambotti M, Baker FC, Colrain IM
    Alcohol, 2020 May;84:1-7.
    PMID: 31539623 DOI: 10.1016/j.alcohol.2019.09.005
    There is evidence for impairment in both central nervous system (CNS) and autonomic nervous system (ANS) function with prolonged alcohol use. While these impairments persist into abstinence, partial recovery of function has been demonstrated in both systems during sleep. To investigate potential ANS dysfunction associated with cortical CNS responses (impairment in CNS-ANS coupling), we assessed phasic heart rate (HR) fluctuation associated with tones that did and those that did not elicit a K-complex (KC) during stable N2 non-rapid eye movement (NREM) sleep in a group of 16 recently abstinent alcohol use disorder (AUD) patients (41.6 ± 8.5 years) and a group of 13 sex- and age-matched control participants (46.6 ± 9.3 years). Electroencephalogram (EEG) and electrocardiogram (ECG) data were recorded throughout the night. Alcohol consumption questionnaires were also administered to the AUD patients. AUD patients had elevated HR compared to controls at baseline prior to tone presentation. The HR fluctuation associated with KCs elicited by tone presentation was significantly smaller in amplitude, and tended to be delayed in time, in the AUD group compared with the control group, and the subsequent deceleration was also smaller in AUD patients. In both groups, the increase in HR was larger and occurred earlier when KCs were produced than when they were not, and there was no difference in the magnitude of the KC effect between groups. Phasic HR changes associated with KCs elicited by tones are impaired in AUD participants, reflecting ANS dysfunction possibly caused by an alteration of cardiac vagal trafficking. However, only the timing of the HR response was found to relate to estimated lifetime alcohol consumption in AUD. The clinical meaning and implications of these novel findings need to be determined.
    Matched MeSH terms: Electrocardiography
  19. Fulltext Association of testosterone, inflammation with the severity of first acute ST-elevation myocardial infarction
    Wickramatilake CM, Mohideen MR, Pathirana C
    Indian Heart J, 2017 02 12;69(2):291.
    PMID: 28460787 DOI: 10.1016/j.ihj.2017.02.002
    Matched MeSH terms: Electrocardiography*
  20. Cardiac sarcoidosis: two cases with autopsy findings
    Wan Muhaizan WM, Swaminathan M, Daud MS
    Malays J Pathol, 2004 Jun;26(1):59-63.
    PMID: 16196153
    Cardiac sarcoidosis is a disease of young adults. In most cases it presents with sudden death, arrhythmias, conduction disorders, heart failure or cardiomyopathy. The authors describe two cases of myocardial involvement by sarcoidosis that lead to death of the patients. Case one was a 26-year-old Indian man who was previously well and presented with sudden death. Autopsy showed nodules of sarcoid granuloma involving the heart, lungs and lymph nodes. Case two was a 47-year-old Indian lady who complained of reduced effort tolerance. Echocardiography showed that she had restrictive hypertrophic cardiomyopathy with heart failure. Seven months after initial presentation, she developed worsening of heart failure and died. Autopsy revealed involvement of the heart, lungs and liver by sarcoidosis.
    Matched MeSH terms: Electrocardiography, Ambulatory
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