Displaying publications 1 - 20 of 208 in total

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  1. Ibrahimy MI, Ahmed F, Mohd Ali MA, Zahedi E
    IEEE Trans Biomed Eng, 2003 Feb;50(2):258-62.
    PMID: 12665042
    An algorithm based on digital filtering, adaptive thresholding, statistical properties in the time domain, and differencing of local maxima and minima has been developed for the simultaneous measurement of the fetal and maternal heart rates from the maternal abdominal electrocardiogram during pregnancy and labor for ambulatory monitoring. A microcontroller-based system has been used to implement the algorithm in real-time. A Doppler ultrasound fetal monitor was used for statistical comparison on five volunteers with low risk pregnancies, between 35 and 40 weeks of gestation. Results showed an average percent root mean square difference of 5.32% and linear correlation coefficient from 0.84 to 0.93. The fetal heart rate curves remained inside a +/- 5-beats-per-minute limit relative to the reference ultrasound method for 84.1% of the time.
    Matched MeSH terms: Electrocardiography, Ambulatory/methods*
  2. Lim MA, Yusof K
    Med J Malaysia, 1973 Dec;28(2):129-31.
    PMID: 4276231
    Matched MeSH terms: Electrocardiography
  3. Sabarudin A, Sun Z, Yusof AK
    Int J Cardiol, 2013 Sep 30;168(2):746-53.
    PMID: 23098849 DOI: 10.1016/j.ijcard.2012.09.217
    This study is conducted to investigate and compare image quality and radiation dose between prospective ECG-triggered and retrospective ECG-gated coronary CT angiography (CCTA) with the use of single-source CT (SSCT) and dual-source CT (DSCT).
    Matched MeSH terms: Electrocardiography/methods; Electrocardiography/standards*
  4. Rajendra Acharya U, Faust O, Adib Kadri N, Suri JS, Yu W
    Comput Biol Med, 2013 Oct;43(10):1523-9.
    PMID: 24034744 DOI: 10.1016/j.compbiomed.2013.05.024
    Diabetes mellitus (DM) affects considerable number of people in the world and the number of cases is increasing every year. Due to a strong link to the genetic basis of the disease, it is extremely difficult to cure. However, it can be controlled to prevent severe consequences, such as organ damage. Therefore, diabetes diagnosis and monitoring of its treatment is very important. In this paper, we have proposed a non-invasive diagnosis support system for DM. The system determines whether or not diabetes is present by determining the cardiac health of a patient using heart rate variability (HRV) analysis. This analysis was based on nine nonlinear features namely: Approximate Entropy (ApEn), largest Lyapunov exponet (LLE), detrended fluctuation analysis (DFA) and recurrence quantification analysis (RQA). Clinically significant measures were used as input to classification algorithms, namely AdaBoost, decision tree (DT), fuzzy Sugeno classifier (FSC), k-nearest neighbor algorithm (k-NN), probabilistic neural network (PNN) and support vector machine (SVM). Ten-fold stratified cross-validation was used to select the best classifier. AdaBoost, with least squares (LS) as weak learner, performed better than the other classifiers, yielding an average accuracy of 90%, sensitivity of 92.5% and specificity of 88.7%.
    Matched MeSH terms: Electrocardiography/methods
  5. Wong, Jackson Sing Ann, Yew, Hoe Tung
    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
  6. Krishnan GD, Yahaya N, Yahya M
    J ASEAN Fed Endocr Soc, 2019;34(1):92-94.
    PMID: 33442142 DOI: 10.15605/jafes.034.01.14
    A 31-year-old male, apparently well, presented with typical chest pain. His ECG showed ST-elevation from V1-V4 and echocardiogram revealed anteroseptal wall hypokinesia with ejection fraction of 45%. Normal coronary arteries were seen on coronary angiogram. A thyroid function test showed elevated free T4 levels with suppressed thyroid stimulating hormone (TSH). Treatment with thionamides and beta-blockers improved symptoms. Upon review 4 months later he was well. Repeat echocardiogram showed good ejection fraction with no hypokinetic area.
    Matched MeSH terms: Electrocardiography
  7. Selvaraj J, Murugappan M, Wan K, Yaacob S
    Biomed Eng Online, 2013;12:44.
    PMID: 23680041 DOI: 10.1186/1475-925X-12-44
    Identifying the emotional state is helpful in applications involving patients with autism and other intellectual disabilities; computer-based training, human computer interaction etc. Electrocardiogram (ECG) signals, being an activity of the autonomous nervous system (ANS), reflect the underlying true emotional state of a person. However, the performance of various methods developed so far lacks accuracy, and more robust methods need to be developed to identify the emotional pattern associated with ECG signals.
    Matched MeSH terms: Electrocardiography/methods*
  8. 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
  9. Wong KC
    Med J Malaysia, 2021 07;76(4):565.
    PMID: 34305119
    No abstract provided.
    Matched MeSH terms: Electrocardiography*
  10. Ngow HA, Wan Khairina WM
    Postgrad Med J, 2010 Oct;86(1020):624-6.
    PMID: 20971714 DOI: 10.1136/pgmj.2010.102236
    Matched MeSH terms: Electrocardiography
  11. Hussein AF, Hashim SJ, Rokhani FZ, Wan Adnan WA
    Sensors (Basel), 2021 Mar 26;21(7).
    PMID: 33810211 DOI: 10.3390/s21072311
    Cardiovascular Disease (CVD) is a primary cause of heart problems such as angina and myocardial ischemia. The detection of the stage of CVD is vital for the prevention of medical complications related to the heart, as they can lead to heart muscle death (known as myocardial infarction). The electrocardiogram (ECG) reflects these cardiac condition changes as electrical signals. However, an accurate interpretation of these waveforms still calls for the expertise of an experienced cardiologist. Several algorithms have been developed to overcome issues in this area. In this study, a new scheme for myocardial ischemia detection with multi-lead long-interval ECG is proposed. This scheme involves an observation of the changes in ischemic-related ECG components (ST segment and PR segment) by way of the Choi-Williams time-frequency distribution to extract ST and PR features. These extracted features are mapped to a multi-class SVM classifier for training in the detection of unknown conditions to determine if they are normal or ischemic. The use of multi-lead ECG for classification and 1 min intervals instead of beats or frames contributes to improved detection performance. The classification process uses the data of 92 normal and 266 patients from four different databases. The proposed scheme delivered an overall result with 99.09% accuracy, 99.49% sensitivity, and 98.44% specificity. The high degree of classification accuracy for the different and unknown data sources used in this study reflects the flexibility, validity, and reliability of this proposed scheme. Additionally, this scheme can assist cardiologists in detecting signal abnormality with robustness and precision, and can even be used for home screening systems to provide rapid evaluation in emergency cases.
    Matched MeSH terms: Electrocardiography
  12. Burns-Cox CJ, Lau LC, Toh BH
    J Electrocardiol, 1971;4(3):211-9.
    PMID: 5126628
    Matched MeSH terms: Electrocardiography*
  13. Chew KT, Raman V, Then PHH
    Sensors (Basel), 2021 Dec 08;21(24).
    PMID: 34960291 DOI: 10.3390/s21248197
    Cardiovascular disease continues to be one of the most prevalent medical conditions in modern society, especially among elderly citizens. As the leading cause of deaths worldwide, further improvements to the early detection and prevention of these cardiovascular diseases is of the utmost importance for reducing the death toll. In particular, the remote and continuous monitoring of vital signs such as electrocardiograms are critical for improving the detection rates and speed of abnormalities while improving accessibility for elderly individuals. In this paper, we consider the design and deployment characteristics of a remote patient monitoring system for arrhythmia detection in elderly individuals. Thus, we developed a scalable system architecture to support remote streaming of ECG signals at near real-time. Additionally, a two-phase classification scheme is proposed to improve the performance of existing ECG classification algorithms. A prototype of the system was deployed at the Sarawak General Hospital, remotely collecting data from 27 unique patients. Evaluations indicate that the two-phase classification scheme improves algorithm performance when applied to the MIT-BIH Arrhythmia Database and the remotely collected single-lead ECG recordings.
    Matched MeSH terms: Electrocardiography*
  14. Sudarshan VK, Acharya UR, Oh SL, Adam M, Tan JH, Chua CK, et al.
    Comput Biol Med, 2017 04 01;83:48-58.
    PMID: 28231511 DOI: 10.1016/j.compbiomed.2017.01.019
    Identification of alarming features in the electrocardiogram (ECG) signal is extremely significant for the prediction of congestive heart failure (CHF). ECG signal analysis carried out using computer-aided techniques can speed up the diagnosis process and aid in the proper management of CHF patients. Therefore, in this work, dual tree complex wavelets transform (DTCWT)-based methodology is proposed for an automated identification of ECG signals exhibiting CHF from normal. In the experiment, we have performed a DTCWT on ECG segments of 2s duration up to six levels to obtain the coefficients. From these DTCWT coefficients, statistical features are extracted and ranked using Bhattacharyya, entropy, minimum redundancy maximum relevance (mRMR), receiver-operating characteristics (ROC), Wilcoxon, t-test and reliefF methods. Ranked features are subjected to k-nearest neighbor (KNN) and decision tree (DT) classifiers for automated differentiation of CHF and normal ECG signals. We have achieved 99.86% accuracy, 99.78% sensitivity and 99.94% specificity in the identification of CHF affected ECG signals using 45 features. The proposed method is able to detect CHF patients accurately using only 2s of ECG signal length and hence providing sufficient time for the clinicians to further investigate on the severity of CHF and treatments.
    Matched MeSH terms: Electrocardiography/methods*
  15. Acharya UR, Oh SL, Hagiwara Y, Tan JH, Adam M, Gertych A, et al.
    Comput Biol Med, 2017 10 01;89:389-396.
    PMID: 28869899 DOI: 10.1016/j.compbiomed.2017.08.022
    The electrocardiogram (ECG) is a standard test used to monitor the activity of the heart. Many cardiac abnormalities will be manifested in the ECG including arrhythmia which is a general term that refers to an abnormal heart rhythm. The basis of arrhythmia diagnosis is the identification of normal versus abnormal individual heart beats, and their correct classification into different diagnoses, based on ECG morphology. Heartbeats can be sub-divided into five categories namely non-ectopic, supraventricular ectopic, ventricular ectopic, fusion, and unknown beats. It is challenging and time-consuming to distinguish these heartbeats on ECG as these signals are typically corrupted by noise. We developed a 9-layer deep convolutional neural network (CNN) to automatically identify 5 different categories of heartbeats in ECG signals. Our experiment was conducted in original and noise attenuated sets of ECG signals derived from a publicly available database. This set was artificially augmented to even out the number of instances the 5 classes of heartbeats and filtered to remove high-frequency noise. The CNN was trained using the augmented data and achieved an accuracy of 94.03% and 93.47% in the diagnostic classification of heartbeats in original and noise free ECGs, respectively. When the CNN was trained with highly imbalanced data (original dataset), the accuracy of the CNN reduced to 89.07%% and 89.3% in noisy and noise-free ECGs. When properly trained, the proposed CNN model can serve as a tool for screening of ECG to quickly identify different types and frequency of arrhythmic heartbeats.
    Matched MeSH terms: Electrocardiography*
  16. Toh KW, Nadesan K, Sie MY, Vijeyasingam R, Tan PS
    Anesth Analg, 2004 Aug;99(2):350-2, table of contents.
    PMID: 15271703
    Arrhythmogenic right ventricular dysplasia is an inherited disease causing fatty replacement of heart tissue. This disease often presents as T-wave inversion in the anterior leads of the electrocardiogram (ECG) with life-threatening ventricular arrhythmias. In older patients, progressive right and left ventricular failure can develop. This is a case report of postoperative death occurring in a 59-yr-old woman with undiagnosed arrhythmogenic right ventricular dysplasia after hepatic cystectomy. The patient had T-wave inversion in the inferior ECG leads and no history of arrhythmias. During general anesthesia, cardiovascular collapse occurred in the absence of arrhythmias that was unresponsive to resuscitation.
    Matched MeSH terms: Electrocardiography
  17. Saedon NI, Zainal-Abidin I, Chee KH, Khor HM, Tan KM, Kamaruzzaman SK, et al.
    Clin Auton Res, 2016 Feb;26(1):41-8.
    PMID: 26695401 DOI: 10.1007/s10286-015-0327-5
    To determine the magnitude of postural blood pressure change, differences in ECG between fallers and non-fallers were measured. Postural blood pressure change is associated with symptoms of dizziness, presyncope, and syncope.
    Matched MeSH terms: Electrocardiography
  18. Chuah JS, Wong WL, Bakin S, Lim RZM, Lee EP, Tan JH
    Ann Med Surg (Lond), 2021 May;65:102294.
    PMID: 33948169 DOI: 10.1016/j.amsu.2021.102294
    Introduction and importance: A totally implantable venous access device (TIVAD), also referred to as 'chemoport', is frequently used for oncology patients. Chemoport insertion via the subclavian vein access may compress the catheter between the first rib and the clavicle, resulting in pinch-off syndrome (POS). The sequela includes catheter transection and subsequent embolization. It is a rare complication with incidence reported to be 1.1-5.0% and can lead to a devastating outcomes.

    Case presentation: 50-year-old male had his chemoport inserted for adjuvant chemotherapy 3 years ago. During the removal, remaining half of the distal catheter was not found. There was no difficulties during the removal. Chest xray revealed that the fractured catheter had embolized to the right ventricle. Further history taking, he did experienced occasional palpitation and chest discomfort for the past six months. Electrocardiogram and cardiac enzymes were normal. Urgent removal of the fractured catheter via the percutaneous endovascular approach, under fluoroscopic guidance by an experience interventional radiologist was done. The procedure was successful without any complication. Patient made an uneventful recovery. He was discharged the following day, and was well during his 3rd month follow up.

    Conclusion: Early detection and preventive measures can be done to prevent pinch-off syndrome. Unrecognized POS can result in fatal complications such as cardiac arrhythmia and septic embolization. Retrieval via the percutaneous endovascular approach provide excellent outcome in the case of embolized fractured catheter.

    Matched MeSH terms: Electrocardiography
  19. Acharya UR, Faust O, Sree V, Swapna G, Martis RJ, Kadri NA, et al.
    Comput Methods Programs Biomed, 2014;113(1):55-68.
    PMID: 24119391 DOI: 10.1016/j.cmpb.2013.08.017
    Coronary artery disease (CAD) is one of the dangerous cardiac disease, often may lead to sudden cardiac death. It is difficult to diagnose CAD by manual inspection of electrocardiogram (ECG) signals. To automate this detection task, in this study, we extracted the heart rate (HR) from the ECG signals and used them as base signal for further analysis. We then analyzed the HR signals of both normal and CAD subjects using (i) time domain, (ii) frequency domain and (iii) nonlinear techniques. The following are the nonlinear methods that were used in this work: Poincare plots, Recurrence Quantification Analysis (RQA) parameters, Shannon entropy, Approximate Entropy (ApEn), Sample Entropy (SampEn), Higher Order Spectra (HOS) methods, Detrended Fluctuation Analysis (DFA), Empirical Mode Decomposition (EMD), Cumulants, and Correlation Dimension. As a result of the analysis, we present unique recurrence, Poincare and HOS plots for normal and CAD subjects. We have also observed significant variations in the range of these features with respect to normal and CAD classes, and have presented the same in this paper. We found that the RQA parameters were higher for CAD subjects indicating more rhythm. Since the activity of CAD subjects is less, similar signal patterns repeat more frequently compared to the normal subjects. The entropy based parameters, ApEn and SampEn, are lower for CAD subjects indicating lower entropy (less activity due to impairment) for CAD. Almost all HOS parameters showed higher values for the CAD group, indicating the presence of higher frequency content in the CAD signals. Thus, our study provides a deep insight into how such nonlinear features could be exploited to effectively and reliably detect the presence of CAD.
    Matched MeSH terms: Electrocardiography
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