Displaying publications 21 - 40 of 212 in total

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  1. Fulltext Bizarre ECG in head injury mimicking ventricular tachycardia
    Loke YK, Lai VM, Tan MH, Gunn A
    Singapore Med J, 1997 Apr;38(4):166-8.
    PMID: 9269397
    Bizarre electrocardiographic (ECG) changes were found in an 18-year-old girl who had a subdural haematoma following head trauma. The initial diagnosis was of ventricular tachycardia (VT) and she was treated with intravenous anti-arrhythmic drugs and electrical cardioversion, but to no effect. It was later concluded that the ECG appearances were not of a ventricular arrhythmia but were the result of the intracranial pathology. ECG abnormalities related to head injuries have been reported on many occasions, and our case report illustrates how this can create difficulties for the attending clinicians.
    Matched MeSH terms: Electrocardiography*
  2. Normal electrocardiogram after myocardial infarction
    Ng WH, Ahmad Z
    Med J Malaysia, 1978 Dec;33(2):128-32.
    PMID: 755162
    Matched MeSH terms: Electrocardiography*
  3. 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*
  4. Fulltext Electrocardiogram-Based Emotion Recognition Systems and Their Applications in Healthcare-A Review
    Hasnul MA, Aziz NAA, Alelyani S, Mohana M, Aziz AA
    Sensors (Basel), 2021 Jul 23;21(15).
    PMID: 34372252 DOI: 10.3390/s21155015
    Affective computing is a field of study that integrates human affects and emotions with artificial intelligence into systems or devices. A system or device with affective computing is beneficial for the mental health and wellbeing of individuals that are stressed, anguished, or depressed. Emotion recognition systems are an important technology that enables affective computing. Currently, there are a lot of ways to build an emotion recognition system using various techniques and algorithms. This review paper focuses on emotion recognition research that adopted electrocardiograms (ECGs) as a unimodal approach as well as part of a multimodal approach for emotion recognition systems. Critical observations of data collection, pre-processing, feature extraction, feature selection and dimensionality reduction, classification, and validation are conducted. This paper also highlights the architectures with accuracy of above 90%. The available ECG-inclusive affective databases are also reviewed, and a popularity analysis is presented. Additionally, the benefit of emotion recognition systems towards healthcare systems is also reviewed here. Based on the literature reviewed, a thorough discussion on the subject matter and future works is suggested and concluded. The findings presented here are beneficial for prospective researchers to look into the summary of previous works conducted in the field of ECG-based emotion recognition systems, and for identifying gaps in the area, as well as in developing and designing future applications of emotion recognition systems, especially in improving healthcare.
    Matched MeSH terms: Electrocardiography*
  5. 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: Electrocardiography*
  6. Intelligent classification of electrocardiogram (ECG) signal using extended Kalman Filter (EKF) based neuro fuzzy system
    Meau YP, Ibrahim F, Narainasamy SA, Omar R
    Comput Methods Programs Biomed, 2006 May;82(2):157-68.
    PMID: 16638620
    This study presents the development of a hybrid system consisting of an ensemble of Extended Kalman Filter (EKF) based Multi Layer Perceptron Network (MLPN) and a one-pass learning Fuzzy Inference System using Look-up Table Scheme for the recognition of electrocardiogram (ECG) signals. This system can distinguish various types of abnormal ECG signals such as Ventricular Premature Cycle (VPC), T wave inversion (TINV), ST segment depression (STDP), and Supraventricular Tachycardia (SVT) from normal sinus rhythm (NSR) ECG signal.
    Matched MeSH terms: Electrocardiography/classification*
  7. Fulltext Images in clinical medicine. Dextrocardia with situs inversus
    Loh KY, Mohamed AL
    N Engl J Med, 2005 Sep 1;353(9):933.
    PMID: 16135838 DOI: 10.1056/NEJMicm040398
    Matched MeSH terms: Electrocardiography*
  8. A novel automated diagnostic system for classification of myocardial infarction ECG signals using an optimal biorthogonal filter bank
    Sharma M, Tan RS, Acharya UR
    Comput Biol Med, 2018 11 01;102:341-356.
    PMID: 30049414 DOI: 10.1016/j.compbiomed.2018.07.005
    Myocardial infarction (MI), also referred to as heart attack, occurs when there is an interruption of blood flow to parts of the heart, due to the acute rupture of atherosclerotic plaque, which leads to damage of heart muscle. The heart muscle damage produces changes in the recorded surface electrocardiogram (ECG). The identification of MI by visual inspection of the ECG requires expert interpretation, and is difficult as the ECG signal changes associated with MI can be short in duration and low in magnitude. Hence, errors in diagnosis can lead to delay the initiation of appropriate medical treatment. To lessen the burden on doctors, an automated ECG based system can be installed in hospitals to help identify MI changes on ECG. In the proposed study, we develop a single-channel single lead ECG based MI diagnostic system validated using noisy and clean datasets. The raw ECG signals are taken from the Physikalisch-Technische Bundesanstalt database. We design a novel two-band optimal biorthogonal filter bank (FB) for analysis of the ECG signals. We present a method to design a novel class of two-band optimal biorthogonal FB in which not only the product filter but the analysis lowpass filter is also a halfband filter. The filter design problem has been composed as a constrained convex optimization problem in which the objective function is a convex combination of multiple quadratic functions and the regularity and perfect reconstruction conditions are imposed in the form linear equalities. ECG signals are decomposed into six subbands (SBs) using the newly designed wavelet FB. Following to this, discriminating features namely, fuzzy entropy (FE), signal-fractal-dimensions (SFD), and renyi entropy (RE) are computed from all the six SBs. The features are fed to the k-nearest neighbor (KNN). The proposed system yields an accuracy of 99.62% for the noisy dataset and an accuracy of 99.74% for the clean dataset, using 10-fold cross validation (CV) technique. Our MI identification system is robust and highly accurate. It can thus be installed in clinics for detecting MI.
    Matched MeSH terms: Electrocardiography
  9. 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
  10. 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: Electrocardiography
  11. Highly-sensitive graphene-based flexible pressure sensor platform
    Muhammad Aniq Shazni, Lee MW, Lee HW
    Sains Malaysiana, 2017;46:1155-1161.
    In this work, graphene has been utilized as the sensing material for the development of a highly-sensitive flexible pressure sensor platform. It has been demonstrated that a graphene-based pressure sensor platform that is able to measure pressure change of up to 3 psi with a sensitivity of 0.042 psi-1 and a non-linearity of less than 1% has been accomplished. The developed device, which resides on a flexible platform, will be applicable for integration in continuous wearables health-care monitoring system for the measurement of blood pressure.
    Matched MeSH terms: Electrocardiography
  12. 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
  13. Prolonged QT predicts prognosis in COVID-19
    Akhtar Z, Gallagher MM, Yap YG, Leung LWM, Elbatran AI, Madden B, et al.
    Pacing Clin Electrophysiol, 2021 05;44(5):875-882.
    PMID: 33792080 DOI: 10.1111/pace.14232
    BACKGROUND: Coronavirus disease-2019 (COVID-19) causes severe illness and multi-organ dysfunction. An abnormal electrocardiogram is associated with poor outcome, and QT prolongation during the illness has been linked to pharmacological effects. This study sought to investigate the effects of the COVID-19 illness on the corrected QT interval (QTc).

    METHOD: For 293 consecutive patients admitted to our hospital via the emergency department for COVID-19 between 01/03/20 -18/05/20, demographic data, laboratory findings, admission electrocardiograph and clinical observations were compared in those who survived and those who died within 6 weeks. Hospital records were reviewed for prior electrocardiograms for comparison with those recorded on presentation with COVID-19.

    RESULTS: Patients who died were older than survivors (82 vs 69.8 years, p 455 ms (males) and >465 ms (females) (p = 0.028, HR 1.49 [1.04-2.13]), as predictors of mortality. QTc prolongation beyond these dichotomy limits was associated with increased mortality risk (p = 0.0027, HR 1.78 [1.2-2.6]).

    CONCLUSION: QTc prolongation occurs in COVID-19 illness and is associated with poor outcome.

    Matched MeSH terms: Electrocardiography
  14. Fulltext A Shallow U-Net Architecture for Reliably Predicting Blood Pressure (BP) from Photoplethysmogram (PPG) and Electrocardiogram (ECG) Signals
    Mahmud S, Ibtehaz N, Khandakar A, Tahir AM, Rahman T, Islam KR, et al.
    Sensors (Basel), 2022 Jan 25;22(3).
    PMID: 35161664 DOI: 10.3390/s22030919
    Cardiovascular diseases are the most common causes of death around the world. To detect and treat heart-related diseases, continuous blood pressure (BP) monitoring along with many other parameters are required. Several invasive and non-invasive methods have been developed for this purpose. Most existing methods used in hospitals for continuous monitoring of BP are invasive. On the contrary, cuff-based BP monitoring methods, which can predict systolic blood pressure (SBP) and diastolic blood pressure (DBP), cannot be used for continuous monitoring. Several studies attempted to predict BP from non-invasively collectible signals such as photoplethysmograms (PPG) and electrocardiograms (ECG), which can be used for continuous monitoring. In this study, we explored the applicability of autoencoders in predicting BP from PPG and ECG signals. The investigation was carried out on 12,000 instances of 942 patients of the MIMIC-II dataset, and it was found that a very shallow, one-dimensional autoencoder can extract the relevant features to predict the SBP and DBP with state-of-the-art performance on a very large dataset. An independent test set from a portion of the MIMIC-II dataset provided a mean absolute error (MAE) of 2.333 and 0.713 for SBP and DBP, respectively. On an external dataset of 40 subjects, the model trained on the MIMIC-II dataset provided an MAE of 2.728 and 1.166 for SBP and DBP, respectively. For both the cases, the results met British Hypertension Society (BHS) Grade A and surpassed the studies from the current literature.
    Matched MeSH terms: Electrocardiography
  15. Fulltext Automatic identification of hypertension and assessment of its secondary effects using artificial intelligence: A systematic review (2013-2023)
    Gudigar A, Kadri NA, Raghavendra U, Samanth J, Maithri M, Inamdar MA, et al.
    Comput Biol Med, 2024 Apr;172:108207.
    PMID: 38489986 DOI: 10.1016/j.compbiomed.2024.108207
    Artificial Intelligence (AI) techniques are increasingly used in computer-aided diagnostic tools in medicine. These techniques can also help to identify Hypertension (HTN) in its early stage, as it is a global health issue. Automated HTN detection uses socio-demographic, clinical data, and physiological signals. Additionally, signs of secondary HTN can also be identified using various imaging modalities. This systematic review examines related work on automated HTN detection. We identify datasets, techniques, and classifiers used to develop AI models from clinical data, physiological signals, and fused data (a combination of both). Image-based models for assessing secondary HTN are also reviewed. The majority of the studies have primarily utilized single-modality approaches, such as biological signals (e.g., electrocardiography, photoplethysmography), and medical imaging (e.g., magnetic resonance angiography, ultrasound). Surprisingly, only a small portion of the studies (22 out of 122) utilized a multi-modal fusion approach combining data from different sources. Even fewer investigated integrating clinical data, physiological signals, and medical imaging to understand the intricate relationships between these factors. Future research directions are discussed that could build better healthcare systems for early HTN detection through more integrated modeling of multi-modal data sources.
    Matched MeSH terms: Electrocardiography
  16. Diagnostic value of electrocardiogram in cardiac tamponade
    Ang KP, Nordin RB, Lee SCY, Lee CY, Lu HT
    Med J Malaysia, 2019 02;74(1):51-56.
    PMID: 30846663
    INTRODUCTION: We aim to study the diagnostic value of electrocardiogram (ECG) in cardiac tamponade.

    METHODS: This study was a single centre, retrospective casecontrol study. We recruited 42 patients diagnosed with cardiac tamponade of various aetiologies confirmed by transthoracic echocardiography and 100 controls between January 2011 and December 2015. The ECG criteria of cardiac tamponade we adopted was as follows: 1) Low QRS voltage in a) the limb leads alone, b) in the precordial leads alone or, c) in all leads, 2) PR segment depression, 3) Electrical alternans, and 4) Sinus tachycardia.

    RESULTS: Malignancy was the most common causes of cardiac tamponade, the two groups were of similar proportion of gender and ethnicity. We calculated the sensitivity (SN), specificity (SP), positive predictive value (PPV), and negative predictive value (NPV) of each ECG criteria. Among the ECG abnormalities, we noted the SN of 'low voltage in all chest leads' (69%), 'low voltage in all limb leads' (67%) and 'sinus tachycardia' (69%) were higher as compared to 'PR depression' (12%) and 'electrical alternan' (5%). On the other hand, 'low voltage in all chest leads' (98%), 'low voltage in all leads' (99%), 'PR depression' (100%) and 'electrical alternans' (100%) has highest SP.

    CONCLUSION: Our study reaffirmed the findings of previous studies that electrocardiography cannot be used as a screening tool for diagnosing cardiac tamponade due to its low sensitivity. However, with clinical correlation, electrocardiography is a valuable adjuvant test to 'rule in' cardiac tamponade because of its high specificity.

    Matched MeSH terms: Electrocardiography*
  17. Automated detection of sleep apnea using sparse residual entropy features with various dictionaries extracted from heart rate and EDR signals
    Viswabhargav CSS, Tripathy RK, Acharya UR
    Comput Biol Med, 2019 05;108:20-30.
    PMID: 31003176 DOI: 10.1016/j.compbiomed.2019.03.016
    Sleep is a prominent physiological activity in our daily life. Sleep apnea is the category of sleep disorder during which the breathing of the person diminishes causing the alternation in the upper airway resistance. The electrocardiogram derived respiration (EDR) and heart rate (RR-time-series) signals are normally used for the detection of sleep apnea as these two signals capture cardio-pulmonary activity information. Hence, the analysis of these two signals provides vital information about sleep apnea. In this paper, we propose the novel sparse residual entropy (SRE) features for the automated detection of sleep apnea using EDR and heart rate signals. The features required for the automated detection of sleep apnea are extracted in three steps: (i) atomic decomposition based residual estimation from both EDR and heart rate signals using orthogonal matching pursuit (OMP) with different dictionaries, (ii) estimation of probabilities from each sparse residual, and (iii) calculation of the entropy features. The proposed SRE features are fed to the combination of fuzzy K-means clustering and support vector machine (SVM) to pick the best performing classifier. The experimental results demonstrate that the proposed SRE features with radial basis function (RBF) kernel-based SVM classifier yielded higher performance with accuracy, sensitivity and specificity values of 78.07%, 78.01%, and 78.13%, respectively with Fourier dictionary and 10-fold cross-validation. For subject-specific or leave-one-out validation case, the SVM classifier has sensitivity and specificity of 85.43% and 92.60%, respectively using SRE features with Fourier dictionary (FD).
    Matched MeSH terms: Electrocardiography*
  18. 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*
  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: Electrocardiography/methods*
  20. Fulltext Reliability of computerised electrocardiographic reports
    Ong HT, Kuah SH, Chew SP
    Singapore Med J, 1993 Feb;34(1):53-4.
    PMID: 8266130
    The aim of this study is to assess the reliability of computerised reporting of electrocardiograms (ECG). Fifty ECG performed consecutively at the outpatient department of the Penang Adventist Hospital on the Marquette 12SL-SC were studied. Two physicians independently reviewed the ECG and the manual readings were compared with each other and to the computer reports. There was no significant difference in the measurement of rate. The PR and QT intervals measured by the two physicians were similar but each was significantly different from the computer reading. The QRS duration assessed by Physician 1 was similar to the computer reading but each was significantly different from that of Physician 2. The overall diagnosis was the same between the two physicians in 76%, between Physician 1 and the computer in 68%, and between Physician 2 and the computer in 78%. No ECG was reported as normal by the computer and said to be abnormal by either physician. Thus, the computer programme is reasonably reliable in ECG reporting with computer-physician variability being comparable to inter-physician variability.

    Study site: outpatient department of the Penang Adventist Hospital
    Matched MeSH terms: Electrocardiography*
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