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Fulltext Coronary artery spasm (CAS) simulating inferior ST elevation myocardial infarction (STEMI)

Liew CK, Leong WS

Med J Malaysia, 2012 Feb;67(1):108-10.
PMID: 22582559 MyJurnal

Coronary arteries vasospasm (CAS) is commonly seen in invasive cardiology laboratory during diagnostic catheterization or coronary intervention. Though the incidence of Printzmetal angina is uncommon, coronary vasospasm resulting in acute myocardial infarct is rare, especially if there is no significant atherosclerotic plaque within the coronary vasculature.

Matched MeSH terms: Electrocardiography
Fulltext Multimodality imaging of left circumflex artery to coronary sinus fistula

Sze TL, Abdul Aziz YF, Abu Bakar N, Mohd Sani F, Oemar H

Iran J Radiol, 2015 Jan;12(1):e6878.
PMID: 25793089 DOI: 10.5812/iranjradiol.6878

Coronary artery fistula (CAF) is a rare anomaly of the coronary artery. Patients with this condition are usually asymptomatic. However, cardiac failure may occur later in life due to progressive enlargement of the fistula. Diagnosis is traditionally made by echocardiogram and conventional angiogram. However with the advantage of new technologies such as computed tomography (CT) coronary angiography, the course and communications of these fistulae can be delineated non-invasively and with greater accuracy. We report a case of a left circumflex artery fistula to the coronary sinus which was suspected on echocardiogram and the diagnosis was clinched on ECG-gated CT.

Matched MeSH terms: Electrocardiography
Fulltext A qualitative study on negative attitude toward electrocardiogram learning among undergraduate medical students

Ohn MH, Souza U, Ohn KM

Tzu Chi Med J, 2020 08 02;32(4):392-397.
PMID: 33163387 DOI: 10.4103/tcmj.tcmj_91_19

Objective: Negative affect state toward learning has a substantial impact on the learning process, academic performance, and practice of a particular subject, but such attitude toward electrocardiogram (ECG) learning has still received relatively little attention in medical education research. In spite of the significant emphasis in investigating ECG teaching method, the educators would not be able to address ECG incompetency without understanding the negative perception and attitude toward ECG learning. The purpose of this study was to assess the undergraduate students' difficulties in ECG learning and hence help educators design appropriate ECG learning curriculum to instill competent skill in ECG interpretation based on this outcome.
Materials and Methods: A total of 324 undergraduate preclinical (year 2) and clinical (year 3-5) medical students participated in this study. The research design used thematic analysis of an open-ended questionnaire to analyze the qualitative data.
Results: The thematic analysis detected five major emergent themes: lack of remembering (18.2%), lack of understanding (28.4%), difficulty in applying (3.6%), difficulty in analysis (15.1%), and difficulty in interpretation (17.8%), of which addressing these challenges could be taken as a foundation step upon which medical educators put an emphasis on in order to improve ECG teaching and learning.
Conclusion: Negative attitude toward ECG learning poses a serious threat to acquire competency in ECG interpretation skill. The concept of student's memorizing ECG is not a correct approach; instead, understanding the concept and vector analysis is an elementary key for mastering ECG interpretation skill. The finding of this study sheds light into a better understanding of medical students' deficient points of ECG learning in parallel with taxonomy of cognitive domain and enables the medical teachers to come up with effective and innovative strategies for innovative ECG learning in an undergraduate medical curriculum.

Matched MeSH terms: Electrocardiography
Automated beat-wise arrhythmia diagnosis using modified U-net on extended electrocardiographic recordings with heterogeneous arrhythmia types

Oh SL, Ng EYK, Tan RS, Acharya UR

Comput Biol Med, 2019 Feb;105:92-101.
PMID: 30599317 DOI: 10.1016/j.compbiomed.2018.12.012

Abnormality of the cardiac conduction system can induce arrhythmia - abnormal heart rhythm - that can frequently lead to other cardiac diseases and complications, and are sometimes life-threatening. These conduction system perturbations can manifest as morphological changes on the surface electrocardiographic (ECG) signal. Assessment of these morphological changes can be challenging and time-consuming, as ECG signal features are often low in amplitude and subtle. The main aim of this study is to develop an automated computer aided diagnostic (CAD) system that can expedite the process of arrhythmia diagnosis, as an aid to clinicians to provide appropriate and timely intervention to patients. We propose an autoencoder of ECG signals that can diagnose normal sinus beats, atrial premature beats (APB), premature ventricular contractions (PVC), left bundle branch block (LBBB) and right bundle branch block (RBBB). Apart from the first, the rest are morphological beat-to-beat elements that characterize and constitute complex arrhythmia. The novelty of this work lies in how we modified the U-net model to perform beat-wise analysis on heterogeneously segmented ECGs of variable lengths derived from the MIT-BIH arrhythmia database. The proposed system has demonstrated self-learning ability in generating class activations maps, and these generated maps faithfully reflect the cardiac conditions in each ECG cardiac cycle. It has attained a high classification accuracy of 97.32% in diagnosing cardiac conditions, and 99.3% for R peak detection using a ten-fold cross validation strategy. Our developed model can help physicians to screen ECG accurately, potentially resulting in timely intervention of patients with arrhythmia.

Matched MeSH terms: Electrocardiography
Fulltext Multirate Processing with Selective Subbands and Machine Learning for Efficient Arrhythmia Classification

Qaisar SM, Mihoub A, Krichen M, Nisar H

Sensors (Basel), 2021 Feb 22;21(4).
PMID: 33671583 DOI: 10.3390/s21041511

The usage of wearable gadgets is growing in the cloud-based health monitoring systems. The signal compression, computational and power efficiencies play an imperative part in this scenario. In this context, we propose an efficient method for the diagnosis of cardiovascular diseases based on electrocardiogram (ECG) signals. The method combines multirate processing, wavelet decomposition and frequency content-based subband coefficient selection and machine learning techniques. Multirate processing and features selection is used to reduce the amount of information processed thus reducing the computational complexity of the proposed system relative to the equivalent fixed-rate solutions. Frequency content-dependent subband coefficient selection enhances the compression gain and reduces the transmission activity and computational cost of the post cloud-based classification. We have used MIT-BIH dataset for our experiments. To avoid overfitting and biasness, the performance of considered classifiers is studied by using five-fold cross validation (5CV) and a novel proposed partial blind protocol. The designed method achieves more than 12-fold computational gain while assuring an appropriate signal reconstruction. The compression gain is 13 times compared to fixed-rate counterparts and the highest classification accuracies are 97.06% and 92.08% for the 5CV and partial blind cases, respectively. Results suggest the feasibility of detecting cardiac arrhythmias using the proposed approach.

Matched MeSH terms: Electrocardiography
Fulltext Acute pancreatitis with electrocardiographic changes mimicking acute coronary syndrome: a diagnostic conundrum

Shamala N., Faizal, A.H.

Medicine & Health, 2018;13(2):195-201.
MyJurnal

Electrocardiographic abnormalities can be associated with acute pancreatitis. However, data regarding the actual causative factor still remains elusive. Many previous cases were reported on non-specific ST and T wave abnormalities concurrent with acute pancreatitis but rarely with an increasing trend of cardiac markers. We describe the case of a 70-year-old female who presented with one such conundrum. Our patient had typical presentation of acute pancreatitis but had dynamic ECG changes with markedly increased cardiac markers. Subsequently after initiation of treatment for acute pancreatitis and observation for the course of several days, the ECG returned to the baseline as pre admission. This substantiates the fact that acute pancreatitis can mimic both biochemical and electrical manifestation of an acute coronary syndrome. Thus, Emergency Physicians should consider acute pancreatitis as a possible diagnosis in patients who present with abnormal electrocardiograms.

Matched MeSH terms: Electrocardiography
Fulltext The brunt of intensive simulation airway management training programme on the final year nursing in one of health training institutions in Sabah

Mazlinda Musa, Fidelia Ferderik Anis, Hamidah Hassan, Syed Sharizman Syed Abdul Rahim, Siti Fatimah Saat

Malaysian Journal of Medicine and Health Sciences, 2020;16(105):18-0.
MyJurnal

Introduction: Airway management is one of the most important steps in emergency patient care, and it is part of the core content of emergency training programme in nursing. Besides, learning in the real clinical area on artificial air- way management is almost impossible due to the complexity of clinical conditions and non-uniform treatment algo- rithms that make the training strategies even more difficult to develop. This study was to evaluate the effectiveness of the simulation airway management training programme developed for the final year nursing students. Methods: This was a quasi-experimental with convenience sampling technique approach used. Students were exposed with the Intensive simulation of airway management technique which includes BLS, oropharyngeal measure and insertion, high flow O2 administration, interpret ECG, use of defib and understanding role of arrest team during emergency. The questionnaire on confident level was given before and after the simulation of airway management. Results: The results showed significant different in the mean score of pre-tests and post-tests (CI95% (-0.53414, -0.09586), t= -3.009, df = 19, p

Matched MeSH terms: Electrocardiography
A case of situs inversus

Banu SZ

Med J Malaysia, 1977 Mar;31(3):236-40.
PMID: 904519
Matched MeSH terms: Electrocardiography
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
Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats

Oh SL, Ng EYK, Tan RS, Acharya UR

Comput Biol Med, 2018 11 01;102:278-287.
PMID: 29903630 DOI: 10.1016/j.compbiomed.2018.06.002

Arrhythmia is a cardiac conduction disorder characterized by irregular heartbeats. Abnormalities in the conduction system can manifest in the electrocardiographic (ECG) signal. However, it can be challenging and time-consuming to visually assess the ECG signals due to the very low amplitudes. Implementing an automated system in the clinical setting can potentially help expedite diagnosis of arrhythmia, and improve the accuracies. In this paper, we propose an automated system using a combination of convolutional neural network (CNN) and long short-term memory (LSTM) for diagnosis of normal sinus rhythm, left bundle branch block (LBBB), right bundle branch block (RBBB), atrial premature beats (APB) and premature ventricular contraction (PVC) on ECG signals. The novelty of this work is that we used ECG segments of variable length from the MIT-BIT arrhythmia physio bank database. The proposed system demonstrated high classification performance in the handling of variable-length data, achieving an accuracy of 98.10%, sensitivity of 97.50% and specificity of 98.70% using ten-fold cross validation strategy. Our proposed model can aid clinicians to detect common arrhythmias accurately on routine screening ECG.

Matched MeSH terms: Electrocardiography
Incidental congenitally corrected transposition of the great arteries (ccTGA) in an adult with suspected coronary artery disease: review on radiological features and pathophysiology

Sayuti KA, Azizi MYSB

BMJ Case Rep, 2020 Apr 22;13(4).
PMID: 32327461 DOI: 10.1136/bcr-2019-234225

We report a case of a 46-year-old woman who has presented to a peripheral hospital with progressive exertional dyspnoea and chest discomfort. The resting ECG showed features of left-sided ventricular hypertrophy. The initial chest radiograph was reported as cardiomegaly. Initial echocardiography revealed left atrial dilatation and 'left ventricular' hypertrophy with normal ejection fraction. She was treated as possible coronary artery disease and was subsequently referred to our centre for CT coronary angiography. Findings from the CT scan were consistent with congenitally corrected transposition of the great arteries (ccTGA). This report describes the radiological features of ccTGA, its associated cardiovascular anomalies, pathophysiology and potential complications.

Matched MeSH terms: Electrocardiography
Fulltext Belhassen ventricular tachycardia-a diagnostic challenge in emergency room: case report

Syed Farid Almufazal Syed Salim, Shamsuriani Md Jamal

Malaysian Journal of Emergency Medicine, 2018;3(1):0-0.
MyJurnal

FascicularVentricular Tachycardia (VT) is a uniqueclinical syndrome, rarelyencountered by physicians.It isalso known as BelhassenSyndrome, named after a physician who reported the case in 1981. The condition,accounts for 10-15% of total idiopathic VTand the rhythm is sensitive tocalcium channel blocker. First described in 1979, the diagnosis of thissyndromeremains challenging,as the electrocardiogram (ECG) changes may be incorrectly diagnosed as Supraventricular Tachycardia (SVT) with aberrant conductions. We describeda patient whopresented to Emergency Department with palpitation. The difficulty in diagnosis and management is illustrated in the reportas he was initially misdiagnosed as SVT with resistance to initial standard treatment.This case report alsodescribedwide complex tachycardia algorithms to assist physician in daily clinicalpractice. Therapeutic options inmanaging this rare syndrome werealso discussed.

Matched MeSH terms: Electrocardiography
Fulltext Multimodality registration of two-dimensional echocardiography and cardiac CT for mitral valve diagnosis and surgical planning

Khalil A, Faisal A, Ng SC, Liew YM, Lai KW

J Med Imaging (Bellingham), 2017 Jul;4(3):037001.
PMID: 28840172 DOI: 10.1117/1.JMI.4.3.037001

A registration method to fuse two-dimensional (2-D) echocardiography images with cardiac computed tomography (CT) volume is presented. The method consists of two major procedures: temporal and spatial registrations. In temporal registration, the echocardiography frames at similar cardiac phases as the CT volume were interpolated based on electrocardiogram signal information, and the noise of the echocardiography image was reduced using the speckle reducing anisotropic diffusion technique. For spatial registration, an intensity-based normalized mutual information method was applied with a pattern search optimization algorithm to produce an interpolated cardiac CT image. The proposed registration framework does not require optical tracking information. Dice coefficient and Hausdorff distance for the left atrium assessments were [Formula: see text] and [Formula: see text], respectively; for left ventricle, they were [Formula: see text] and [Formula: see text], respectively. There was no significant difference in the mitral valve annulus diameter measurement between the manually and automatically registered CT images. The transformation parameters showed small deviations ([Formula: see text] deviation in translation and [Formula: see text] for rotation) between manual and automatic registrations. The proposed method aids the physician in diagnosing mitral valve disease as well as provides surgical guidance during the treatment procedure.

Matched MeSH terms: Electrocardiography
Left bundle branch area pacing for heart failure patients requiring cardiac resynchronization therapy: A meta-analysis

Jin C, Dai Q, Li P, Lam P, Cha YM

J Cardiovasc Electrophysiol, 2023 Sep;34(9):1933-1943.
PMID: 37548113 DOI: 10.1111/jce.16013

INTRODUCTION: Left bundle branch area pacing (LBBP) is a novel conduction system pacing method to achieve effective physiological pacing and an alternative to cardiac resynchronization therapy (CRT) with biventricular pacing (BVP) for patients with heart failure with reduced ejection fraction (HFrEF). We conduted this meta-analysis and systemic review to review current data comparing BVP and LBBP in patients with HFrEF and indications for CRT.
METHODS: We searched PubMed/Medline, Web of Science, and Cochrane Library from the inception of the database to November 2022. All studies that compared LBBP with BVP in patients with HFrEF and indications for CRT were included. Two reviewers performed study selection, data abstraction, and risk of bias assessment. We calculated risk ratios (RRs) with the Mantel-Haenszel method and mean difference (MD) with inverse variance using random effect models. We assessed heterogeneity using the I2 index, with I2 > 50% indicating significant heterogeneity.
RESULTS: Ten studies (9 observational studies and 1 randomized controlled trial; 616 patients; 15 centers) published between 2020 and 2022 were included. We observed a shorter fluoroscopy time (MD: 9.68, 95% confidence interval [CI]: 4.49-14.87, I2 = 95%, p

Matched MeSH terms: Electrocardiography
Evaluation of Wrist-Worn Photoplethysmography Trackers with an Electrocardiogram in Patients with Ischemic Heart Disease: A Validation Study

Ibrahim NS, Rampal S, Lee WL, Pek EW, Suhaimi A

Cardiovasc Eng Technol, 2024 Feb;15(1):12-21.
PMID: 37973701 DOI: 10.1007/s13239-023-00693-z

PURPOSE: Photoplethysmography measurement of heart rate with wrist-worn trackers has been introduced in healthy individuals. However, additional consideration is necessary for patients with ischemic heart disease, and the available evidence is limited. The study aims to evaluate the validity and reliability of heart rate measures by a wrist-worn photoplethysmography (PPG) tracker compared to an electrocardiogram (ECG) during incremental treadmill exercise among patients with ischemic heart disease.
METHODS: Fifty-one participants performed the standard incremental treadmill exercise in a controlled laboratory setting with 12-lead ECG attached to the patient's body and wearing wrist-worn PPG trackers.
RESULTS: At each stage, the absolute percentage error of the PPG was within 10% of the standard acceptable range. Further analysis using a linear mixed model, which accounts for individual variations, revealed that PPG yielded the best performance at the baseline low-intensity exercise. As the stages progressed, heart rate validity decreased but was regained during recovery. The reliability was moderate to excellent.
CONCLUSIONS: Low-cost trackers AMAZFIT Cor and Bip validity and reliability were within acceptable ranges, especially during low-intensity exercise among patients with ischemic heart disease recovering from cardiac procedures. Though using the tracker as part of the diagnosis tool still requires more supporting studies, it can potentially be used as a self-monitoring tool with precautions.

Matched MeSH terms: Electrocardiography
Automated identification of normal and diabetes heart rate signals using nonlinear measures

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
Fulltext Classification of emotional states from electrocardiogram signals: a non-linear approach based on Hurst

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*
An MLP neural network for ECG noise removal based on Kalman filter

Moein S

Adv Exp Med Biol, 2010;680:109-16.
PMID: 20865492 DOI: 10.1007/978-1-4419-5913-3_13

In this paper, application of Artificial Neural Network (ANN) for electrocardiogram (ECG) signal noise removal has been investigated. First, 100 number of ECG signals are selected from Physikalisch-Technische Bundesanstalt (PTB) database and Kalman filter is applied to remove their low pass noise. Then a suitable dataset based on denoised ECG signal is configured and used to a Multilayer Perceptron (MLP) neural network to be trained. Finally, results and experiences are discussed and the effect of changing different parameters for MLP training is shown.

Matched MeSH terms: Electrocardiography/statistics & numerical data*
Newborn seizure detection based on heart rate variability

Malarvili MB, Mesbah M

IEEE Trans Biomed Eng, 2009 Nov;56(11):2594-603.
PMID: 19628449 DOI: 10.1109/TBME.2009.2026908

In this paper, we investigate the use of heart rate variability (HRV) for automatic newborn seizure detection. The proposed method consists of a sequence of processing steps, namely, obtaining HRV from the ECG, extracting a discriminating HRV feature set, selecting an optimal subset from the full feature set, and, finally, classifying the HRV into seizure/nonseizure using a supervised statistical classifier. Due to the fact that HRV signals are nonstationary, a set of time-frequency features from the newborn HRV is proposed and extracted. In order to achieve efficient HRV-based automatic newborn seizure detection, a two-phase wrapper-based feature selection technique is used to select the feature subset with minimum redundancy and maximum class discriminability. Tested on ECG recordings obtained from eight newborns with identified EEG seizure, the proposed HRV-based neonatal seizure detection algorithm achieved 85.7% sensitivity and 84.6% specificity. These results suggest that the HRV is sensitive to changes in the cardioregulatory system induced by the seizure, and therefore, can be used as a basis for an automatic seizure detection.

Matched MeSH terms: Electrocardiography/methods
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*

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