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A Comparative Effectiveness Systematic Review and Meta-Analysis of Drugs for the Prophylaxis of Junctional Ectopic Tachycardia

Mendel B, Christianto, Setiawan M, Prakoso R, Siagian SN

Curr Cardiol Rev, 2021 Jun 03.
PMID: 34082685 DOI: 10.2174/1573403X17666210603113430

BACKGROUND: Junctional ectopic tachycardia (JET) is an arrhythmia originating from the AV junction, which may occur following congenital heart surgery, especially when the intervention is near the atrioventricular junction.
OBJECTIVE: The aim of this systematic review and meta-analysis is to compare the effectiveness of amiodarone, dexmedetomidine and magnesium in preventing JET following congenital heart surgery.
METHODS: This meta-analysis was conducted according to the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) statement, where 11 electronic databases were searched from date of inception to August 2020. The incidence of JET was calculated with the relative risk of 95% confidence interval (CI). Quality assessment of the included studies was assessed using the Consolidated Standards of Reporting Trials (CONSORT) 2010 statement.
RESULTS: Eleven studies met the predetermined inclusion criteria and were included in this meta-analysis. Amiodarone, dexmedetomidine and magnesium significantly reduced the incidence of postoperative JET [Amiodarone: risk ratio 0.34; I2= 0%; Z=3.66 (P=0.0002); 95% CI 0.19-0.60. Dexmedetomidine: risk ratio 0.34; I2= 0%; Z=4.77 (P<0.00001); 95% CI 0.21-0.52. Magnesium: risk ratio 0.50; I2= 24%; Z=5.08 (P<0.00001); 95% CI 0.39-0.66].
CONCLUSION: All three drugs show promise in reducing the incidence of JET. Our systematic review found that dexmedetomidine is better in reducing the length of ICU stays as well as mortality. In addition, dexmedetomidine also has the least pronounced side effects among the three. However, it should be noted that this conclusion was derived from studies with small sample sizes. Therefore, dexmedetomidine may be considered as the drug of choice for preventing JET.

Matched MeSH terms: Arrhythmias, Cardiac
Fulltext A Hybrid Deep CNN Model for Abnormal Arrhythmia Detection Based on Cardiac ECG Signal

Ullah A, Rehman SU, Tu S, Mehmood RM, Fawad, Ehatisham-Ul-Haq M

Sensors (Basel), 2021 Feb 01;21(3).
PMID: 33535397 DOI: 10.3390/s21030951

Electrocardiogram (ECG) signals play a vital role in diagnosing and monitoring patients suffering from various cardiovascular diseases (CVDs). This research aims to develop a robust algorithm that can accurately classify the electrocardiogram signal even in the presence of environmental noise. A one-dimensional convolutional neural network (CNN) with two convolutional layers, two down-sampling layers, and a fully connected layer is proposed in this work. The same 1D data was transformed into two-dimensional (2D) images to improve the model's classification accuracy. Then, we applied the 2D CNN model consisting of input and output layers, three 2D-convolutional layers, three down-sampling layers, and a fully connected layer. The classification accuracy of 97.38% and 99.02% is achieved with the proposed 1D and 2D model when tested on the publicly available Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database. Both proposed 1D and 2D CNN models outperformed the corresponding state-of-the-art classification algorithms for the same data, which validates the proposed models' effectiveness.

Matched MeSH terms: Arrhythmias, Cardiac/diagnosis
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: Arrhythmias, Cardiac
A deep convolutional neural network model to classify heartbeats

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: Arrhythmias, Cardiac/diagnosis; Arrhythmias, Cardiac/physiopathology*
Fulltext A diagnostic workup of perioperative anaphylaxis reveals a selective type 1 hypersensitivity to cefazolin

Meera Thalayasingam, Shek, Lynette Pei-Chi

International e-Journal of Science, Medicine & Education, 2015;9(2):37-39.
MyJurnal

Anaphylaxis in the operating room although infrequent can be potentially fatal. The diagnosis of perioperative anaphylaxis is complex due to a multitude of factors. Firstly, patients under anesthesia cannot verbalize their complaints, the anesthetic agents themselves can alter vital parameters (e.g. heart rate and blood pressure) and cutaneous signs in a completely draped patient may be missed. Secondly, the differential diagnosis of intraoperative anaphylaxis is wide. Conditions such as asthma exacerbation, arrhythmia, hemorrhage, angioedema, mastocytosis, acute myocardial infarction, drug overdose, pericardial tamponade, pulmonary edema, pulmonary embolus, sepsis, tension pneumothorax, vasovagal reaction, venous air embolism, laryngospasm, blood transfusion reaction and malignant hyperthermia need to be considered. Thirdly, the diagnostic workup is challenging due to the multiple medications administered and other exposures encountered such as latex and chlorhexidene. However, through a timely allergy consultation and a systematic approach, identification of the culprit agent and safe alternatives can be established to prevent future occurrences as illustrated in the case below.

Matched MeSH terms: Arrhythmias, Cardiac
Fulltext A large left ventricular tumour presenting with arrhythmias in a 2-month-old infant

Wan Adlina Wan Yusuf, Amelia Alias, Wan Hanifah Wan Hussin1, Mohd Nasir Abdul Kadir, Abdul Rahim Wong

Malaysian Journal of Paediatrics and Child Health, 2018;24(2):1-5.
MyJurnal

Primary cardiac tumours (PCT) are rare in the paediatric population. They can present in a variety of ways – from being asymptomatic, obstructive with heart failure, strokes, arrhythmias or sudden death. We present a 2-month-old child who was admitted because of heart failure from varying types of arrhythmias and was found on echocardiography to have a large left ventricular tumour. A high clinical suspicion in any infant or child who presents with an unexplained heart murmur, arrhythmias or congestive heart failure should prompt relevant investigations ruling out this entity.

Matched MeSH terms: Arrhythmias, Cardiac
Fulltext A leadless intracardiac transcatheter pacing system

Reynolds D, Duray GZ, Omar R, Soejima K, Neuzil P, Zhang S, et al.

N Engl J Med, 2016 Feb 11;374(6):533-41.
PMID: 26551877 DOI: 10.1056/NEJMoa1511643

BACKGROUND: A leadless intracardiac transcatheter pacing system has been designed to avoid the need for a pacemaker pocket and transvenous lead.
METHODS: In a prospective multicenter study without controls, a transcatheter pacemaker was implanted in patients who had guideline-based indications for ventricular pacing. The analysis of the primary end points began when 300 patients reached 6 months of follow-up. The primary safety end point was freedom from system-related or procedure-related major complications. The primary efficacy end point was the percentage of patients with low and stable pacing capture thresholds at 6 months (≤2.0 V at a pulse width of 0.24 msec and an increase of ≤1.5 V from the time of implantation). The safety and efficacy end points were evaluated against performance goals (based on historical data) of 83% and 80%, respectively. We also performed a post hoc analysis in which the rates of major complications were compared with those in a control cohort of 2667 patients with transvenous pacemakers from six previously published studies.
RESULTS: The device was successfully implanted in 719 of 725 patients (99.2%). The Kaplan-Meier estimate of the rate of the primary safety end point was 96.0% (95% confidence interval [CI], 93.9 to 97.3; P<0.001 for the comparison with the safety performance goal of 83%); there were 28 major complications in 25 of 725 patients, and no dislodgements. The rate of the primary efficacy end point was 98.3% (95% CI, 96.1 to 99.5; P<0.001 for the comparison with the efficacy performance goal of 80%) among 292 of 297 patients with paired 6-month data. Although there were 28 major complications in 25 patients, patients with transcatheter pacemakers had significantly fewer major complications than did the control patients (hazard ratio, 0.49; 95% CI, 0.33 to 0.75; P=0.001).
CONCLUSIONS: In this historical comparison study, the transcatheter pacemaker met the prespecified safety and efficacy goals; it had a safety profile similar to that of a transvenous system while providing low and stable pacing thresholds. (Funded by Medtronic; Micra Transcatheter Pacing Study ClinicalTrials.gov number, NCT02004873.).

Matched MeSH terms: Arrhythmias, Cardiac/therapy*
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: Arrhythmias, Cardiac/classification; Arrhythmias, Cardiac/diagnosis*
Fulltext Acute myocardial infarction survival rate and complications after streptokinase therapy in Hospital Universiti Sains Malaysia, Kelantan--a comparative study

Hishamuddin HM, Azmi NN, Jackson N

Singapore Med J, 1993 Aug;34(4):316-8.
PMID: 8266202

Thrombolytic therapy is a well-established therapy in acute myocardial infarction (AMI), reducing mortality and infarct size. This study is a retrospective analysis of survival and complications after the use of streptokinase at Hospital Universiti Sains Malaysia. Streptokinase was first used here in March 1990. Between then and February 1992, 126 patients were admitted to the Coronary Care Unit. Thirty-two patients who fulfilled our criteria for thrombolytic treatment were given an hour intravenous infusion of 1.5 MU streptokinase, and started on aspirin. A control group of 64 patients selected from before March 1990, and matched for age, sex and site of infarct, was given standard therapy. The survival at 4 weeks post-AMI was 91% in the streptokinase therapy group and 91% in both groups (p > 0.05). The complications encountered were reperfusion arrhythmias (2 patients), hypotension(1), maculopapular rash(1) and gum bleeding(1). None of these complications were statistically increased when compared to the control group and none resulted in the death of a patient. We conclude that streptokinase therapy can be given safely in a rural Malaysian setting. Our survival and complication rates are comparable with other published series.

Matched MeSH terms: Arrhythmias, Cardiac/etiology
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: Arrhythmias, Cardiac
Adverse effect of intraoperative phenylephrine 10%: case report

Lai YK

Br J Ophthalmol, 1989 Jun;73(6):468-9.
PMID: 2751981

The case is reported of a patient who suffered severe acute hypertension, cardiac arrhythmia, and myocardial infarction probably as a direct effect of phenylephrine overdose. Instillation of the drops during surgery probably enhanced the systemic absorption of a significant amount of the drug. Therefore it should be used during surgery with caution, especially in elderly patients and those with cardiovascular disease.

Matched MeSH terms: Arrhythmias, Cardiac/chemically induced*
Fulltext Age-dependent atrial arrhythmic phenotype secondary to mitochondrial dysfunction in Pgc-1β deficient murine hearts

Valli H, Ahmad S, Chadda KR, Al-Hadithi ABAK, Grace AA, Jeevaratnam K, et al.

Mech Ageing Dev, 2017 Oct;167:30-45.
PMID: 28919427 DOI: 10.1016/j.mad.2017.09.002

INTRODUCTION: Ageing and several age-related chronic conditions including obesity, insulin resistance and hypertension are associated with mitochondrial dysfunction and represent independent risk factors for atrial fibrillation (AF).
MATERIALS AND METHODS: Atrial arrhythmogenesis was investigated in Langendorff-perfused young (3-4 month) and aged (>12 month), wild type (WT) and peroxisome proliferator activated receptor-γ coactivator-1β deficient (Pgc-1β-/-) murine hearts modeling age-dependent chronic mitochondrial dysfunction during regular pacing and programmed electrical stimulation (PES).
RESULTS AND DISCUSSION: The Pgc-1β-/- genotype was associated with a pro-arrhythmic phenotype progressing with age. Young and aged Pgc-1β-/- hearts showed compromised maximum action potential (AP) depolarization rates, (dV/dt)max, prolonged AP latencies reflecting slowed action potential (AP) conduction, similar effective refractory periods and baseline action potential durations (APD90) but shortened APD90 in APs in response to extrasystolic stimuli at short stimulation intervals. Electrical properties of APs triggering arrhythmia were similar in WT and Pgc-1β-/- hearts. Pgc-1β-/- hearts showed accelerated age-dependent fibrotic change relative to WT, with young Pgc-1β-/- hearts displaying similar fibrotic change as aged WT, and aged Pgc-1β-/- hearts the greatest fibrotic change. Mitochondrial deficits thus result in an arrhythmic substrate, through slowed AP conduction and altered repolarisation characteristics, arising from alterations in electrophysiological properties and accelerated structural change.

Matched MeSH terms: Arrhythmias, Cardiac/genetics*
Fulltext Ageing in Pgc-1β-/- mice modelling mitochondrial dysfunction induces differential expression of a range of genes regulating ventricular electrophysiology

Edling CE, Fazmin IT, Chadda KR, Ahmad S, Valli H, Grace AA, et al.

Biosci Rep, 2019 04 30;39(4).
PMID: 30914453 DOI: 10.1042/BSR20190127

Mice deficient in mitochondrial promoter peroxisome proliferator activated receptor-γ co-activator-1β (Pgc-1β-/- ) is a valuable model for metabolic diseases and has been found to present with several pathologies including ventricular arrhythmia. In the present study, our aim was to shed light on the molecular mechanisms behind the observed arrhythmic substrate by studying how the expression of selected genes critical for cardiac function differs in wild-type (WT) compared with Pgc-1β knockout mice and young compared with aged mice. We found that a clear majority of genes are down-regulated in the Pgc-1β-/- ventricular tissue compared with the WT. Although most individual genes are not significantly differentially expressed, a pattern is apparent when the genes are grouped according to their functional properties. Genes encoding proteins relating to ATPase activity, potassium ion channels relating to repolarisation and resting membrane potential, and genes encoding proteins in the cAMP pathway are found to be significantly down-regulated in the Pgc-1β deficient mice. On the contrary, the pacemaker channel genes Hcn3 and Hcn4 are up-regulated in subsets of the Pgc-1β deficient tissue. Furthermore, we found that with age, especially in the Pgc-1β-/- genotype, most genes are up-regulated including genes relating to the resting membrane potential, calcium homeostasis, the cAMP pathway, and most of the tested adrenoceptors. In conclusion, we here demonstrate how a complex pattern of many modest changes at gene level may explain major functional differences of the action potential related to ageing and mitochondrial dysfunction.

Matched MeSH terms: Arrhythmias, Cardiac/metabolism; Arrhythmias, Cardiac/physiopathology
Fulltext An uncommon life-threatening cause of electric alternans with sudden onset of breathlessness and chest pain

Benjamin Ng Han Sim

Borneo Journal of Medical Sciences, 2020;14(2):0-0.
MyJurnal

Phasic ECG voltage changes or electrical alternans is a well-described ECG changes seen in the pericardial effusion and cardiac tamponade. Popular as once believed, this ECG features are no longer considered pathognomonic for pericardial effusion and cardiac tamponade. Electric alternans is observed in pneumothorax especially left-sided pneumothorax. This is a case of a 41-year-old man who presented with chest pain and breathlessness to the emergency department. Assessment in the emergency unit revealed an obvious distress man with a respiratory rate of 60 breaths/min with cyanosis There were generalised rhonchi and prolonged expiratory breath sound appreciated. Chest X-ray (CXR) was done and diagnosed to have left tension pneumothorax. Initial electrocardiogram (ECG) showed electrical alternans in all leads. He was intubated for respiratory distress followed by chest tube insertion. His initial ECG findings resolved after treatment of the tension pneumothorax. Doctors need to evaluate the cardiac findings along with respiratory findings.

Matched MeSH terms: Arrhythmias, Cardiac
Fulltext Anterior Myocardial Infarction Associated with Myocardial Bridging in a Young Man

Ngow, H.A., Wan Khairina, W.M.N.

International Medical Journal Malaysia, 2010;9(2):67-70.
MyJurnal

A 43-year-old man presented with acute extensive anterior ST-segment elevation myocardial infarction. During coronary angiogram, a segment of myocardial bridging was noted in the mid-segment of left anterior descending artery. The association of myocardial bridging and an anterior ST segment elevation is rarely reported in the medical literature. Myocardial bridging is caused by systolic compression of a coronary artery by overlying myocardium tissue. It is a rare coronary artery anomaly, which usually has a benign prognosis despite some case reports of myocardial ischemia leading to myocardial infarction, lethal arrhythmias and sudden cardiac death. We report one such case of myocardial bridging that was complicated with acute extensive anterior myocardial infarction.

Matched MeSH terms: Arrhythmias, Cardiac
Arrhythmia detection using deep convolutional neural network with long duration ECG signals

Yıldırım Ö, Pławiak P, Tan RS, Acharya UR

Comput Biol Med, 2018 11 01;102:411-420.
PMID: 30245122 DOI: 10.1016/j.compbiomed.2018.09.009

This article presents a new deep learning approach for cardiac arrhythmia (17 classes) detection based on long-duration electrocardiography (ECG) signal analysis. Cardiovascular disease prevention is one of the most important tasks of any health care system as about 50 million people are at risk of heart disease in the world. Although automatic analysis of ECG signal is very popular, current methods are not satisfactory. The goal of our research was to design a new method based on deep learning to efficiently and quickly classify cardiac arrhythmias. Described research are based on 1000 ECG signal fragments from the MIT - BIH Arrhythmia database for one lead (MLII) from 45 persons. Approach based on the analysis of 10-s ECG signal fragments (not a single QRS complex) is applied (on average, 13 times less classifications/analysis). A complete end-to-end structure was designed instead of the hand-crafted feature extraction and selection used in traditional methods. Our main contribution is to design a new 1D-Convolutional Neural Network model (1D-CNN). The proposed method is 1) efficient, 2) fast (real-time classification) 3) non-complex and 4) simple to use (combined feature extraction and selection, and classification in one stage). Deep 1D-CNN achieved a recognition overall accuracy of 17 cardiac arrhythmia disorders (classes) at a level of 91.33% and classification time per single sample of 0.015 s. Compared to the current research, our results are one of the best results to date, and our solution can be implemented in mobile devices and cloud computing.

Matched MeSH terms: Arrhythmias, Cardiac
Fulltext Association of Co-Morbidity and Treatment with the Complications of Acute Coronary Syndrome in Young Patients Less Than 45 Years

Norsa'adah B, Che-Muzaini CM

Malays J Med Sci, 2018 Feb;25(1):42-52.
PMID: 29599634 MyJurnal DOI: 10.21315/mjms2018.25.1.6

Background: Approximately 5%-10% of acute coronary syndrome (ACS) cases occur in people younger than 45 years of age. This study aimed to identify complications of ACS and the associated factors in young patients.
Methods: In this cross-sectional study, data from 147 ACS patients aged less than 45 years were analysed.
Results: The mean age was 39.1 (4.9) years, the male to female ratio was 3:1; 21.2% of patients presented with unstable angina, 58.5% had non-ST elevation myocardial infarction and 20.4% had ST elevation myocardial infarction. The most frequent risk factor of ACS was dyslipidaemia (65.3%), followed by hypertension (43.5%). In total, 49.7% of patients had inpatient complication(s), with the most common being heart failure (35.4%), followed by arrhythmia (20.4%). The significant factors associated with ACS complications were current smoking [adjusted odds ratio (AOR) 4.03; 95% confidence interval (CI): 1.33, 12.23;P-value = 0.014], diabetic mellitus [AOR 3.03; 95% CI: 1.19, 7.71;P-value = 0.020], treatments of fondaparinux [AOR 0.18; 95% CI: 0.08, 0.39;P-value < 0.001] and oral nitrates [AOR 0.18; 95% CI: 0.08, 0.42;P-value < 0.001].
Conclusions: Smoking status and diabetes mellitus were modifiable risk factors while pharmacological treatment was an important protective factor for ACS complications in young patients.

Matched MeSH terms: Arrhythmias, Cardiac
Automated characterization of cardiovascular diseases using relative wavelet nonlinear features extracted from ECG signals

Adam M, Oh SL, Sudarshan VK, Koh JE, Hagiwara Y, Tan JH, et al.

Comput Methods Programs Biomed, 2018 Jul;161:133-143.
PMID: 29852956 DOI: 10.1016/j.cmpb.2018.04.018

Cardiovascular diseases (CVDs) are the leading cause of deaths worldwide. The rising mortality rate can be reduced by early detection and treatment interventions. Clinically, electrocardiogram (ECG) signal provides useful information about the cardiac abnormalities and hence employed as a diagnostic modality for the detection of various CVDs. However, subtle changes in these time series indicate a particular disease. Therefore, it may be monotonous, time-consuming and stressful to inspect these ECG beats manually. In order to overcome this limitation of manual ECG signal analysis, this paper uses a novel discrete wavelet transform (DWT) method combined with nonlinear features for automated characterization of CVDs. ECG signals of normal, and dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy (HCM) and myocardial infarction (MI) are subjected to five levels of DWT. Relative wavelet of four nonlinear features such as fuzzy entropy, sample entropy, fractal dimension and signal energy are extracted from the DWT coefficients. These features are fed to sequential forward selection (SFS) technique and then ranked using ReliefF method. Our proposed methodology achieved maximum classification accuracy (acc) of 99.27%, sensitivity (sen) of 99.74%, and specificity (spec) of 98.08% with K-nearest neighbor (kNN) classifier using 15 features ranked by the ReliefF method. Our proposed methodology can be used by clinical staff to make faster and accurate diagnosis of CVDs. Thus, the chances of survival can be significantly increased by early detection and treatment of CVDs.

Matched MeSH terms: Arrhythmias, Cardiac/diagnosis
Fulltext Basics of electrocardiogram (ECG) and its application in diagnosis of heart ailments: an educational series

Chodankar, Nagesh N., May, Honey Ohn, D’Souza, Urban John Arnold

Borneo Journal of Medical Sciences, 2018;12(1):3-22.
MyJurnal

Electrocardiogram (ECG) is a record of electrical activity of the heart. PQRST waves represent
the electrical activities of atria and ventricles. A complete three-dimensional electrical activity is
possible to be recorded using a 12-lead ECG. The normal and different routinely-met clinical ECG
are elaborated and discussed. This routine, normal and abnormal ECG, like arrhythmias and heart
block records as well as their clinical notes shall be educational information for the medical students.

Matched MeSH terms: Arrhythmias, Cardiac
Cardiac arrhythmia after succinylcholine administration in a patient with Guillain-Barré syndrome--a case report

Hor JY

Middle East J Anaesthesiol, 2010 Oct;20(6):881-3.
PMID: 21526679

We report a case of cardiac arrhythmia occurring in a Guillain-Barré syndrome (GBS) patient after succinylcholine administration during third endotracheal intubation, on day 13 of illness. The probable cause of arrhythmia is succinylcholine-induced hyperkalemia. Of interest, this case demonstrated in the same patient that arrhythmia only occurred during third intubation, when duration of illness is prolonged, and not during previous two intubation episodes, despite succinylcholine was also being used. In GBS, muscle denervation resulted in up-regulation of acetylcholine receptors at neuromuscular junctions, causing the muscle cell membrane to become supersensitive to succinylcholine, leading to severe hyperkalemia and arrhythmia when succinylcholine was administered.

Matched MeSH terms: Arrhythmias, Cardiac/chemically induced*

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