Ventricular Arrhythmia in the Fontan Circulation:Prevalence,Risk Factors and Clinical Implications

Objective:Sudden cardiac death(SCD)and malignant ventricular arrhythmia(VA)are increasingly recognized as important issues for people living with a Fontan circulation,but data are lacking.We sought to characterize the cohort who had sudden cardiac death,most likely related to VA and/or documented VA in the Australia and New Zealand Fontan Registry including risk factors and clinical outcomes.Methods:A retrospective cohort study was performed.Inclusion criteria were documented non-sustained ventricular tachycardia,sustained ventricular tachycardia,ventricular fibrillation,resuscitated cardiac arrest or SCD>30 days post-Fontan completion.Results:Of 1611 patients,20(1.2%)had VA;14(1.0%)had VA without SCD and 6(<1%)had SCD(6%of all deaths recorded in Registry;5 of those had documented VA at the time of arrest and 1 was presumed to be VA-associated).The median age at first VA was 20.5(14–32)years,10(50%)were females,and the median age at Fontan operation was 8(4–17)years.On univariable analysis,hypoplastic left heart syndrome(p=0.03)and older age Fontan operation(p<0.001)were associated with VA.Earlier Fontan era(p<0.003),atriopulmonary Fontan(p<0.001),pre-Fontan atrioventricular valve repair(p=0.013)pre-or post-Fontan atrial arrhythmia(p=0.010)were associated with SCD.Patients with VA had a 3 times higher risk of death or heart transplant(HR 3.27(1.19,8.98),p=0.02).Conclusions:A proportion of people living with a Fontan circulation have malignant VA.Routine VA screening in this cohort is essential.More data are needed to aid risk stratification.

Current knowledge for the risk factors of early permanent pacemaker implantation following transcatheter aortic valve replacement and what is next for the primary prevention?

In this editorial,we comprehensively summarized the preoperative risk factors of early permanent pacemaker implantation after transcatheter aortic valve replacement(TAVR)among patients with severe aortic stenosis from several renowned clinical studies and focused on the primary prevention of managing the modifiable factors,e.g.,paroxysmal atrial fibrillation before the TAVR.

Cardiac-targeted PIASy gene silencing mediates deSUMOylation of caveolin-3 and prevents ischemia/reperfusion-induced Na_(v)1.5 downregulation and ventricular arrhythmias

Background:Abnormal myocardial voltage-gated sodium channel 1.5(Nav1.5)expression and function cause lethal ventricular arrhythmias during myocardial ischemia–reperfusion(I/R).Protein inhibitor of activated STAT Y(PIASy)-mediated caveolin-3(Cav-3)small ubiquitin-related modifier(SUMO)modification affects Cav-3 binding to the Nav1.5.PIASy activity is increased after myocardial I/R,but it is unclear whether this is attributable to plasma membrane Nav1.5 downregulation and ventricular arrhythmias.Methods:Using recombinant adeno-associated virus subtype 9(AAV9),rat cardiac PIASy was silenced using intraventricular injection of PIASy short hairpin RNA(shRNA).After two weeks,rat hearts were subjected to I/R and electrocardiography was performed to assess malignant arrhythmias.Tissues from peri-infarct areas of the left ventricle were collected for molecular biological measurements.Results:PIASy was upregulated by I/R(P<0.01),with increased SUMO2/3 modification of Cav-3 and reduced membrane Nav1.5 density(P<0.01).AAV9-PIASy shRNA intraventricular injection into the rat heart down-regulated PIASy after I/R,at both mRNA and protein levels(P<0.05 vs.Scramble-shRNA+I/R group),decreased SUMO-modified Cav-3 levels,enhanced Cav-3 binding to Nav1.5,and prevented I/R-induced decrease of Nav1.5 and Cav-3co-localization in the intercalated disc and lateral membrane.PIASy silencing in rat hearts reduced I/R-induced fatal arrhythmias,which was reflected by a modest decrease in the duration of ventricular fibrillation(VF;P<0.05 vs.Scramble-shRNA+I/R group)and a significantly reduced arrhythmia score(P<0.01 vs.Scramble-shRNA+I/R group).The anti-arrhythmic effects of PIASy silencing were also evidenced by decreased episodes of ventricular tachycardia(VT),sustained VT and VF,especially at the time 5–10 min after ischemia(P<0.05 vs.Scramble-shRNA+IR group).Using in vitro human embryonic kidney 293 T(HEK293T)cells and isolated adult rat cardiomyocyte models exposed to hypoxia/reoxygenation(H/R),we confirmed that increased PIASy promoted Cav-3 modification by SUMO2/3 and Nav1.5/Cav-3 dissociation after H/R.Mutation of SUMO consensus lysine sites in Cav-3(K38R or K144R)altered the membrane expression levels of Nav1.5 and Cav-3 before and after H/R in HEK293T cells.Conclusions:I/R-induced cardiac PIASy activation increased Cav-3 SUMOylation by SUMO2/3 and dysregulated Nav1.5-related ventricular arrhythmias.Cardiac-targeted PIASy silencing mediated Cav-3 deSUMOylation and partially prevented I/R-induced Nav1.5 downregulation in the plasma membrane of cardiomyocytes,and subsequent ventricular arrhythmias in rats.PIASy was identified as a potential therapeutic target for life-threatening arrhythmias in patients with ischemic heart diseases.

Ensemble Approach Combining Deep Residual Networks and BiGRU with Attention Mechanism for Classification of Heart Arrhythmias

This research introduces an innovative ensemble approach,combining Deep Residual Networks(ResNets)and Bidirectional Gated Recurrent Units(BiGRU),augmented with an Attention Mechanism,for the classification of heart arrhythmias.The escalating prevalence of cardiovascular diseases necessitates advanced diagnostic tools to enhance accuracy and efficiency.The model leverages the deep hierarchical feature extraction capabilities of ResNets,which are adept at identifying intricate patterns within electrocardiogram(ECG)data,while BiGRU layers capture the temporal dynamics essential for understanding the sequential nature of ECG signals.The integration of an Attention Mechanism refines the model’s focus on critical segments of ECG data,ensuring a nuanced analysis that highlights the most informative features for arrhythmia classification.Evaluated on a comprehensive dataset of 12-lead ECG recordings,our ensemble model demonstrates superior performance in distinguishing between various types of arrhythmias,with an accuracy of 98.4%,a precision of 98.1%,a recall of 98%,and an F-score of 98%.This novel combination of convolutional and recurrent neural networks,supplemented by attention-driven mechanisms,advances automated ECG analysis,contributing significantly to healthcare’s machine learning applications and presenting a step forward in developing non-invasive,efficient,and reliable tools for early diagnosis and management of heart diseases.

AutoRhythmAI: A Hybrid Machine and Deep Learning Approach for Automated Diagnosis of Arrhythmias

In healthcare,the persistent challenge of arrhythmias,a leading cause of global mortality,has sparked extensive research into the automation of detection using machine learning(ML)algorithms.However,traditional ML and AutoML approaches have revealed their limitations,notably regarding feature generalization and automation efficiency.This glaring research gap has motivated the development of AutoRhythmAI,an innovative solution that integrates both machine and deep learning to revolutionize the diagnosis of arrhythmias.Our approach encompasses two distinct pipelines tailored for binary-class and multi-class arrhythmia detection,effectively bridging the gap between data preprocessing and model selection.To validate our system,we have rigorously tested AutoRhythmAI using a multimodal dataset,surpassing the accuracy achieved using a single dataset and underscoring the robustness of our methodology.In the first pipeline,we employ signal filtering and ML algorithms for preprocessing,followed by data balancing and split for training.The second pipeline is dedicated to feature extraction and classification,utilizing deep learning models.Notably,we introduce the‘RRI-convoluted trans-former model’as a novel addition for binary-class arrhythmias.An ensemble-based approach then amalgamates all models,considering their respective weights,resulting in an optimal model pipeline.In our study,the VGGRes Model achieved impressive results in multi-class arrhythmia detection,with an accuracy of 97.39%and firm performance in precision(82.13%),recall(31.91%),and F1-score(82.61%).In the binary-class task,the proposed model achieved an outstanding accuracy of 96.60%.These results highlight the effectiveness of our approach in improving arrhythmia detection,with notably high accuracy and well-balanced performance metrics.

Prognostic relevance of ventricular arrhythmias in surgical patients with gastrointestinal tumors

BACKGROUND Individuals diagnosed with gastrointestinal tumors are at an increased risk of developing cardiovascular diseases.Among which,ventricular arrhythmia is a prevalent clinical concern.This suggests that ventricular arrhythmias may have predictive value in the prognosis of patients with gastrointestinal tumors.AIM To explore the prognostic value of ventricular arrhythmias in patients with gastrointestinal tumors receiving surgery.METHODS We retrospectively analyzed data from 130 patients undergoing gastrointestinal tumor resection.These patients were evaluated by a 24-h ambulatory electrocardiogram(ECG)at the Sixth Affiliated Hospital of Sun Yat-sen University from January 2018 to June 2020.Additionally,41 general healthy age-matched and sexmatched controls were included.Patients were categorized into survival and non-survival groups.The primary endpoint was all-cause mortality,and secondary endpoints included major adverse cardiovascular events(MACEs).RESULTS Colorectal tumors comprised 90%of cases.Preoperative ambulatory ECG monitoring revealed that among the 130 patients with gastrointestinal tumors,100(76.92%)exhibited varying degrees of premature ventricular contractions(PVCs).Ten patients(7.69%)manifested non-sustained ventricular tachycardia(NSVT).The patients with gastrointestinal tumors exhibited higher PVCs compared to the healthy controls on both conventional ECG[27(21.3)vs 1(2.5),P=0.012]and 24-h ambulatory ECG[14(1.0,405)vs 1(0,6.5),P<0.001].Non-survivors had a higher PVC count than survivors[150.50(7.25,1690.50)vs 9(0,229.25),P=0.020].During the follow-up period,24 patients died and 11 patients experienced MACEs.Univariate analysis linked PVC>35/24 h to all-cause mortality,and NSVT was associated with MACE.However,neither PVC burden nor NSVT independently predicted outcomes according to multivariate analysis.CONCLUSION Patients with gastrointestinal tumors exhibited elevated PVCs.PVCs>35/24 h and NSVT detected by 24-h ambulatory ECG were prognostically significant but were not found to be independent predictors.

Facing ethical concerns in the age of precise gene therapy:Outlook on inherited arrhythmias

This editorial,comments on the article by Spartalis et al published in the recent issue of the World Journal of Cardiology.We here provide an outlook on potential ethical concerns related to the future application of gene therapy in the field of inherited arrhythmias.As monogenic diseases with no or few therapeutic options available through standard care,inherited arrhythmias are ideal candidates to gene therapy in their treatment.Patients with inherited arrhythmias typically have a poor quality of life,especially young people engaged in agonistic sports.While genome editing for treatment of inherited arrhythmias still has theoretical application,advances in CRISPR/Cas9 technology now allows the generation of knock-in animal models of the disease.However,clinical translation is somehow expected soon and this make consistent discussing about ethical concerns related to gene editing in inherited arrhythmias.Genomic off-target activity is a known technical issue,but its relationship with ethnical and individual genetical diversity raises concerns about an equitable accessibility.Meanwhile,the costeffectiveness may further limit an equal distribution of gene therapies.The economic burden of gene therapies on healthcare systems is is increasingly recognized as a pressing concern.A growing body of studies are reporting uncertainty in payback periods with intuitive short-term effects for insurance-based healthcare systems,but potential concerns for universal healthcare systems in the long term as well.Altogether,those aspects strongly indicate a need of regulatory entities to manage those issues.

Efficient ECG classification based on Chi-square distance for arrhythmia detection

This study introduces a new classifier tailored to address the limitations inherent in conventional classifiers such as K-nearest neighbor(KNN),random forest(RF),decision tree(DT),and support vector machine(SVM)for arrhythmia detection.The proposed classifier leverages the Chi-square distance as a primary metric,providing a specialized and original approach for precise arrhythmia detection.To optimize feature selection and refine the classifier’s performance,particle swarm optimization(PSO)is integrated with the Chi-square distance as a fitness function.This synergistic integration enhances the classifier’s capabilities,resulting in a substantial improvement in accuracy for arrhythmia detection.Experimental results demonstrate the efficacy of the proposed method,achieving a noteworthy accuracy rate of 98% with PSO,higher than 89% achieved without any previous optimization.The classifier outperforms machine learning(ML)and deep learning(DL)techniques,underscoring its reliability and superiority in the realm of arrhythmia classification.The promising results render it an effective method to support both academic and medical communities,offering an advanced and precise solution for arrhythmia detection in electrocardiogram(ECG)data.

Arrhythmia Detection by Using Chaos Theory with Machine Learning Algorithms

Heart monitoring improves life quality.Electrocardiograms(ECGs or EKGs)detect heart irregularities.Machine learning algorithms can create a few ECG diagnosis processing methods.The first method uses raw ECG and time-series data.The second method classifies the ECG by patient experience.The third technique translates ECG impulses into Q waves,R waves and S waves(QRS)features using richer information.Because ECG signals vary naturally between humans and activities,we will combine the three feature selection methods to improve classification accuracy and diagnosis.Classifications using all three approaches have not been examined till now.Several researchers found that Machine Learning(ML)techniques can improve ECG classification.This study will compare popular machine learning techniques to evaluate ECG features.Four algorithms—Support Vector Machine(SVM),Decision Tree,Naive Bayes,and Neural Network—compare categorization results.SVM plus prior knowledge has the highest accuracy(99%)of the four ML methods.QRS characteristics failed to identify signals without chaos theory.With 99.8%classification accuracy,the Decision Tree technique outperformed all previous experiments.

Analysis of the Efficacy of Low-Dose Betaloc Combined with Amiodarone in Treating Ventricular Arrhythmia

Objective:To explore and analyze the clinical effect of low-dose Betaloc combined with amiodarone in treating ventricular arrhythmia.Methods:70 patients with ventricular arrhythmia who were admitted to the Department of Cardiology of our hospital between August 2022 and August 2023 were selected as research subjects.They were divided into two groups using the coin-tossing method:the combination group(n=35)and the reference group(n=35).The combination group was treated with low-dose Betaloc and amiodarone,and the control group was treated with low-dose Betaloc alone.The treatment efficacy,cardiac function indicators,and related tested indicators of the two groups were compared.Results:The total efficacy of the treatment received by the combination group was much higher than that of the control group(P<0.05).Besides,after treatment,the cardiac function indicators such as left ventricular ejection fraction(LVEF),left ventricular end-systolic volume(LVESV),and cardiac index(CI)of the patients in the combination group were significantly better than those of the reference group(P<0.05).Furthermore,the high-sensitivity C-reactive protein(Hs-CRP),N-terminal prohormone of brain natriuretic peptide(NT-proBNP),adiponectin(APN),and other related test indicators of the patients in the combination group were significantly better than those of the reference group(P<0.05).Conclusion:Low-dose Betaloc combined with amiodarone has a noticeable effect in treating ventricular arrhythmia and deserves to be widely promoted.

Clinical Efficacy of Metoprolol Succinate Extended-Release Tablets in the Treatment of Post-Myocardial Infarction Ventricular Arrhythmias

Objective:To investigate the clinical efficacy of metoprolol succinate extended-release tablets in the treatment of post-myocardial infarction ventricular arrhythmias.Methods:The clinical data of 84 patients with post-myocardial infarction ventricular arrhythmia included in the study were collected and they were divided into Groups A and B with 42 cases each using the randomization method.Group A was treated with oral glucosamine hydrochloride,while Group B was administered oral metoprolol succinate extended-release tablets.Combined indicators were used to evaluate the improvement of clinical indicators,therapeutic effects,and the incidence of adverse reactions in the two groups.Results:The baseline data of the two groups of patients were not statistically significant(Pall>0.05);after treatment,the QT dispersion,corrected QT dispersion,and heart rate of Group B were lower than that of Group A(Pall=0.000<0.001);the 2 total clinical effectiveness of Group B was 95.24%,which was significantly higher than 80.95%in Group A(χ=4.087,P=0.043<0.05);the total incidence of adverse reactions in Group B was 4.76%,which was significantly lower than 219.04%in Group A(χ=4.087,P=0.043<0.05).Conclusion:In the treatment of post-myocardial infarction ventricular arrhythmia,the use of metoprolol succinate extended-release tablets can effectively correct the QT dispersion of patients,improve their heart rate,increase clinical effectiveness,and reduce the incidence of adverse reactions.

A Potential Novel Indication for Preventing Thromboembolism in Patients with Atrial Arrhythmias: Remodeling of the Left Atrium

Objective:Thrombosis in patients with atrial arrhythmias might be associated with remodeling of the left atrium(LA).We aimed to describe this relationship and identify a novel factor,in addition to the CHA_(2)DS_(2)-VASc score,to guide therapeutic strategies for preventing thromboembolism in the hope of improving the prognosis for such patients.Methods:Patients diagnosed with atrial arrhythmias and who met our inclusion criteria were enrolled in this study.Various clinical parameters were recorded;diameters reflecting remodeling of the LA were measured and thrombosis was diagnosed by enhanced CT.Results:Totally,192 patients were enrolled in the study.The overall prevalence of left atrial thrombosis was 8.3%.Patients with persistent atrial fibrillation exhibited the highest anteroposterior diameter of the LA(45.1±7.2 mm)and prevalence of thrombosis(15.6%).An anteroposterior LA diameter of≥50 mm was a strong independent impact risk factor for thrombosis(OR=10.1,95%CI:2.8-36.9,P<0.OO1).The prevalence of LA thrombosis in patients with and without anteroposterior LA diameter of≥50 mm was 25.8%and 5.0%,respectively.Conclusion:Marked remodeling of the LA characterized by an anteroposterior LA diameter of≥50 mm is a strong independent risk factor of thrombosis,and should be considered for the prevention of thromboembolism through various strategies.

THE EFFECT OF ACUPUNCTURE ON PREVENTION AND TREATMENT OF ISCHEMIC ARRHYTHMIA AND THE RELATION BETWEEN EFFECT OF ACUPUNCTURE AND CHOLINERGIC MUSCARINE-LIKE RECEPTORS

For investigating the effect of acupuncture on ischemic arrhythmia and its mech-anism, adult albino rats with ligated anterior descending branch of coronary artery as experimentalmodel were treated with or without acupuncture, and others with imitative operation but without bothcoronary artery ligation and acupuncture treatment were used as control. It was found in acupuncturegroup that the fibrillation-liability of ischemic myocardium was efficiently decreased, the affinity ofAch-M receptors on membranes of ischemic myocardium was markedly increased, and the tolerance ofischemic myocardium to atropine was elevated in the experiment of atropine inducing fibrillation.These results indicate that acupuncture may play a therapeutic role on ischemic arrhythmia throughactivating the activity of muscarine-like receptors of cholinergic nervous system.

Cardiac Arrhythmia Disease Classifier Model Based on a Fuzzy Fusion Approach

Cardiac diseases are one of the greatest global health challenges.Due to the high annual mortality rates,cardiac diseases have attracted the attention of numerous researchers in recent years.This article proposes a hybrid fuzzy fusion classification model for cardiac arrhythmia diseases.The fusion model is utilized to optimally select the highest-ranked features generated by a variety of well-known feature-selection algorithms.An ensemble of classifiers is then applied to the fusion’s results.The proposed model classifies the arrhythmia dataset from the University of California,Irvine into normal/abnormal classes as well as 16 classes of arrhythmia.Initially,at the preprocessing steps,for the miss-valued attributes,we used the average value in the linear attributes group by the same class and the most frequent value for nominal attributes.However,in order to ensure the model optimality,we eliminated all attributes which have zero or constant values that might bias the results of utilized classifiers.The preprocessing step led to 161 out of 279 attributes(features).Thereafter,a fuzzy-based feature-selection fusion method is applied to fuse high-ranked features obtained from different heuristic feature-selection algorithms.In short,our study comprises three main blocks:(1)sensing data and preprocessing;(2)feature queuing,selection,and extraction;and(3)the predictive model.Our proposed method improves classification performance in terms of accuracy,F1measure,recall,and precision when compared to state-of-the-art techniques.It achieves 98.5%accuracy for binary class mode and 98.9%accuracy for categorized class mode.

Dysglycemia and arrhythmias

Disorders in glucose metabolism can be divided into three separate but interrelated domains,namely hyperglycemia,hypoglycemia,and glycemic variability.Intensive glycemic control in patients with diabetes might increase the risk of hypoglycemic incidents and glucose fluctuations.These three dysglycemic states occur not only amongst patients with diabetes,but are frequently present in other clinical settings,such as during critically ill.A growing body of evidence has focused on the relationships between these dysglycemic domains with cardiac arrhythmias,including supraventricular arrhythmias(primarily atrial fibrillation),ventricular arrhythmias(malignant ventricular arrhythmias and QT interval prolongation),and bradyarrhythmias(bradycardia and heart block).Different mechanisms by which these dysglycemic states might provoke cardiac arrhythmias have been identified in experimental studies.A customized glycemic control strategy to minimize the risk of hyperglycemia,hypoglycemia and glucose variability is of the utmost importance in order to mitigate the risk of cardiac arrhythmias.

Inherited arrhythmias and gene therapy: Are there any ethical considerations to take into account?

Interventional electrophysiology represents a relatively recent subspecialty within the field of cardiology.In the past half-century,there has been significant advan-cement in the development and implementation of innovative ablation treatments and approaches.However,the treatment of arrhythmias continues to be inade-quate.Several arrhythmias,such as ventricular tachycardia and atrial fibrillation,pose significant challenges in terms of therapeutic efficacy,whether through interventional procedures or the administration of antiarrhythmic drugs.Cardio-logists are engaged in ongoing research to explore innovative methodologies,such as genome editing,with the purpose of effectively managing arrhythmias and meeting the growing needs of patients afflicted with rhythm disturbances.The field of genome editing has significant promise and has the potential to serve as a highly effective personalized therapy for rhythm disorders in patients.However,several ethical issues must be considered.

Classification of Electrocardiogram Signals for Arrhythmia Detection Using Convolutional Neural Network

With the help of computer-aided diagnostic systems,cardiovascular diseases can be identified timely manner to minimize the mortality rate of patients suffering from cardiac disease.However,the early diagnosis of cardiac arrhythmia is one of the most challenging tasks.The manual analysis of electrocardiogram(ECG)data with the help of the Holter monitor is challenging.Currently,the Convolutional Neural Network(CNN)is receiving considerable attention from researchers for automatically identifying ECG signals.This paper proposes a 9-layer-based CNN model to classify the ECG signals into five primary categories according to the American National Standards Institute(ANSI)standards and the Association for the Advancement of Medical Instruments(AAMI).The Massachusetts Institute of Technology-Beth Israel Hospital(MIT-BIH)arrhythmia dataset is used for the experiment.The proposed model outperformed the previous model in terms of accuracy and achieved a sensitivity of 99.0%and a positivity predictively 99.2%in the detection of a Ventricular Ectopic Beat(VEB).Moreover,it also gained a sensitivity of 99.0%and positivity predictively of 99.2%for the detection of a supraventricular ectopic beat(SVEB).The overall accuracy of the proposed model is 99.68%.

Characterizing the Impact of Caffeine on Heart Arrhythmias

Caffeine is one of the most commonly consumed stimulants and is found in many items like coffee and energy drinks. Heart arrhythmias are irregular heart rhythms, which can occur when the electrical signals that control the heart’s rhythm are not functioning properly. Due to the stimulant properties of caffeine, it is theorized that caffeine consumption may cause tachycardias-like ventricular arrhythmias. This review article describes the relationship between caffeine intake and heart arrhythmias using a comprehensive Pub-Med search. A comprehensive search was conducted using the search terms “caffeine arrhythmia” which was conducted and a total of 26 search results were obtained. The majority of clinical studies suggest that there are no strong associations between caffeine consumption and arrhythmias. There is little evidence suggesting a direct relationship between caffeine and ventricular arrhythmias (relative Risk 1.00, 95% CI 0.94 - 1.06;13.5%, p = 0.32). Conversely, caffeine consumption has an inverse relationship with the risk of atrial fibrillation (p for overall trend = 0.015;p for nonlinearity = 0.27). Caffeine related deaths are uncommon, but certain groups such as infants, psychiatric patients, and athletes may have an increased risk of arrhythmias following caffeine consumption. Overall, caffeine consumption is not strongly linked to heart arrhythmias and limited studies suggest it may reduce the risk of arrhythmias. Although there is not a strong relationship between caffeine intake and heart arrhythmias, it does cause other cardiovascular problems including high blood pressure and hence should be consumed responsibly (40 - 180 mg/day).

Prevalence of arrhythmias in heavy vehicle drivers

Objective: In our study we aimed to determine the frequency of arrhythmias that we believe may affect driving safety. Methods and Results: Two hundred drivers were randomly selected from the heavy vehicle driver population (82 bus and 118 truck drivers, p = 0.08, q = 0.92, N = 1200, α = 0.01, d = 0.045). A questionnaire was completed via face to face interviews with the individuals including questions about their personal socio-demographic characteristics and symptoms for arrhythmias. An electrocardiography (ECG) was taken of the study participants using the Cardioline Delta 3 Plus Digital ECG machine. The cardiologist at the clinic evaluated the questionnaire and ECG for presence of arrhythmias. When indicated, ambulatory electrocardiography (Holter Monitoring) was performed for 24 hours in 133 individuals (71 driver and 62 control). In cases that had Holter examination;ventricular ectopy was identified in 25.4% and 22.6%, and supra-ventricular ectopy in 45.1% and 35.5% in the driver and the control groups;respectively. Ventricular tachycardia was detected in 2 patients. Arrhythmia frequencies were 59.1%, 54.8% and 57.1% in drivers, control and both groups respectively. Statistical differences between drivers and control group for rhythm disorders were not detected. Conclusions: Arrhythmias with lethal and devastating potential;need to be diagnosed and treated in professional drivers with extreme caution. The follow up and screening for heart diseases has a crucial role in preventing accidents and occupational diseases in drivers.

Meta-Heuristic Optimized Hybrid Wavelet Features for Arrhythmia Classification

The non-invasive evaluation of the heart through EectroCardioG-raphy(ECG)has played a key role in detecting heart disease.The analysis of ECG signals requires years of learning and experience to interpret and extract useful information from them.Thus,a computerized system is needed to classify ECG signals with more accurate results effectively.Abnormal heart rhythms are called arrhythmias and cause sudden cardiac deaths.In this work,a Computerized Abnormal Heart Rhythms Detection(CAHRD)system is developed using ECG signals.It consists of four stages;preprocessing,feature extraction,feature optimization and classifier.At first,Pan and Tompkins algorithm is employed to detect the envelope of Q,R and S waves in the preprocessing stage.It uses a recursive filter to eliminate muscle noise,T-wave interference and baseline wander.As the analysis of ECG signal in the spatial domain does not provide a complete description of the signal,the feature extraction involves using frequency contents obtained from multiple wavelet filters;bi-orthogonal,Symlet and Daubechies at different resolution levels in the feature extraction stage.Then,Black Widow Optimization(BWO)is applied to optimize the hybrid wavelet features in the feature optimization stage.Finally,a kernel based Support Vector Machine(SVM)is employed to classify heartbeats into five classes.In SVM,Radial Basis Function(RBF),polynomial and linear kernels are used.A total of∼15000 ECG signals are obtained from the Massachusetts Institute of Technology-Beth Israel Hospital(MIT-BIH)arrhythmia database for performance evaluation of the proposed CAHRD system.Results show that the proposed CAHRD system proved to be a powerful tool for ECG analysis.It correctly classifies five classes of heartbeats with 99.91%accuracy using an RBF kernel with 2nd level wavelet coefficients.The CAHRD system achieves an improvement of∼6%over random projections with the ensemble SVM approach and∼2%over morphological and ECG segment based features with the RBF classifier.