The budget impact of implementing atrial fibrillation-screening in European countries.

A budget impact analysis estimates the short-term difference between the cost of the current treatment strategy and a new treatment strategy, in this case to implement population screening for atrial fibrillation (AF). The aim of this study is to estimate the financial impact of implementing population-based AF-screening of 75-year-olds compared with the current setting of no screening from a healthcare payer perspective in eight European countries. The net budget impact of AF-screening was estimated in country-specific settings for Denmark, Germany, Ireland, Italy, Netherlands, Serbia, Spain, and Sweden. Country-specific parameters were used to allow for variations in healthcare systems and to reflect the healthcare sector in the country of interest. Similar results can be seen in all countries AF-screening incurs savings of stroke-related costs since AF treatment reduces the number of strokes. However, the increased number of detected AF and higher drug acquisition will increase the drug costs as well as the costs of physician- and control visits. The net budget impact per invited varied from €10 in Ireland to €122 in the Netherlands. The results showed the increased costs of implementing AF-screening were mainly driven by increased drug costs and screening costs. In conclusion, across Europe, though the initial cost of screening and more frequent use of oral anti-coagulants will increase the healthcare payers' costs, introducing population screening for AF will result in savings of stroke-related costs.

Brugada Phenotype Following a Cocaine Overdose.

Brugada syndrome is a rare cardiac condition characterized by distinctive electrocardiogram patterns, predisposing individuals to fatal arrhythmias. While primarily linked to a loss-of-function mutation in the SCN5A gene, acquired forms of the syndrome have been associated with various factors, including drug use. We present a case of a 31-year-old female who presented to the emergency department unresponsive following cocaine use and developed type 1 Brugada ECG patterns alongside an incomplete right bundle branch block in V1-V3, ST elevations with biphasic waves, and diffuse repolarization abnormalities with J point deviations while in the intensive care unit. This study aimed to discuss the complexity of managing drug-induced Brugada-like findings and highlights the need for further research into the mechanisms underlying cocaine-induced cardiac effects. We aimed to discuss potential mechanisms for the impact of cocaine as its role as a sodium channel blocker and its potential effects on connexin 43 in the context of Brugada syndrome. This study also reinforced the importance of differentiating between true Brugada syndrome and other similar ECG changes for appropriate care management.

HRV and EEG correlates of well-being using ultra-short, portable, and low-cost measurements.

Wearable electroencephalography (EEG) and electrocardiography (ECG) devices may offer a non-invasive, user-friendly, and cost-effective approach for assessing well-being (WB) in real-world settings. However, challenges remain in dealing with signal artifacts (such as environmental noise and movements) and identifying robust biomarkers. We evaluated the feasibility of using portable hardware to identify potential EEG and heart-rate variability (HRV) correlates of WB. We collected simultaneous ultrashort (2-min) EEG and ECG data from 60 individuals in real-world settings using a wrist ECG electrode connected to a 4-channel wearable EEG headset. These data were processed, assessed for signal quality, and analyzed using the open-source EEGLAB BrainBeats plugin to extract several theory-driven metrics as potential correlates of WB. Namely, the individual alpha frequency (IAF), frontal and posterior alpha asymmetry, and signal entropy for EEG. SDNN, the low/high frequency (LF/HF) ratio, the Poincaré SD1/SD2 ratio, and signal entropy for HRV. We assessed potential associations between these features and the main WB dimensions (hedonic, eudaimonic, global, physical, and social) implementing a pairwise correlation approach, robust Spearman's correlations, and corrections for multiple comparisons. Only eight files showed poor signal quality and were excluded from the analysis. Eudaimonic (psychological) WB was positively correlated with SDNN and the LF/HF ratio. EEG posterior alpha asymmetry was positively correlated with Physical WB (i.e., sleep and pain levels). No relationships were found with the other metrics, or between EEG and HRV metrics. These physiological metrics enable a quick, objective assessment of well-being in real-world settings using scalable, user-friendly tools.

A narrative review on tip navigation and tip location of central venous access devices in the neonate: Intracavitary ECG or real time ultrasound?

The proper location of the tip of a central venous access device plays a crucial role in minimizing the risks potentially associated with its use. Recent guidelines strongly recommend preferring real-time, intra-procedural methods of tip location since they are more accurate, more reliable and more cost-effective than post-procedural methods. Intracavitary electrocardiography and real time ultrasound can both be applied in the neonatal setting, but they offer different advantages or disadvantages depending on the type of central venous access device. Reviewing the evidence currently available about the use of these two methods in neonates, in terms of applicability, feasibility and accuracy, it can be concluded that (a) real time ultrasound is the only acceptable methodology for tip navigation for any central venous access device in neonates, (b) intracavitary electrocardiography is the preferred method of tip location for central catheters inserted by ultrasound-guided cannulation of the internal jugular vein or the brachiocephalic vein, and (c) real time ultrasound is the preferred method of tip location for umbilical venous catheters, epicutaneo-cava catheters, and central catheters inserted by ultrasound-guided cannulation of the common femoral vein.

A Systematic Review on the Effectiveness of Machine Learning in the Detection of Atrial Fibrillation.

Recent endeavors have led to the exploration of Machine Learning (ML) to enhance the detection and accurate diagnosis of heart pathologies. This is due to the growing need to improve efficiency in diagnostics and hasten the process of delivering treatment. Several institutions have actively assessed the possibility of creating algorithms for advancing our understanding of atrial fibrillation (AF), a common form of sustained arrhythmia. This means that artificial intelligence is now being used to analyze electrocardiogram (ECG) data. The data is typically extracted from large patient databases and then subsequently used to train and test the algorithm with the help of neural networks. Machine learning has been used to effectively detect atrial fibrillation with more accuracy than clinical experts, and if applied to clinical practice, it will aid in early diagnosis and management of the condition and thus reduce thromboembolic complications of the disease. In this text, a review of the application of machine learning in the analysis and detection of atrial fibrillation, a comparison of the outcomes (sensitivity, specificity, and accuracy), and the framework and methods of the studies conducted have been presented.

An ECG denoising technique based on AHIN block and gradient difference max loss.

The electrocardiogram (ECG) signal is susceptible to interference from unknown noises during the acquisition process due to their low frequency and amplitude, resulting in the loss of significant information in the signals. Recent advancements in deep learning models have shown promising results in signal processing. However, these models lack robustness against various types of noise and often overlook the gradient difference between denoised and original signals. In this study, we propose a novel deep learning denoising method based on an attention half instance normalization block (AHIN block) and a gradient difference max loss function (GDM Loss). Our approach consists of two stages: firstly, we input the noisy ECG signal to obtain a denoised version; secondly, we reconstruct the denoised signal by fusing preliminary results from the first stage while correcting waveform distortions caused by initial denoising to minimize information loss. Additionally, we introduce a new loss function that considers differences between slopes of the denoised ECG signal and clean ECG signal. To validate our proposed method's effectiveness, extensive experiments were conducted on both our model architecture and loss function compared with other state-of-the-art methods. Results demonstrate that our approach achieves excellent performance in terms of both signal-to-noise ratio (SNR) and root-mean-square error (RMSE). The proposed noise reduction method improves 8.86%, 12.05% and 10.50% respectively under BW, MA and EM noise.

Case report: Electrocardiographic (ECG) recording during the hanging process.

A 48-year-old woman was found hanged in the bathroom. She was wearing a Holter monitor, which was later analysed by a cardiologist. During autopsy, findings congruent with atypical hanging were collected. The ECG showed a 20 s asystole and four minutes later bradycardia, which progressed to a second-degree AV-block Mobitz I, then Mobitz II, then to a third-degree AV-block. Finally, only P waves could be observed, before heart action ceased. This is one of few cases reporting ECG-changes during hanging and might give further insight into the complex pathophysiology of this type of death.

Clinical differences between drug-induced type 1 Brugada pattern and syndrome.

Background: Diagnosis of Brugada syndrome (BrS) may be established by exposing a Type 1 Brugada pattern using a sodium channel blocker. Data on the outcomes of different patient populations with drug-induced Type 1 Brugada pattern are limited. The present study reports on the characteristics and outcome of subjects with ajmaline induced Type 1 Brugada pattern. Methods: A multicenter retrospective study including all consecutive cases of ajmaline-induced Type 1 Brugada pattern from seven centers. Results: A total of 260 patients (69.9% males, mean age 43.4 ± 13.5) were included. Additional characteristics included history of syncope (n = 56, 21.5%), family history of BrS (n = 58, 22.3%) or sudden cardiac death (n = 47, 18.1%) and ventricular fibrillation (n = 3, 1.2%). Patients were divided into those meeting current diagnostic criteria for drug-induced BrS (DIBrS) and compared to the drug-induced Brugada pattern (DIBrECG). Females were significantly overrepresented in the DIBrS group (n = 50, 40% vs. n = 29, 21.5%, p = .001). A significantly higher prevalence of type 2/3 Brugada ECG at baseline was found in the DIBrECG group (n = 108, 80.8% vs. n = 75, 60% in the DIBrS, p = .026). During a median follow up of three (IQR 1.50-5.32) years, a single event of significant arrhythmia occurred in the DIBrS group. Conclusion: Less than half of subjects with ajmaline-induced Brugada pattern met current criteria for BrS. These individuals had very low rate of adverse outcomes during a follow up of 3 years, irrespective of the indication for the test or eligibility for the BrS diagnosis.

Explainable localization of premature ventricular contraction using deep learning-based semantic segmentation of 12-lead electrocardiogram.

Background: Predicting the origin of premature ventricular contraction (PVC) from the preoperative electrocardiogram (ECG) is important for catheter ablation therapies. We propose an explainable method that localizes PVC origin based on the semantic segmentation result of a 12-lead ECG using a deep neural network, considering suitable diagnosis support for clinical application. Methods: The deep learning-based semantic segmentation model was trained using 265 12-lead ECG recordings from 84 patients with frequent PVCs. The model classified each ECG sampling time into four categories: background (BG), sinus rhythm (SR), PVC originating from the left ventricular outflow tract (PVC-L), and PVC originating from the right ventricular outflow tract (PVC-R). Based on the ECG segmentation results, a rule-based algorithm classified ECG recordings into three categories: PVC-L, PVC-R, as well as Neutral, which is a group for the recordings requiring the physician's careful assessment before separating them into PVC-L and PVC-R. The proposed method was evaluated with a public dataset which was used in previous research. Results: The evaluation of the proposed method achieved neutral rate, accuracy, sensitivity, specificity, F1-score, and area under the curve of 0.098, 0.932, 0.963, 0.882, 0.945, and 0.852 on a private dataset, and 0.284, 0.916, 0.912, 0.930, 0.943, and 0.848 on a public dataset, respectively. These quantitative results indicated that the proposed method outperformed almost all previous studies, although a significant number of recordings resulted in requiring the physician's assessment. Conclusions: The feasibility of explainable localization of premature ventricular contraction was demonstrated using deep learning-based semantic segmentation of 12-lead ECG.Clinical trial registration: M26-148-8.

The effectiveness of coronary computed tomography angiography and functional testing for the diagnosis of obstructive coronary artery disease: results from the individual patient data Collaborative Meta-Analysis of Cardiac CT (COME-CCT).

AIM: To determine the effectiveness of functional stress testing and computed tomography angiography (CTA) for diagnosis of obstructive coronary artery disease (CAD). METHODS AND RESULTS: Two-thousand nine-hundred twenty symptomatic stable chest pain patients were included in the international Collaborative Meta-Analysis of Cardiac CT consortium to compare CTA with exercise electrocardiography (exercise-ECG) and single-photon emission computed tomography (SPECT) for diagnosis of CAD defined as ≥ 50% diameter stenosis by invasive coronary angiography (ICA) as reference standard. Generalised linear mixed models were used for calculating the diagnostic accuracy of each diagnostic test including non-diagnostic results as dependent variables in a logistic regression model with random intercepts and slopes. Covariates were the reference standard ICA, the type of diagnostic method, and their interactions. CTA showed significantly better diagnostic performance (p < 0.0001) with a sensitivity of 94.6% (95% CI 92.7-96) and a specificity of 76.3% (72.2-80) compared to exercise-ECG with 54.9% (47.9-61.7) and 60.9% (53.4-66.3), SPECT with 72.9% (65-79.6) and 44.9% (36.8-53.4), respectively. The positive predictive value of CTA was ≥ 50% in patients with a clinical pretest probability of 10% or more while this was the case for ECG and SPECT at pretest probabilities of ≥ 40 and 28%. CTA reliably excluded obstructive CAD with a post-test probability of below 15% in patients with a pretest probability of up to 74%. CONCLUSION: In patients with stable chest pain, CTA is more effective than functional testing for the diagnosis as well as for reliable exclusion of obstructive CAD. CTA should become widely adopted in patients with intermediate pretest probability. SYSTEMATIC REVIEW REGISTRATION: PROSPERO Database for Systematic Reviews-CRD42012002780. CRITICAL RELEVANCE STATEMENT: In symptomatic stable chest pain patients, coronary CTA is more effective than functional testing for diagnosis and reliable exclusion of obstructive CAD in intermediate pretest probability of CAD. KEY POINTS: Coronary computed tomography angiography showed significantly better diagnostic performance (p < 0.0001) for diagnosis of coronary artery disease compared to exercise-ECG and SPECT. The positive predictive value of coronary computed tomography angiography was ≥ 50% in patients with a clinical pretest probability of at least 10%, for ECG ≥ 40%, and for SPECT 28%. Coronary computed tomography angiography reliably excluded obstructive coronary artery disease with a post-test probability of below 15% in patients with a pretest probability of up to 74%.

Insights from assessment for arrhythmia after CoViD-19 vaccination among Indians.

Post-vaccination myocarditis is usually moderate and transient, recovering quickly with conservative treatment. Therefore, it is of interest to assess for arrhythmia after CoViD-19 vaccination among Indians. We looked for ECG abnormalities in a small cohort of 50 participants after 52 weeks after receiving the Oxford/AstraZeneca CoViD-19 vaccination. Data shows that post-vaccination myocarditis is typically mild and transient, with most cases resolving swiftly through conservative management. Thus, it is unlikely that this vaccine will induce severe arrhythmias or life-threatening cardiac events in the general population.

Diagnostic utility of electrocardiogram for screening of cardiac injury on cardiac magnetic resonance in post-hospitalised COVID-19 patients: A prospective multicenter study.

BACKGROUND: The role of ECG in ruling out myocardial complications on cardiac magnetic resonance (CMR) is unclear. We examined the clinical utility of ECG in screening for cardiac abnormalities on CMR among post-hospitalised COVID-19 patients. METHODS: Post-hospitalised patients (n = 212) and age, sex and comorbidity-matched controls (n = 38) underwent CMR and 12­lead ECG in a prospective multicenter follow-up study. Participants were screened for routinely reported ECG abnormalities, including arrhythmia, conduction and R wave abnormalities and ST-T changes (excluding repolarisation intervals). Quantitative repolarisation analyses included corrected QT (QTc), corrected QT dispersion (QTc disp), corrected JT (JTc) and corrected T peak-end (cTPe) intervals. RESULTS: At a median of 5.6 months, patients had a higher burden of ECG abnormalities (72.2% vs controls 42.1%, p = 0.001) and lower LVEF but a comparable cumulative burden of CMR abnormalities than controls. Patients with CMR abnormalities had more ECG abnormalities and longer repolarisation intervals than those with normal CMR and controls (82% vs 69% vs 42%, p < 0.001). Routinely reported ECG abnormalities had poor discriminative ability (area-under-the-receiver-operating curve: AUROC) for abnormal CMR, AUROC 0.56 (95% CI 0.47-0.65), p = 0.185; worse among female than male patients. Adding JTc and QTc disp improved the AUROC to 0.64 (95% CI 0.55-0.74), p = 0.002, the sensitivity of the ECG increased from 81.6% to 98.0%, negative predictive value from 84.7% to 96.3%, negative likelihood ratio from 0.60 to 0.13, and reduced sex-dependence variabilities of ECG diagnostic parameters. CONCLUSION: Post-hospitalised COVID-19 patients have more ECG abnormalities than controls. Normal ECGs, including normal repolarisation intervals, reliably exclude CMR abnormalities in male and female patients.

Assessment of B-type natriuretic peptide as an early predictor of mortality in acutely poisoned patients with cardiotoxicities.

Background: Cardiotoxicity is a major toxic effect induced by several types of drugs. An electrocardiogram is done routinely in cardiovascular drug exposures. Cardiac troponin I (cTnI) is the usual biomarker for diagnosing myocardial injury. B-type natriuretic peptide (BNP) is a well-established predictor of disease state in suspected heart failure. Aim: The study aimed to assess BNP's role as an early predictor of mortality compared with cTnI and ECG changes in acutely poisoned patients with cardiotoxicities. Methodology: This study enrolled 70 patients with acute cardiotoxicity by drugs and toxins known to cause cardiac injury admitted to Tanta University Poison Control Center (TUPCC). Collected data included socio-demographic data, toxicological history, vital signs, ECG changes, Poison Severity Score (PSS), BNP, and cTnI serum levels. Result: Patients were classified as survivors and non-survivors. Significantly more delay time was recorded in non-survivors. Moreover, vital signs were significantly abnormal in non-survivors. There was no statistical significance regarding the initial ECG abnormalities between survivors and non-survivors. BNP and cTnI levels were significantly higher among non-survivors. For mortality prediction, BNP had good predictive power (AUC = 0.841) with 100% sensitivity and 79.7% specificity while cTnI had an acceptable predictive power (AUC = 0.786), with 83.3% sensitivity and 78.1% specificity with insignificant difference between both biomarkers. Conclusion: BNP and cTnI levels can predict mortality in acute cardiotoxicity compared to ECG which has no statistically significant prediction. BNP has a higher discriminatory power than cTnI for the prediction of mortality.

Apical Acute Myocardial Infarction Due to Occluded Posterior Descending Branch of Right Coronary Artery Concomitant With Short Left Anterior Descending Artery: Multi-imaging Modality Assessment.

Regional wall motion abnormality in the left ventricular (LV) apex detected on transthoracic echocardiography is commonly interpreted as the presence of a distal left anterior descending (LAD) artery lesion in clinical practice. Herein, we reported a rare case of apical acute myocardial infarction (AMI) caused by an occluded posterior descending branch of the right coronary artery (RCA), in which the correspondence between coronary arterial anatomy and supplied LV apex was evaluated by multi-imaging modalities. Despite the presence of regional wall motion abnormality in the LV apex, left coronary angiography showed no significant coronary artery diseases. It was of note that LAD terminated before the LV apex. Right coronary angiography showed total occlusion of the posterior descending branch. Cardiac computed tomography (CT) clearly demonstrated that the spontaneously recanalized posterior descending branch extended toward the LV apex. Cardiac magnetic resonance imaging (MRI) clearly revealed regional wall motion abnormality corresponding to myocardial edema in the LV apex. Cardiac CT and MRI were powerful tools in clarifying the correspondence between coronary arterial anatomy and supplied LV apex. Clinicians should be aware that localized apical AMI can occur under the condition of occluded posterior descending branch of RCA concomitant with short LAD.

Direct comparison of whole heart quantifications between different retrospective and prospective gated 4D flow CMR acquisitions.

Introduction: 4D flow cardiovascular magnetic resonance (CMR) is a versatile technique to non-invasively assess cardiovascular hemodynamics. With developing technology, choice in sequences and acquisition parameters is expanding and it is important to assess if data acquired with these different variants can be directly compared, especially when combining datasets within research studies. For example, sequences may allow a choice in gating techniques or be limited to one method, yet there is not a direct comparison investigating how gating selection impacts quantifications of the great vessels, semilunar and atrioventricular valves and ventricles. Thus, this study investigated if quantifications across the heart from contemporary 4D flow sequences are comparable between two commonly used 4D flow sequences reliant on different ECG gating techniques. Methods: Forty participants (33 healthy controls, seven patients with coronary artery disease and abnormal diastolic function) were prospectively recruited into a single-centre observational study to undergo a 3T-CMR exam. Two acquisitions, a k-t GRAPPA 4D flow with prospective gating (4Dprosp) and a modern compressed sensing 4D flow with retrospective gating (4Dretro), were acquired in each participant. Images were analyzed for volumes, flow rates and velocities in the vessels and four valves, and for biventricular kinetic energy and flow components. Data was compared for group differences with paired t-tests and for agreement with Bland-Altman and intraclass correlation (ICC). Results: Measurements primarily occurring during systole of the great vessels, semilunar valves and both left and right ventricles did not differ between acquisition types (p > 0.05 from t-test) and yielded good to excellent agreement (ICC: 0.75-0.99). Similar findings were observed for the majority of parameters dependent on early diastole. However, measurements occurring in late diastole or those reliant on the entire-cardiac cycle such as flow component volumes along with diastolic kinetic energy values were not similar between 4Dprosp and 4Dretro acquisitions resulting in poor agreement (ICC < 0.50). Discussion: Direct comparison of measurements between two different 4D flow acquisitions reliant on different gating methods demonstrated systolic and early diastolic markers across the heart should be compatible when comparing these two 4D flow sequences. On the other hand, late diastolic and intraventricular parameters should be compared with caution.

Cardiac alterations induced by Heloderma horridum horridum venom in rats: An experimental study with ECG analysis using a linear regression algorithm.

Envenomation by reptile venom, particularly from lizards, poses significant health risks and can lead to physiological and cardiovascular changes. The venom of Heloderma horridum horridum, endemic to Colima, Mexico, was tested on Wistar rats. Electrocardiographic (ECG) data were collected pre-treatment and at 5-min intervals for 1 h post-envenomation. A specially designed computational linear regression algorithm (LRA) was used for the segmentation analysis of the ECG data to improve the detection of fiducial points (P, Q, R, S, and T) in ECG waves. Additionally, heart tissue was analyzed for macroscopic and microscopic changes. The results revealed significant electrocardiographic alterations, including pacemaker migration, junctional extrasystoles, and intraventricular conduction aberrations. By applying a linear regression algorithm, the study compensated for noise and anomalies in the isoelectric line in an ECG signal, improving the detection of P and T waves and the QRS complex with an efficiency of 97.5%. Cardiac enzyme evaluation indicated no statistically significant differences between the control and experimental groups. Macroscopic and microscopic examination revealed no apparent signs of damage or inflammatory responses in heart tissues. This study enhances our understanding of the cardiovascular impact of Heloderma venom, suggesting a greater influence on changes in conduction and arrhythmias than on direct cardiac damage to the myocardium.

Atrial Fibrillation Prediction Based on Recurrence Plot and ResNet.

Atrial fibrillation (AF) is the most prevalent form of arrhythmia, with a rising incidence and prevalence worldwide, posing significant implications for public health. In this paper, we introduce an approach that combines the Recurrence Plot (RP) technique and the ResNet architecture to predict AF. Our method involves three main steps: using wavelet filtering to remove noise interference; generating RPs through phase space reconstruction; and employing a multi-level chained residual network for AF prediction. To validate our approach, we established a comprehensive database consisting of electrocardiogram (ECG) recordings from 1008 AF patients and 48,292 Non-AF patients, with a total of 2067 and 93,129 ECGs, respectively. The experimental results demonstrated high levels of prediction precision (90.5%), recall (89.1%), F1 score (89.8%), accuracy (93.4%), and AUC (96%) on our dataset. Moreover, when tested on a publicly available AF dataset (AFPDB), our method achieved even higher prediction precision (94.8%), recall (99.4%), F1 score (97.0%), accuracy (97.0%), and AUC (99.7%). These findings suggest that our proposed method can effectively extract subtle information from ECG signals, leading to highly accurate AF predictions.

Certain investigation on hybrid neural network method for classification of ECG signal with the suitable a FIR filter.

The Electrocardiogram (ECG) records are crucial for predicting heart diseases and evaluating patient's health conditions. ECG signals provide essential peak values that reflect reliable health information. Analyzing ECG signals is a fundamental technique for computerized prediction with advancements in Very Large-Scale Integration (VLSI) technology and significantly impacts in biomedical signal processing. VLSI advancements focus on high-speed circuit functionality while minimizing power consumption and area occupancy. In ECG signal denoising, digital filters like Infinite Impulse Response (IIR) and Finite Impulse Response (FIR) are commonly used. The FIR filters are preferred for their higher-order performance and stability over IIR filters, especially in real-time applications. The Modified FIR (MFIR) blocks were reconstructed using the optimized adder-multiplier block for better noise reduction performance. The MIT-BIT database is used as reference where the noises are filtered by the MFIR based on Optimized Kogge Stone Adder (OKSA). Features are extracted and analyzed using Discrete wavelet transform (DWT) and Cross Correlation (CC). At this modern era, Hybrid methods of Machine Learning (HMLM) methods are preferred because of their combined performance which is better than non-fused methods. The accuracy of the Hybrid Neural Network (HNN) model reached 92.3%, surpassing other models such as Generalized Sequential Neural Networks (GSNN), Artificial Neural Networks (ANN), Support Vector Machine with linear kernel (SVM linear), and Support Vector Machine with Radial Basis Function kernel (SVM RBF) by margins of 3.3%, 5.3%, 23.3%, and 24.3%, respectively. While the precision of the HNN is 91.1%, it was slightly lower than GSNN and ANN but higher than both SVM linear and SVM -RBF. The HNN with various features are incorporated to improve the ECG classification. The accuracy of the HNN is switched to 95.99% when the DWT and CC are combined. Also, it improvises other parameters such as precision 93.88%, recall is 0.94, F1 score is 0.88, Kappa is 0.89, kurtosis is 1.54, skewness is 1.52 and error rate 0.076. These parameters are higher than recently developed models whose algorithms and methods accuracy is more than 90%.

Screening Sportsmen and Sportswomen Over Age 35: The Relevance of an Exercise Electrocardiogram. Data From the SEEPRED Study.

INTRODUCTION: The importance of exercise electrocardiogram (ECG) is still controversial in the prevention of cardiovascular events among sportsmen and sportswomen. The aim of this study was to assess the relevance of exercise ECG as a screening tool to prevent cardiovascular events when any cardiovascular disease (CVD) risk factors are present. METHODS: The study included leisure time asymptomatic sportsmen and sportswomen over age 35 evaluated from 2011 to 2016 at the University Hospital of Saint-Etienne (France). Major adverse cardiovascular events (MACE) and atrial fibrillation were collected at 3 years. RESULTS: Of the cohort of 2457 sportsmen and sportswomen (mean age 50.2 ± 9.4 years), 50 (2%) had a high-risk SCORE2. A total of 256 exercise ECGs (10%) were defined as positive, most of them due to silent myocardial ischemia (SMI) (n = 196; 8%). These 196 SMI cases led to 33 coronary angiograms (1%), which revealed 23 significant coronary stenoses requiring revascularization. In multivariate logistic regression analysis, having at least two CVD risk factors was independently associated with (1) positive exercise ECG (OR = 1.80 [95% CI: 1.29-2.52], p = 0.0006), with (2) suspected SMI (OR = 2.57 [95% CI: 1.10-6.02], p = 0.0304), with (3) confirmed SMI (OR = 8.20 [95% CI: 3.46-19.46], p < 0.0001) and with (4) cardiovascular events (MACE or atrial fibrillation) (OR = 6.95 [95% CI: 3.49-13.81], p < 0.0001) at 3 years (median). CONCLUSIONS: The study supports the European recommendations for the use of exercise ECG in evaluation of asymptomatic leisure time sportsmen over age 35. Having at least two CVD risk factors was the best predictor for presence of coronary artery stenosis that may increase the risk for adverse events. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT06024863.

Unusual Presentation of De Winter's Sign Due to Bezold-Jarisch Reflex in a Patient With Severe Aortic Valve Stenosis.

The de Winter electrocardiogram (ECG) pattern, marked by upsloping ST depression in leads V2-V6, ST elevation in lead aVR, and tall symmetric T waves, typically indicates left anterior descending artery (LAD) occlusion. Traditionally linked to LAD occlusion, it is rare in severe aortic stenosis and the Bezold-Jarisch reflex (BJR). We report an 83-year-old man with severe aortic stenosis who developed hypotension due to bleeding and exhibited the de Winter ECG pattern. This case highlights how severe aortic stenosis and BJR can lead to significant hemodynamic instability and ischemic ECG changes, resolving after hemodynamic stabilization.