Journal of electrocardiology最新文献

{"title":"A dragon in a barn","authors":"José Nunes de Alencar MD","doi":"10.1016/j.jelectrocard.2025.153904","DOIUrl":"10.1016/j.jelectrocard.2025.153904","url":null,"abstract":"","PeriodicalId":15606,"journal":{"name":"Journal of electrocardiology","volume":"90 ","pages":"Article 153904"},"PeriodicalIF":1.3,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143529687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ventricular preexcitation and narrow QRS tachycardia. What is the diagnosis?","authors":"Utkarsh Kohli MD , Jack R. Stines MD","doi":"10.1016/j.jelectrocard.2025.153906","DOIUrl":"10.1016/j.jelectrocard.2025.153906","url":null,"abstract":"","PeriodicalId":15606,"journal":{"name":"Journal of electrocardiology","volume":"90 ","pages":"Article 153906"},"PeriodicalIF":1.3,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143527152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Challenges in localisation of culprit artery in STEMI","authors":"Shivam Goel, Rajiv Narang, Atit A. Gawalkar","doi":"10.1016/j.jelectrocard.2025.153901","DOIUrl":"10.1016/j.jelectrocard.2025.153901","url":null,"abstract":"","PeriodicalId":15606,"journal":{"name":"Journal of electrocardiology","volume":"90 ","pages":"Article 153901"},"PeriodicalIF":1.3,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143509721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A practical review of generative AI in cardiac electrophysiology medical education","authors":"Shaun A. Hanycz ,&nbsp;Pavel Antiperovitch","doi":"10.1016/j.jelectrocard.2025.153903","DOIUrl":"10.1016/j.jelectrocard.2025.153903","url":null,"abstract":"<div><div>Generative artificial intelligence (AI) is a component of artificial intelligence that creates synthetic multi-modal output in the form of text, images, and audio. Multiple approaches have been implemented into teaching surface ECG interpretation. However, learner performance remains poor. Generative AI in the form of Generative Adversarial Networks (GANs) is a novel AI model that has the potential to augment trainee ECG interpretation via creation of synthetic ECGs and anatomical depiction of conduction defects. Generative AI may be implemented in medical education to customize trainee surface ECG interpretation to improve learning and retention.</div></div>","PeriodicalId":15606,"journal":{"name":"Journal of electrocardiology","volume":"90 ","pages":"Article 153903"},"PeriodicalIF":1.3,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143520130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Expect the unexpected","authors":"Pierre Groussin,&nbsp;Melvyn Dezecot,&nbsp;Philippe Mabo,&nbsp;Raphaël P. Martins","doi":"10.1016/j.jelectrocard.2025.153902","DOIUrl":"10.1016/j.jelectrocard.2025.153902","url":null,"abstract":"","PeriodicalId":15606,"journal":{"name":"Journal of electrocardiology","volume":"90 ","pages":"Article 153902"},"PeriodicalIF":1.3,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143487427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High precision ECG digitization using artificial intelligence","authors":"Anthony Demolder MD, PhD ,&nbsp;Viera Kresnakova MSc, PhD ,&nbsp;Michal Hojcka MSc, PhD ,&nbsp;Vladimir Boza MSc, PhD ,&nbsp;Andrej Iring MSc ,&nbsp;Adam Rafajdus MEng ,&nbsp;Simon Rovder MInf ,&nbsp;Timotej Palus MSc ,&nbsp;Martin Herman ,&nbsp;Felix Bauer BSc ,&nbsp;Viktor Jurasek ,&nbsp;Robert Hatala MD, PhD ,&nbsp;Jozef Bartunek MD, PhD ,&nbsp;Boris Vavrik MSc ,&nbsp;Robert Herman MD, PhD","doi":"10.1016/j.jelectrocard.2025.153900","DOIUrl":"10.1016/j.jelectrocard.2025.153900","url":null,"abstract":"<div><h3>Background</h3><div>Digitization of paper-based electrocardiograms (ECGs) enables long-term preservation, fast transmission, and advanced analysis. Traditional methods for digitizing ECGs face significant challenges, particularly in real-world scenarios with varying image quality. State-of-the-art solutions often require manual input and are limited by their dependence on high-quality scans and standardized layouts.</div></div><div><h3>Methods</h3><div>This study introduces a fully automated, deep learning-based approach for high precision ECG digitization. In the normalization phase, a standardized grid structure is detected, and image distortions are corrected. Next, the reconstruction phase uses deep learning techniques to extract and digitize the leads, followed by post-processing to refine the signal. This approach was evaluated using the publicly available PMcardio ECG Image Database (PM-ECG-ID), comprising 6000 ECG images reflecting diverse real-world scenarios and smartphone-based image acquisitions. Performance was assessed using Pearson's correlation coefficient (PCC), root mean squared error (RMSE), and signal-to-noise ratio (SNR).</div></div><div><h3>Results</h3><div>The ECG digitization solution demonstrated an average PCC consistently exceeding 0.91 across all leads, SNR above 12.5 dB and RMSE below 0.10 mV. The time to ECG digitization was consistently less than 7 s. The average failure rate was 6.62 % across leads, with most failures occurring under extreme conditions such as severe blurring or significant image degradation. The solution maintained robust performance even under challenging scenarios, such as low-resolution images, distorted grids, and overlapping signals.</div></div><div><h3>Conclusion</h3><div>Our deep learning-based approach for ECG digitization delivers high-precision signals, effectively addressing real-world challenges. This fully automated method enhances the accessibility and utility of ECG data by enabling convenient digitization via smartphones, unlocking advanced AI-driven analysis.</div></div>","PeriodicalId":15606,"journal":{"name":"Journal of electrocardiology","volume":"90 ","pages":"Article 153900"},"PeriodicalIF":1.3,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143487426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Electrocardiographic abnormalities in chagasic patients compared to the general population: A systematic review with updated meta-analysis","authors":"Bruno Felipe Santos de Oliveira ,&nbsp;Cárita Victória Carvalho de Santana ,&nbsp;Luiz Filippe Vago Pereira,&nbsp;Gabriele Eliza Assis,&nbsp;Ana Beatriz Cazé,&nbsp;Jackson Pedro Barros-Pereira,&nbsp;Rafaela Góes Bispo,&nbsp;Roque Aras Júnior","doi":"10.1016/j.jelectrocard.2025.153893","DOIUrl":"10.1016/j.jelectrocard.2025.153893","url":null,"abstract":"<div><h3>Introduction</h3><div>Chagas disease (CD) remains a serious public health issue. It can progress from the acute phase to the chronic phase, manifesting as a cardiomyopathy with some typical electrocardiogram abnormality.</div></div><div><h3>Objectives</h3><div>This systematic review with meta-analysis aims to evaluate the overall prevalence of cardiac arrhythmias in patients with CD and discriminative values to clinical and epidemiological variables for specific arrhythmias.</div></div><div><h3>Methods</h3><div>Articles that included the prevalence of electrocardiographic abnormalities in CD compared to other groups were searched in the following databases: MEDLINE/PubMed, Embase and Cochrane Library. The results obtained were pooled in meta-analyses</div></div><div><h3>Results</h3><div>A total of 322.608 participants from 88 studies were included. The meta-analyses with single electrocardiographic changes showed a significant association with positive CD serology compared to non-chagasic individuals, (OR: 2.86 95 % CI 2.53; 3.23; I² = 85 %). Significant associations were found to complete right bundle branch block [CRBBB], CRBBB + left anterior fascicular block [LAFB], first-degree atrioventricular block [AVB], incomplete right bundle branch block [IRBBB], second-degree AVB, LAFB, supraventricular ectopic beats, third-degree AVB, ventricular ectopic beats and atrial fibrillation or flutter. For the presence of a single electrocardiographic alterations, the discriminative capacity variables showed: sensitivity 0.511 (0.505, 0.517), specificity 0.622 (0.620, 0.624), Positive Predictive Value (PPV) 0.101 (0.099, 0.103), Negative Predictive Value (NPV) 0.939 (0.937, 0.940), Positive Likelihood Ratio (PLR) 1.352 (1.334, 1.370) and Negative Likelihood Ratio (NLR) 0.786 (0.776, 0.797).</div></div><div><h3>Conclusion</h3><div>Our findings show a significant association between CD and several electrocardiographic abnormalities, which highlights the need for ongoing surveillance and targeted therapeutic interventions to improve clinical outcomes for patients with chagasic cardiomyopathy.</div></div>","PeriodicalId":15606,"journal":{"name":"Journal of electrocardiology","volume":"90 ","pages":"Article 153893"},"PeriodicalIF":1.3,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143548429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Narrow complex tachycardia with cycle length alternans","authors":"Swasthi S. Kumar MD,&nbsp;Sudipta Mondal MD, DM,&nbsp;Jyothi Vijay MD, DM,&nbsp;Narayanan Namboodiri MD, DM","doi":"10.1016/j.jelectrocard.2025.153898","DOIUrl":"10.1016/j.jelectrocard.2025.153898","url":null,"abstract":"<div><div>An elderly lady without any comorbidities, presented with paroxysmal palpitations and was documented to have an adenosine-responsive narrow complex tachycardia (NCT). Baseline electrocardiogram (ECG) showed normal sinus rhythm with no preexcitation. Tachycardia ECG showed a regularly irregular short RP NCT at the rate of around 150/min with alternating cycle lengths of 320 ms and 360 ms.</div><div>What is the likely diagnosis?</div></div>","PeriodicalId":15606,"journal":{"name":"Journal of electrocardiology","volume":"90 ","pages":"Article 153898"},"PeriodicalIF":1.3,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143548430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ECG based human activity-specific cardiac pattern detection using machine-learning and deep-learning models","authors":"Kusum Tara ,&nbsp;Md Hasibul Islam ,&nbsp;Takenao Sugi","doi":"10.1016/j.jelectrocard.2025.153899","DOIUrl":"10.1016/j.jelectrocard.2025.153899","url":null,"abstract":"<div><div>Monitoring cardiac patterns under relaxed, cognitive, and physical stressors is crucial for identifying early signs of cardiac stress or abnormalities. This study analyzes ECG signals recorded during diverse activities such as sitting, math-reasoning, walking, jogging, and hand-biking, simulating these stressors. A deep-learning image-based convolutional neural network (CNN) model utilizing bispectrum-based contours was proposed to classify cardiac patterns by capturing the non-linear dynamics of cardiac behavior. Two approaches were employed: a feature-based random forest (RF) machine-learning model using time-domain, frequency-domain, and statistical features, and an image-based CNN model utilizing Continuous wavelet transform (CWT) based scalograms and bispectrum-based contours. Feature selection techniques, including Pearson correlation and least absolute shrinkage and selection operator (LASSO) regularization, were used to identify significant features for RF model input. RF model achieved 96.80 % accuracy and an F1-score of 92.22 %. CNN model outperformed it, achieving 98.44 % accuracy and a 96.11 % F1-score with CWT scalograms, and 99.16 % accuracy and a 97.89 % F1-score with bispectrum-based contours. Key features such as stress index and SNS-to-PNS ratio increased with cognitive and physical stressors, highlighting autonomic responses. Based on the results of analysis, the proposed CNN model with bispectrum-based contours demonstrated superior accuracy and reliability, showcasing significant potential for monitoring cardiac functions across diverse activities.</div></div>","PeriodicalId":15606,"journal":{"name":"Journal of electrocardiology","volume":"90 ","pages":"Article 153899"},"PeriodicalIF":1.3,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143453695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Irregular rhythm with wide-QRS complexes and repolarization abnormalities in the emergency department: A nightmare ECG","authors":"Antonino Micari M.D,&nbsp;Giulia Cavolina M.D,&nbsp;Pasquale Crea M.D. Ph.D","doi":"10.1016/j.jelectrocard.2025.153896","DOIUrl":"10.1016/j.jelectrocard.2025.153896","url":null,"abstract":"<div><div>We present the ECG findings of a 49-year-old woman recorded in the emergency department, which reveal a wide-QRS, irregular rhythm. The ECG shows a broad terminal wave in the right precordial leads, along with a convex ST segment elevation, and is associated with a right axis deviation. While the initial presentation may appear complex, several diagnostic possibilities must be considered, as each could suggest a distinct management for this clinical case.</div></div>","PeriodicalId":15606,"journal":{"name":"Journal of electrocardiology","volume":"90 ","pages":"Article 153896"},"PeriodicalIF":1.3,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143453696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}