Journal of electrocardiology最新文献

{"title":"Bridging ECG learning with emerging technologies: Advancing clinical excellence","authors":"Anthony H. Kashou ,&nbsp;Peter A. Noseworthy ,&nbsp;Nandan S. Anavekar ,&nbsp;Ian Rowlandson ,&nbsp;Adam M. May","doi":"10.1016/j.jelectrocard.2024.153765","DOIUrl":"10.1016/j.jelectrocard.2024.153765","url":null,"abstract":"<div><p>As ECG technology rapidly evolves to improve patient care, accurate ECG interpretation will continue to be foundational for maintaining high clinical standards. Recent studies have exposed significant educational gaps, with many healthcare professionals lacking sufficient training and proficiency. Furthermore, integrating new software and hardware ECG technologies poses challenges about potential knowledge and skill erosion. This underscores the need for clinicians who are adept at integrating clinical expertise with technological proficiency. It also highlights the need for innovative solutions to enhance ECG interpretation among healthcare professionals in this rapidly evolving environment. This work explores the importance of aligning ECG education with technological advancements and proposes how this synergy could advance patient care in the future.</p></div>","PeriodicalId":15606,"journal":{"name":"Journal of electrocardiology","volume":"86 ","pages":"Article 153765"},"PeriodicalIF":1.3,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141847405","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":"Brugada syndrome in a patient with AKAP9 mutation: Case report and review of the literature","authors":"Dongli Zhou ,&nbsp;Min Cheng","doi":"10.1016/j.jelectrocard.2024.153763","DOIUrl":"10.1016/j.jelectrocard.2024.153763","url":null,"abstract":"<div><p>Brugada syndrome (BrS) is a rare autosomal dominant inherited channel disorder characterized by a specific electrocardiographic pattern of right precordial ST-segment elevation. Clinically, patients may experience polymorphic ventricular tachycardia and ventricular fibrillation, leading to recurrent syncope and sudden cardiac death (SCD) in the absence of structural cardiomyopathy. The A-kinase anchor protein 9 (<em>AKAP9</em>) gene, located on chromosome 7, encodes the <em>AKAP9</em> protein, which plays a crucial role in regulating the phosphorylation of slowly activating delayed rectifier potassium channels (IKs). Here, we present a rare case of BrS associated with an insertion mutation in <em>AKAP9</em>, resulting in a frameshift mutation.</p></div>","PeriodicalId":15606,"journal":{"name":"Journal of electrocardiology","volume":"86 ","pages":"Article 153763"},"PeriodicalIF":1.3,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0022073624002334/pdfft?md5=8542c68933d9345652dabb761fbf0203&pid=1-s2.0-S0022073624002334-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141843623","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":"An ECG denoising technique based on AHIN block and gradient difference max loss","authors":"Ruixia Liu ,&nbsp;Huichen Hu ,&nbsp;Shuaishuai Zhang ,&nbsp;Yanjun Deng ,&nbsp;Zhaoyang Liu ,&nbsp;Yongjian Chen ,&nbsp;Zhe Chen","doi":"10.1016/j.jelectrocard.2024.153761","DOIUrl":"10.1016/j.jelectrocard.2024.153761","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":15606,"journal":{"name":"Journal of electrocardiology","volume":"86 ","pages":"Article 153761"},"PeriodicalIF":1.3,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141852683","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":"Simulating left atrial arrhythmias with an interactive N-body model","authors":"Bryant Wyatt ,&nbsp;Gavin McIntosh ,&nbsp;Avery Campbell ,&nbsp;Melanie Little ,&nbsp;Leah Rogers ,&nbsp;Brandon Wyatt","doi":"10.1016/j.jelectrocard.2024.153762","DOIUrl":"10.1016/j.jelectrocard.2024.153762","url":null,"abstract":"<div><h3>Background</h3><p>Heart disease and strokes are leading global killers. While atrial arrhythmias are not deadly by themselves, they can disrupt blood flow in the heart, causing blood clots. These clots can travel to the brain, causing strokes, or to the coronary arteries, causing heart attacks. Additionally, prolonged periods of elevated heart rates can lead to structural and functional changes in the heart, ultimately leading to heart failure if untreated. The left atrium, with its more complex topology, is the primary site for complex arrhythmias. Much remains unknown about the causes of these arrhythmias, and computer modeling is employed to study them.</p></div><div><h3>Methods</h3><p>We use N-body modeling techniques and parallel computing to build an interactive model of the left atrium. Through user input, individual muscle attributes can be adjusted, and ectopic events can be placed to induce arrhythmias in the model. Users can test ablation scenarios to determine the most effective way to eliminate these arrhythmias.</p></div><div><h3>Results</h3><p>We set up muscle conditions that either spontaneously generate common arrhythmias or, with a properly timed and located ectopic event, induce an arrhythmia. These arrhythmias were successfully eliminated with simulated ablation.</p></div><div><h3>Conclusions</h3><p>We believe the model could be useful to doctors, researchers, and medical students studying left atrial arrhythmias.</p></div>","PeriodicalId":15606,"journal":{"name":"Journal of electrocardiology","volume":"86 ","pages":"Article 153762"},"PeriodicalIF":1.3,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141766230","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":"Prognostic value of the electrocardiogram in patients with bicuspid aortic valve disease","authors":"Paul M. Hendriks ,&nbsp;Zoë A. Keuning ,&nbsp;Jan A. Kors ,&nbsp;Allard T. van den Hoven ,&nbsp;Laurie W. Geenen ,&nbsp;Jannet A. Eindhoven ,&nbsp;Vivan J.M. Baggen ,&nbsp;Judith A.A.E. Cuypers ,&nbsp;Robert M. Kauling ,&nbsp;Jolien W. Roos-Hesselink ,&nbsp;Annemien E. van den Bosch","doi":"10.1016/j.jelectrocard.2024.153760","DOIUrl":"10.1016/j.jelectrocard.2024.153760","url":null,"abstract":"<div><h3>Background</h3><p>Identifying bicuspid aortic valve (BAV) patients at risk for cardiac events remains challenging and the role of the electrocardiogram (ECG) has not yet been described. Therefore, this study aims to describe ECG parameters in BAV patients, and investigate their prognostic value.</p></div><div><h3>Methods</h3><p>In this single-center prospective study patients with BAV without a prior aortic valve replacement (AVR) were included. Transthoracic echocardiogram and 12‑lead resting-ECG were obtained. Associations between ECG parameters and the composite endpoint of all-cause mortality and AVR were assessed using Cox-proportional hazard analysis.</p></div><div><h3>Results</h3><p>120 patients with BAV were included (median age 30 years, 61% male). Median aortic jet velocity was 2.4 m/s [IQR: 1.7–3.4] and 5 patients (4%) had severe aortic regurgitation. All patients were in sinus rhythm. Any ECG abnormality was present in 57 patients (48%). Median PR-interval was 156 [IQR: 138–170] msec. A deviating QRS axis was found in 17 patients (14%) and Cornell criteria for LVH were fulfilled in 20 patients (17%). Repolarization abnormalities were present in 12 patients (10%). Median follow-up duration was 7.0 [6.3–9.8] years, during which 23 patients underwent AVR and 2 patients died. After adjusting for age, a longer PR-interval was associated with worse intervention-free survival (HR 1.02, 95% CI: 1.01–1.04).</p></div><div><h3>Conclusion</h3><p>Almost half of the patients with BAV had abnormalities on their ECG. Moreover, the PR-interval may be an interesting prognostic marker for intervention-free survival in BAV patients.</p></div>","PeriodicalId":15606,"journal":{"name":"Journal of electrocardiology","volume":"86 ","pages":"Article 153760"},"PeriodicalIF":1.3,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0022073624002309/pdfft?md5=1390aba17af0b5c5e675bb174af2803b&pid=1-s2.0-S0022073624002309-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141852268","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":"From sleep patterns to heart rhythm: Predicting atrial fibrillation from overnight polysomnograms","authors":"Zuzana Koscova ,&nbsp;Ali Bahrami Rad ,&nbsp;Samaneh Nasiri ,&nbsp;Matthew A. Reyna ,&nbsp;Reza Sameni ,&nbsp;Lynn M. Trotti ,&nbsp;Haoqi Sun ,&nbsp;Niels Turley ,&nbsp;Katie L. Stone ,&nbsp;Robert J. Thomas ,&nbsp;Emmanuel Mignot ,&nbsp;Brandon Westover ,&nbsp;Gari D. Clifford","doi":"10.1016/j.jelectrocard.2024.153759","DOIUrl":"10.1016/j.jelectrocard.2024.153759","url":null,"abstract":"<div><h3>Background</h3><p>Atrial fibrillation (AF) is often asymptomatic and thus under-observed. Given the high risks of stroke and heart failure among patients with AF, early prediction and effective management are crucial. Given the prevalence of obstructive sleep apnea among AF patients, electrocardiogram (ECG) analysis from polysomnography (PSG) offers a unique opportunity for early AF prediction. Our aim is to identify individuals at high risk of AF development from single‑lead ECGs during standard PSG.</p></div><div><h3>Methods</h3><p>We analyzed 18,782 single‑lead ECG recordings from 13,609 subjects undergoing PSG at the Massachusetts General Hospital sleep laboratory. AF presence was identified using ICD-9/10 codes. The dataset included 15,913 recordings without AF history and 2054 recordings from patients diagnosed with AF between one month to fifteen years post-PSG. Data were partitioned into training, validation, and test cohorts ensuring that individual patients remained exclusive to each cohort. The test set was held out during the training process.</p><p>We employed two different methods for feature extraction to build a final model for AF prediction: Extraction of hand-crafted ECG features and a deep learning method. For extraction of ECG-hand-crafted features, recordings were split into 30-s windows, and those with a signal quality index (SQI) below 0.95 were discarded. From each remaining window, 150 features were extracted from the time, frequency, time-frequency domains, and phase-space reconstructions of the ECG. A compilation of 12 statistical features summarized these window-specific features per recording, resulting in 1800 features (12 × 150).</p><p>A pre-trained deep neural network from the PhysioNet Challenge 2021 was updated using transfer learning to discriminate recordings with and without AF. The model processed PSG ECGs in 16-s windows to generate AF probabilities, from which 13 statistical features were extracted. Combining 1800 features from feature extraction with 13 from the deep learning model, we performed a feature selection and subsequently trained a shallow neural network to predict future AF and evaluated its performance on the test cohort.</p></div><div><h3>Results</h3><p>On the test set, our model exhibited sensitivity, specificity, and precision of 0.67, 0.81, and 0.3, respectively, for AF prediction. Survival analysis revealed a hazard ratio of 8.36 (<em>p</em>-value: 1.93 × 10⁻⁵²) for AF outcomes using the log-rank test.</p></div><div><h3>Conclusions</h3><p>Our proposed ECG analysis method, utilizing overnight PSG data, shows promise in AF prediction despite modest precision, suggesting false positives. This approach could enable low-cost screening and proactive treatment for high-risk patients. Refinements, including additional physiological parameters, may reduce false positives, enhancing clinical utility and accuracy.</p></div>","PeriodicalId":15606,"journal":{"name":"Journal of electrocardiology","volume":"86 ","pages":"Article 153759"},"PeriodicalIF":1.3,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141788162","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":"Inferior ST elevation myocardial infarction with ST elevation in V1 and V6","authors":"Yochai Birnbaum MD ,&nbsp;Kjell Nikus MD","doi":"10.1016/j.jelectrocard.2024.07.002","DOIUrl":"10.1016/j.jelectrocard.2024.07.002","url":null,"abstract":"","PeriodicalId":15606,"journal":{"name":"Journal of electrocardiology","volume":"86 ","pages":"Article 153758"},"PeriodicalIF":1.3,"publicationDate":"2024-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141701837","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":"Artificial intelligence, the challenge of maintaining an active role","authors":"Ricardo Lopez Santi ,&nbsp;Shyla Gupta ,&nbsp;Adrian Baranchuk","doi":"10.1016/j.jelectrocard.2024.07.001","DOIUrl":"10.1016/j.jelectrocard.2024.07.001","url":null,"abstract":"","PeriodicalId":15606,"journal":{"name":"Journal of electrocardiology","volume":"86 ","pages":"Article 153757"},"PeriodicalIF":1.3,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141703347","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":"Endocardial and epicardial biventricular mapping of ventricular fibrillation","authors":"Elena Tolkacheva","doi":"10.1016/j.jelectrocard.2024.06.027","DOIUrl":"10.1016/j.jelectrocard.2024.06.027","url":null,"abstract":"","PeriodicalId":15606,"journal":{"name":"Journal of electrocardiology","volume":"85 ","pages":"Page 14"},"PeriodicalIF":1.3,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141950679","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":"Deep learning model-enabled electrocardiogram to localize premature ventricular contractions in patients referred for catheter ablation","authors":"Abhishek Deshmukh,&nbsp;Tiffany Woelber","doi":"10.1016/j.jelectrocard.2024.06.010","DOIUrl":"10.1016/j.jelectrocard.2024.06.010","url":null,"abstract":"","PeriodicalId":15606,"journal":{"name":"Journal of electrocardiology","volume":"85 ","pages":"Page 4"},"PeriodicalIF":1.3,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141952016","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}