Journal of electrocardiology最新文献

{"title":"Mortality in Modena","authors":"Mario Jorge Mc Loughlin MD","doi":"10.1016/j.jelectrocard.2024.153852","DOIUrl":"10.1016/j.jelectrocard.2024.153852","url":null,"abstract":"","PeriodicalId":15606,"journal":{"name":"Journal of electrocardiology","volume":"88 ","pages":"Article 153852"},"PeriodicalIF":1.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142885840","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":"An ECG-based machine-learning approach for mortality risk assessment in a large European population","authors":"Martina Doneda ,&nbsp;Ettore Lanzarone ,&nbsp;Claudio Giberti ,&nbsp;Cecilia Vernia ,&nbsp;Andi Vjerdha ,&nbsp;Federico Silipo ,&nbsp;Paolo Giovanardi","doi":"10.1016/j.jelectrocard.2024.153850","DOIUrl":"10.1016/j.jelectrocard.2024.153850","url":null,"abstract":"<div><h3>Aims</h3><div>Through a simple machine learning approach, we aimed to assess the risk of all-cause mortality after 5 years in a European population, based on electrocardiogram (ECG) parameters, age, and sex.</div></div><div><h3>Methods</h3><div>The study included patients between 40 and 90 years old who underwent ECG recording between January 2008 and October 2022 in the metropolitan area of Modena, Italy. Exclusion criteria established a patient cohort without severe ECG abnormalities, namely, tachyarrhythmias, bradyarrhythmias, Wolff-Parkinson-White syndrome, second- or third- degree AV block, bundle-branch blocks, more than three premature beats, poor signal quality, and presence of pacemakers and implantable cardioverter- defibrillators. Mortality was assessed using a set of logistic regression models, differentiated by age group, to which the Akaike Information Criterion was applied. Model fitting was evaluated using confusion matrix-related performance metrics, the area under the receiver operating characteristic (ROC) curve (AUC), and the predictive significance against the no-information rate (NIR).</div></div><div><h3>Results</h3><div>53692 patients were enrolled, of whom 14353 (26.73 %) died within 5 years of ECG registration. The logistic regression model distinguished between those who died and those who survived based on the predicted mortality probability for all age groups, obtaining a significant difference between the predicted mortality and the NIR in 14 of the 55 age groups. Good accuracy and performance metrics were observed, resulting in an average AUC of 0.779.</div></div><div><h3>Conclusions</h3><div>The proposed model showed a good predictive performance in patients without severe ECG abnormalities. Therefore, this study highlights the potential of ECGs as prognostic rather than diagnostic tools.</div></div>","PeriodicalId":15606,"journal":{"name":"Journal of electrocardiology","volume":"88 ","pages":"Article 153850"},"PeriodicalIF":1.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142822274","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":"MrSeNet: Electrocardiogram signal denoising based on multi-resolution residual attention network","authors":"Zhen Wang ,&nbsp;Hanshuang Xie ,&nbsp;Yamin Liu ,&nbsp;Huaiyu Zhu ,&nbsp;Hongpo Zhang ,&nbsp;Zongmin Wang ,&nbsp;Yun Pan","doi":"10.1016/j.jelectrocard.2024.153858","DOIUrl":"10.1016/j.jelectrocard.2024.153858","url":null,"abstract":"<div><div>Electrocardiography (ECG) is a widely used, non-invasive, and cost-effective diagnostic method that plays a crucial role in the early detection and management of cardiac conditions. However, the ECG signal is easily disrupted by various noise signals in the real world, leading to a decrease in signal quality and potentially compromising accurate clinical interpretation. With the goal of reducing noise in ECG signals, this research proposes an end-to-end multi-resolution deep learning network with attention mechanism, namely the MrSeNet to perform effective denoising of ECG data. Our MrSeNet fuses features at different scales for effective denoising with the squeeze-and-excitation module to enhance the features of the ECG signal channel. CPSC2018 database and the MIT-BIH database were used to verify the validity of the model by adding different intensity noises based on NSTDB. Using Pearson correlation coefficient, signal-to-noise ratio, and root mean square error performance evaluation model, the experimental results show that MrSeNet performs better than the traditional method, the model can achieve a good denoising effect to different degrees of noise signal data, and has a good future application prospect.</div></div>","PeriodicalId":15606,"journal":{"name":"Journal of electrocardiology","volume":"89 ","pages":"Article 153858"},"PeriodicalIF":1.3,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142915048","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 marker of ventricular action potential triangulation (the simulation study)","authors":"Natalia V. Arteyeva PhD ,&nbsp;Oleg E. Osadchii MD, PhD","doi":"10.1016/j.jelectrocard.2024.153857","DOIUrl":"10.1016/j.jelectrocard.2024.153857","url":null,"abstract":"<div><h3>Background</h3><div>The aim of this simulation was to examine the utility of a novel ECG-based index of cardiac action potential (AP) triangulation, the Tstart-to-Tpeak (TsTp) interval-to-JTstart (JTs) interval ratio, for assessment of changes in AP profile imposed through variations in the duration of the plateau phase and the phase 3 repolarization.</div></div><div><h3>Methods</h3><div>ECGs were simulated using a realistic rabbit model based on experimental data. The AP plateau was measured at APD30, and the phase 3 was assessed as APD90-to-APD30 difference (AP durations at 90 % and 30 % repolarization, respectively). AP triangulation was quantified as (APD90-APD30)-to-APD30 ratio. The baseline durations of AP plateau and phase 3 were modified through (1) 50 % shortening, (2) 50 % to 100 % lengthening, and (3) concurrent, non-uniform lengthening in both the AP plateau and phase 3, replicating the long QT syndrome type 2 (LQT2) and type 3 (LQT3) models.</div></div><div><h3>Results</h3><div>When simulating the isolated changes in either the AP plateau or the phase 3 durations, the (APD90-APD30)-to-APD30 ratios were the same for all model cells. The TsTp/JTs ratios calculated from the simulated ECGs closely approximated the (APD90-APD30)-to-APD30 ratios, despite the lead-to‑lead variability in TsTp and JTs intervals. When simulating the concurrent changes in AP plateau and the phase 3, the (APD90-APD30)-to-APD30 ratios were variable in cells from different layers across the ventricular wall. Nevertheless, the TsTp/JTs ratios were found to be within the range of the minimum-to-maximum values for the (APD90-APD30)-to-APD30 ratio.</div></div><div><h3>Conclusions</h3><div>The TsTp interval-to-JTs interval ratio can serve as an electrocardiographic marker of cardiac AP triangulation.</div></div>","PeriodicalId":15606,"journal":{"name":"Journal of electrocardiology","volume":"89 ","pages":"Article 153857"},"PeriodicalIF":1.3,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142925990","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":"Acute myocardial infarction with ST elevation isolated to lead III (and aVR)","authors":"Yochai Birnbaum ,&nbsp;Kjell Nikus","doi":"10.1016/j.jelectrocard.2024.153855","DOIUrl":"10.1016/j.jelectrocard.2024.153855","url":null,"abstract":"","PeriodicalId":15606,"journal":{"name":"Journal of electrocardiology","volume":"89 ","pages":"Article 153855"},"PeriodicalIF":1.3,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142927346","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":"Case report: Improved detection of electrocardiographic abnormalities using precordial bipolar leads","authors":"Mario J. Mc Loughlin MD","doi":"10.1016/j.jelectrocard.2024.153856","DOIUrl":"10.1016/j.jelectrocard.2024.153856","url":null,"abstract":"","PeriodicalId":15606,"journal":{"name":"Journal of electrocardiology","volume":"89 ","pages":"Article 153856"},"PeriodicalIF":1.3,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142909664","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":"Real-world evidence for passive video-based cardiac monitoring from smartphones used by patients with a history of AF","authors":"J.-P. Couderc,&nbsp;A. Page,&nbsp;M. Lutz,&nbsp;T. Pham,&nbsp;G.R. Tsouri,&nbsp;B. Hall","doi":"10.1016/j.jelectrocard.2024.153860","DOIUrl":"10.1016/j.jelectrocard.2024.153860","url":null,"abstract":"<div><div>Passive cardiac monitoring has become synonymous with wearable technologies, necessitating patients to incorporate new devices into their daily routines. While this requirement may not be a burden for many, it is a constraint for individuals with chronic diseases who already have their daily routine. In this study, we introduce an innovative technology that harnesses the front-facing camera of smartphones to capture pulsatile signals discreetly when users engage in other activities on their device.</div><div>We conducted a clinical study to gather real world evidence that passive video-based cardiac monitoring is feasible and it can be used to gather daily information about cardiac status of patients with a history of atrial fibrillation (AF). The study involved 16 patients who used an application called HealthKam AFib (HK) on their Android smartphone for a period of 14 days. They also wore an ECG patch during the first 7 days that was used as a reference device. Subjects were asked to also perform self testing procedures using video selfies twice a day, but measurements were also collected in the background during normal device usage. The 16 subjects had the HK app installed on their device during an average time period of 12.8<span><math><mo>±</mo></math></span>2.3 days. On average, the measurement rate was 2.1<span><math><mo>±</mo></math></span>1.6 measurements per hour of utilization of the smartphone. Heart rate measurements were found to be highly accurate, with a mean error equal to −0.3 bpm. The study revealed that passive facial video monitoring collected reliable data in real-world conditions.</div></div>","PeriodicalId":15606,"journal":{"name":"Journal of electrocardiology","volume":"89 ","pages":"Article 153860"},"PeriodicalIF":1.3,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142926755","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":"Spatial QRS-T angle can indicate presence of myocardial fibrosis in pediatric and young adult patients with hypertrophic cardiomyopathy","authors":"Anna Wålinder Österberg ,&nbsp;Robert Jablonowski ,&nbsp;Ingegerd Östman-Smith ,&nbsp;Marcus Carlsson ,&nbsp;Todd T. Schlegel ,&nbsp;Henrik Green ,&nbsp;Cecilia Gunnarsson ,&nbsp;Eva Fernlund","doi":"10.1016/j.jelectrocard.2024.153859","DOIUrl":"10.1016/j.jelectrocard.2024.153859","url":null,"abstract":"<div><h3>Background</h3><div>Myocardial fibrosis, expressed as late gadolinium enhancement (LGE) on cardiac magnetic resonance imaging (CMR), is an important risk factor for malignant cardiac events in hypertrophic cardiomyopathy (HCM). However, CMR is not easily available, expensive, also needing intravenous access and contrast.</div></div><div><h3>Objective</h3><div>To determine if derived vectorcardiographic spatial QRS-T angles, an aspect of advanced ECG (A-ECG), can indicate LGE to appropriately prioritize young HCM-patients for CMR.</div></div><div><h3>Methods</h3><div>Young patients (age 7–31 years) with clinical HCM (<em>N</em> = 19) or genotype-positive but phenotype-negative (G+ P-) results (<em>N</em> = 6) and nine healthy volunteers were evaluated for LGE by CMR at a single centre between 2011 and 2018. A-ECG was performed within 4 months before and 6 months after CMR and evaluated for spatial mean and peaks QRS-T angles. ECG Risk-score and frontal, two-dimensional QRS-T angle were also calculated from the 12‑lead ECG.</div></div><div><h3>Results</h3><div>All QRS-T angles were significantly higher in the HCM group with LGE as compared to the HCM group without LGE, and the G+ P- and Healthy groups. Only HCM-patients showed LGE (11/19). The optimal cut-offs for indicating LGE were &gt; 50° for the spatial peaks (AUC = 0.98 [95 %CI 0.95–1.00], sensitivity 100 %, specificity 93 %; <em>p</em> &lt; 0.001), &gt;80° for the spatial mean (AUC = 0.91; <em>p</em> &lt; 0.001), and &gt; 60° for the frontal QRS-T angles (AUC = 0.85; <em>p</em> &lt; 0.001), and &gt; 2 points for an established ECG risk-score (AUC = 0.90, <em>p</em> &lt; 0.001).</div></div><div><h3>Conclusion</h3><div>A spatial peaks QRS-T angle &gt;50° has excellent sensitivity and specificity as a marker of myocardial fibrosis in a young patients with HCM, and can be useful for management and follow-up of such patients.</div></div>","PeriodicalId":15606,"journal":{"name":"Journal of electrocardiology","volume":"89 ","pages":"Article 153859"},"PeriodicalIF":1.3,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142964512","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":"The role of telemetry monitoring: From diagnosing arrhythmia to predictive models of patient instability","authors":"Ran Xiao PhD","doi":"10.1016/j.jelectrocard.2024.153861","DOIUrl":"10.1016/j.jelectrocard.2024.153861","url":null,"abstract":"<div><div>Over the past sixty years, telemetry monitoring has become integral to hospital care, offering critical insights into patient health by tracking key indicators like heart rate, respiratory rate, blood pressure, and oxygen saturation. Its primary application, continuous electrocardiographic (ECG) monitoring, is essential in diverse settings such as emergency departments, step-down units, general wards, and intensive care units for the early detection of cardiac rhythms signaling acute clinical deterioration. Recent advancements in data analytics and machine learning have expanded telemetry's role from observation to prognostication, enabling predictive models that forecast inhospital events indicative of patient instability. This short communication reviews the current applications and benefits of telemetry monitoring, including its vital role in identifying arrhythmias and predicting conditions like sepsis and cardiac arrest, while also addressing challenges such as alarm fatigue and the economic impact on health systems. It further explores opportunities for developing algorithms to enhance the practical use of telemetry data in clinical settings.</div></div>","PeriodicalId":15606,"journal":{"name":"Journal of electrocardiology","volume":"89 ","pages":"Article 153861"},"PeriodicalIF":1.3,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142909666","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":"12 lead surface ECGs as a surrogate of atrial electrical remodeling - a deep learning based approach","authors":"Ishan Vatsaraj ,&nbsp;Yazan Mohsen ,&nbsp;Lukas Grüne ,&nbsp;Lucas Steffens ,&nbsp;Shane Loeffler ,&nbsp;Marc Horlitz ,&nbsp;Florian Stöckigt ,&nbsp;Natalia Trayanova","doi":"10.1016/j.jelectrocard.2024.153862","DOIUrl":"10.1016/j.jelectrocard.2024.153862","url":null,"abstract":"<div><h3>Background and purpose</h3><div>Atrial fibrillation (AF), a common arrhythmia, is linked with atrial electrical and structural changes, notably low voltage areas (LVAs) which are associated with poor ablation outcomes and increased thromboembolic risk. This study aims to evaluate the efficacy of a deep learning model applied to 12‑lead ECGs for non-invasively predicting the presence of LVAs, potentially guiding pre-ablation strategies and improving patient outcomes.</div></div><div><h3>Methods</h3><div>A retrospective analysis was conducted on 204 AF patients, who underwent catheter ablation. Pre-procedural sinus rhythm ECGs and electroanatomical maps (EAM) were utilized alongside demographic data to train a deep learning model combining Long Short-Term Memory networks and Convolutional Neural Networks with a cross-attention layer. Model performance was evaluated using a 5-fold cross-validation strategy.</div></div><div><h3>Results</h3><div>The model effectively identified the presence of LVA on the examined atrial walls, achieving accuracies of 78 % for both the anterior and posterior walls, and 82 % for the LA roof. Moreover, it accurately predicted the global left atrial (LA) average voltage &lt;0.7 mV, with an accuracy of 88 %.</div></div><div><h3>Conclusion</h3><div>The study showcases the potential of deep learning applied to 12‑lead ECGs to effectively predict regional LVAs and global LA voltage in AF patients non-invasively. This model offers a promising tool for the pre-ablation assessment of atrial substrate, facilitating personalized therapeutic strategies and potentially enhancing ablation success rates.</div></div>","PeriodicalId":15606,"journal":{"name":"Journal of electrocardiology","volume":"89 ","pages":"Article 153862"},"PeriodicalIF":1.3,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142915045","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}