Journal of electrocardiology最新文献

Background: Electrode positioning directly influences the interpretation and diagnostic quality of ECG recordings. While current solutions mainly focus on detecting lead swaps in standard full-lead configurations, the growing adoption of portable and reduced-lead devices underscores the need for effective methods to identify and quantify electrode misplacement in various settings.

Methods: We developed and evaluated an end-to-end, personalized, uncertainty-aware framework that took ECG waveforms as input and automatically detected and estimated potential electrode misplacement, using an annotated dataset of 4608 Mayo Clinic 12-lead ECGs. The pipeline combined a deep convolutional encoder to identify the lead source area with a regression head that leveraged the learned representation to estimate misplacement direction and magnitude. It also incorporated patient-specific ECG morphology for personalization and integrated an uncertainty quantification mechanism based on Monte Carlo dropout to enhance decision confidence.

Results: The proposed method achieved over 94% classification accuracy in detecting the lead source area and estimated lead misplacement with a mean absolute error (MAE) of 2.2 cm. Incorporating personalized information enhanced results, reaching 97.5% accuracy and an MAE of 2.0 cm, while also largely maintaining performance for ECG determinations such as myocardial infarction and atrial fibrillation. The uncertainty-aware layer further reduced false corrections by flagging unfamiliar or ambiguous cases, boosting accuracy to 98.6% and lowering the MAE to 1.8 cm.

Conclusion: This study introduced a practical solution to improve ECG lead placement accuracy, enabling self-validating lead positioning that can enhance diagnostic reliability and support broader adoption of ECG technology in both clinical and decentralized care.

Background: Electrical injuries are a significant cause of cardiovascular morbidity and life-threatening arrhythmias. This study aimed to evaluate markers of ventricular repolarization dispersion and atrial conduction heterogeneity, specifically, the Tp-e interval, Tp-e/QT ratio, and P-wave dispersion, in patients presenting with electrical injury METHODS: In this retrospective case-control study, 50 patients with electrical injury were compared with 59 age- and sex-matched healthy controls. Standard 12‑lead electrocardiograms were obtained for all participants. Key parameters, Tp-e interval, Tp-e/QT ratio, Tp-e/QTc ratio, and P-wave dispersion, were manually measured by two blinded cardiologists with excellent interobserver reliability. A subgroup analysis was also performed within the injury cohort based on troponin status RESULTS: The Tp-e interval was significantly prolonged in the electrical injury group compared to controls (median 85.0 ms vs. 80.0 ms, p < 0.001). The Tp-e/QT ratio (0.24 vs. 0.21, p < 0.001), Tp-e/QTc ratio (0.20 vs. 0.19, p = 0.005), and P-wave dispersion (45.0 ms vs. 25.0 ms, p < 0.001) were also elevated in patients with electrical injury. Notably, no significant differences in these electrocardiographic parameters were observed between troponin-positive and troponin-negative subgroups CONCLUSION: Electrical injury is associated with significant acute abnormalities in ventricular repolarization and atrial conduction heterogeneity, independent of troponin elevation. The Tp-e interval, Tp-e/QT ratio, and P-wave dispersion may serve as potential electrocardiographic markers of arrhythmic risk in this population, though their prognostic utility requires validation in prospective studies.

Background: Left ventricular (LV) mechanical dyssynchrony results from nonuniform myocardial activation, leading to inefficient LV contraction and worse clinical outcomes. Synchromax® is a noninvasive system that performs real-time spatial variance analysis of QRS complexes from a standard electrocardiogram, generating an electrical synchrony index (ESI) that may be a potential marker of LV mechanical dyssynchrony. This study evaluated the efficacy of the ESI in predicting LV mechanical dyssynchrony compared to the gold standard: LV mechanical dispersion (LVMD) > 60 ms as measured by speckle-tracking strain echocardiography.

Methods: A cross-sectional study was conducted with consecutive adult patients undergoing echocardiography in San Salvador, El Salvador. Clinical, electrocardiographic (rhythm, QRS duration, ESI), and echocardiographic (LVMD, LV ejection fraction, global longitudinal strain) data were collected.

Results: Eighty-four studies from 83 patients were analyzed. Mean ESI was 0.36 ± 0.31 and mean LVMD was 58.4 ± 27.1 ms. The ESI showed a sensitivity of 70.0% and a specificity of 88.9%, with negative and positive predictive values of 84.2% and 77.8%, respectively. Agreement with the gold standard was moderate (kappa = 0.60; p < 0.001). ROC curve analysis demonstrated good discriminative performance (area under the curve = 0.81), superior to QRS duration (area under the curve = 0.71) for identifying LV mechanical dyssynchrony.

Conclusions: The optimal ESI cutoff was 0.42. ESI correlated consistently and significantly with LVMD, indicating that it may be a more sensitive functional marker than QRS duration, especially in cases without evident dyssynchrony. The ESI is a simple, accessible tool for complementary assessment of ventricular electromechanical synchrony.