Advances in Electrocardiology: Diagnostic Innovations and Clinical Applications

Dear Colleagues,

Electrocardiology has entered a new era characterized by the integration of advanced signal analytics, computational modeling, and artificial intelligence. Beyond traditional ECG morphology and rhythm interpretation, modern approaches now explore subtle temporal and spectral features that reflect autonomic regulation and myocardial integrity. Parameters such as heart rate variability (HRV), deceleration and acceleration capacity (DC and AC), and ventricular late potentials have emerged as robust markers of autonomic imbalance, myocardial vulnerability, and arrhythmogenic substrate. When analyzed jointly, these indices provide valuable insight into the complex interplay between neural and electrophysiological control of the heart, supporting early detection of cardiovascular dysfunction even before overt clinical manifestation.

Parallel to these physiological metrics, machine learning and deep neural networks have demonstrated unprecedented ability to identify invisible electrocardiographic signatures-patterns beyond human perception-that correlate with left ventricular dysfunction, ischemic burden, or impending arrhythmia. The convergence of AI-driven analytics with classical physiological interpretation offers a powerful new framework for personalized risk stratification, bridging quantitative signal analysis with clinical decision support.

This Special Issue aims to highlight cutting-edge developments in electrocardiologic diagnostics and their translation into clinical applications. We invite submissions addressing methodological innovations, validation of novel ECG-derived parameters, multimodal integration of autonomic and hemodynamic signals, and the implementation of AI in predictive modeling and decision support. Contributions exploring the role of advanced ECG analytics in syncope, heart failure, myocardial infarction, cardiomyopathies, or sudden cardiac death prevention are particularly encouraged.

By combining physiologic insight with data-driven intelligence, this Special Issue seeks to define the next frontier in electrocardiology—transforming the ECG from a static recording into a dynamic, predictive biomarker of cardiovascular health.

Prof. Dr. Branislav Milovanovic
Guest Editor

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