Advances in Electrocardiology: Diagnostic Innovations and Clinical Applications
A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging and Theranostics".
Deadline for manuscript submissions: 31 August 2026 | Viewed by 2794
Editor
Interests: cardiovascular; neurocardiology; autonomic
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
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
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- heart rate variability (HRV)
- deceleration capacity
- acceleration capacity
- late potentials
- artificial intelligence
- machine learning
- autonomic nervous system
- electrocardiographic biomarkers
- risk stratification
- predictive analytics
- signal processing
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