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Biomedicines
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Cardiac Arrhythmias: Technological Frontiers, Therapeutic Approaches and Future Directions
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Cardiac Arrhythmias: Technological Frontiers, Therapeutic Approaches and Future Directions

A special issue of Biomedicines (ISSN 2227-9059). This special issue belongs to the section "Molecular and Translational Medicine".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 1746

Special Issue Editors

Dr. Panteleimon Pantelidis

E-Mail Website
Guest Editor
School of Medicine, National & Kapodistrian, University of Athens, Athens, Greece
Interests: cardiology; arrhythmias; electrophysiology; e-cardiology; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Polychronis E. Dilaveris

E-Mail Website
Guest Editor
School of Medicine, National & Kapodistrian, University of Athens, Athens, Greece
Interests: cardiology; arrhythmias; electrophysiology; e-cardiology; artificial intelligence; telemonitoring
Special Issues, Collections and Topics in MDPI journals
Dr. Raffaele De Lucia

E-Mail Website
Guest Editor
2nd Division of Cardiology, Cardiac Thoracic and Vascular Department, Azienda Ospedaliero Universitaria Pisana, 56124 Pisa, Italy
Interests: cardiology; arrhythmias; electrophysiology; e-cardiology; artificial intelligence
Dr. Evangelos Oikonomou

grade E-Mail Website
Guest Editor
Medical School, National and Kapodistrian, University of Athens, Athens, Greece
Interests: atherosclerosis; endothelium; heart failure; cardiovascular imaging; epidemiology; e-cardiology; artificial intelligence; telemonitoring
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue, “Cardiac Arrhythmias: Technological Frontiers, Therapeutic Approaches and Future Directions,” aims to explore the rapidly evolving landscape of arrhythmia research, from fundamental mechanisms to translational breakthroughs.

We are witnessing a paradigm shift driven by innovations across the digital and experimental spectrum. Digital health—including e-cardiology, telemonitoring, and artificial intelligence—is providing unprecedented insights into arrhythmia prediction, detection, and risk stratification. Concurrently, the devices landscape and the electrophysiology lab are being transformed by novel tools. Technologies like Pulsed-Field Ablation, advanced, AI-assisted mapping systems, and leadless implantable devices present new therapeutic potentials but also new challenges and questions for the science around arrhythmia diagnostics, therapeutics, and overall understanding.

This issue will focus on the pre-clinical, experimental, and translational aspects of these advancements. We invite original research and review articles investigating the following:

  • Novel diagnostic biomarkers and in vitro/in vivo experimental models of arrhythmogenesis;
  • The molecular basis of new therapeutic strategies;
  • Advanced cardiovascular imaging for arrhythmia substrate characterization;
  • The application of AI in guiding diagnostics and personalized treatment models;
  • Translational studies on the mechanisms, safety, and efficacy of new interventional technologies and devices.

By highlighting the frontier of laboratory and experimental medicine in arrhythmology, this Special Issue hopes to pave the way for future, precision-based patient care.

Dr. Panteleimon Pantelidis
Dr. Polychronis E. Dilaveris
Dr. Raffaele De Lucia
Dr. Evangelos Oikonomou
Guest Editors

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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Biomedicines is an international peer-reviewed open access monthly 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

  • cardiac arrhythmias
  • electrophysiology
  • implantable devices
  • artificial intelligence
  • translational medicine
  • arrhythmogenesis
  • e-cardiology
  • biomarkers
  • telemonitoring

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

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15 pages, 841 KB  
Article
An Implantable Loop Recorder in the Diagnosis of Cardiac Arrhythmias: The Importance of Drug Treatment in Predicting Pacemaker Requirement
by Jelena Vučković-Filipović, Vladimir Ignjatović, Isidora Stanković, Neda Ćićarić, Vesna Ignjatović, Goran Davidović, Vladimir Miloradović, Violeta Irić-Ćupić, Ivan Simić and Natasa Djordjevic
Biomedicines 2026, 14(2), 466; https://doi.org/10.3390/biomedicines14020466 - 19 Feb 2026
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Abstract
Background: An implantable loop recorder (ILR) represents the gold standard in the diagnosis of cardiac arrhythmias in patients with neurological or cardiac symptoms. Our study aimed to determine the real-world diagnostic effectiveness of ILRs in detecting arrhythmias requiring permanent pacemaker implantation. Methods [...] Read more.
Background: An implantable loop recorder (ILR) represents the gold standard in the diagnosis of cardiac arrhythmias in patients with neurological or cardiac symptoms. Our study aimed to determine the real-world diagnostic effectiveness of ILRs in detecting arrhythmias requiring permanent pacemaker implantation. Methods: The study enrolled and followed up for two years 62 ILR recipients from the Cardiology Clinic of the Clinical Center Kragujevac, Serbia. Results: The most common indication for pacemaker implantation was pauses in cardiac activity (83%). The use of oral anticoagulants (OR: 11.80; 95% CI: 1.76, 79.4), ACE inhibitors or AT receptor blockers (OR: 3.87; 95% CI: 1.21, 12.35), and diuretics (OR: 5.29; 95% CI: 1.55, 18.04) had a statistically significant impact on the detection of pacemaker-requiring arrhythmias by an ILR. After adjustment for other factors of influence, oral anticoagulants (OR: 7.82; 95% CI: 1.08, 56.9) and diuretics (OR: 3.68; 95% CI: 1.04, 13.00) remained significant in predicting pacemaker requirement in ILR recipients. Conclusions: An ILR represents an effective diagnostic approach in detecting cardiac arrhythmias requiring permanent pacemaker implantation, especially in patients treated with oral anticoagulants or diuretics. Full article
(This article belongs to the Special Issue Cardiac Arrhythmias: Technological Frontiers, Therapeutic Approaches and Future Directions)
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Review

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19 pages, 785 KB  
Review
Artificial Intelligence for Prediction and Detection of Atrial Fibrillation from Sinus-Rhythm Electrocardiograms and Ambulatory Monitoring
by Panteleimon Pantelidis, Nikolaos Vythoulkas-Biotis, Athanasios Samaras, Panagiotis Theofilis, Raffaele De Lucia, Polychronis Dilaveris, Theodore G. Papaioannou, Evangelos Oikonomou and Gerasimos Siasos
Biomedicines 2026, 14(5), 1058; https://doi.org/10.3390/biomedicines14051058 - 7 May 2026
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Abstract
Atrial fibrillation (AF) is a highly prevalent arrhythmia associated with stroke, heart failure and excess mortality. Yet, “silent” AF episodes remain undetected, leading to underestimation of disease burden. Additionally, paroxysms occur in an “unpredictable” way, and available clinical scores only stratify long-term AF [...] Read more.
Atrial fibrillation (AF) is a highly prevalent arrhythmia associated with stroke, heart failure and excess mortality. Yet, “silent” AF episodes remain undetected, leading to underestimation of disease burden. Additionally, paroxysms occur in an “unpredictable” way, and available clinical scores only stratify long-term AF risk with moderate discrimination, lacking the ability to evaluate near-term events. Artificial intelligence (AI) applied to sinus rhythm from short or continuous electrocardiogram (ECG) recordings shows that such predictive information is hidden in “plain sight.” This complementary approach seeks to uncover latent AF substrate and forecast imminent AF episodes. Deep-learning models trained on 10-s, 12-lead ECGs can identify individuals with prevalent or long- or near-term AF with areas under the curve (AUCs) up to 0.90, outperforming established clinical scores. Image-based AI-ECG models extend these capabilities to paper or scanned ECGs. Furthermore, AI algorithms applied to 24-h Holter and multi-day patch recordings achieve AUCs ≥0.80 for detecting occult AF or predicting it within 14 days, consistently surpassing risk scores like C2HEST and HATCH. Short-term models utilizing heart-rate variability features further demonstrate that AF can be anticipated minutes to hours before onset, with accuracies around 90% in curated datasets. However, most AI-AF studies remain retrospective, single-system and focused on diagnostic yield rather than clinical outcomes like stroke or mortality. Moreover, few pragmatic trials have evaluated AI-guided AF screening and its translation into clinical benefit. Robust prospective trials and standardized evaluation frameworks are needed before AI-guided AF prediction can be routinely integrated into clinical decision-making. Full article
(This article belongs to the Special Issue Cardiac Arrhythmias: Technological Frontiers, Therapeutic Approaches and Future Directions)
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