Research and Technology in Electrocardiology

Dear Colleagues,

Electrocardiology provides a snapshot of heart activity and plays a central role in diagnosing and managing cardiovascular diseases. Electrocardiogram (ECG) interpretation is essential in modern medicine and the early detection and management of cardiac conditions associated with high mortality, including arrhythmia and left ventricular hypertrophy. Key ECG markers, such as the P wave, QT interval duration and dispersion, Tpeak–Tend interval, and ST segment elevation, provide vital diagnostic information but often require expert analysis for accurate interpretation. Additionally, achieving and maintaining competency can be challenging for healthcare professionals.

ECG is important in clinical medicine, research, and diagnostics because it provides a non-invasive way to understand heart function and enables interdisciplinary collaboration. Recent advancements in artificial intelligence (AI) algorithms have significantly enhanced the capabilities of ECG-based biosignal analysis, enabling the more precise and rapid identification of pathological patterns. Integrated into wearable devices and remote monitoring systems, AI-driven ECG interpretation may improve patient outcomes through real-time detection and intervention. Continued innovation and training are essential to fully realize electrocardiology’s potential to improve patient care.

Research on arrhythmias continues to benefit from high-resolution ECG data and the development of signal quality indices which improve the reliability of recordings, especially those from portable and wearable ECG devices. Furthermore, considering the pharmacological impacts on ECG parameters, such as QT interval prolongation, is crucial for assessing drug safety and tailoring therapeutic strategies. In electrocardiology, personalized medicine emphasizes the use of individual ECG profiles, AI-based risk stratification, and modeling approaches to optimize the treatment outcomes. Animal experimental models and in silico simulations further support the translational potential of ECG findings, enhancing our understanding of electrophysiological mechanisms and therapeutic interventions.

Collectively, these advancements are transforming electrocardiography into a more precise, predictive, and patient-centered discipline. Electrocardiography remains a cornerstone of cardiovascular diagnostics, and its integration in modern technologies has opened new frontiers in both clinical and research settings.

For this Special Issue, we welcome the submission of original research articles and reviews. The research areas may include (but are not limited to) the following:

• Artificial intelligence (AI) in electrocardiology;
• Electrocardiographic biomarkers in acute and chronic disorders;
• Crosstalk between electrocardiographic and other paraclinical biomarkers;
• Arrhythmia research;
• The electrocardiographic effects of drugs;
• Standard 12-lead ECG, wearable ECG devices, and exercise ECG;
• Personalized medicine and electrocardiography;
• ECG analysis techniques: ECG body surface mapping and signal-averaged ECG;
• Heart rate and QT variability and T-wave alternans;
• Modeling in electrocardiography;
• Signal quality indices for electrocardiograms;
• Animal experimental models.

I look forward to receiving your contributions.

Prof. Dr. Ioana Mozos
Guest Editor

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• electrocardiogram
• QT interval
• ST segment elevation
• sudden cardiac death
• arrhythmia
• left ventricular hypertrophy
• signal-averaged ECG
• ECG body surface mapping
• AI algorithms
• biosignal analysis