Engineering, Coding, and AI Essentials for Electrophysiologists

Learning objectives:

After completing this eLearning course, participants will be able to: 

• Explain the fundamental principles of artificial intelligence (AI) in cardiac electrophysiology (EP).
• Describe how bioelectrical concepts influence signal generation, filtering, processing, and recording in EP studies.
• Recognise how signal processing parameters affect electrogram quality and clinical interpretation.
• Collaborate effectively with technical and engineering teams by applying a basic understanding of biomedical engineering concepts in EP.
• Interpret the structure and logic of simple Python scripts for data analysis, signal processing, and visualisation in electrophysiology.
• Apply fundamental programming skills to automate basic analyses and enhance efficiency in EP research and clinical workflows.
• Explain key artificial intelligence (AI) and machine learning concepts (e.g. ANN, CNN, RNN) and their relevance to electrophysiology.
• Evaluate AI applications in EP by understanding model training, validation, interpretation, and associated ethical considerations.
• Implement good research data management practices, including data curation, quality assurance, and security in digital EP projects.
• Critically assess current and emerging AI-driven and digital solutions in electrophysiology using structured frameworks (e.g. EHRA AI checklist) to ensure reliability, transparency, and clinical value.

Pre-requisites:

• Basic understanding of EP and some practical experience of workflows in an EP center.

Needs assessment:

• Rapid digitalisation of cardiac electrophysiology has created a clear need for clinicians to understand engineering, data science, and AI principles underpinning modern EP practice. Many professionals lack training in signal processing, programming, and data management, limiting their ability to interpret complex data and collaborate with technical teams. This course addresses these gaps by introducing biomedical engineering fundamentals, coding for EP data handling, AI and machine learning concepts, and principles of data quality and ethics. It also promotes critical evaluation of AI-driven tools using the EHRA AI checklist and explores their clinical applications in AF management, ECG diagnostics, and EP procedures.

The following topics are covered:

• e-Cardiology/Digital Health :
  • Cardiovascular Image Processing
  • Cardiovascular Signal Processing - Electrocardiogram (ECG) and Arrhythmia Analysis
  • Cardiovascular Signal Processing - Cardiovascular Signal Processing,
  • Artificial Intelligence (Machine Learning, Deep Learning)
  • Big Data and Digital Twin
  • In-Silico Medicine and Virtual Physiologic Patient
• e-CARDIOLOGY/DIGITAL HEALTH. PUBLIC HEALTH. HEALTH ECONOMICS. RESEARCH METHODOLOGY
  • Big Data Analysis
  • Research Ethics

This course can be purchased by both members and non-members: 

• €25 for EHRA Members (VAT excluded)
• €50 for non-EHRA members (VAT excluded)

Discount applies if all seven topics are purchased simultaneously:

• €98 for EHRA members (VAT excluded)
• €250 for non-EHRA members (VAT excluded)

7 main topics are available as follows:

•  Electrocardiography

•  Anatomy for EP-CIED cardiologists

•  Physiology and Pathophysiology

•  Genetics

•  Introduction to interventional electrophysiology

•  Introduction to CIED: Pacing, ICD and CRT

•  Engineering Coding and AI Essentials for Electrophysiologists

Once you have purchased the course, you will have 12 months to complete it.