Development of a Python-based graphic interface for custom cardiac signal design in the EP-David electrophysiology simulator.

The EP-David simulator is a hands-on training tool designed to replicate a patient under an electrophysiological procedure. It simulates key physiological parameters such as respiration and temperature, and it generates realistic cardiac signals, including normal sinus rhythm and various types of arrhythmias, for diagnostic and therapeutic practice. However, its original signal creation workflow was fragmented and heavily dependent on MATLAB, limiting testing efficiency and physiological flexibility. This thesis presents the design and implementation of a fully Python-based graphical user interface (GUI) that enables the creation of both surface ECG and intracardiac signals (iECG) with customizable morphology, propagation timing, and amplitude. The interface allows users to fine-tune a pre-designed set of signals, construct activation sequences, visualize them in real-time, define region-specific signals, and export a structure compatible with the simulator’s runtime engine. This file replaces previous CSV-based inputs and allows direct integration with the simulator without manual preprocessing. The new system facilitates faster signal validation, supports the simulation of complex arrhythmias, and significantly improves the physiological accuracy and usability of EP David in training contexts. The system was tested and validated with the involvement of clinical experts during the creation of complex arrhythmic scenarios. The GUI-generated signals enabled a physiologically correct propagation of electrical activity across cardiac regions, resolving previous inconsistencies in signal onset and timing. The experts provided positive feedback, highlighting the tool’s ability to replicate clinically relevant activation patterns and its potential to enhance the educational value of the simulator. Compared to the original MATLAB-based approach, the new workflow reduces design complexity, enhances visual feedback, and aligns signal morphology with expert recommendations. The result is a scalable and accessible tool that bridges the gap between signal modeling and hardware execution, strengthening EP-David’s role as a didactic simulator for cardiac electrophysiology.