Machine Learning-Based Multi-Chamber Anomaly Detection and Intelligently Guided Rf Ablation Catheter Navigation in A 4d Beating-Heart Simulation Framework

Abstract

This paper presents a fully self-contained, toolbox-free MATLAB simulation framework for artificial-intelligence-guided catheter navigation during radiofrequency (RF) ablation of cardiac arrhythmias. A patient-specific three-dimensional (3D) heart mesh, parsed with a custom binary/ASCII STL reader, is animated with a physiologically motivated four-dimensional (4D) deformation model that reproduces atrial and ventricular contraction together with an atrioventricular (AV) conduction delay. Four fixed sensing catheters continuously sample electrical activity from all four cardiac chambers; a rolling window of each chamber's readings is reduced to a compact three-feature descriptor and passed through a lightweight two-layer artificial neural network (3 inputs → 6 hidden tanh units → 1 sigmoid output) trained from scratch by plain gradient descent on synthetic electrogram data, without any deep-learning toolbox. Because each chamber maintains an independent normal/confirming/queued/treating state, the classifier can flag multiple simultaneous abnormalities rather than a single global fault. A single RF ablation catheter is then dispatched by an AI-based target-selection rule that jointly weighs classifier confidence and travel distance, and it is routed along a smooth cubic-Bézier S-curve rendered as a rotation-minimizing 3D tube, delivering energy and rechecking rhythm with the same trained classifier before advancing to the next queued chamber. The complete procedure is rendered frame-by-frame at a fixed camera angle and exported as an annotated MP4.

A from-scratch re-implementation of the classifier reported in this paper reproduces its training procedure and achieves 100% classification accuracy on both the training set and an independent, differently-seeded 1,600-sample synthetic test set, confirming that the two engineered features (mean/maximum absolute deviation and standard deviation of the rolling window) are linearly-in-feature-space separable for the amplitude and noise contrast used. The framework offers a reproducible, GPU-free, low-cost testbed for prototyping AI-assisted, multi-chamber ablation strategies and for teaching cardiac-conduction and electrophysiology workflows.