IKrNet: A Neural Network for Detecting Specific Drug-Induced Patterns in Electrocardiograms Amidst Physiological Variability
Journal:
arXiv
Published Date:
May 12, 2025
Abstract
Monitoring and analyzing electrocardiogram (ECG) signals, even under varying
physiological conditions, including those influenced by physical activity,
drugs and stress, is crucial to accurately assess cardiac health. However,
current AI-based methods often fail to account for how these factors interact
and alter ECG patterns, ultimately limiting their applicability in real-world
settings. This study introduces IKrNet, a novel neural network model, which
identifies drug-specific patterns in ECGs amidst certain physiological
conditions. IKrNet's architecture incorporates spatial and temporal dynamics by
using a convolutional backbone with varying receptive field size to capture
spatial features. A bi-directional Long Short-Term Memory module is also
employed to model temporal dependencies. By treating heart rate variability as
a surrogate for physiological fluctuations, we evaluated IKrNet's performance
across diverse scenarios, including conditions with physical stress, drug
intake alone, and a baseline without drug presence. Our assessment follows a
clinical protocol in which 990 healthy volunteers were administered 80mg of
Sotalol, a drug which is known to be a precursor to Torsades-de-Pointes, a
life-threatening arrhythmia. We show that IKrNet outperforms state-of-the-art
models' accuracy and stability in varying physiological conditions,
underscoring its clinical viability.
