ConvexECG: Lightweight and Explainable Neural Networks for Personalized, Continuous Cardiac Monitoring

TL;DR

ConvexECG is an explainable, resource-efficient neural network model that accurately reconstructs six-lead ECGs from single-lead data, suitable for personalized, continuous cardiac monitoring in low-resource settings.

Contribution

This paper introduces ConvexECG, a convex reformulation of a neural network that enables efficient training, explainability, and deployment for ECG reconstruction.

Findings

Achieves accuracy comparable to larger neural networks.

Reduces computational overhead significantly.

Demonstrates potential for real-time, low-resource cardiac monitoring.

Abstract

We present ConvexECG, an explainable and resource-efficient method for reconstructing six-lead electrocardiograms (ECG) from single-lead data, aimed at advancing personalized and continuous cardiac monitoring. ConvexECG leverages a convex reformulation of a two-layer ReLU neural network, enabling the potential for efficient training and deployment in resource constrained environments, while also having deterministic and explainable behavior. Using data from 25 patients, we demonstrate that ConvexECG achieves accuracy comparable to larger neural networks while significantly reducing computational overhead, highlighting its potential for real-time, low-resource monitoring applications.