ConvexECG: Lightweight and Explainable Neural Networks for Personalized, Continuous Cardiac Monitoring
This work addresses continuous cardiac monitoring in resource-constrained environments, but it is incremental as it builds on existing neural network methods with a convex reformulation.
The authors tackled the problem of reconstructing six-lead ECGs from single-lead data for personalized cardiac monitoring, achieving accuracy comparable to larger networks with reduced computational overhead.
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.