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

Rayan Ansari, John Cao, Sabyasachi Bandyopadhyay, Sanjiv M. Narayan, Albert J. Rogers +1 more
9/19/2024
cs.LGeess.SPmath.OC

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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.

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Cite this paper

@article{ansari2024convexecg,
  title  = {ConvexECG: Lightweight and Explainable Neural Networks for Personalized, Continuous Cardiac Monitoring},
  author = {Rayan Ansari and John Cao and Sabyasachi Bandyopadhyay and Sanjiv M. Narayan and Albert J. Rogers and Mert Pilanci},
  year   = {2024},
  eprint = {2409.12493},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2409.12493v1}
}

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