# Evaluator-Dependent Patient-Adaptive ECG Lead-Channel Allocation

arXiv: https://arxiv.org/abs/2608.26827 · HTML: https://mlsanity.com/paper/2608.26827/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 22 references that are in the map.
- First posted on arXiv 27 Aug 2026.

## Builds on (most cited first, 12 of 12)

- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Evaluating model calibration in classification](https://mlsanity.com/paper/1902.06977/) (2019)
- [Automatic diagnosis of the 12-lead ECG using a deep neural network](https://mlsanity.com/paper/1904.01949/) (2019)
- [Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL](https://mlsanity.com/paper/2004.13701/) (2020)
- [EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE](https://mlsanity.com/paper/1809.11142/) (2018)
- [Learning to Maximize Mutual Information for Dynamic Feature Selection](https://mlsanity.com/paper/2301.00557/) (2023)
- [Lead-agnostic Self-supervised Learning for Local and Global Representations of Electrocardiogram](https://mlsanity.com/paper/2203.06889/) (2022)
- [Classification with Costly Features using Deep Reinforcement Learning](https://mlsanity.com/paper/1711.07364/) (2017)
- [Flow Models for Arbitrary Conditional Likelihoods](https://mlsanity.com/paper/1909.06319/) (2019)
- [Learning Sensor Multiplexing Design through Back-propagation](https://mlsanity.com/paper/1605.07078/) (2016)
- [Multi-Channel Masked Autoencoder and Comprehensive Evaluations for Reconstructing 12-Lead ECG from Arbitrary Single-Lead ECG](https://mlsanity.com/paper/2407.11481/) (2024)
- [Why Pay More When You Can Pay Less: A Joint Learning Framework for Active Feature Acquisition and Classification](https://mlsanity.com/paper/1709.05964/) (2017)

## Abstract (arXiv)

The diagnostic value of an ECG channel depends on the model that interprets it. We test whether adaptive acquisition retains its advantage over fixed protocols when that model changes. Two policies developed with a logistic evaluator are frozen and assessed with a masked waveform ResNet1D, using exhaustive, metric-matched fixed comparators. On PTB-XL, replacing the evaluator reverses the mean adaptive advantage in negative log-likelihood and Brier score across the tested budgets, while calibration responds less uniformly. The shift persists with common fixed references and broader training-mask exposure. Training a policy against the stronger evaluator partly recovers the lost advantage. These retrospective exploratory results show that acquisition quality cannot be assessed independently of the downstream evaluator and motivate validating adaptive policies with the model intended for diagnosis.