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Main Authors: Stenhede, Elias, Ranjbar, Arian
Format: Preprint
Published: 2026
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Online Access:https://arxiv.org/abs/2604.09782
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author Stenhede, Elias
Ranjbar, Arian
author_facet Stenhede, Elias
Ranjbar, Arian
contents Chagas disease screening via ECGs is limited by scarce and noisy labels in existing datasets. We propose a biomarker-based pretraining approach, where an ECG feature extractor is first trained to predict percentile-binned blood biomarkers from the MIMIC-IV-ECG dataset. The pretrained model is then fine-tuned on Brazilian datasets for Chagas detection. Our 5-model ensemble, developed by the Ahus AIM team, achieved a challenge score of 0.269 on the hidden test set, ranking 5th in Detection of Chagas Disease from the ECG: The George B. Moody PhysioNet Challenge 2025. Source code and the model are shared on GitHub: github.com/Ahus-AIM/physionet-challenge-2025
format Preprint
id arxiv_https___arxiv_org_abs_2604_09782
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Biomarker-Based Pretraining for Chagas Disease Screening in Electrocardiograms
Stenhede, Elias
Ranjbar, Arian
Computer Vision and Pattern Recognition
Chagas disease screening via ECGs is limited by scarce and noisy labels in existing datasets. We propose a biomarker-based pretraining approach, where an ECG feature extractor is first trained to predict percentile-binned blood biomarkers from the MIMIC-IV-ECG dataset. The pretrained model is then fine-tuned on Brazilian datasets for Chagas detection. Our 5-model ensemble, developed by the Ahus AIM team, achieved a challenge score of 0.269 on the hidden test set, ranking 5th in Detection of Chagas Disease from the ECG: The George B. Moody PhysioNet Challenge 2025. Source code and the model are shared on GitHub: github.com/Ahus-AIM/physionet-challenge-2025
title Biomarker-Based Pretraining for Chagas Disease Screening in Electrocardiograms
topic Computer Vision and Pattern Recognition
url https://arxiv.org/abs/2604.09782