# HRVConformer: Neonatal Hypoxic-Ischemic Encephalopathy Classification from the Heart Rate signals

Source: [HRVConformer: Neonatal Hypoxic-Ischemic Encephalopathy Classification from the Heart Rate signals](<https://www.sota2.com/research/paper/6a167a6ddbd7150506f7fe9e>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-05-28T05:00:05.405Z

Authors: Shuwen Yu, William P Marnane, Geraldine B. Boylan, Gordon Lightbody

Published: 2026-05-25T09:22:11.000Z

## Abstract

This paper presents the HRVConformer, a novel deep learning architecture for the classification of hypoxic-ischemic encephalopathy (HIE) using the instantaneous heart rate (HR) signal. Unlike conventional approaches that rely on handcrafted features, HRVConformer directly processes raw HR signals in an end-to-end manner, capturing both local and long-range dependencies through a hybrid Convolution-Transformer framework. By integrating convolutional layers for local feature extraction and Transformer-based attention mechanisms for global context modelling, the architecture effectively enhances signal representation and classification performance. The model was trained using supervised learning on a large HR dataset consisting of 1,573 one-hour epochs, including 259 one-hour expert-annotated epochs and a substantial set of weakly labelled data. A 314-hour validation set provided a robust performance estimation, while an independent 215-hour dataset with expert annotations was reserved for final testing. HR signals were extracted from electrocardiogram (ECG) recordings using an improved Pan-Tompkins algorithm, which significantly enhanced both signal quality and data availability. Experimental results demonstrate that the HRVConformer achieves an AUC of 83.23\\% and accuracy of 74.56\\% on the test set. These results surpass the performance of the Transformer, ResNet50 and fully convolutional networks baselines, highlighting the advantages of integrating convolutional and Transformer-based components for HR-based HIE classification. The proposed method provides a promising step toward a more accurate and automated assessment of HIE using HR signals. The code is available at: https://github.com/syu-kylin/HRVConformer.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Epoch-level classification on HIE (test)](<https://www.sota2.com/research/sota/epoch-level-classification-on-hie-test.md>): Epoch-level classification; HIE (test); Accuracy: 74.56.

- [Epoch-level classification on HIE (val)](<https://www.sota2.com/research/sota/epoch-level-classification-on-hie-val.md>): Epoch-level classification; HIE (val); Accuracy: 87.87.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2605.26190>)

[PDF](<https://arxiv.org/pdf/2605.26190.pdf>)