HRVConformer: Neonatal Hypoxic-Ischemic Encephalopathy Classification from the Heart Rate signals
arXiv:2605.26190v1 Announce Type: new 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 m
The continuous advancements in deep learning architectures, particularly the integration of Transformers with convolutional networks, enable more sophisticated analysis of complex biological signals like heart rate for medical diagnostics. This research represents the ongoing trend of applying advanced AI to improve healthcare outcomes.
This development can significantly improve the early and accurate diagnosis of neonatal HIE, a critical condition with severe long-term consequences, ultimately leading to better patient outcomes and reduced healthcare burdens. It demonstrates the growing capability of AI to extract diagnostic insights from subtle physiological data.
HIE diagnosis can now move towards non-invasive, continuous monitoring and AI-driven classification from raw heart rate signals, potentially reducing reliance on subjective interpretations or more complex diagnostic procedures. This shifts the paradigm for early detection in a vulnerable population.
- · Neonatal care providers
- · Medical AI companies
- · Infant patients and their families
- · Deep learning researchers
- · Traditional diagnostic methods for HIE
- · Specialists relying solely on manual signal interpretation
Improved early diagnosis and intervention for neonatal HIE, reducing morbidity and mortality.
Increased adoption of AI-powered continuous monitoring systems in neonatal intensive care units globally.
Extension of similar hybrid AI diagnostic models to other complex physiological conditions, leading to a broader revolution in non-invasive medical diagnostics.
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Read at arXiv cs.LG