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Analysis of Respiratory Sinus Arrhythmia with Neural Networks

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Computer Science > Machine Learning

arXiv:2609.05698 (cs)
[Submitted on 4 Sep 2026]

Title:Analysis of Respiratory Sinus Arrhythmia with Neural Networks

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Abstract:The paper introduces a neural network-based approach for analyzing ECG signals to estimate respiratory rate by leveraging the phe- nomenon of Respiratory Sinus Arrhythmia (RSA). Our method employs a deep learning model trained to predict respiratory waveforms directly from ECG input data. To achieve this, we developed and evaluated three different neural network architectures capable of automatically extract- ing relevant features from ECG signals without the need for manual preprocessing. The proposed approach offers a robust and scalable solu- tion for non-invasive respiratory monitoring, with potential applications in healthcare and wearable technology
Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as: arXiv:2609.05698 [cs.LG]
  (or arXiv:2609.05698v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2609.05698
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Julian Szymanski JS [view email]
[v1] Fri, 4 Sep 2026 20:09:30 UTC (268 KB)
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