Analysis of Respiratory Sinus Arrhythmia with Neural Networks

A paper submitted to arXiv on 4 Sep 2026 by Julian Szymanski introduces a neural network-based method for estimating respiratory rate from ECG signals by exploiting Respiratory Sinus Arrhythmia (RSA). The approach trains a deep learning model to predict respiratory waveforms directly from ECG input, and the authors developed and evaluated three different neural network architectures that automatically extract relevant features without manual preprocessing. The authors present the method as a robust, scalable solution for non-invasive respiratory monitoring with potential applications in healthcare and wearable technology.

Computer Science Machine Learning Submitted on 4 Sep 2026 Title:Analysis of Respiratory Sinus Arrhythmia with Neural Networks View PDF /pdf/2609.05698 HTML experimental https://arxiv.org/html/2609.05698v1 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 Submission history From: Julian Szymanski JS view email /show-email/85f7619a/2609.05698 v1 Fri, 4 Sep 2026 20:09:30 UTC 268 KB References & Citations Loading... Bibliographic and Citation Tools Bibliographic Explorer What is the Explorer? https://info.arxiv.org/labs/showcase.html arxiv-bibliographic-explorer Connected Papers What is Connected Papers? https://www.connectedpapers.com/about Litmaps What is Litmaps? https://www.litmaps.co/ scite Smart Citations What are Smart Citations? https://www.scite.ai/ Code, Data and Media Associated with this Article alphaXiv What is alphaXiv? https://alphaxiv.org/ CatalyzeX Code Finder for Papers What is CatalyzeX? https://www.catalyzex.com DagsHub What is DagsHub? https://dagshub.com/ Gotit.pub What is GotitPub? http://gotit.pub/faq Hugging Face What is Huggingface? https://huggingface.co/huggingface ScienceCast What is ScienceCast? https://sciencecast.org/welcome Demos Recommenders and Search Tools Influence Flower What are Influence Flowers? https://influencemap.cmlab.dev/ CORE Recommender What is CORE? https://core.ac.uk/services/recommender IArxiv Recommender What is IArxiv? https://iarxiv.org/about arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs https://info.arxiv.org/labs/index.html .