aiqbench analysis
This research applies deep learning architectures to extract respiratory waveforms directly from ECG signals, eliminating the need for manual preprocessing in non-invasive monitoring. By leveraging Respiratory Sinus Arrhythmia (RSA), the study demonstrates a scalable neural network solution with direct implications for healthcare wearables and remote patient monitoring systems.
Analysis of Respiratory Sinus Arrhythmia with Neural Networks
arXiv:2609.05698v1 Announce Type: new 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
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
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2609.05698 [cs.LG]
(or arXiv:2609.05698v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.05698 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 189 words
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