[Submitted on 4 Sep 2026]
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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
Submission history #
From: Julian Szymanski JS [
[view email](/show-email/85f7619a/2609.05698)]
**[v1]** Fri, 4 Sep 2026 20:09:30 UTC (268 KB)
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