Analysis of Respiratory Sinus Arrhythmia with Neural Networks

2026-09-04

International Conference on Artificial Intelligence and Soft Computing

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

Problem

• Respiratory Sinus Arrhythmia
• ECG signals
• respiratory rate

Innovation

• neural networks
• ECG signals
• respiratory rate estimation
• Respiratory Sinus Arrhythmia (RSA)
• automatic feature extraction