用神经网络从心电图推呼吸率，无需手动预处理
Analysis of Respiratory Sinus Arrhythmia with Neural Networks
- 用深度学习模型直接从心电图预测呼吸波形
- 三种网络架构自动提取特征，准确估计呼吸速率
- 适合可穿戴设备和非侵入式医疗监测场景
本文提出一种基于神经网络的心电图分析方法，通过呼吸性窦性心律不齐（RSA）现象估算呼吸频率。该方法采用深度学习模型，直接从心电图输入数据预测呼吸波形。为实现这一目标，我们设计并评估了三种不同的神经网络架构，能够自动提取心电图中的相关特征，无需人工预处理。所提方法为非侵入式呼吸监测提供了一种稳健且可扩展的解决方案，具有在医疗健康和可穿戴技术中的应用潜力。
原文摘要 · Abstract (English)
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
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