The paper introduces a neural network-based approach for analyzing ECG signals to estimate respiratory rate by leveraging the phenomenon 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 extracting relevant features from ECG signals without the need for manual preprocessing. The proposed approach offers a robust and scalable solution for non-invasive respiratory monitoring, with potential applications in healthcare and wearable technology.