Aims: Breathing rate (BR) is a key vital sign routinely used in clinical practice to assess pulmonary function. Traditional BR monitoring systems are often bulky or specialized, limiting their use in remote or wearable applications. This study aims to evaluate an open-source approach for estimating BR using single-lead ECG signals. Method: We applied a Python-based algorithm that estimates BR by analyzing time- and frequency-domain modulations in the root mean square (RMS) amplitude of each QRS complex, using a 16-beat overlapping moving window. The algorithm was tested on data from 40 healthy adult subjects, acquired using a high-density 128-electrode array distributed over the thorax. BR was estimated independently for each electrode's ECG signal and compared to reference respiratory data obtained via plethysmography belt. The method's performance was quantified using the Mean Absolute Error (MAE) between estimated and ground-truth BR. Results: The algorithm showed strong consistent performance across the electrodes, with an average MAE of 2,46 bpm, ranging from 1.74 to 3.47 bpm. Spatial visualization of the estimation error (Fig.1) revealed clear difference between the upper and lower regions of the thorax. The algorithm generally performed better in the signals from the electrodes in the upper right thorax (MAE of 1.98 bpm), compared to the lower right region (MAE = 2.74 bpm) and especially over the heart area (MAE = 3.22 bpm), where the estimation was less reliable. Interestingly, electrodes in the standard 12-lead precordial ECG positions did not provide optimal performance for BR estimation. In contrast, electrodes located on the subject's right lateral thorax yielded more accurate and stable BR estimates. Conclusion: We demonstrate the feasibility and robustness of using single-lead ECG for spatial BR mapping and highlight the importance of electrode placement. For practical applications in wearable systems, the electrode position should be carefully considered to ensure reliable BR monitoring.