## 5 Discussion  
During the presented research, we acquired the EBI signals using the selected electrode placements in perpendicular and distal placements relative to the wrist. Firstly, we analyzed the acquired signals concerning the modulation depth caused by the cardiac activity. The measurement results demonstrate the significance of the electrode positioning relative to the arteries in the forearm. As can be seen from Table 2, distal placement provides a generally higher value of \(\Delta Z(t)\). Irrespective of the electrode width, the highest values of \(\Delta Z(t)\) are gained by using configuration B of both electrode widths--the longest distance between the electrodes. The same phenomenon appears also in perpendicular placement. Therefore, a longer distance between the electrodes is preferred in wearable device design, clearly related to the artery's locations. In the cases of such a wide gap between the electrodes as represented by the configurations B-b10 and B-b17 (Table 1), we expect the contribution from both arteries: radial and ulnar. While the tetrapolar system provides the lowest result in terms of \(\Delta Z(t)\), it provides remarkably higher \(S\) (sensitivity)--as seen in Figure 9. The bipolar system delivers higher values of \(\Delta Z(t)\), while the signal is weaker and more interfered with noise (Figure 9). The tetrapolar system is practical when targeting arteries can be implemented, as reported in the literature before [15]. Such knowledge is important for algorithm development--a reasonable electrode configuration choice enables accurate HR estimation. Secondly, the acquired signals underwent complex processing with the goal of adopting techniques and algorithms for HR estimation from the EBI signals. In Figure 10, the left plot demonstrates the performance of the 'findpeaks' method when applying BPF and Savitzky-Golay filters. The scatter of points across the SNR and BPM difference ranges is noticeable, indicating variability in performance. Notably, the BPM consistently achieves higher SNR values, distributed more centrally and towards the upper third of the plot. To better analyze the scatter plot, the centroid of both data clusters was added as a diamond symbol with the same color as the corresponding data clusters. As can be observed from the centroid locations, the BPM achieves the highest SNR values and lowest BPM differences in the 'findpeaks' method's case, leaving the Savitzky-Golay processing method behind with 10 points of BPM differences, as well as with an approximately 1 dB lower SNR result for the relevant dataset. In Figure 10, the right plot illustrates the 'BPM DL' method's performance under the same filtering conditions. Here, both results from filtered data show a pronounced clustering of points in the lower BPM difference region. Similar to 'findpeaks' scatter plot,the BPM calculation indicates better performance in terms of SNR and DIFF\({}_{\text{BPM}}\) than the Savitzky-Golay filtering method. In this case, the difference between these two methods is much closer compared to DIFF\({}_{\text{BPM}}\) and SNR values. In summary, across both plots, the BPF filter exhibits higher SNR values, suggesting effective noise management as well as in the BPM difference case. Since the signal is more smoothed compared to the one obtained with the Savitzky-Golay filter, see Figure 6, BPM estimation algorithms are less likely to detect any false peaks. The 'BPM DL' method outperforms 'findpeaks' in terms of noise robustness and BPM accuracy, particularly evident in its ability to cluster data points at lower BPM differences across a broad range of SNRs. This analysis underscores the critical impact of choosing appropriate filtering techniques and processing algorithms on the accuracy and reliability of BPM measurements. In evaluating the performance of 'findpeaks' and 'BPM DL' algorithms based on the presented colormap graphs and scatter plots, it is clear that 'BPM DL' demonstrates significantly better results.