## 6 Conclusions  
To summarize, this work presents a small-scale experimental study on electrode placement and number for recording bioimpedance signals at the wrist. The signals are processed with a custom signal processing pipeline and algorithm to: (1) detect signal quality using a custom SNR metric and (2) estimate the subject's heart rate (HR). The HR detection results are validated using PPG as the reference signal.  
The main findings include:  
* Confirmation that focusing on the signal source (i.e., the pulsating flow of arterial blood) offers benefits, demonstrating that it is feasible to use peripheral body sites, such as the wrist, for noninvasive and accurate HR acquisition. The distal placement provides higher \(\Delta Z(t)\) (up to 0.231 \(\Omega\)) when electrodes are positioned on both wrist arteries.
* Quantification of the advantages of the tetrapolar electrode setup, with further evidence that bipolar setups are also feasible. Generally, the bipolar system results in a larger \(\Delta Z(t)\), while the tetrapolar setup provides greater sensitivity.
* Comparison of bandpass and Savitzky-Golay filters during the preprocessing stage. The bandpass filter consistently yields higher SNR metrics and lower BPM estimation errors, when used with either of the two HR estimation algorithms. Specifically, the bandpass filter achieves 3.6 dB average SNR for the best bipolar setup and 4.8 dB average SNR for the tetrapolar setup, while the Savitzky-Golay filter shows 2.6 and 3.3 dB SNR, respectively.
* Comparison of the custom HR estimation algorithm ('BPM DL') with the standard 'findpeaks' method as implemented in MATLAB. The 'BPM DL' algorithm shows a reduction in both relative and absolute HR estimation errors. Specifically, 'BPM DL' achieves an absolute error as low as 1.8 BPM (with 3.1 BPM STD) for the best bipolar setup and 2.4 BPM absolute error (with 4.3 BPM STD) for the best tetrapolar setup combined with the preprocessing algorithm. The respective values for the 'findpeaks' method are 17.2 BPM (7.2 BPM STD) and 6.2 BPM (7.1 BPM STD). The differences in relative errors are similar.
* Of the four combinations of filtering algorithms paired with HR estimation algorithms, the 'BPM DL' algorithm combined with the bandpass filter demonstrates the best results across a range of noisy signals.  
Conceptualization, D.L. and M.M.; methodology, D.L., M.M. and A.K.; validation, D.L. and M.M.; formal analysis, D.L., M.M. and A.K.; investigation, D.L. and M.M.; data curation, D.L.; writing--original draft preparation, D.L., M.M. and A.K.; writing--review and editing, D.L., M.M., R.J., O.M. and A.E.; visualization, D.L. and M.M.; supervision, O.M. and A.E.; project administration, D.L.; funding acquisition, O.M. and A.E. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Estonian Research Council (Grant PRG1483) and in part by the European Union, Grant Agreement n. 101095672 (project PRAESIIDIIUM \"Physics informed machine learning-based prediction and reversion of impaired fasting glucose management\").  
The study was conducted in accordance with the Declaration of Helsinki, and approved by the Life and Medical Sciences Research Ethics Committee of the University of Latvia (protocol number 71-35/160, approval date: 10 November 2023) for studies involving humans.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the ethical reasons as it contains sensitive personal data (cardiac information).  
The authors declare no conflicts of interest.