## 1 Introduction
However, these studies were either limited to simulation models [15], small trials with a single configuration [16], or focused on the electrode's physical properties without attention to the exact configuration and setup on the wrist surface [17]. The tetrapolar system has traditionally been used for measuring small impedance changes [18], such as in pulse wave detection [13]. Bipolar systems are typically used for body composition analysis [19] or to detect slow physiological processes, such as wound healing [20] or dehydration [21]. The potential applicability of the bipolar system for EBI-based pulse wave monitoring has been highlighted in [16], noting only a slightly lower signal-to-noise ratio (SNR) compared to other methods. Various SNR evaluation approaches have been described in the literature (e.g., [22]). The SNR evaluation method applied in this research is thoroughly described in [17; 23]. EBI measurements are highly susceptible to motion artifacts, particularly in bipolar systems, as these artifacts stem from the skin-electrode interface and tissue movements like muscle contractions [24; 25]. Estimating hemodynamic parameters during daily activities is complex and requires advanced solutions. Approaches to handling motion artifacts include: (1) reducing artifacts at the sensor level [26], (2) post-processing the signals using multi-sensory methods such as accelerometers [27], or (3) deriving useful information directly from the signal [27]. This research combines EBI signal acquisition techniques with custom algorithms for HR estimation from the forearm, while also considering efforts to capture hemodynamic data from peripheral sites like the carotid, posterior tibial, and brachial arteries [28; 29; 30], accounting for both noisy and high-quality signals in algorithm development. Signal processing is a crucial component, especially in wearables. While it is typically focused on photoplethysmography (PPG) sensors, where the signal quality is higher [31], applying it to EBI signals poses a significant challenge and remains a relatively under-researched area [30; 32; 33]. Traditionally relying on peak detection [34], the preprocessing stage is of paramount importance [35]. Although the dynamic properties of heart rate (HR) have been addressed in the literature before [36], there has been less focus on practical, wearable-targeted algorithm development. The novelty of this paper lies in two key aspects:  
1. The evaluation of bipolar EBI measurement electrode system configurations (and electrode dimensions) for pulse wave acquisition from the wrist. 2. The development of an approach and algorithm for HR estimation from EBI signals acquired from the wrist. The combination of signal acquisition methods (e.g., electrode placements and configurations) and signal processing techniques is expected to improve the accuracy of HR and cardiovascular parameter extraction. The proposed approach introduces advances in preprocessing, including baseline removal, normalization, and cross-correlation. It is tested on EBI pulse signals of varying quality measured from the wrist, including signals acquired by both bipolar and tetrapolar electrode EBI configurations. The results help to identify electrode configurations suitable for practical applications. Specifically, we demonstrate that certain bipolar configurations can achieve similar signal-to-noise ratios (SNR) to the more widely studied tetrapolar setups. This has practical significance as the wearable devices with bipolar electrode setups are more robust (as the number of required skin-to-electrode contacts is reduced) and less cumbersome to wear.