## 1 Introduction  
Heart rate (HR) is one of the four vital signs, serving as a key indicator of human psychophysiological condition. For example, heart rate variability (HRV) provides insights into the functionality of the autonomic nervous system [1]. An overview of the physiological basis of both HR and HRV can be found in [2]. Resting heart rate (HR) typically ranges between 60 and 100 beats per minute (BPM). The primary types of arrhythmias are classified as bradycardia (\(<\)60 BPM), tachycardia (\(>\)100 BPM), fibrillation, and premature contraction [3]. Therefore, accurate HR and cardiovascular parameter estimation are critical tasks in medical and personal healthcare practices (see an excellent review in [4]). Electrical bioimpedance (EBI), which estimates the electrical properties of biological tissues, is a promising tool for noninvasive and unobtrusive cardiovascular parameter acquisition [5]. The volume changes in arterial blood flow appear in the measured EBI signal as amplitude modulation, reflecting hemodynamic parameters including cardiovascular condition and heart functionality [6]. A well-known application of EBI is impedance cardiography (ICG) [7], which relies on the first derivative of the thoracic EBI signal. ICG can output timely changes in impedance, from which numerous cardiodynamic parameters can be estimated, such as stroke volume and cardiac output [6]. Recent studies have also shown that ICG can be extracted from the arm and wrist [8; 9]. The importance of the HR acquisition site has been emphasized in the literature (e.g., in [10]) For instance, the thorax is a classical site for heart-related signal acquisition, as seen in ICG [7]. Another traditional method involves detecting HR-related volume changes between two limbs (such as the legs [11]). While the wrist is a convenient location for wearing a device, being a common site for a wristwatch, the cardiac volume change-related modulation in the measured signal is shallower and more prone to motion artifacts, which necessitates more intensive signal processing. EBI-based measurement systems, particularly those designed for autonomous wearable use on the wrist, are relatively new. As a result, there is no widely accepted standard HR estimation algorithm specifically tailored for this method. While established HR estimation algorithms exist for photoplethysmography (PPG) and electrocardiography (ECG) [12], due to the fundamental differences in signal acquisition, these algorithms cannot be directly applied to EBI signals. Tailored algorithms, like the one presented in this paper, are needed to optimize HR estimation in EBI systems and address the unique challenges posed by this modality. At the same time, EBI-based HR estimation offers several advantages over traditional methods such as PPG and ECG, particularly in wearable applications. They include:  
* Increased resistance to measurement errors caused by varying skin tones, which can affect the accuracy of PPG systems. EBI measurements are based on the electrical properties of tissues, making the signal consistent across all skin types [13]. * Ability to measurement additional physiological parameters such as breathing, hydration, and sweating. This added capability makes EBI a more versatile tool for comprehensive health monitoring. The two basic electrode systems for EBI measurement are bipolar (1) and tetrapolar (2) [6]. In the first case, a single pair of electrodes is used for both excitation and measurement [6; 14]. In the second case, separate pairs of electrodes are used for these tasks. When using the bipolar system, the electrode impedances, lead wires, and contact interface are measured as a sum of the impedance values in the object [6]. The applicability of the bipolar system for forearm EBI measurements has been reported in the literature before [15; 16; 17].