## 1 Introduction  
Cardiac health is one of the most critical issues in modern times. Every year, millions of people die from various cardiovascular diseases (CVDs) [1]. Early diagnosis of CVDs through electrocardiography (ECG) can significantly reduce mortality rates, as timely intervention allows more effective cardiological treatments [2]. Therefore, numerous studies are conducted, and many sensor-based ECG processing devices are proposed. The accuracy of these electronic devices depends on the precision of the R-peak detectors. The detection is usually performed using real-time ECG signal acquisition. The process typically involves monitoring and measuring heart activity for a certain period by placing electrodes in different combinations at specific points on the human body [3] and then applying a peak detection algorithm to it. However, this is only possible if the algorithm is previously validated on publicly available ECG datasets. The publicly available ECG datasets can be categorized into two groups. The first group consists of general datasets that cover a wide range of clinical real-life conditions, including different ECG noises and various heart arrhythmic anomalies. Examples of these include the MIT-BIH arrhythmia dataset [4], QT database (QTDB) [5], Physikalisch-Technische Bundesanstalt eXtended (PTB-XL) diagnostic database [6], and St. PetersburgInstitute of Cardiological Technics (INCART) database [7]. The second group consists of specific datasets dedicated to addressing certain noisy conditions or a particular arrhythmic heart anomalies, such as the MIT-BIH Noise Stress Test database (NSTDB) [8], European ST-T database [9], and Long-Term Atrial Fibrillation database [10]. The MIT-BIH arrhythmia database is the benchmark for evaluating R-peak detectors before applying methodologies to real-time sensor-based ECG devices [11; 12]. This is due to its historical significance as the first publicly available dataset designed for arrhythmia analysis, featuring diverse cardiac abnormalities, clinical noise, and artifacts. It is a widely adopted dataset by researchers for comparison with earlier studies. This dataset is also well-organized and comes with dedicated tools to simplify its processing. Each recording in these datasets consists of multiple ECG signals, including components like the P-wave, QRS complex, and T-wave, which offer valuable insights into the heart's electrical activity and overall health condition [13]. CVDs, especially arrhythmic pathologies, are analyzed by detecting R-peaks within QRS complexes [14; 15]. R-peaks are important for arrhythmia detection and analysis for several reasons. First, they are the gold standard for calculating heart rate, measured by the time interval between consecutive R-peaks (RR intervals) [16; 17]. Since arrhythmias often involve irregular heart rhythms, detecting R-peaks enables precise measurement of these fluctuations, whether too fast or too slow [18]. Second, R-peaks mark ventricular depolarization, a key phase of the cardiac cycle. Their timing and shape, in relation to other ECG components (P-wave, T-wave), help diagnose arrhythmic pathologies and assess their effect on heart function [19]. Third, R-peaks are crucial for arrhythmia classification, as specific RR interval patterns are key to distinguishing different arrhythmias [20]. For example, atrial fibrillation shows irregular RR intervals, while ventricular tachycardia presents rapid, repetitive R-peaks [21; 22]. Therefore, accurate R-peak detection is vital for properly classifying these conditions. Once these R-peaks are detected while developing a detection algorithm, the next step is to validate them by comparing the results with reference annotations using the window concept. This validation can be performed using tools provided by the waveform database (WFDB) or by developing a custom testing methodology.