## 4 Discussion  
In this database [25], four patient recordings did not reach 48 h, and several included artifact-laden segments, visually identifiable but unlabeled. Although QRS and PVC labeling was reviewed by an independent cardiologist to minimize bias, the absence of reference standards or published comparative methods limits the generalizability of the findings, emphasizing the need for future validation studies using standardized protocols. The QRS complex identification algorithm balances decomposition, coefficient handling, and reconstruction [26]. It reconstructs ECG signals to retain only QRS-related information and, when applied in 5 min blocks, achieved a PPV value of 97.53 \(\pm\) 0.05% for R-wave identification--comparable to other studies reporting 99.68% using similar techniques in different databases [26,27]. The algorithm's structure supports adaptive thresholding and resists heart rate variations and artifacts, making it suitable for long-term ECG recordings. The algorithm's moderate computational complexity enables processing of a 48 h record in about 13.1 min--an acceptable time for retrospective studies. The db4 wavelet's properties optimize spectral decomposition for QRS complex morphology. However, using only the lead with the highest R-wave amplitude, though optimal under ideal conditions, reduces its generalizability in multichannel recordings or low-amplitude signals. Since the analyzed data correspond to patients with low physical activity, motion artifacts are minimized. As shown in Figure 1b, time-frequency tools could complement this method for improved PVC identification. PVC identification using the CWT leverages time-frequency resolution to capture non-stationary signal features. The 2.6-3.2 Hz band was selected in line with previous findings linking arrhythmias to mid-frequency components between 0 and 4 Hz [28]. This range excludes low-frequency drift and high-frequency muscle noise. A dynamic threshold (40% of the maximum amplitude vector) adjusts to signal variability, avoiding fixed threshold limitations. Energy from wavelet coefficients is transformed into a one-dimensional vector for spike analysis. Using spike prominence (Figure 2b) enhances noise resistance but does not account for morphology variability. As PVCs differ across and within patients, from broad, high-amplitude complexes to narrower, asymmetric beats, detection remains challenging. Even so, the algorithm achieved a PPV of 85.83%, which is competitive with more complex systems that report 92.47% and 93.18% using expert systems and deep learning on other databases [29,30]. The correction of artifacts in RR intervals is crucial for HRV analysis reliability. The method employed--a moving average with an amplitude threshold--offers a simple and effective solution. However, performance depends on careful parameter tuning; too low a threshold yields false positives, while too high risks omitting significant artifacts. The use of neighbor-based averaging ensures continuity but is less accurate at signals extremes. Advanced interpolation techniques could improve performance in these edge cases. Dividing an hour into fourteen 5 min blocks for HRV analysis provides a finer resolution and aligns with Task Force recommendations [11]. This method improves the detection of intra-hour autonomic fluctuations, which are not reflected in hourly averages. It also allows the identification of critical periods needing closer attention, as suggested in earlier work [14]. Histograms of PVCs over 48 h recordings reveal that hemodialysis significantly impacts autonomic regulation and cardiac electrical stability [7, 8, 9]. SDNN drops during dialysis while PVCs' occurrence increases. Post-hemodialysis, SDNN partially recovers though PVCs persist, indicating residual instability. During the interdialytic period, SDNN remains low and PVCs decrease, but late increases in PVCs signal renewed instability, asseen in the green bars and orange trendline. These findings underscore the autonomic vulnerability of this period.