## 3 Results
### Patterns of the Presence of PVCs  
The analysis of the occurrence of PVCs for the 51 patients revealed three distinct patterns. The classification into three groups grounds in methodological considerations to ensure a robust subset of 24 records with unambiguous PVCs patterns, which was analyzed to minimize noise and enhance physiological relevance; gender balance (four men/four women per group) was enforced to mitigate confounding and improve generalizability; and the analysis focused on the hour of peak PVCs activity to prioritize clinically significant arrhythmic burden.  
Class 1 (Biphasic Peaks Post-Dialysis): Patients exhibited two PVC peaks separated by 6-12 h after hemodialysis, showing a delayed or biphasic physiological response. Figure 3a shows patient F1008 as an example of the identified pattern. This group is made up of patients F1001, F1008, F1017, F1020, M1014, M1022, M1023, and M1041, where F indicates female, and M is male. This group shows the highest occurrence of PVCs among the three classes analyzed, with a total of 2597 PVCs.  
Class 2 (Acute Peak During/Post-Dialysis): A single PVC cluster during or immediately after dialysis implies acute stress from the procedure itself, such as rapid fluid shifts, transient ischemia, or sympathetic activation. Figure 3b uses patient F1060 as an example of the identified pattern. This group is composed of patients F1005, F1015, F1035, F1060, M1007, M1030, M1049, and M1051. In this class, a total of 820 PVCs were found.  
Class 3 (Uniform PVC Distribution): Evenly distributed PVCs over 48 h indicate chronic arrhythmogenic factors unrelated to dialysis timing. Figure 3c uses patient M1016 as an example of the identified pattern. This group comprises patients F1013, F1028, F1029, F1044, M1002, M1016, M1018 and M1046. For this class, a total of 362 PVCs were found.  
Figure 2: Patient 1023; Graphical analysis of PVC identification with the proposed algorithm. (**a**) ECG signal; from the database annotation file, the PVCs (red dots) are identified. (**b**) The black triangle corresponds to the peak whose prominence value satisfies Equation (15) and identify a PVC. Relative amplitude corresponds to energy contributions from the PVCs-associated frequency band.  
Figure 3: The distribution and patterns of PVCs in three classes were analyzed. (**a**) Class 1 has the highest prevalence, with 2597 PVCs at the hour of highest occurrence. Example of the pattern proposed for Class 1 using patient F1008. (**b**) In Class 2, the PVCs are grouped in a single peak region, with a total of 820 PVCs at the hour of highest occurrence. Example of the pattern for Class 2 with patient F1060. (**c**) In Class 3, the PVCs have a lower prevalence, with 362 at the hour of highest occurrence the PVCs are distributed over the 48 h. Example of the pattern for Class 3 with patient M1016. The patterns for the three classes are described using a running average, with a window size of 4 points.  
The validation of the algorithm designed to identify PVCs in the ECG records was carried out using the same performance metrics described in Section 3.1, as seen in Table 3. An average PPV value in the 24 patients of 85.83 \(\pm\) 12.88% was obtained. The sensitivity obtained an average value of 80.95 \(\pm\) 17.38%. Finally, for the F-score, an average value of 0.8495 \(\pm\) 0.1193 was obtained.