## 4 Discussion  
No pathological change in the ECG record was found by supervising physician during the measurement. This statement is also supported by the results where most parameters did not show a statistically significant difference between the compared time series. Therefore, there are no obvious changes in records before, during, and after the HIMS application. However, in the case of long-term variability parameters, i.e., SDHR parameter, a statistically significant difference among data obtained before, during, and after the HIMS application was observed. Similarly, in the case of the RMSSD parameter, significant differences are observed. The RMSSD parameter is then linked to a short-term variability, i.e., variability between individual heartbeats [19]. Furthermore, significant differences were observed in the NN50 and pNN50 parameters, which also relate to variability [19]. Thus, it is becoming clear that the signal variability varied during the experiment, and therefore the HIMS application affects the variability of the heart rhythm in some way, although this is not obvious at first sight. Looking at the results related to frequency analysis, it is evident that there are almost no significant transitions. The only significant transition had shown between the state during and after the HIMS application in the low-frequency band. This band is mainly related to parasympathetic activity [19]. We can declare, however, that it is not possible to form conclusions on influencing the heart rhythm of HIMS application based on the frequency analysis, and from the perspective of frequency analysis, it appears that the HIMS application may not have a more substantial influence on cardiac activity. As was previously mentioned, biological processes are burdened with fluctuations, and therefore linear analysis does not seem to be an appropriate choice [45]. On the other hand, these results can be used as a baseline for further progress. By using the standard methods, it is possible to assume the influence on the variability of the heart rhythm through parameters that correspond to short and long-term variability. For these reasons, we carried out a recurrence quantification analysis. The analysis is based on chaos theory and takes into account the nonlinear behavior of the signal [23]. In the case of the RQA, particularly transitions between states pre and during the HIMS application were observed. This was the case with the RR parameter, which indicates the variability of the system. The results show that in the case of the dataset with the application of HIMS, there is an increase of RR and, thus, a decrease of variability. Other transitions were found in the parameter L, representing the average time at which 2 segments in the phase space are close together [23], in the ENT parameter or rather Shannon entropy indicating the degree of chaos in the system [46], and finally in the TND parameter quantifying the stationarity of the system [23]. Taking into account the fact that RQA parameters are used as indicators of complexity, or in other words, complex variability [22], especially the ENT, it is clear that the HIMS application in the chest area can induce some changes in complexity. On the other hand, there were relatively few significant transitions, and it seems that the subjects' hearts (i.e., hearts functioning without any pathologies) appeared to be highly resistant to the HIMS application. The high resistance of the physiologically functioning electrical conduction system of the heart and the minimal possibility of affecting the operation of the sinoatrial node by low-frequency electromagnetic fields in a healthy heart is evident. The explanation for this phenomenon can be hypothetically found in the excellent electrical conductivity of the pectoral muscles as well as of the conductive serous fluids found between the _pleura parietalis_ and the _pleura visceralis_, as well as between the _pericardium_ and _epicardium_.