## 4 Discussion
One limitation of our study became apparent following changes in HbO\({}_{2}\), HHb, total hemoglobin, and CBFV which were influenced by simultaneous occurrences such as spontaneous baseline shifts, the administration of anesthesia and epinephrine, and various attempts to induce VF prior to its occurrence. The CHS model may be too simple to properly account for all such events and artifacts. This can at least partially explain why the application of the model resulted in good data fits when R\({}^{2}\) > 0.5 in only 63% of the datasets (17 out of 27). In future research, we aim to incorporate established techniques for manipulating CBF during the baseline and ROSC phases, including inducing hypercapnia, adjusting ventilation rates, and implementing similar approaches. Another possible reason behind the differences between the model results and the readings for HHB and HbO\({}_{2}\), in particular those seen in Figure 2 after the beginning of CA, is that in the current version of the CHS model, the parameters listed in Table 1 are considered to be fixed for a given time interval. However, as our analysis shows, at least \(cHb\) and \(F^{(c)}\)--and possibly other parameters--can change during and after VF, leading to deviations between the real HHB and HbO\({}_{2}\) temporal traces and those predicted by the model with the fixed parameters. We are currently working on the further development of our dynamic model and an inversion algorithm to account for the dynamic changes in the model parameters. Another important aspect of this study is its applicability to human studies. While there have been studies that have demonstrated significant structural and cellular compatibility between pigs and humans in various organs, such as the liver [32] and those of the cardiovascular system [33], the compatibility of the microvasculature of the brain in pigs, which was the focus of our investigation, requires more comprehensive examination. Although laser Doppler flowmetry (LDF) is an established and commercially available technique [34], it cannot measure the blood flow in the human brain non-invasively. Diffuse correlation spectroscopy (DCS) is a novel technique that measures average microvascular blood flow in tissue volumes [35]. A fast increase in the rate at which DCS is applied to brain measurements has recently started. In particular, in [36], simultaneous spatially resolved DCS and NIRS were used to non-invasively measure cerebral microvascular parameters in humans during transient hypotension. The cerebral blood flow measurements were separated from the extracerebral contributions to the DCS signals using a combination of the short (1.5 cm) and long (2.5 cm) source-detector distances. The feasibility of using a combined hNIRS/DCS system in cardiac patients during cardiac surgery was shown in [37]. The application of the CHS model to the hNIRS data from the pig CA experiments added a practical dimension to the application of CHS, although transitioning to human parameters presents challenges. Continuing exploration and refinement of this model may lead to the elucidation of optimal CPR outcomes for the brain. This endeavor, in turn, could significantly contribute to the improvement of recovery rates following sudden CA. Hemodynamic models can play a pivotal role in the interpretation of data from invasive and non-invasive optical imaging methods like NIRS across various microvascular hemodynamic and oxygen metabolism scenarios. This model's utility extends to scenarios involving oscillations, where utilizing the model in the frequency domain is effective. In our study of cardiac arrest in pigs, because of the singular nature of cardiac arrest and the absence of repetitive phenomena, we employed the model in the time domain. Our investigation was initially focused on two models: the CHS model ultimately employed in our study, and another developed by a research team at the University of Essex [7]. Integrating the first model into our research marked a significant step forward.