# Correlation Mapping of Perfusion Patterns in Cutaneous Tissue
## 1 Introduction  
The number of people suffering from diabetes mellitus (DM) is increasing worldwide. According to [1], the number of registered people with diabetes mellitus has quadrupled over the past 40 years and is still rising while we can also observe an increase in DM mortality. In addition, DM is the leading cause of blindness, kidney failure, heart attacks, strokes, and lower-limb amputations. This information also points to the fact that more attention needs to be paid to this disease. It is not just a metabolic disorder in the body but a complex disease that affects the kidneys, the nervous system, and the cardiovascular system in the long term. Diabetic foot is the leading cause of lower extremity amputation worldwide. Therefore, it is crucial to investigate methods of early diagnosis of this disease, as well as what methodology or instruments should be used to prevent not only macrovascular but also microvascular damage in diabetic foot syndrome. One way to monitor microcirculation changes induced by DM is non-invasive and non-contact cardiovascular diagnostics based on optoelectronic methods--photoplethysmography imaging. This technique of investigating cutaneous tissue perfusion represents a significant step forward and has seen increased development in recent years, which is also related to technological advances [2]. Photoplethysmography imaging (PPGI) is based on conventional photoplethysmography (PPG) first published in [3] but operated without contactwith patient skin. A significant milestone were the works of [4; 5] which transformed contact PPG into a completely contact-free method of investigating cutaneous perfusion using a camera system. The PPGI technology finds its application in the monitoring of many physiological or pathophysiological processes, among which we can include vital signs monitoring [6; 7; 8; 9; 10; 11], heart rate variability [12], oximetry [13], pulse transit time assessment [14], wound assessment [15; 16], driver state estimation [17; 18; 19], or pain evaluation [20]. Tissue perfusion changes are also related to respiratory activity. For example, [21] confirm changes in perfusion measured by PPG in the fingers during brachial artery occlusion. The shape of the PPG curve varies according to the measurement site as well as depending on the breathing pattern [22]. Thus, the PPG signal can also be used to extract information related to respiratory activity [23; 24]. During spontaneous breathing and its controlled form, the pulse wave propagation velocity also changes, as documented, e.g., in [25]. Early assessment of the vascular system state could be beneficiary also for patients suffering from DM. Therefore, this work combines the PPGI measurements and correlation mapping to understand better and evaluate quantitative and qualitative changes in the skin perfusion of patients suffering from DM. Here, the non-invasiveness of PPGI makes it possible to sense skin perfusion during all phases of diabetic foot syndrome. In this way, we also avoid the discomfort caused by cables connected to probands. To track perfusion changes, we need to induce them. Several tests elicit complex cardiovascular responses allowing for assessment of changes, e.g., in heart rate, blood pressure, and length of RR intervals. The results of these tests depend on several tracked physiological parameters and used systems. Validated cardiovascular tests include the deep breathing test (DBT) [26], the orthostatic test [27], and the Valsalva test [28]. For our experiment, the deep breathing test was chosen, representing a relatively simple cardiovascular test used clinically to assess the effect of ANS on heart rate and peripheral perfusion regulation [29]. This work aims to create an appropriate methodology for processing and evaluating perfusion data obtained through a camera synchronously with auxiliary PPG, ECG, and respiratory waveform. These auxiliary data should allow for improved processing and analysis of PPGI signals. The proposed methodology is tested in our case on a small sample of subjects.