Detecting the probability of stroke using blood plasma measurement and ECG examination using fuzzy logic

Disorders of the autonomic nervous system (ANS) complicate the clinical course in patients with acute stroke. This research presents a model that can be used to detect any deficiency in the autonomic nervous system and show its relationship with the occurrence of stroke. The data used in the present research is provided by using the studies conducted on the functioning of the autonomic nervous system in patients suffering from stroke (brainstem). The focus of this research is to examine the studies that present the function of the autonomic nervous system in the early period after stroke. This study determines the effect of stroke location on autonomic nervous system function. And by examining the vital parameters of the body, he tries to relate them to the functioning of the autonomic nervous system. In the final step, using other research, vital data associated with the functioning of the autonomic nervous system is identified. Then, using these data and using a fuzzy system based on Takagi-Sugno’s logic, the correct functioning of the autonomic nervous system is estimated with parameters in the heart, brain, and blood plasma measurements. This estimate helps to identify the patient's condition and his proximity and distance to stroke.