The objective of this work is the study of biosensors for monitoring parameters and diagnosing vital functions during first medical emergencies. The study and analysis of vital parameters are extremely important in emergency medicine.
The principle is based on the combination of signals coming from the patient (vital functions) and involves the measurement and comparison of the phase of active and reactive components of biologically active points (BAPs), the transduction of these acquired signals, and the processing of the obtained information. One of the advantages of reflex diagnostic methods is that the response of BAPs reflects changes in the internal structure of the human body. These signals provide instantaneous information about the functional state of 20 basic organs and systems in the human body.
The method will use one input variable (classical physiological parameters and/or signals detected using additional sensors) and one output variable, which is correlated with the clinical condition of the patient. High information volume, accuracy, reliability, and reproducibility of data are maintained in parallel with emergency diagnostics. A model will establish an association between the input variables and the output variable using a dataset developed in collaboration with the medical team.
The proposed methodology enhances standard systems such as reflex diagnostics, track-and-trigger mechanisms, and threshold-based approaches (e.g., Early Warning Score). It has been shown to achieve good results in the prediction and early diagnosis of first medical emergencies through the adoption of Fuzzy Set Theory.
