Determination of Hypertension Disease with Optimal Frequency Range of Short-Time Photopletismography Signals

Abstract

Hypertension is the condition where the normal blood pressure is high. This situation is manifested by the high pressure of the blood in the vein towards the vessel wall. Hypertension mostly affects the brain, kidneys, eyes, arteries and heart. Therefore, the diagnosis of this common disease is important. It may take days, weeks or even months for diagnosis. Often a device called a blood pressure holter is connected to the person for 24 or 48 hours and the person's blood pressure is recorded at certain intervals. Diagnosis can be made by the specialist physician considering these results. In recent years, various physiological measurement techniques have been used to accelerate this time-consuming diagnostic phase and propose intelligent models. One of these techniques is photopletesmography (PPG). In this study, a model for the detection of hypertension disease in individuals using the optimal frequency ranges of 2.1 second short-time PPG signals was proposed. The proposed model was tested with PPG data of 219 people and the disease was determined with classification accuracy of 76.15%. The results showed that the diagnosis of hypertension based on machine learning can be performed effectively by using frequency ranges of 1.4-5.7 Hz of short time PPG signals.