Determination of hypertension disease using chirp z-transform and statistical features of optimal band-pass filtered short-time photoplethysmography 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 h 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 intelligent models have been proposed. One of these techniques is photopletesmography (PPG). In this study, a model for the detection of hypertension disease in individuals was proposed using chirp z-transform and statistical features (total band power, autoregressive model parameters, standard deviation of signal’s derivative and zero crossing rate) of optimal band-pass filtered short-time PPG signals. The proposed method was successfully applied to 657 PPG trials, which each of them had only 2.1 s signal length and achieved a classification accuracy rate of 77.52% on the test data. The results showed that the diagnosis of hypertension can be performed effectively by chirp z-transform and statistical features and support vector machine classifier using optimal frequency range of 1.4–6 Hz.