Senkron Sikiştirma Dönüşümü Ve Deri̇n Öğrenme Kullanilarak Fotopleti̇smografi̇ Tabanli Kan Basinci Kesti̇ri̇mi̇

Abstract

Cardiovascular diseases are one of the deadliest health problems. Hypertension is the most common reason for cardiovascular diseases. Keeping the blood pressure (BP) level under control is the only way to protect against the deadly results of hypertension. Therefore, monitoring BP regularly makes it possible to detect dangerous conditions in patients with hypertension. With the rapid developments in computers and sensor technologies, it is becoming possible to monitor BP levels continuously by using photoplethysmogram (PPG) signals. This work presents a non-invasive BP prediction method using one channel PPG signal. We employed the Synchrosqueezing Transform to obtain Time-Frequency (TF) images of the PPG signals. The TF images were used to feed a pre-trained deep neural network. We estimated the BP levels inside the 5-second intervals. Our method estimates BP levels with a mean error (ME) of 0.2148 mmHg and -0.0370 mmHg in the systolic and diastolic blood pressure (SBP and DBP) respectively. The ME values of our method are in the applicable levels. The standard deviation (SD) of our method is 5.0642 mmHg for DBP and 10.9904 mmHg for SBP. The upper limit specified by the AAMI is 8 mmHg. Also, our method is coherent with grades A and B according to the BHS standard.