Blood Pressure Estimation Using Photoplethysmogram Signal and Its Morphological Features

In this paper, we present a machine learning model to estimate the blood pressure (BP) of a person using only his photoplethysmogram (PPG) signal. We propose algorithms to better detect some critical points of the PPG signal, such as systolic and diastolic peaks, dicrotic notch and inflection point. These algorithms are applicable to different PPG signal morphologies and improve the precision of feature extraction. We show that the logarithm of dicrotic notch reflection index, the ratio of low- to high-frequency components of heart rate (HR) variability signal, and the product of HR multiplied by the modified Normalized Pulse Volume (mNPV) are the key features in accurately estimating the BP using PPG signal. Our proposed method has achieved higher accuracies in estimating BP compared to the previously reported methods that only use PPG signal. For the systolic BP, the achieved correlation coefficient between the estimated values and the real values is 0.78, the mean absolute error of the estimated values is 8.22 mmHg, and their standard deviation is 10.38 mmHg. For the diastolic BP, the achieved correlation coefficient between the estimated and the real values is 0.72, the mean absolute error of the estimated values is 4.17 mmHg, and their standard deviation is 4.22 mmHg. The achieved results fall within Grade A for diastolic, Grade C for systolic and Grade B for mean BP based on BHS standard.