Comparison of Different Methods for Estimation of Arterial Blood Pressure Using PPG Signals

Abstract

The use of photoplethysmography (PPG) signals to predict the arterial blood pressure (ABP) waveform has gained popularity in recent years. Currently, there is a limited number of studies investigating this approach. This chapter elaborates a comparative analysis of two methodologies: a deep neural network approach and an encoder–decoder model for ABP waveform estimation with different window sizes expressed in seconds: 1s (175 signal points) and 4s (512 signal points). By estimating the systolic blood pressure (SBP) and diastolic blood pressure (DBP) scalars, this approach differs from conventional regression models that predict blood pressure through direct estimation; and it also enables another feature—evaluation of cardiovascular anomalies by analyzing the waveform patterns derived from the input PPG signal, which enables further medical analysis. The best obtained results are an R2 score of 0.76 for ABP, an MAE of 6.52 mmHg for DBP, using an encoder–decoder model on a sequence of 4s, and an MAE of 10.48 mmHg for SBP using GRU neural network on a sequence of 1s.