Novel Data Augmentation Employing Multivariate Gaussian Distribution for Neural Network-Based Blood Pressure Estimation

Abstract

In this paper, we propose a novel data augmentation technique employing multivariate Gaussian distribution (DA-MGD) for neural network (NN)-based blood pressure (BP) estimation, which incorporates the relationship between the features in a multi-dimensional feature vector to describe the correlated real-valued random variables successfully. To verify the proposed algorithm against the conventional algorithm, we compare the results in terms of mean error (ME) with standard deviation and Pearson correlation using 110 subjects contributed to the database (DB) which includes the systolic BP (SBP), diastolic BP (DBP), photoplethysmography (PPG) signal, and electrocardiography (ECG) signal. For each subject, 3 times (or 6 times) measurements are accomplished in which the PPG and ECG signals are recorded for 20 s. And, to compare with the performance of the BP estimation (BPE) using the data augmentation algorithms, we train the BPE model using the two-stage system, called the stacked NN. Since the proposed algorithm can express properly the correlation between the features than the conventional algorithm, the errors turn out lower compared to the conventional algorithm, which shows the superiority of our approach.

Using Data Augmentation to Improve the Accuracy of Blood Pressure Measurement Based on Photoplethysmography

“…Although some research shows that generating synthetic time series data can improve the performance of regression models [30,31], there is a lack of research on data augmentation in the regression problem of BP measurement based on PPG. Song et al [32] paid attention to data augmentation in BP measurement, but they used multivariate Gaussian distribution to generate PPG features rather than raw PPG data. Wu et al [33] designed GAN to generate synthetic remote photoplethysmography (rPPG), which can reduce errors in BP measurement.…”