Cuffless Deep Learning-Based Blood Pressure Estimation for Smart Wristwatches

Abstract

In this article, we propose a cuffless blood pressure (BP) estimation technique based on deep learning for smart wristwatches. Photoplethysmography (PPG) and electrocardiography (ECG) signals are first collected from the sensors installed at a smart wristwatch. Ground-truth systolic BP (SBP) and diastolic BP (DBP) measurements are then obtained by a reference device, a mercury sphygmomanometer. In order to estimate the SBP and DBP, we extract feature vectors and reconstruct them through a feature selection process. Next, we design a two-stage system of stacked deep neural network (DNN)-based SBP and DBP estimation models and compare our results with those obtained using estimation techniques in the previously reported algorithms such as the polynomial regression (PR), support vector machine (SVM), artificial neural network (ANN), and deep belief network (DBN)-DNN. In order to verify the proposed algorithm against the conventional algorithms, we quantitatively compare the results in terms of mean error (ME) with standard deviation, mean absolute error (MAE) with standard deviation, Pearson correlation, box plot, and Bland-Altman plot. For this, 110 subjects contributed to the database (DB), each of which is collected three times for 20 s. The quantitative errors turn out to be lower than that of the existing methods, which shows the superiority of our approach. To enhance the BP estimation performance for each individual user further, we devise the personal adaptation algorithm for the BP estimation algorithm that yields better BP estimates.