Blood Pressure Estimation Using Deep Learning based on Attention Mechanism

Abstract

Abstract Recently, several studies have proposed methods for measuring cuffless blood pressure (BP) using finger photoplethysmogram (PPG) signals. This study presents a new BP estimation system that measures PPG signals under progressive finger pressure, making the system relatively robust to errors caused by finger position when using the cuffless oscillometric method. To reduce errors caused by finger position, we developed a sensor that can simultaneously measure multi-channel PPG and force signals in a wide field of view (FOV). We propose a deep-learning-based algorithm that can learn to focus on the optimal PPG channel from multi channel PPG using an attention mechanism. The errors (ME±STD) of the proposed multi channel system were 0.43±9.35 mmHg and 0.21±7.72 mmHg for SBP and DBP, respectively. Through extensive experiments, we found a significant performance difference depending on the location of the PPG measurement in the BP estimation system using finger pressure.

Heesang Eom

Researcher, Seoul National University Hospital

His research interests include bio-signal processing, deep learning algorithms and model optimization.

Seungwoo Han

PhD Student, Tokyo University of Agriculture and Technology

His research interests include graph signal processing and representation learning.

Cheolsoo Park

Professor

His research interests include machine learning, adaptive signal processing, computational neuroscience, and wearable technology.