Advancing cuffless blood pressure estimation: A PPG-based multi-task learning model for enhanced feature extraction and fusion

Abstract

Cuffless continuous blood pressure (BP) monitoring is essential for personalized health management. Although existing cuffless BP estimation applies advanced machine learning techniques and integrates PPG signals, it is deficient in feature extraction and fusion. In addition, it is inefficient to train the model separately for different tasks. In this study, an advanced multi-domain and local–global feature parallel multi-task learning network (MDLG-MTLNet) is introduced. The MDLG-MTLNet was designed with three key aspects: first, temporal and multi-scale spatial features were extracted from PPG signals and their derivatives via a multi-scale spatial and temporal feature block; interaction of features from different domains was facilitated by the introduction of a local–global attention module that captured and efficiently fused local–global information; and lastly, intrinsic correlation between systolic (SBP) and diastolic blood pressure (DBP) was taken into account via a multi-task learning network to improve the overall performance of the model. On the MIMIC-II dataset, the MAEs of MDLG-MTLNet for DBP, SBP, and MBP were 2.64 mmHg, 1.57 mmHg, and 2.02 mmHg, respectively. These errors were superior to those of the existing methods, meeting the AAMI criteria, and earning an A grade according to the BHS protocol. The experimental results confirm the effectiveness of our proposed model in feature extraction and fusion.