Blood Pressure Estimation from Single Photoplethysmogram via Multi-modal Data Generation and Feature Fusion using Deep Learning.

With the increasing prevalence of cardiovascular diseases, accurate prediction and monitoring of human blood pressure have become increasingly important. In the context of continuous interdisciplinary advancements, non-invasive blood pressure monitoring techniques have also seen rapid development. However, most existing research relies on conventional computational or machine learning algorithms for feature engineering and is validated only on a small number of test sets, resulting in limited generalization performance and a lack of universality. In this study, we propose an innovative approach that combines multimodal data generation and feature fusion for model construction and training, utilizing the MIMIC dataset. This approach enables us to achieve an accurate estimation of human blood pressure using single PPG as input. Testing the trained model on a dataset that contains more than 60,000 sample segments produced encouraging experimental results. Additionally, through ablation experiments, we substantiated the efficacy of the data generation and feature fusion approaches advanced within this study. Furthermore, using a customized dataset as well as the University of Queensland Vital Signs Dataset, we carried out personalized calibration experiments for each participant. After the calibration procedure, the Mean Absolute Error (MAE) of SBP and DBP showed improvements of 9.60 mmHg and 4.32 mmHg, respectively. The result underscores the practicality of utilizing deep learning for blood pressure estimation while also highlighting the imperative nature of individualized calibration techniques.