Deep learning models for cuffless blood pressure monitoring from PPG signals using attention mechanism

Abstract Hypertension or high blood pressure is a major health problem worldwide and primary risk factor for cardiovascular disease. Blood pressure is one of the four vital signs that provides important information regarding patients’ cardiovascular system conditions. Continuous and regular blood pressure monitoring is essential for early diagnosis and prevention of cardiovascular disease. Considering the existing invasive or cuff-based blood pressuring monitoring techniques in clinical practice, several studies have identified motivation and advantages of a new non-invasive and cuffless blood pressuring measurement technique using Photoplethysmogram (PPG) signals. In this study, we propose several systolic and diastolic blood pressure estimation models using recurrent neural networks with bidirectional connections and attention mechanism utilising only PPG signals. The models were evaluated on PPG and blood pressure signals derived from the Multiparameter Intelligent Monitoring in Intensive Care II database. In the process, 22 characteristic features were extracted from the PPG waveform followed by various dimensionality reduction techniques to eliminate redundancies and reduce computational complexity. The proposed models were evaluated on both the 22-feature set and the reduced input feature vector, respectively. The models were compared with four machine learning techniques commonly used in the literature. Experimental results demonstrated that the proposed models could capture the non-linear relationship between the PPG features and blood pressure with high accuracy and outperformed the conventional machine learning methods on both datasets. The results for all the proposed models were acceptable by the global standards set by the Association for the Advancement of Medical Instrumentation for cuffless blood pressure estimation.