Dual - Stage Learning Approach Towards Continuous Cuffless Blood Pressure Monitoring

Abstract

Hypertension, being one of the associated factors of cardiovascular diseases needs to be monitored on the continuous manner to track the rapid BP changes. The paper proposes a dual-stage blood pressure estimation approach using the suitable features from Photoplethysmogram and machine learning models. The method initially classifies the given data among 4 classes given by British Hypertension Society (BHS). Further, the classified data is predicted from one of the four models. The 105 medical records consisting of clinical and digitalized signal data of 125 Hz are taken from the MIMIC-III database for the process. The dual-stage approach for the classification and estimation of BP outperforms the existing method by relative improvement in the MAE and RMSE by 64.4% and 36.37 % for systolic BP and 40.1% and 22.9% for diastolic BP respectively.