Beat-to-beat ambulatory blood pressure estimation based on random forest

Abstract

Ambulatory blood pressure is critical in predicting some major cardiovascular events; therefore, cuff-less and noninvasive beat-to-beat ambulatory blood pressure measurement is of great significance. Machine-learning methods have shown the potential to derive the relationship between physiological signal features and ABP. In this paper, we apply random forest method to systematically explorer the inherent connections between photoplethysmography signal, electrocardiogram signal and ambulatory blood pressure. To archive this goal, 18 features were extracted from PPG and ECG signals. Several models with most significant features as inputs and beat-to-beat ABP as outputs were trained and tested on data from the Multi-Parameter Intelligent Monitoring in Intensive Care II database. Results indicate that compared with the common pulse transit time method, the RF method gives a better performance for one-hour continuous estimation of diastolic blood pressure and systolic blood pressure under both the Association for the Advancement of Medical Instrumentation and British Hyper-tension Society standard.