Cuff-less continuous blood pressure measurement based on multiple types of information fusion

Abstract

Non-invasive and cuff-less real-time continuous blood pressure monitoring plays an important role in both intensive care unit (ICU) and the management of chronic diseases such as hypertension and cardiovascular disease. Although pulse wave transit time (PTT) and pulse waveform parameters (PWPs) can be extracted from photoplethysmography (PPG) and electrocardiographic (ECG) to estimate blood pressure, the value of blood pressure is also constrained by personal characteristics parameters (PCPs). For example, the blood pressure may vary with height, weight and age. In this paper, we present a cuff-less continuous blood pressure measurement method based on multiple types of information fusion. First, we recruited 186 volunteers and measured their personal data (PPG signal, ECG signal, height, age, weight, gender). Second, extract the PTT and PWPs from the PPG signal and ECG signal. Then, according to the extracted parameters, the blood pressure models of control group and experimental group were established respectively (the experimental group uses the multiple types of information fusion method to model, the control group only considers PTT and PWPs). Experiments show that, comparing with the control group, the experimental group systolic blood pressure (SBP) correlation was increased from 0.9355 to 0.9948, root mean square error (RMSE) was reduced from 5.2 mmHg to 1.5 mmHg, while the diastolic blood pressure (DBP) correlation was improved from 0.9331 to 0.9931, RMSE was reduced from 2.9 mmHg to 0.9 mmHg. Therefore, the modeling method proposed in this paper can get more accurate blood pressure values. Further promotion, to achieve the measurement with the "modeling" method, all "restriction" elements must be taken into account in order to obtain a more accurate and stable model.