Cuff-less continuous blood pressure estimation from Electrocardiogram(ECG) and Photoplethysmography (PPG) signals with artificial neural network

Abstract

Continuous blood measurement important information about the health status of the individuals. Conventional methods use a cuff for blood pressure measurement and cannot be measured continuously. In this study, we proposed a system that estimates systolic blood pressure (SP) and diastolic blood pressure (DP) for each heart beat by extracting attributes from ECG and PPG signals. Simultaneous ECG and PPG signals from the PhysioNet Database are pre-processed (denoising, artifact cleaning and baseline wandering) to remove noise and artifacts and segmented into R-R peaks. For each heartbeat, 22-time domain features were extracted from ECG and PPG signals. SP and DP values were estimated by introducing these 22 attributes to the model of Lavenberg-Marquardt artificial neural networks (ANN). Arterial blood pressure (ABP) was also taken from the PhysioNet MIMIC II database along with ECG and PPG signals. ABP signals have been used as targets in the artificial neural network. The system performance has been evaluated by calculating the difference between the estimated ABP values and the actual by the ANN model. The performance value between the predicted SP and actual SP values is −0.14 ± 2.55 (mean ± standard deviation) and the performance value between estimated DP and actual DP values is −0.004 ± 1.6. The obtained results have shown that the proposed model has predicted blood pressure with high accuracy. In this study, SP and DP values can also be measured directly without any calibration in blood pressure estimation.