Photoplethysmogram-based Blood pressure evaluation using Kalman filtering and Neural Networks

The paper deals with the accurate evaluation of the Blood pressure (BP) by an Artificial Neural Network (ANN) and the Photoplethysmogram (PPG) signal. The proposed method allows evaluating the blood pressure for each heart beat, without using a cuff or invasive tool. For each heart beat, a fixed number of features, which characterize the PPG pulse, are extracted and given as the input to the ANN. A systolic, diastolic and mean BP are obtained as the output. The improvement of the BP evaluation accuracy is obtained by removing artifacts from the references used to train the ANN. The filtering of the reference inputs is performed with Kalman based filter in order to take into account the variability of the human pulse rate and cardiovascular system. Preliminary experimental results confirm the suitability of the proposal and asses the BP evaluation accuracy within 5±8 mmHg.