A sparse regression based approach for cuff-less blood pressure measurement

This paper proposes a sparse regression based approach for accurate continuous Blood Pressure (BP) monitoring. ECG and Finger PPG signals serve as the input; from which 32 parameters are extracted. Not all parameters are indicative of BP; to automatically trim the redundant parameters a sparse regression based approach is proposed. To build the BP predicting model the necessary parameters and their corresponding weights are learned using data from 99 subjects. The learned model is applied on 10 test subjects. The ground truth BP is measured using a clinically proven, professional automatic digital BP monitor OMRON HBP1300., The BP prediction results show that the SBP/DBP mean absolute error and error standard deviation, with OMRON monitor as a reference, is 4.43/2.46 and 4.90/3.31 mmHg respectively, which falls under the standard allowable error mentioned by Association for the Advancement of Medical Instrumentation for estimation of BP. We have compared our work with other BP prediction techniques (Linear Regression and Feed Forward Neural Network) and have seen that our proposed method yields considerably better results, especially for diastolic BP.