Blood pressure estimation using neural networks

Abstract

Oscillometry is an indirect method to determine blood pressure. An inflatable and debatable cuff is placed on arm to observe oscillations at different pressure levels. Thus, an envelope obtained from the oscillations is related to the blood pressure. In our work, we extract few features from the oscillometric waveforms, and estimate blood pressure using feedforward neural networks. Feature strength is evaluated by computing the standard deviation of the errors. The results are compared with the traditional maximum amplitude pressure algorithm. A large noninvasively collected database is used for this purpose.