Reduction of interference in oscillometric arterial blood pressure measurement using fuzzy logic.

In oscillometry, oscillation amplitudes (OAs) embedded in the cuff pressure are drastically affected by a variety of artifacts and cardiovascular diseases, leading to inaccurate arterial blood pressure (ABP) measurement. The purpose of this paper is to improve the accuracy in the arterial pressure measurement by reducing interference in the OAs using a recursive weighted regression algorithm (RWRA). This method includes a fuzzy logic discriminator (FLD) and a recursive regression algorithm. The FLD is used to reduce the effect of artifacts caused by measurement motion disturbance or cardiovascular diseases, and to determine the truthfulness of the oscillation pulse. According to the truth degree, the relationship between the cuff pressure and OA is reconstructed using the regression algorithm. Because the regression method must utilize inverse matrix operation, which will be difficult to implement in an automatic or ambulatory monitor, the recursive regression method is proposed to solve this problem. To test the performance of this RWRA, 47 subjects underwent the ABP measurement using both the auscultation and the oscillometry combined with the RWRA. It was found that the average difference between the pooled blood pressures measured by the auscultation and those by the oscillometry combined with the RWRA was found to be only 4.9 mmHg. Clinical results demonstrated that the proposed RWRA is more robust than the traditional curve fitting algorithm (TCFA). We conclude that the proposed RWRA can be applied to effectively improve the accuracy of the oscillometric blood pressure measurement.

Model-based synthetic fuzzy logic controller for indirect blood pressure measurement.

In this paper, a new measurement system for the noninvasive monitoring of the continuous blood pressure waveform in the radial artery is presented. The proposed system comprises a model-based fuzzy logic controller, an arterial tonometer and a micro syringe device. The flexible diaphragm tonometer registers the continuous blood pressure waveform. To obtain accurate measurement without distortion, the tonometer's mean chamber pressure must be kept equal to the mean arterial pressure (MAP), the so-called optimal coupling condition, such that the arterial vessel has the maximum compliance. Since the MAP cannot be measured directly, to keep the optimal coupling condition becomes a tracking control problem with unknown desired trajectory. To solve this dilemma, a model-based fuzzy logic controller is designed to compensate the change of MAP by applying a counter pressure on the tonometer chamber through the micro syringe device. The proposed controller consists of a model-based predictor and a synthetic fuzzy logic controller (SFLC). The model-based predictor estimates the MAPs changing tendency based on the identified arterial pressure-volume model.