Shing-Hong Liu

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 tonometers 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.

A model-based fuzzy logic controller with Kalman filtering for tracking mean arterial pressure

This paper proposes a new noninvasive measurement method for tracking the tendency of mean arterial pressure (MAP) in the radial artery. The designed system consists of a tonometer, a microsyringe device, and a model-based fuzzy logic controller. The modified flexible diaphragm tonometer is to detect the continuous blood pressure waveform and vessel volume pulse. A precise mathematical model describing the interaction between the tonometer and artery is derived. To reach accurate measurement without distortion, a model-based fuzzy logic control system is designed to compensate the change of MAP by applying a counter pressure on the tonometer chamber through the microsyringe device. The proposed control system consists of a linear predictor, a Kalman filter, and a synthetic fuzzy logic controller (SFLC). Simulation results show that, for the real physiologic MAP with changing rates up to 20 or -20 mm-Hg/minute, the model-based SFLC can beat-to-beat adjust the tonometers chamber pressure to follow the tendency of MAP accurately.

The short-time fractal scaling of heart rate variability to estimate the mental stress of driver

Traffic accidents happen everywhere, especially if the car driver cannot concentrate when he/she is under mental stress. The main purpose of this research is to utilize the method of the nonlinear dynamic system for analyzing the drivers mental stress. In physiology, the autonomic nervous system, including sympathetic and parasympathetic nervous systems, controls the cardiovascular system. Humans heart rate and blood pressure violently fluctuate while under mental stress. Therefore, the short-time fractal scaling of the heart rate variability (HRV) is analyzed to estimate the drivers mental stress. In our virtual-reality lab, a subject sat on a 6-D motion platform designed to emulate a driver-vehicle environment. The electrocardiogram of the subject was recorded. It was found that the Lyapunov exponent could effectively represent the HRV. Therefore, we conclude that the nonlinear dynamic system can be applied to analyze the drivers HRV under mental stress.

A model-based fuzzy logic controller for tracking mean arterial pressure

Proposes a noninvasive measurement method for tracking the tendency of mean arterial pressure in the radial artery. The designed system consists of a tonometer, a micro syringe device, and a model-based fuzzy logic controller. The proposed control system consists of a linear predictor, and a synthetic fuzzy logic controller. The design of the fuzzy rules in each subcontroller is based on the oscillometric principle saying that the arterial vessel has the maximum compliance when the detected vessel volume pulse reaches its maximum amplitude.

Using the system identify theorem for constructing the dynamic compliance of the brachial artery

A noninvasive measurement technique with oscillometry, system identify, and the related measured circuits is investigated to detect the dynamic compliance of brachial artery. In oscillometry, oscillation amplitudes (OAs) embedded in the cuff pressure are effected by the arterial characteristic, body tissue, and cuff characteristic. In cuff deflation, pressure transducer and micro flower meter were used to detect the variation of cuff pressure and volume. A system identify theorem was used to reconstruct the cuff model. Using the cuff pressure and OAs, the arterial volume change was calculated under the different transmural pressure. This measurement system also detected the systolic and diastolic pressure, simultaneously. Therefore, the dynamic pressure-volume (P-V) curve of artery was made.

An optimal controller with synthetic fuzzy logic for tracking mean arterial pressure

Proposes a noninvasive measurement method for tracking the tendency of mean arterial pressure (MAP) in the radial artery. The designed system consists of a tonometer, a micro syringe device, and a model-based fuzzy logic controller. The modified flexible diaphragm tonometer detects the continuous blood pressure waveform and vessel volume pulse. A precise mathematical model describing the interaction between the tonometer and artery is derived. To reach accurate measurement without distortion, an optimal controller is designed to compensate the change of MAP by applying a counter pressure on the tonometer chamber through the micro syringe device. Simulation results show that, for the real physiologic MAP with changing rates up to 20 or -20 mm-Hg/minute, the optimal controller can beat-to-beat adjust the tonometers chamber pressure to follow the tendency of MAP accurately.