Continuous Blood Pressure Monitoring Method Based on Multiple Photoplethysmography Features

Continuous blood pressure monitoring is of great significance for the prevention and early diagnosis of cardiovascular diseases. However, the existing continuous blood pressure monitoring methods, especially the sleeveless blood pressure monitoring methods, are complex and computationally heavy. In this paper, we propose a method, using plethysmography (PPG) signals alone, to estimate continuous blood pressure by extracting multiple PPG features related to intravascular blood flow characteristics. The performance of our method was evaluated using ten minutes synchronized PPG signals and continuous blood pressure from 21 healthy volunteers and 19 patients with hypertension and diabetes. The test results have shown that the absolute mean errors and standard deviation errors between the estimated and referenced blood pressure are 3.22±0.66 mmHg for systolic blood pressure and 2.11±1.11 mmHg for diastolic blood pressure, which meet AAMI (the association for the advancement of medical instrumentation) error acceptance.

Consideration Factors in Application of Thermocouple Sensors for RCS Temperature Instrumentation

Because of harsh radiated environmental conditions, it is necessary to use thermocouples (TCs) in the temperature instrumentation channels of a reactor coolant system (RCS) in an integrated pressurized water reactor vessel. Conventionally, resistance temperature detectors (RTDs) have been adopted for RCS temperature measurement. Therefore, we have conducted an analysis and review of instrumentation error factors in the measurement circuits of RTD and TC sensors to specify the influence on measurement accuracy for application of TCs instead of RTDs for RCS temperature instrumentation. From the review and analysis results, it is anticipated that a measurement accuracy deterioration would be an issue and that a drift range should be investigated for the anticipated operational temperature conditions.

Statistic Inference for Inertial Instrumentation Error Model Using Bayesian Network

For the parameter estimation problem of inertial instrumentation error models, a Bayesian network is founded to fuse the calibration data and make error coefficients statistical inference in this paper. First the fundamental of Bayesian network is stated and then how to establish network for a typical case of inertial instrumentation error coefficients estimation is illustrated. Since the difficult high-dimension integral calculus for model parameter can be avoidable, WinBUGS software based on MCMC method is used for calculation and inference. The simulated results show that using Bayesian network to make statistical inference for inertial instrumentation error model is reasonable and effective.