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Abstract: Objective: In the current market, there are all kinds of blood pressure monitors that use different filtering algorithms. Therefore, their calculation accuracy varies. Through research, it’s determined that the calculation accuracy of a sphygmomanometer’s filtering algorithm can be effectively improved. This is proven via experimental data obtained from the processing of various filter algorithms. A comparison of this data with the gains from the bidirectional filter algorithm shows that the bidirectional filter algorithm improves the calculation accuracy of the blood pressure cuff.
Key Words: Sphygmomanometer, Calculation accuracy of the meter, Bidirectional digital filtering
How it works
As we all know, high blood pressure is an important cause of heart failure in humans, and it also threatens the life and health of patients. Therefore, accurate measurement of blood pressure has important clinical significance. Typically, electronic sphygmomanometers use oscillometric methods. Generally, data obtained by the oscillometric method must be filtered to eliminate noise and filtering interference, so that the measurement data is as accurate as possible. Popular filtering algorithms include interpolation filtering, median filtering, bidirectional filtering, and Gaussian filtering. We chose the interpolation filtering method and the median filtering method to compare with the bidirectional filtering algorithm. We studied the advantages of the bidirectional filtering algorithm and improved the accuracy of the sphygmomanometer calculation.
The diagram of blood pressure measurement is shown in Fig. 1. In the figure, Ps corresponds to systolic pressure, Pd corresponds to diastolic pressure, and Pm corresponds to average pressure.
A bidirectional filter, also known as a bilateral filter, is a nonlinear filtering method in which the image space is seen as a compromise combination of close and similar pixels, taking into account both spatial information and grayscale similarity. This method has achieved the purpose of protection and denoising . It is a simple, non-linear, and non-comprehensive method that utilizes a Gaussian filter function based on spatial distribution, ensuring that data far from the gray edge does not affect the edge. As such, pixel values close to the edges are not significantly blurred, thereby serving as a protective boundary and achieving a relatively ideal filtering effect .
The algorithm for systolic pressure is given in Equation 2-1: Ps = P/Vi = Ks*Um. The algorithm for diastolic pressure is provided in Equation 2-2: Pd = P/Vi = Kd*Um.
Data from subjects with different physiological conditions was used as the original data source during the internship utilizing the Beijing Yueqi ABP-1000S sphygmomanometer. Following this, the data from 10 subjects using the mercury sphygmomanometer were considered as actual data, and the original data was interpolated. The subsequent table data was obtained by the filtering method, the median filtering method, and the bidirectional filtering method.
An intuitive comparison should be made between the actual data, the measured data, and the measured data processed by the two-way filtering algorithm and the interpolation filtering algorithm, along with the median filtering algorithm. The actual data is measured by the mercury sphygmomanometer, whereas the measured data is raw data, without any application of an algorithm. DBP and SBP stand for diastolic blood pressure and systolic blood pressure, as shown in Table 1 and Table 2:
The difference between the 20 sets of measured values in the above two tables, and the difference between the blood pressure data obtained by the interpolation filtering algorithm, the median filtering algorithm, and the bidirectional filtering algorithm in comparison to the actual blood pressure values (systolic and diastolic), were respectively calculated as A, B, C. Upon comparing these three values, the following results were calculated: A = 30, B = 71, C = 68.
It can be discerned that after the blood pressure data was processed by the three filtering algorithms, the blood pressure value obtained by the bidirectional filtering process was closest to the actual value. The error was significantly reduced by the bidirectional filtering algorithm. To summarize, compared with the median and interpolation filtering algorithm, the bidirectional filtering algorithm has considerable advantages in improving the calculation accuracy of the sphygmomanometer, significantly enhancing the device’s calculation accuracy.
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