Traditional Chinese medicine pulse manifestation acquisition apparatus, noise reduction system and noise reduction method

A collection device and pulse condition technology, which is applied in the field of medical instruments, can solve the problems that the sensor is susceptible to external interference, and the repeatability and accuracy of the traditional Chinese medicine pulse conditioner are low, so as to achieve the effect of improving the repeatability and accuracy

Inactive Publication Date: 2016-01-20

刘垚 +1

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AI-Extracted Technical Summary

Problems solved by technology

[0005] In order to solve the problem that the sensor in the pulse conditioner of traditional Chinese medicine is susceptible to external interference, which leads to the problem of low repeatability and accuracy ...

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Method used

The structure of the internal circuit of the pulse condition acquisition device of traditional Chinese medical science is as shown in Figure 2, and this circuit 204 also comprises multi-channel signal conditioning circuit 201, multi-channel synchronous A/D circuit 202 and microcontroller 203 . The multi-channel signal conditioning circuit 201 is responsible for filtering and amplifying the signals collected by the pressure sensor array. The multi-channel synchronous A/D circuit 202 is used to collect the analog signals of all pressure sensors sync...

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Abstract

The present invention belongs to the technical field of medical instruments, and specifically relates to a traditional Chinese medicine pulse manifestation acquisition apparatus. The apparatus comprises a sensor array, a multipath signal conditioning circuit, a multipath synchronous A/D circuit and a single chip microcomputer; the sensor array comprises pulse sensors and noise sensors, and the noise sensors are used for acquiring vibration noise data introduced by a body; the multipath signal conditioning circuit is used for filtering and amplifying signals acquired by the sensors; the multipath synchronous A/D circuit is used for synchronously acquiring the signals processed by the multipath signal conditioning circuit in real time and converting the signals into digital signals; and the single chip microcomputer is used for sending the converted digital signals into an intelligent terminal. The present invention also provides a noise reduction system and a noise reduction method. The pulse sensor data are corrected by utilization of noise sensor data, interference of vibration noise on a traditional Chinese medicine pulse manifestation instrument can be effectively reduced, and therefore repeatability and accuracy of pulse manifestation instrument diagnosis can be improved.

Application Domain

CatheterDiagnostic recording/measuring +1

Technology Topic

Sensor arrayTraditional Chinese medicine +11

Image

Examples

Experimental program(3)

Example Embodiment

[0045] Example 1:

[0046] The TCM pulse collection device includes a pressure sensor array and an internal circuit. The location of the pressure sensor is as figure 1 Shown. Among them, the pressure sensor array element 101, the pressure sensor array element 102 and the pressure sensor array element 103 are arranged corresponding to the "inch", "off" and "ruler" pulse points of the human wrist, and are used to collect the "inch, off, and off" pulse points of the human wrist. This type of sensor can be called a pulse sensor; the pressure sensor array element 104 can be placed near the pulse sensor on the wrist, but it will not be pressed against the position of arteries and blood vessels, and is used to collect Incoming vibration and noise, this type of sensor can be called a noise sensor. In order to avoid errors caused by each pulse sensor array element not accurately corresponding to the pulse points of "inch", "off", and "ruler" when the traditional Chinese medicine pulse collection device is in use, the pulse sensor array element can also be a pulse sensor array.

[0047] The structure of the internal circuit of the TCM pulse collection device is as figure 2 As shown, the circuit 204 includes not only the array elements 101, 102, 103, and 104 of the aforementioned pressure sensor array, but also a multi-channel signal conditioning circuit 201, a multi-channel synchronous A/D circuit 202, and a single-chip 203. The multi-channel signal conditioning circuit 201 is responsible for filtering and amplifying the signals collected by the pressure sensor array. The multi-channel synchronous A/D circuit 202 is used to collect the analog signals of all pressure sensors in real time and synchronously, and convert them into digital signals. The single-chip microcomputer 203 is used to store the converted digital signals and forward them to smart terminals such as computers, mobile phones, tablets, etc. through any interface such as Bluetooth, WIFI, USB, etc., and use the powerful computing capabilities of the smart terminals to perform digital signal processing and pulse conditions Recognition related operations can finally realize the identification of users’ pulse characteristics, assist doctors in diagnosis and user health management.

[0048] After the intelligent terminal receives the signal collected by the pressure sensor array, before performing the pulse analysis, the noise reduction method is used to preprocess the data to improve the accuracy of the pulse analysis result. The preprocessing steps are as image 3 As shown, the specific steps are as follows:

[0049] Step 301: After the smart terminal receives all the data collected by the pressure sensor, it is stored locally;

[0050] Step 302: The smart terminal extracts all pressure sensor data, determines whether the pressure sensor is a pulse sensor or a noise sensor, and divides its data into pulse sensor data and noise sensor data accordingly, thereby separating the pulse sensor data and the noise sensor data;

[0051] Step 305: Before processing each pulse sensor data, the intelligent terminal uses the noise sensor data to perform adaptive filtering or frequency domain spectrum subtraction processing on the pulse sensor data, thereby suppressing the noise signal doped in the original pulse signal;

[0052] Step 306: Output the pulse sensor data after noise reduction, and the preprocessing is completed.

[0053] In order to further improve the accuracy of the pulse analysis result, between step 302 and step 305, it also includes:

[0054] Step 303: The smart terminal first analyzes the noise sensor data, calculates the power spectrum of the noise, and judges whether the noise power reaches a certain threshold, if yes, skip to step 304; if not, skip to step 305;

[0055] Step 304: The smart terminal prompts the user that the vibration and noise are too large, please keep quiet or change to a quiet environment, and then skip to step 301 to perform data collection again.

[0056] In addition, you can also use the noise reduction method to preprocess the data:

[0057] Step 31: After the smart terminal receives all the data collected by the pressure sensor, it is stored locally;

[0058] Step 32: The smart terminal extracts all pressure sensor data, determines whether the pressure sensor is a pulse sensor or a noise sensor, and divides its data into pulse sensor data and noise sensor data accordingly, thereby separating the pulse sensor data and noise sensor data;

[0059] Step 33: The smart terminal first analyzes the noise sensor data, calculates the noise power spectrum, and judges whether the noise power reaches a certain threshold. If it is, it will prompt the user that the vibration and noise are too large, please keep quiet or change to a quiet environment, and then skip to Step 31: Perform data collection again.

[0060] Among them, the preferred adaptive filtering algorithm model in step 305 is as Figure 4 As shown, the following describes the adaptive filtering algorithm in conjunction with the model.

[0061] Suppose the input signal of the pulse sensor is d(k), d(k)=s(k)+n(k), where s(k) is the pulse wave signal, and n(k) is the noise signal of the pulse sensor.

[0062] Let the input signal of the noise sensor be X(k).

[0063] Suppose the output signal of the filter is y(k), y(k) satisfies the following equation:

[0064] y ( k ) = W T ( k ) X ( k ) = X i = 0 N - 1 w i x ( k - i )

[0065] In the formula, k is the time series, N is the filter order, W T (k) represents the filter weight coefficient, w i Represents the i-th order filter coefficient, x(ki) represents the input sequence to complete the convolution calculation, X[x(k),x(k-1),...,x(k-N+1)] represents the input vector , W[w 0 (k),w 1 (k),...,w N-1 (k)) T Represents the filter weight coefficient vector.

[0066] Calculate the error signal as e(k)=d(k)-y(k)

[0067] The updated estimated value of the calculated filter weight coefficient is W(k+1)=W(k)+2μe(k)X(k), where μ is the step size factor, which depends on whether the selected step size is fixed or variable .

[0068] The rules for judging whether the error signal reaches the steady state include the minimum mean square error criterion and the least square criterion, as well as the deformation based on these two criteria. Those skilled in the art can also choose other discriminating rules.

[0069] Minimum mean square error criterion: E[e 2 (n)] Minimization, that is, the square statistical mean of the error is minimized.

[0070] Least squares criterion: Minimize, that is, the sum of squared errors is the smallest.

[0071] The implementation steps of the adaptive filtering algorithm are as follows Figure 5 Shown.

[0072] Step 1: Choose filter order N;

[0073] Step 2: Initialize the filter weight coefficient W(0) and step factor, the initial value of W(0) is 0, and the initial value of step factor is 1;

[0074] Step 3: Calculate the error signal according to the filter weight coefficient and the input signal according to the model of the adaptive filtering algorithm;

[0075] Step 4: Determine whether the error signal has reached a steady state according to the discriminant rule, if not, update the filter weight coefficient and step factor according to the judgment rule, and return to step 3 to recalculate the error signal; if yes, the implementation step ends .

Example Embodiment

[0076] Example 2:

[0077] The foregoing embodiment 1 is an example in which three pressure sensor array elements or arrays are used as pulse sensors, and one pressure sensor array element is used as a noise sensor. Taking into account the different physiological structures of the human body, each person’s blood vessel position and degree of curvature are different, and for the convenience of people who do not have any medical knowledge to use this product, the pressure sensor array elements that make up the pressure sensor array 401 in embodiment 2 are on the wrist The position of the position is not set in advance, that is, the positional relationship between the pressure sensor and the pulse point and the arterial blood vessel is not limited, but the signal collected by the pressure sensor is used to determine whether it is a pulse sensor or a noise sensor; the pressure sensor can be arranged as Image 6 The rectangular structure shown can also adopt other structures, as long as it is ensured that some pressure sensor array elements are located in the pulse area and some pressure sensor array elements are located in the non-arterial blood vessel area.

[0078] The processing flow of this embodiment is roughly the same as that of the first embodiment, please refer to image 3 The method flow chart shown.

Example Embodiment

[0079] Example 3:

[0080] The following describes the implementation of determining the sensor type (whether the pulse is collected or the noise is collected) in Embodiments 1 and 2. Figure 7 Shows the step flow chart of the first judgment method. The process of this step is described as follows:

[0081] First, perform frequency domain conversion on all sensor data;

[0082] Secondly, perform spectrum analysis on the converted data to find the corresponding frequency with the strongest spectrum;

[0083] After that, according to the characteristics of the pulse spectrum, the pulse sensor and the noise sensor are distinguished. The frequency can be compared with the normal pulse range of the human body (normal heartbeat is 45 to 120 beats per minute). If it falls within this range, it is considered normal, otherwise it is considered that there is no pulse signal in the spectrum.

[0084] Figure 8 Shows the step flow chart of the second judgment method. The basic principle is that the signal is not related to noise. The process of this step is described as follows:

[0085] First, perform cross-correlation processing on different sensor data, which can be done through time domain or frequency domain processing;

[0086] Secondly, compare the obtained cross-correlation coefficients, 0-0.3 is micro-correlation, 0.3-0.5 is real correlation, 0.5-0.8 is significant correlation, 0.8-1 is high correlation;

[0087] Then, through the above comparison, the sensor array is divided into two areas;

[0088] Finally, determine the types of the two areas in the previous step. The center area (which can be slightly left or right) near the sensor array is the pulse sensor area, and the edge area near the sensor array is the noise sensor area.

[0089] When the number of sensors increases and the number of noise sensors is more than one, the data of two or more noise sensors can be selected to perform noise spectrum estimation and noise reduction algorithm processing together to increase the robustness of the algorithm.

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Traditional Chinese medicine pulse manifestation acquisition apparatus, noise reduction system and noise reduction method

AI-Extracted Technical Summary

Problems solved by technology

Method used

Abstract

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Examples

Example Embodiment

Example Embodiment

Example Embodiment

PUM

Description & Claims & Application Information

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