Device and method for detecting blood pressure module of arteriosclerosis detector

Abstract

The invention provides an arteriosclerosis detector blood pressure module detection device and method, and the method comprises the steps: S1, obtaining static pressure data collected by an arteriosclerosis detector at a plurality of preset static pressure points, complete pressure-time waveform sequences corresponding to four channels of four limbs of the user under the standard ambulatory blood pressure waveform sequence and corresponding time sequence mark data are collected; s2, sequentially performing filtering and denoising, time alignment correction and zero drift correction on the static pressure data, the standard dynamic blood pressure waveform sequence, the complete pressure-time waveform sequence and the time sequence marking data; and S3, performing accuracy analysis, synchronism analysis and consistency analysis on the basis of the corrected data to obtain a corresponding accuracy result, a synchronism result and a consistency result. The method has the beneficial effects that efficient, comprehensive and real measurement performance evaluation can be realized, and the measurement accuracy and reliability of the arteriosclerosis detector are ensured.

Description

Technical Field

[0001] This invention relates to the technical field of arteriosclerosis detection, and more specifically, to an arteriosclerosis detection instrument with a blood pressure module detection device and a detection method. Background Technology

[0002] Arteriosclerosis detectors are important medical devices for assessing vascular elasticity and the degree of hardening. A key function is measuring blood pressure in the limbs and calculating parameters such as the ankle-brachial index (ABI). Currently, advanced arteriosclerosis detectors can simultaneously collect and measure blood pressure in all four limbs, significantly improving detection efficiency and ensuring data consistency over time, which is crucial for accurately assessing vascular condition.

[0003] However, the metrological calibration technology and equipment to match the functions of such instruments are severely lagging behind. Currently, the industry generally uses the traditional single-channel electronic blood pressure monitor calibration scheme for calibrating such multi-channel synchronous blood pressure monitoring modules. For example, single-channel non-invasive blood pressure simulators such as the BP Pump 2 manufactured by Fluke Corporation are typically used for calibration. During calibration, the operator must manually connect the simulator to each of the instrument's four blood pressure cuff interfaces one by one, completing the static pressure point test of all channels in four independent operation procedures. Verification of dynamic waveform performance is even more cumbersome and even difficult to carry out systematically.

[0004] This serial calibration method based on single-channel devices has obvious technical drawbacks: Inefficient and cumbersome to operate: The complete calibration of a limb arteriosclerosis detector is time-consuming. The calibration process requires repeated disconnection and reconnection of cables and repeated setting of pressure points, which greatly increases the workload of operators and the probability of errors. Synchronization cannot be assessed: Because it is a time-division measurement, the existing methods are completely unable to verify the time synchronization performance of the four channels in the actual working state of the instrument. Any timing delay between channels will affect the accuracy of calculation results such as ABI, which is the blind spot of the existing calibration technology. It is difficult to realistically simulate the working state: the blood pressure values ​​of the four limbs of the arteriosclerosis detector are measured synchronously, and time-division calibration cannot reproduce this real scenario of synchronous pressure input, resulting in a significant difference between the calibration environment and the usage environment, and insufficient reliability and representativeness of the calibration results; Lack of consistency verification: There is a lack of effective means to quickly compare the consistency of the measurement results of the four channels, i.e. the differences between channels, under the same input and at the same time, and it is impossible to determine whether the performance of each channel of the instrument is balanced.

[0005] Therefore, there is an urgent need in this field for a calibration and testing method and device that can match the simultaneous measurement function of the arteriosclerosis detector in the limbs, so as to achieve efficient, comprehensive and accurate metrological performance evaluation and ensure the measurement accuracy and reliability of the arteriosclerosis detector. Summary of the Invention

[0006] The technical problem to be solved by this invention is how to achieve efficient, comprehensive and accurate metrological performance evaluation and ensure the measurement accuracy and reliability of arteriosclerosis detectors. In order to overcome the defects of the above-mentioned prior art (or related arteries), this invention provides a blood pressure module detection device and detection method for arteriosclerosis detectors.

[0007] This invention provides a method for detecting blood pressure using an arteriosclerosis detector, comprising the following steps: Step S1: Acquire static pressure data collected by the arteriosclerosis detector at multiple preset static pressure points, and acquire complete pressure-time waveform sequences and corresponding time-series marker data for four channels corresponding to the user's limbs under the standard dynamic blood pressure waveform sequence. Step S2: The static pressure data, the standard dynamic blood pressure waveform sequence, the complete pressure-time waveform sequence, and the time-series marker data are sequentially filtered for noise reduction, time alignment correction, and zero-point drift correction. Step S3: Based on the corrected static pressure data, the standard dynamic blood pressure waveform sequence, the complete pressure-time waveform sequence, and the time-series marker data, perform accuracy analysis, synchronicity analysis, and consistency analysis respectively to obtain the corresponding accuracy results, synchronicity results, and consistency results.

[0008] Compared with existing technologies, the blood pressure module detection method of the arteriosclerosis detector of the present invention has the following advantages: In this invention, multi-dimensional data acquisition, including static pressure data, complete pressure-time waveform sequences, and time-series marker data, is performed in step S1. Data preprocessing and correction are performed in step S2. A three-dimensional analysis of data accuracy, synchronicity, and consistency is performed in step S3. By acquiring, uniformly preprocessing, and comprehensively analyzing the four-channel pressure data of the user's limbs, a full-function integrated calibration of the arteriosclerosis detector is achieved. This solves the problem that existing calibration methods can only perform time-segmented calibration on a single channel, significantly improving the integrity, efficiency, and realistic simulation capability of the calibration, and ensuring the measurement accuracy and reliability of the arteriosclerosis detector.

[0009] In one possible implementation, in step S1, 0 mmHg, 50 mmHg, 100 mmHg, 150 mmHg, 200 mmHg, 250 mmHg, and 300 mmHg are used as the preset static pressure points, and the systolic pressure, diastolic pressure, and mean pressure of the limbs at each preset static pressure point are collected as the static pressure data.

[0010] Compared with existing technologies, the above technical solution can ensure that the calibration process can fully cover the actual working range of the arteriosclerosis detector by defining the preset static pressure points as multiple typical clinical pressure points from 0 mmHg to 300 mmHg. This setting makes the calibration results more clinically representative and systematic, and can detect the linearity and accuracy of the arteriosclerosis detector in different pressure ranges.

[0011] In one possible implementation, the complete pressure-time waveform sequence includes dynamic blood pressure waveform sequences of the four channels at each of the preset static pressure points, and the filtering and noise reduction in step S2 includes: The dynamic blood pressure waveform sequences are first filtered using a low-pass filter with a cutoff frequency of 40Hz to obtain the low-pass filtered waveform. Then, the low-pass filtered waveforms are second filtered using a band-stop filter with a center frequency of 50Hz to obtain the band-stop filtered waveform. Finally, the band-stop filtered waveforms are smoothed using a moving average method with a window width of 5 sampling points to obtain the moving average waveform.

[0012] Compared with existing technologies, the above-mentioned technical solution can preprocess the dynamic blood pressure waveform sequence by using a three-stage cascaded filter including a low-pass filter, a band-stop filter, and a moving average method. This effectively filters out high-frequency noise and power frequency interference and smooths the waveform, significantly improving the signal quality and anti-interference capability of subsequent analysis, thereby ensuring the reliability of accurate, synchronous, and consistent analysis results.

[0013] In one possible implementation, the time-series marker data includes the timestamp of the synchronization trigger signal output by the arteriosclerosis detector and the acquisition timestamp of the waveform start point of each of the dynamic blood pressure waveform sequences. The time alignment correction in step S2 includes: Using the timestamp of the synchronization trigger signal as a reference, the time deviation between the acquisition timestamp and the timestamp of the synchronization trigger signal of each of the moving average waveforms is calculated, and the moving average waveforms are time-shifted based on the time deviation to align them on the time axis to obtain time-aligned waveforms.

[0014] Compared with existing technologies, the above technical solution can perform time deviation correction and waveform alignment based on the timestamp of the synchronous trigger signal, ensuring strict synchronization of the four-channel data on the time axis. This directly solves the analysis error caused by the inconsistency of the timing of multi-channel signals and provides an accurate time basis for subsequent synchronization analysis and consistency comparison.

[0015] In one possible implementation, the zero-point drift correction in step S2 includes: The measured value of the arteriosclerosis detector when there is no pressure input is taken as the zero-point offset. Based on the zero-point offset, the time-aligned waveforms are subtracted to obtain the corrected waveforms.

[0016] Compared with existing technologies, the above technical solution can eliminate systematic errors caused by sensor drift or environmental factors through zero-point drift correction, which makes the corrected waveform more accurate and stable.

[0017] In one possible implementation, the accuracy analysis in step S3 includes: Step A1: For each preset standard static pressure point, calculate the absolute error based on the preset standard static pressure point and the systolic pressure value, diastolic pressure value, or mean pressure value measured at the preset standard static pressure point, and obtain a first accuracy result based on the absolute error and a pre-configured first reference threshold. Step A2: For each corrected waveform, calculate the PCC correlation coefficient and root mean square error based on the corrected waveform and the standard dynamic blood pressure waveform sequence, and obtain a second accuracy result based on the PCC correlation coefficient, the root mean square error, and the pre-configured second reference threshold and third reference threshold. Step A3: Combine the first accuracy result and the second accuracy result as the accuracy result.

[0018] Compared with existing technologies, the above-mentioned technical solution can achieve a comprehensive evaluation of the static accuracy and dynamic response characteristics of the arteriosclerosis detector by using a two-level accuracy assessment of static error and dynamic waveform similarity. By introducing PCC correlation coefficient and root mean square error as dynamic waveform evaluation indicators, the accuracy results are more scientific and better meet the real needs of clinical waveform reproduction.

[0019] In one possible implementation, the synchronization analysis in step S3 includes: Step B1: For each corrected waveform, extract the waveform peak time of the corrected waveform; Step B2: Calculate the channel time difference between the peak times of the waveforms in each channel; Step B3: Select the maximum channel time difference from each of the channel time differences, and obtain the synchronization analysis result based on the maximum channel time difference and a pre-configured fourth reference threshold.

[0020] Compared with existing technologies, the above technical solution can quantitatively evaluate the synchronization of limb signals by extracting the peak time of the waveform and calculating the maximum channel time difference. This method is intuitive and easy to calculate, and can effectively identify channel delay or timing deviation problems, ensuring the reliability of the arteriosclerosis detector in synchronous measurement scenarios.

[0021] In one possible implementation, the consistency analysis in step S3 includes: Step C1: Under the same preset standard static pressure point, calculate the range of systolic pressure, diastolic pressure or mean pressure values ​​of the four channels; Step C2: Obtain the consistency analysis result based on the range and the pre-configured fifth reference threshold.

[0022] Compared with existing technologies, the above-mentioned technical solution can quickly and effectively assess the consistency between channels by calculating the range of limb channel measurements under the same pressure input. This method is simple and intuitive, and can directly reflect the balance of limb blood pressure measurements, which is of great significance for ensuring the consistency of clinical diagnosis.

[0023] This invention provides a blood pressure module detection device for an arteriosclerosis detector, applied to the aforementioned blood pressure module detection method for an arteriosclerosis detector, comprising: The arteriosclerosis detector is equipped with four loops for wearing on the user's limbs. Each loop is used to collect the complete pressure-time waveform sequence and the corresponding time-series marker data of one channel. The static pressure data of the user can also be collected at multiple preset static pressure points through any of the loops. The side wall of the arteriosclerosis detector has four output interfaces, and each of the four output interfaces corresponds to one of the loops. An arteriosclerosis calibrator is provided, which has four input interfaces for connecting to four output interfaces via data cables to receive the complete pressure-time waveform sequence, the time-series marker data, and the static pressure data. The calibrator also has a CPU chip for sequentially performing filtering and noise reduction, time alignment correction, and zero-point drift correction on the static pressure data, the standard dynamic blood pressure waveform sequence, the complete pressure-time waveform sequence, and the time-series marker data. Based on the corrected static pressure data, the standard dynamic blood pressure waveform sequence, the complete pressure-time waveform sequence, and the time-series marker data, accuracy analysis, synchronization analysis, and consistency analysis are performed to obtain accuracy results, synchronization results, and consistency results, respectively.

[0024] Compared with the prior art, the blood pressure module detection device for arteriosclerosis detection of the present invention has the following advantages: This invention utilizes a dedicated output interface corresponding to the four channels on the arteriosclerosis detector and a dedicated input interface corresponding to the output interface on the arteriosclerosis calibrator. A CPU chip is configured to automate data acquisition, processing, and analysis. By collecting, preprocessing, and comprehensively analyzing the four-channel pressure data of the user's limbs from multiple dimensions, this invention achieves full-function integrated calibration of the arteriosclerosis detector. This solves the problem that existing calibration methods can only perform time-segmented calibration on a single channel, significantly improving the completeness, efficiency, and realistic simulation capabilities of the calibration, ensuring the measurement accuracy and reliability of the arteriosclerosis detector. Attached Figure Description

[0025] Figure 1 This is a flowchart of the steps of the present invention; Figure 2 This is a flowchart illustrating the steps of the accuracy analysis of the present invention; Figure 3 This is a flowchart illustrating the steps of the synchronization analysis of the present invention; Figure 4 This is a flowchart of the consistency analysis steps of the present invention; Figure 5 This is a schematic diagram of the overall structure of the device of the present invention; Explanation of reference numerals in the attached figures: 1. Arteriosclerosis detector; 2. Cuff; 3. Output interface; 4. Arteriosclerosis calibrator; 5. Input interface. Detailed Implementation

[0026] First, those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention. Those skilled in the art can make adjustments as needed to adapt to specific application scenarios.

[0027] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0028] See Figure 1 This invention discloses a blood pressure module detection method for an arteriosclerosis detector 1, comprising the following steps: Step S1: Acquire static pressure data collected by the arteriosclerosis detector 1 at multiple preset static pressure points, and acquire complete pressure-time waveform sequences and corresponding time-series marker data for the four channels corresponding to the user's limbs under the standard dynamic blood pressure waveform sequence. Step S2 involves sequentially filtering and denoising the static pressure data, standard dynamic blood pressure waveform sequence, complete pressure-time waveform sequence, and time-marked data, performing time alignment correction, and zero-point drift correction. Step S3: Based on the corrected static pressure data, standard dynamic blood pressure waveform sequence, complete pressure-time waveform sequence, and time-marked data, perform accuracy analysis, synchronicity analysis, and consistency analysis to obtain the corresponding accuracy results, synchronicity results, and consistency results.

[0029] In this embodiment of the invention, the standard dynamic blood pressure waveform sequence can be pre-stored in the arteriosclerosis calibrator 4, including but not limited to normal blood pressure waveform, hypertension waveform, hypotension waveform and simulated waveform containing arrhythmia characteristics, with a sampling frequency of not less than 200 Hz to ensure complete reproduction of waveform details.

[0030] In this embodiment of the invention, in step S1, 0 mmHg, 50 mmHg, 100 mmHg, 150 mmHg, 200 mmHg, 250 mmHg, and 300 mmHg are used as preset static pressure points to cover the common clinical blood pressure range and ensure the comprehensiveness of the calibration. During data acquisition, the pressure source is controlled to gradually and stably output to the above preset static pressure points, and is maintained at each preset static pressure point for at least 3 seconds. After the readings stabilize, the systolic blood pressure, diastolic blood pressure, and mean blood pressure of the limbs at the preset static pressure point are recorded as static pressure data.

[0031] In this embodiment of the invention, the complete pressure-time waveform sequence includes a dynamic blood pressure waveform sequence of four channels at each preset static pressure point, and the filtering and denoising in step S2 includes: A Butterworth low-pass filter with a cutoff frequency of 40Hz was used to filter each dynamic blood pressure waveform sequence to remove high-frequency noise such as muscle tremors and electromagnetic interference, resulting in a low-pass filtered waveform. Next, a notch filter with a center frequency of 50Hz, i.e. a band-stop filter, is used to perform secondary filtering on each low-pass filtered waveform to eliminate power frequency interference and obtain the band-stop filtered waveform. Finally, to smooth the waveform and reduce random noise, the moving average method was used to smooth the band-stop filtered waveforms with a window width of 5 sampling points, resulting in the moving average waveform.

[0032] In this embodiment of the invention, the specific process of filtering and denoising is as follows: Input dynamic blood pressure waveform sequence ; Low-pass filtering (cutoff frequency) ): in, This represents the waveform after low-pass filtering. This indicates low-pass filtering. Indicates the channel index. , respectively representing the left upper limb channel, right upper limb channel, left lower limb channel, and right lower limb channel; Band-stop filtering (center frequency) ): in, This represents the waveform after band-stop filtering. This indicates band-stop filtering. Smoothing with moving average (window width) ): in, This represents the waveform after the moving average. Indicates the waveform sampling point index. , Indicates the waveform sampling time interval; Output the waveform after moving average .

[0033] In this embodiment of the invention, the time-series marker data includes the timestamp of the synchronization trigger signal output by the arteriosclerosis detector 1 and the acquisition timestamp of the waveform start point of each dynamic blood pressure waveform sequence. The time alignment correction in step S2 includes: Based on the timestamp of the synchronous trigger signal, the time deviation between the acquisition timestamp of each moving average waveform and the timestamp of the synchronous trigger signal is calculated, and the moving average waveform is time-shifted based on the time deviation to align it on the time axis to obtain the time-aligned waveform.

[0034] In this embodiment of the invention, the specific process of time alignment correction is as follows: Calculate time deviation : in, Indicates the first The acquisition timestamp of the channel waveform start point. Indicates the timestamp of the synchronization trigger signal; Alignment correction: in, This represents the waveform after time alignment.

[0035] In this embodiment of the invention, the zero-point drift correction in step S2 includes: The measurement value of the arteriosclerosis detector 1 when there is no pressure input is taken as the zero point offset. The corrected waveform is obtained by subtracting the waveform after each time alignment based on the zero point offset. The specific process of zero-point drift correction is as follows: Calculate zero offset (Without pressure input): in, This represents a channel index, used to represent another channel; Indicates the adjustable coefficient; Waveform correction: in, This indicates the corrected waveform.

[0036] See Figure 2 In this embodiment of the invention, the accuracy analysis in step S3 includes: Step A1: For each preset standard static pressure point, calculate the absolute error based on the preset standard static pressure point and the systolic pressure value, diastolic pressure value, or mean pressure value measured at the preset standard static pressure point, and obtain the first accuracy result based on the absolute error and the pre-configured first reference threshold. Step A2: For each corrected waveform, calculate the PCC correlation coefficient and root mean square error based on the corrected waveform and the standard dynamic blood pressure waveform sequence, and obtain the second accuracy result based on the PCC correlation coefficient, root mean square error and the pre-configured second reference threshold and third reference threshold. Step A3: Combine the first accuracy result and the second accuracy result into a single accuracy result.

[0037] In this embodiment of the invention, the accuracy analysis includes static pressure accuracy analysis and dynamic waveform accuracy analysis. The specific process of static pressure accuracy analysis is as follows: Absolute error calculation: in, Indicates the first The channel is in The absolute error of a preset static pressure point Indicates the first The channel is in The systolic blood pressure value measured at a preset static pressure point. Indicates the first One preset static pressure point; Accuracy determination, if all and satisfy: If the static pressure accuracy is qualified, the first accuracy result is output.

[0038] In this embodiment of the invention, the specific process of dynamic waveform accuracy analysis is as follows: Waveform sequence list: in, This represents a standard dynamic blood pressure waveform sequence; Correlation coefficient calculate: in, The first part of the standard ambulatory blood pressure waveform sequence One sampling point, This represents the mean of a standard ambulatory blood pressure waveform sequence. Indicates the first The waveform after channel correction One sampling point, Indicates the first The mean value of the waveform after channel correction; Root mean square error calculation: in, Indicates the first The root mean square error of the waveform after channel correction; Waveform accuracy determination, if the following conditions are met: and ; If the accuracy of the dynamic waveform is qualified, the second accuracy result is output.

[0039] See Figure 3 In this embodiment of the invention, the synchronization analysis in step S3 includes: Step B1: For each corrected waveform, extract the peak time of the corrected waveform. Step B2: Calculate the channel time difference between the peak times of the waveforms in each channel; Step B3: Select the maximum channel time difference from the time differences of each channel, and obtain the synchronization analysis result based on the maximum channel time difference and the pre-configured fourth reference threshold.

[0040] In this embodiment of the invention, the specific process of synchronization analysis is as follows: Extracting waveform peak time: in, Indicates the first The peak time of the waveform after channel correction; Calculate the channel time difference: in, Indicates the first Channel and the Channel time difference between channels Indicates the first The peak time of the waveform after channel correction; Statistical synchronicity indicators: in, Indicates the maximum time difference between channels; Synchronization determination, if the following conditions are met: If the synchronization is satisfactory, the synchronization analysis results will be output.

[0041] See Figure 4 The consistency analysis in step S3 includes: Step C1: Under the same preset standard static pressure point, calculate the range of systolic pressure, diastolic pressure or mean pressure values ​​for the four channels; Step C2 yields consistency analysis results based on the range and a pre-configured fifth reference threshold.

[0042] In this embodiment of the invention, the specific process of consistency analysis is as follows: Take the systolic blood pressure measurements from four channels under the same dynamic waveform. : Calculate the range : Consistency determination, if the following conditions are met: If the consistency is satisfactory, the consistency analysis results will be output.

[0043] See Figure 5The present invention also discloses an arteriosclerosis detector 1 blood pressure module detection device, which uses the above-described arteriosclerosis detector 1 blood pressure module detection method, including: An arteriosclerosis detector 1, also known as a calibration device, is equipped with four cuff-type rings 2 for wearing on the user's limbs. Each ring 2 integrates a pressure sensor, which is used to collect a complete pressure-time waveform sequence and corresponding time-series marker data for one channel. The instrument can be controlled by a program to collect static pressure data at multiple preset static pressure points under the drive of an external pressure source. The side wall of the arteriosclerosis detector 1 is provided with four output interfaces 3 (e.g., using USB-C or a dedicated aviation interface). The four output interfaces 3 are respectively connected to the pressure sensor signal output terminal of one of the rings 2 through internal circuitry. The arteriosclerosis calibrator 4, or calibration device, is the core of this invention. The arteriosclerosis calibrator 4 has four input interfaces 5 on its upper surface, which are used to connect to the four output interfaces 3 of the arteriosclerosis detector 1 being calibrated through shielded data cables to receive complete pressure-time waveform sequences, time-series marker data, and static pressure data. The arteriosclerosis calibrator 4 is equipped with a high-performance CPU chip (e.g., based on an ARM Cortex-A series processor), memory, and an analog-to-digital / digital-to-analog conversion module. Its software system embeds and implements all the algorithms of the blood pressure module detection method of the aforementioned arteriosclerosis detector 1.

[0044] In this embodiment of the invention, four data cables are used to connect the four input interfaces 5 of the arteriosclerosis calibrator 4 to the four output interfaces 3 of the arteriosclerosis detector 1 being calibrated, one by one. The calibration mode (such as full-function calibration), preset static pressure point, and standard dynamic blood pressure waveform sequence are selected through the touch screen of the arteriosclerosis calibrator 4. Then the calibration program is started. The high-precision pressure source inside the arteriosclerosis calibrator 4 applies the preset static pressure and standard dynamic blood pressure waveform to the four collars 2 through the internal air path. At the same time, the CPU chip synchronously collects all the data returned by the arteriosclerosis detector 1 being calibrated through the input interface 5. The CPU chip automatically executes all the data processing and analysis algorithms in steps S2 and S3. The accuracy result, synchronization result, and consistency result are displayed on the touch screen in real time, and an electronic report can be generated for storage or printing.

[0045] In the description of this invention, the references to "one embodiment," "some embodiments," "in this embodiment," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0046] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. An arterial stiffness detector blood pressure module detection method, characterized in that, Includes the following steps: Step S1: Acquire static pressure data collected by the arteriosclerosis detector at multiple preset static pressure points, and acquire complete pressure-time waveform sequences and corresponding time-series marker data for four channels corresponding to the user's limbs under the standard dynamic blood pressure waveform sequence. Step S2: The static pressure data, the standard dynamic blood pressure waveform sequence, the complete pressure-time waveform sequence, and the time-series marker data are sequentially filtered for noise reduction, time alignment correction, and zero-point drift correction. Step S3: Based on the corrected static pressure data, the standard dynamic blood pressure waveform sequence, the complete pressure-time waveform sequence, and the time-series marker data, perform accuracy analysis, synchronicity analysis, and consistency analysis respectively to obtain the corresponding accuracy results, synchronicity results, and consistency results.

2. The blood pressure module detection method for an arteriosclerosis detector according to claim 1, characterized in that, In step S1, 0 mmHg, 50 mmHg, 100 mmHg, 150 mmHg, 200 mmHg, 250 mmHg, and 300 mmHg are used as preset static pressure points, and the systolic pressure, diastolic pressure, and mean pressure of the limbs at each preset static pressure point are collected as static pressure data.

3. The blood pressure module detection method for an arteriosclerosis detector according to claim 1, characterized in that, The complete pressure-time waveform sequence includes dynamic blood pressure waveform sequences of the four channels at each preset static pressure point. The filtering and noise reduction in step S2 includes: The dynamic blood pressure waveform sequences are first filtered using a low-pass filter with a cutoff frequency of 40Hz to obtain the low-pass filtered waveform. Then, the low-pass filtered waveforms are second filtered using a band-stop filter with a center frequency of 50Hz to obtain the band-stop filtered waveform. Finally, the band-stop filtered waveforms are smoothed using a moving average method with a window width of 5 sampling points to obtain the moving average waveform.

4. The blood pressure detection method of the arteriosclerosis detector according to claim 3, characterized in that, The time-series marker data includes the timestamp of the synchronous trigger signal output by the arteriosclerosis detector and the acquisition timestamp of the waveform start point of each of the dynamic blood pressure waveform sequences. The time alignment correction in step S2 includes: Using the timestamp of the synchronization trigger signal as a reference, the time deviation between the acquisition timestamp and the timestamp of the synchronization trigger signal of each of the moving average waveforms is calculated, and the moving average waveforms are time-shifted based on the time deviation to align them on the time axis to obtain time-aligned waveforms.

5. The blood pressure detection method of the arteriosclerosis detector according to claim 4, characterized in that, The zero-point drift correction in step S2 includes: The measured value of the arteriosclerosis detector when there is no pressure input is taken as the zero-point offset. Based on the zero-point offset, the time-aligned waveforms are subtracted to obtain the corrected waveforms.

6. The blood pressure module detection method for an arteriosclerosis detector according to claim 5, characterized in that, The accuracy analysis in step S3 includes: Step A1: For each preset standard static pressure point, calculate the absolute error based on the preset standard static pressure point and the systolic pressure value, diastolic pressure value, or mean pressure value measured at the preset standard static pressure point, and obtain a first accuracy result based on the absolute error and a pre-configured first reference threshold. Step A2: For each corrected waveform, calculate the PCC correlation coefficient and root mean square error based on the corrected waveform and the standard dynamic blood pressure waveform sequence, and obtain a second accuracy result based on the PCC correlation coefficient, the root mean square error, and the pre-configured second reference threshold and third reference threshold. Step A3: Combine the first accuracy result and the second accuracy result as the accuracy result.

7. The blood pressure module detection method for an arteriosclerosis detector according to claim 5, characterized in that, The synchronicity analysis in step S3 includes: Step B1: For each corrected waveform, extract the waveform peak time of the corrected waveform; Step B2: Calculate the channel time difference between the peak times of the waveforms in each channel; Step B3: Select the maximum channel time difference from each of the channel time differences, and obtain the synchronization analysis result based on the maximum channel time difference and a pre-configured fourth reference threshold.

8. The blood pressure module detection method for an arteriosclerosis detector according to claim 1, characterized in that, The consistency analysis in step S3 includes: Step C1: Under the same preset standard static pressure point, calculate the range of systolic pressure, diastolic pressure or mean pressure values ​​of the four channels; Step C2: Obtain the consistency analysis result based on the range and the pre-configured fifth reference threshold.

9. A blood pressure module detection device for an arteriosclerosis detector, characterized in that, The blood pressure detection method using the arteriosclerosis detector blood pressure module as described in any one of claims 1-8 includes: The arteriosclerosis detector is equipped with four rings for wearing on the user's limbs. Each ring is used to collect the complete pressure-time waveform sequence and the corresponding time-series marker data of one channel. The static pressure data of the user can also be collected at multiple preset static pressure points through any of the rings. The side wall of the arteriosclerosis detector is provided with four output interfaces, and the four output interfaces correspond to the data transmission channels of one of the rings. An arteriosclerosis calibrator is provided, which has four input interfaces for connecting to four output interfaces via data cables to receive the complete pressure-time waveform sequence, the time-series marker data, and the static pressure data. The calibrator also has a CPU chip for sequentially performing filtering and noise reduction, time alignment correction, and zero-point drift correction on the static pressure data, the standard dynamic blood pressure waveform sequence, the complete pressure-time waveform sequence, and the time-series marker data. Based on the corrected static pressure data, the standard dynamic blood pressure waveform sequence, the complete pressure-time waveform sequence, and the time-series marker data, accuracy analysis, synchronization analysis, and consistency analysis are performed to obtain accuracy results, synchronization results, and consistency results, respectively.

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