A method, device, system, and instrument for detecting arteriosclerosis.

Abstract

This invention discloses a method, device, system, and instrument for detecting arteriosclerosis, relating to the field of medical device technology. The method includes: preprocessing and differentiating carotid and femoral artery pulse waveforms, then dividing them according to the heartbeat cycle; obtaining the maximum value of the carotid and femoral artery signals within each heartbeat cycle; determining the conduction time within each heartbeat cycle based on the maximum values ​​of the carotid and femoral artery signals; determining the conduction velocity of the subject's pulse wave in the arterial system within each heartbeat cycle based on the distance between the carotid and femoral artery measurement points and the conduction time within each heartbeat cycle; determining the average conduction velocity based on the conduction velocity of the subject's pulse wave in the arterial system within each heartbeat cycle; and determining the degree of arteriosclerosis based on the average conduction velocity. This addresses the technical problem of low accuracy in arteriosclerosis measurement in existing technologies.

Description

Technical Field

[0001] This invention relates to the field of medical device technology, and in particular to a method, device, system and instrument for detecting arteriosclerosis. Background Technology

[0002] Currently, early screening and diagnosis of arteriosclerosis mainly rely on non-invasive blood pressure measurement, ultrasound detection, and pulse wave velocity (PWV) analysis. Traditional non-invasive detection methods (such as cuff blood pressure measurement) have low accuracy in acquiring pulse wave signals, making it difficult to meet the needs of high-resolution vascular elasticity measurement. Ultrasound and other imaging methods are limited by sampling rate and signal processing, making it difficult to achieve high temporal resolution real-time monitoring of arterial hemodynamics.

[0003] Pulse wave velocity (PWV) is a key indicator of arterial stiffness, with carotid-femoral pulse wave velocity (PWV) considered the gold standard for central arterial stiffness. Therefore, accurate PWV calculation is crucial for assessing arterial stiffness. Common PWV calculations involve finding characteristic points on electrocardiograms (ECGs) and arterial waveforms, or on the carotid-femoral or brachial artery waveforms, to calculate pulse wave transit time before calculating PWV. The selection of these characteristic points has a significant impact on the results; a 20ms error can cause a deviation of approximately 1m / s. The most common characteristic point selection methods are the "foot" identification method and the second derivative identification method. Both work well with very high-quality signals, but with slightly lower-quality signals, the results become severely discrepancies, leading to unreliable calculations.

[0004] In summary, most existing methods require direct contact with the skin and are greatly affected by individual differences (such as skin thickness and blood vessel depth), which may affect measurement stability and repeatability. Furthermore, existing measurement devices are usually large, expensive, difficult to operate, and difficult to measure accurately, making them difficult to popularize in primary healthcare or home testing environments. Summary of the Invention

[0005] This invention provides a method, device, system, and instrument for detecting arteriosclerosis, in order to solve the technical problem of low accuracy in arteriosclerosis measurement in existing technologies.

[0006] In a first aspect, embodiments of the present invention provide a method for detecting arteriosclerosis, comprising:

[0007] After preprocessing and differential processing of the carotid and femoral pulse waveforms, they are divided according to the heartbeat cycle;

[0008] Obtain the maximum carotid artery signal and the maximum femoral artery signal within each heartbeat cycle;

[0009] The conduction time within each heartbeat cycle is determined based on the maximum values ​​of the carotid artery signal and the femoral artery signal within each heartbeat cycle.

[0010] Based on the distance between the carotid artery measurement point and the femoral artery measurement point, and the conduction time within each heartbeat cycle, the conduction velocity of the subject's carotid-femoral pulse wave within each heartbeat cycle is determined.

[0011] The average conduction velocity is determined based on the conduction velocity of the neck and femoral pulse waves of the subject during each heartbeat cycle.

[0012] The degree of arteriosclerosis is determined based on the average conduction velocity.

[0013] Optionally, after preprocessing and differentiating the carotid and femoral pulse waveforms and before dividing them according to the heartbeat cycle, the method further includes:

[0014] Acquire pulse data, including carotid pulse data and femoral pulse data;

[0015] Compare the length of the pulse data with the preset data length;

[0016] When it is determined that the length of the pulse data is greater than the preset data length, calculate the autocorrelation curve of the pulse data within the current window;

[0017] Calculate the number of peak values ​​of the curve based on the autocorrelation curve;

[0018] Compare the number of peak values ​​of the curve with the preset number;

[0019] When the number of peak values ​​of the curve exceeds the preset number, the formal collection of carotid pulse data and femoral pulse data begins.

[0020] The distance between the carotid artery measurement point and the femoral artery measurement point, the carotid artery pulse waveform, and the femoral artery pulse waveform are obtained.

[0021] The heart rate of the subject is determined based on the carotid pulse waveform and the femoral pulse waveform.

[0022] Optionally, after determining the conduction time within each heartbeat cycle based on the maximum carotid artery signal value and the maximum femoral artery signal value within each heartbeat cycle, the method further includes:

[0023] The conduction time range is estimated based on the subject's heart rate and age;

[0024] The conduction time within the specified conduction time range is retained, and the conduction time outside the specified conduction time range is discarded.

[0025] Optionally, after preprocessing and differentiating the carotid artery pulse waveform and the femoral artery pulse waveform, the waveform is divided according to the heartbeat cycle, including:

[0026] The carotid pulse waveform and the femoral pulse waveform are subjected to a first filtering process;

[0027] The carotid pulse waveform and the femoral pulse waveform after the first filtering process are respectively subjected to the first differentiation process.

[0028] A second filtering process is performed on the carotid pulse waveform and the femoral pulse waveform after the first differentiation process;

[0029] The carotid pulse waveform and the femoral pulse waveform after the second filtering process are respectively subjected to a second differential process;

[0030] A third filtering process is performed on the carotid pulse waveform and the femoral pulse waveform after the second differentiation process;

[0031] The carotid pulse waveform and the femoral pulse waveform after the third filtering process are divided according to the heartbeat cycle.

[0032] Optionally, determining the conduction time within each heartbeat cycle based on the maximum carotid artery signal and the maximum femoral artery signal within each heartbeat cycle includes:

[0033] The conduction time within each heartbeat cycle is determined as the difference between the maximum value of the carotid artery signal and the maximum value of the femoral artery signal within each heartbeat cycle.

[0034] Optionally, determining the conduction velocity of the carotid-femoral pulse wave of the subject based on the distance between the carotid artery measurement point and the femoral artery measurement point and the conduction time includes:

[0035] The conduction velocity of the carotid-femoral pulse wave of the subject is determined as the ratio of the distance between the carotid artery measurement point and the femoral artery measurement point to the conduction time.

[0036] Optionally, after determining the average conduction velocity based on the conduction velocity of the subject's carotid-femoral pulse wave during each heartbeat cycle, the method further includes:

[0037] The average conduction time is determined based on the conduction time within each heartbeat cycle;

[0038] The standard deviation of the conduction time is determined based on the conduction time and the average conduction time within each heartbeat cycle;

[0039] The evaluation score is determined based on the number of heartbeat cycles remaining after excluding conduction times outside the specified conduction time range, the total number of heartbeat cycles, the average conduction time, and the standard deviation of the conduction time.

[0040] Secondly, embodiments of the present invention also provide an arteriosclerosis detection device, comprising:

[0041] The waveform processing module is used to preprocess and differentiate the carotid pulse waveform and femoral pulse waveform, and then divide them according to the heartbeat cycle.

[0042] The maximum value acquisition module is used to acquire the maximum value of the carotid artery signal and the maximum value of the femoral artery signal within each heartbeat cycle;

[0043] The conduction time determination module is used to determine the conduction time of each heartbeat cycle based on the maximum value of the carotid artery signal and the maximum value of the femoral artery signal obtained by the maximum value acquisition module in each heartbeat cycle.

[0044] The conduction velocity determination module is used to determine the conduction velocity of the subject's carotid-femoral pulse wave in each heartbeat cycle based on the distance between the carotid artery measurement point and the femoral artery measurement point and the conduction time in each heartbeat cycle.

[0045] The average conduction velocity determination module is used to determine the average conduction velocity based on the conduction velocity of the neck and femoral pulse waves of the subject in each heartbeat cycle.

[0046] An arteriosclerosis degree determination module is used to determine the degree of arteriosclerosis based on the average conduction velocity determined by the average conduction velocity determination module.

[0047] Thirdly, embodiments of the present invention also provide an arteriosclerosis detection system, comprising: a device connection module, a user information management module, a data acquisition and processing module, a parameter setting module, and a report generation module; the data acquisition and processing module includes the arteriosclerosis early warning device described in the second aspect;

[0048] The device connection module communicates with the arteriosclerosis detector via an interface module.

[0049] The user information management module is used to store the information of the test subjects;

[0050] The data acquisition and processing module is used to acquire and process data and display waveforms in real time;

[0051] The parameter setting module is used to set filter parameters, laser parameters, and sampling parameters;

[0052] The report generation module is used to generate a temporary report after the data collection is completed.

[0053] Fourthly, embodiments of the present invention also provide an arteriosclerosis detector, including an optical fiber system, a lens assembly, and a main unit;

[0054] The host unit is connected to a host computer, which includes the arteriosclerosis detection system described in the third aspect.

[0055] This invention provides a method, device, system, and instrument for detecting arteriosclerosis. After preprocessing and differentiating the carotid and femoral artery pulse waveforms, they are divided according to the heartbeat cycle. The maximum signal values ​​of the carotid and femoral arteries within each heartbeat cycle are obtained. The conduction time within each heartbeat cycle is determined based on these maximum signal values. The conduction velocity of the subject's carotid-femoral pulse wave within each heartbeat cycle is determined based on the distance between the carotid and femoral artery measurement points and the conduction time within each heartbeat cycle. The average conduction velocity is determined based on the conduction velocity of the subject's carotid-femoral pulse wave within each heartbeat cycle. The degree of arteriosclerosis is determined based on the average conduction velocity. Preprocessing the carotid and femoral pulse waveforms removes the portions affected by respiratory and motion artifacts while retaining information characterizing physiological features. Determining the average conduction velocity based on the conduction velocity of the subject's carotid-femoral pulse wave within each heartbeat cycle, and then determining the degree of arteriosclerosis based on the average conduction velocity, yields relatively stable measurement results. It can improve the reliability of test results and solve the technical problem of low accuracy in the measurement of arteriosclerosis in existing technologies.

[0056] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0057] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0058] Figure 1 This is a flowchart of an arteriosclerosis detection method provided in an embodiment of the present invention;

[0059] Figure 2This is a flowchart of another method for detecting arteriosclerosis provided in an embodiment of the present invention;

[0060] Figure 3 This is a waveform diagram of carotid and femoral artery pulses provided in an embodiment of the present invention;

[0061] Figure 4 This is a conduction time distribution diagram provided in an embodiment of the present invention;

[0062] Figure 5 This is a schematic diagram of the structure of an arteriosclerosis detection device provided in an embodiment of the present invention;

[0063] Figure 6 This is a schematic diagram of the structure of an arteriosclerosis detection system provided in an embodiment of the present invention;

[0064] Figure 7 This is a schematic diagram of the layout of a software main interface provided in an embodiment of the present invention;

[0065] Figure 8 This is a schematic diagram of a detailed parameter setting interface provided in an embodiment of the present invention;

[0066] Figure 9 This is an operation flowchart of a software main interface provided in an embodiment of the present invention;

[0067] Figure 10 This is a schematic diagram of the structure of an arteriosclerosis detector provided in an embodiment of the present invention. Detailed Implementation

[0068] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0069] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0070] Figure 1 This is a flowchart of an arteriosclerosis detection method provided by an embodiment of the present invention. This embodiment is applicable to detecting the degree of arteriosclerosis. The method can be executed by an arteriosclerosis detection device, which can be implemented in hardware and / or software and can be configured in a processor. (Reference) Figure 1 The method includes the following steps:

[0071] S110. After preprocessing and differential processing of the carotid pulse waveform and femoral pulse waveform, they are divided according to the heartbeat cycle.

[0072] It should be noted that the carotid pulse waveform and the femoral pulse waveform are electrical signal waveforms that contain displacement information of the skin at the carotid and femoral arteries of the subject due to pulse beats, respectively. Specifically, through... Figure 10 The infrared laser inside the main unit 1 emits an infrared laser to the skin at the carotid artery and femoral artery of the subject. After being reflected by the skin at the carotid artery and femoral artery, the laser is coupled back to the silicon photonic chip. After being mixed by the silicon photonic chip, the waveform of the electrical signal is converted by the photodetector.

[0073] Preprocessing is accomplished through filtering.

[0074] S120: Obtain the maximum value of the carotid artery signal and the maximum value of the femoral artery signal within each heartbeat cycle.

[0075] Understandably, preprocessing and differentiating the carotid and femoral pulse waveforms before determining the maximum values ​​of the carotid and femoral signals within each heartbeat cycle yields more reliable results.

[0076] S130. Determine the conduction time in each heartbeat cycle based on the maximum value of the carotid artery signal and the maximum value of the femoral artery signal in each heartbeat cycle.

[0077] S140. Based on the distance between the carotid artery measurement point and the femoral artery measurement point, and the conduction time within each heartbeat cycle, determine the conduction velocity of the subject's carotid-femoral pulse wave within each heartbeat cycle.

[0078] S150. Determine the average conduction velocity based on the conduction velocity of the neck and femoral pulse waves of the subject in each heartbeat cycle.

[0079] Understandably, determining the average conduction velocity based on the conduction velocity of the neck and femoral pulse waves in each heartbeat cycle can yield more stable and reliable results.

[0080] S160. Determine the degree of arteriosclerosis based on the average conduction velocity.

[0081] It is understandable that, since the carotid pulse waveform and femoral pulse waveform of this application are electrical signal waveforms containing displacement information of the skin at the carotid artery and femoral artery of the subject due to the pulse, the obtained carotid pulse waveform and femoral pulse waveform will be affected by respiratory and motion artifacts, thus affecting the accuracy of arteriosclerosis measurement.

[0082] This invention preprocesses the carotid and femoral artery pulse waveforms, removing artifacts caused by respiration and motion while retaining physiological information. The average conduction velocity is determined based on the conduction velocity of the carotid and femoral pulse waves during each heartbeat cycle, and the degree of arteriosclerosis is then determined based on this average conduction velocity, resulting in more stable measurement results. This improves the reliability of the detection results and solves the technical problem of low accuracy in arteriosclerosis measurement in existing technologies.

[0083] Figure 2 This is a flowchart of another method for detecting arteriosclerosis provided in an embodiment of the present invention, see reference. Figure 2 The method includes the following steps:

[0084] S210. Acquire pulse data, including carotid pulse data and femoral pulse data.

[0085] S220. Compare the length of the pulse data with the preset data length.

[0086] S230. When the length of the pulse data is determined to be greater than the preset data length, calculate the autocorrelation curve of the pulse data within the current window.

[0087] The preset data length can be freely set according to the required calculation accuracy. For example, the preset data length can be 5 seconds.

[0088] S240. Calculate the number of peak values ​​of the curve based on the autocorrelation curve.

[0089] S250, compare the number of peak values ​​of the curve with the preset number.

[0090] Among them, different measurement results can be obtained by using different numbers of curve peaks through experiments. When the evaluation score of the measurement results reaches 90 points or above, the number of curve peaks at this time is set as the preset number.

[0091] Understandably, when the number of peak values ​​exceeds the set value, we consider the subject's condition to be stable, environmental interference to be minimal, and the subject to be in a suitable testing state, and formal data collection can begin. If the amount of data is insufficient or the number of correlation peak values ​​in the current window is insufficient, the judgment cannot be passed, and we must wait for new data to enter and repeat the judgment process until the requirements are met.

[0092] S260. When the number of curve peaks exceeds the preset number, the formal collection of carotid pulse data and femoral pulse data begins.

[0093] In this embodiment of the invention, data quality is assessed and selected during data acquisition. Carotid and femoral pulse data are only formally acquired when the number of curve peaks exceeds a preset number, resulting in more reliable results. By assessing signal quality before formal measurement, only data of acceptable quality is saved, ensuring data reliability and improving measurement efficiency.

[0094] S270. Obtain the distance between the carotid artery measurement point and the femoral artery measurement point, the carotid artery pulse waveform, and the femoral artery pulse waveform.

[0095] Understandably, after the carotid and femoral pulse data are collected, the data will be automatically input into the algorithm. The algorithm will extract the distance between the carotid and femoral measurement points, the carotid pulse waveform, and the femoral pulse waveform. The heart rate will be calculated using the carotid and femoral pulse waveforms. The heart rate will be used to search for signal markers, remove data, and evaluate results.

[0096] S280. Determine the subject's heart rate based on the carotid artery pulse waveform and the femoral artery pulse waveform.

[0097] Optionally, based on the above embodiments, before step S110 of the above embodiments, steps S210 to S280 are further included.

[0098] S290. After preprocessing and differential processing of the carotid pulse waveform and femoral pulse waveform, they are divided according to the heartbeat cycle.

[0099] Optionally, based on the above embodiments, step S290 includes: performing a first filtering process on the carotid pulse waveform and the femoral pulse waveform; performing a first differentiation process on the carotid pulse waveform and the femoral pulse waveform after the first filtering process; performing a second filtering process on the carotid pulse waveform and the femoral pulse waveform after the first differentiation process; performing a second differentiation process on the carotid pulse waveform and the femoral pulse waveform after the second filtering process; performing a third filtering process on the carotid pulse waveform and the femoral pulse waveform after the second differentiation process; and dividing the carotid pulse waveform and the femoral pulse waveform after the third filtering process according to the heartbeat cycle.

[0100] This invention performs three preprocessing steps on the carotid and femoral artery pulse waveforms to remove components affected by respiratory and motion artifacts while retaining information characterizing physiological features. Through two differential processing steps, velocity and acceleration information are obtained. This invention provides highly reliable results for most situations.

[0101] Figure 3 This is a waveform diagram of the carotid and femoral artery pulses provided in an embodiment of the present invention, specifically the waveform diagrams before and after preprocessing and differential processing of the carotid and femoral artery pulse waveforms. (Reference) Figure 3 , Figure 3 The blue waveform represents the carotid pulse, and the red waveform represents the femoral pulse. Before preprocessing and differentiation, the carotid and femoral pulse waveforms are concentrated in the upper half; after preprocessing and differentiation, they are concentrated in the lower half. Figure 3 The green dots marked on the waveform before processing are feature points. Figure 3 The time point where the feature point is located corresponds to the maximum value of the carotid or femoral artery signal obtained from the processed waveform within the heartbeat cycle at that time point.

[0102] S2100: Obtain the maximum value of the carotid artery signal and the maximum value of the femoral artery signal within each heartbeat cycle.

[0103] S2110. Determine the conduction time in each heartbeat cycle based on the maximum value of the carotid artery signal and the maximum value of the femoral artery signal in each heartbeat cycle.

[0104] Optionally, based on the above embodiments, step S2110 includes: determining the conduction time within each heartbeat cycle as the difference between the maximum value of the carotid artery signal and the maximum value of the femoral artery signal within each heartbeat cycle.

[0105] Optionally, based on the above embodiments, after step S2110, the method further includes: calculating the conduction time range based on the subject's heart rate and age; retaining the conduction time within the conduction time range and discarding the conduction time outside the conduction time range.

[0106] Understandably, after multiple preprocessing steps, most data can be processed correctly and relatively accurate results can be obtained. However, there may still be occasional interference factors such as the subject's cough or movement that render the results unusable. Therefore, after preprocessing and differential processing, the conduction time limit range can be calculated based on heart rate and age. By eliminating unreasonable data within this range, more reliable test results can be obtained.

[0107] In this embodiment of the invention, the conduction time range is calculated based on the subject's heart rate and age. The conduction time within the range is retained, while the conduction time outside the range is removed, resulting in more reliable detection results.

[0108] S2120. Based on the distance between the carotid artery measurement point and the femoral artery measurement point, and the conduction time within each heartbeat cycle, determine the conduction velocity of the subject's carotid-femoral pulse wave within each heartbeat cycle.

[0109] S2130. Determine the average conduction velocity based on the conduction velocity of the neck and femoral pulse waves of the subject in each heartbeat cycle.

[0110] Optionally, based on the above embodiments, step S2130 includes: determining the conduction velocity of the carotid-femoral pulse wave of the subject as the ratio of the distance between the carotid artery measurement point and the femoral artery measurement point to the conduction time.

[0111] Optionally, after step S2130, the method further includes: determining the average conduction time based on the conduction time within each heartbeat cycle; determining the standard deviation of the conduction time based on the conduction time within each heartbeat cycle and the average conduction time; and determining an evaluation score based on the number of heartbeat cycles remaining after removing conduction times outside the conduction time range, the total number of heartbeat cycles, the average conduction time, and the standard deviation of the conduction time.

[0112] Specifically, the assessment score = 0.5 [(number of heartbeat cycles remaining after excluding conduction time outside the conduction time range / total number of heartbeat cycles) + (mean conduction time - conduction time standard deviation) / mean conduction time].

[0113] The embodiments of the present invention evaluate the detection results and provide a score for the quality of a single measurement and calculation result, which can help users understand the accuracy of the measurement results.

[0114] S2140. Determine the degree of arteriosclerosis based on the average conduction velocity.

[0115] Figure 4 This is a conduction time distribution diagram provided by an embodiment of the present invention. For example, after actual measurement, the distance between the carotid artery measurement point and the femoral artery measurement point is 50cm. After preprocessing and differential processing of the original pulse waveform, the maximum values ​​of the carotid artery signal and the femoral artery signal within each heartbeat cycle are obtained through feature points. The distribution results of a single measurement are as follows: Figure 4 As shown. The distance between the combined carotid and femoral artery measurement points determines the conduction velocity of the carotid-femoral pulse wave, and outputs the average conduction velocity, average conduction time, result error, and result evaluation. The calculated conduction velocity of the carotid-femoral pulse wave was 7.0175 m / s; the average conduction time was 71.25 ms; the standard deviation of the conduction time was 4.1136 ms; the sample size was 8; and the evaluation score was 94.24 points. A score above 90 indicates good data and reliable results; a score between 80 and 90 indicates average data and weak reference value; and a score below 80 indicates poor data and meaningless results, suggesting remeasurement.

[0116] In summary, this invention, through preprocessing the carotid and femoral artery pulse waveforms, removes the portions affected by respiratory and motion artifacts while retaining information characterizing physiological features. The average conduction velocity is determined based on the conduction velocity of the carotid and femoral pulse waves within each heartbeat cycle, and the degree of arteriosclerosis is determined based on the average conduction velocity, resulting in more stable measurement results. This improves the reliability of the detection results and solves the technical problem of low accuracy in arteriosclerosis measurement in existing technologies. Data quality is assessed and selected during data acquisition; formal acquisition of carotid and femoral pulse data only begins when the number of curve peaks exceeds a preset number, resulting in more reliable results. Signal quality is assessed before formal measurement; only qualified data is saved, ensuring data reliability and improving measurement efficiency. Three preprocessing steps on the carotid and femoral pulse waveforms remove the portions affected by respiratory and motion artifacts while retaining information characterizing physiological features. Through two differential processing steps, velocity and acceleration information can be obtained, and the results acquired in most cases have high reliability. The conduction time range is estimated based on the subject's heart rate and age. Conduction times within the range are retained, while those outside the range are discarded, resulting in even more reliable test results. The test results are evaluated, and a score is given for the quality of each measurement and calculation, helping users understand the accuracy of the measurement results.

[0117] Figure 5 This is a schematic diagram of the structure of an arteriosclerosis detection device provided in an embodiment of the present invention, for reference. Figure 5 The device includes: a waveform processing module 510, a maximum value acquisition module 520, a conduction time determination module 530, a conduction velocity determination module 540, an average conduction velocity determination module 550, and an arteriosclerosis degree determination module 560.

[0118] In this embodiment of the invention, the waveform processing module 510 is used to preprocess and differentiate the carotid pulse waveform and the femoral pulse waveform, and then divide them according to the heartbeat cycle; the maximum value acquisition module 520 is used to acquire the maximum value of the carotid signal and the maximum value of the femoral signal in each heartbeat cycle; the conduction time determination module 530 is used to determine the conduction time in each heartbeat cycle based on the maximum value of the carotid signal and the maximum value of the femoral signal in each heartbeat cycle acquired by the maximum value acquisition module 520; the conduction velocity determination module 540 is used to determine the conduction velocity of the subject's carotid-femoral pulse wave in each heartbeat cycle based on the distance between the carotid measurement point and the femoral measurement point and the conduction time in each heartbeat cycle; the average conduction velocity determination module 550 is used to determine the average conduction velocity based on the conduction velocity of the subject's carotid-femoral pulse wave in each heartbeat cycle; and the arteriosclerosis degree determination module 560 is used to determine the degree of arteriosclerosis based on the average conduction velocity determined by the average conduction velocity determination module 550.

[0119] The arteriosclerosis detection device provided in this embodiment of the invention can execute the arteriosclerosis detection method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method. For content not described in detail in the embodiments of the invention, please refer to the arteriosclerosis detection method provided in the above embodiments.

[0120] Figure 6 This is a schematic diagram of the structure of an arteriosclerosis detection system provided in an embodiment of the present invention, for reference. Figure 6 The system includes: a device connection module 610, a user information management module 620, a data acquisition and processing module 630, a parameter setting module 640, and a report generation module 650; the data acquisition and processing module 630 includes the arteriosclerosis early warning device provided in the above embodiment. The device connection module 610 is communicatively connected to the arteriosclerosis detector via an interface module; the user information management module 620 is used to store the information of the subject; the data acquisition and processing module 630 is used to acquire and process data and display waveforms in real time; the parameter setting module 640 is used to set filter parameters, laser parameters, and sampling parameters; the report generation module 650 is used to generate a temporary report after the acquisition is completed.

[0121] Figure 7This is a schematic diagram of the layout of a software main interface provided in an embodiment of the present invention. Figure 8 This is a schematic diagram of a detailed parameter setting interface provided in an embodiment of the present invention. Specifically, the arteriosclerosis detection system provided in this embodiment is a self-developed host computer software. This software is specifically designed for the arteriosclerosis detector provided in this embodiment. Through an intuitive graphical interface and flexible parameter settings, it can realize functions such as device connection, data acquisition, real-time data display, data storage, and automatic report generation. The interface module includes USB and serial ports. The device connection module 610 supports establishing communication with the arteriosclerosis detector via USB and serial ports, establishing communication, and automatically searching for and identifying the device status. Users can utilize... Figure 7 The device search function area shown is triggered by clicking the "Search Devices" button, displaying status information such as "Waiting for Connection," "Connection Successful," or "Connection Failed" to ensure reliable data communication. The user information management module 620 requires users to fill in their name, gender, age, height, weight, and cf distance (the distance between the carotid artery measurement point and the femoral artery measurement point) in the personal basic information input area before formal data collection, and saves it via the "Confirm" button to ensure the correspondence between the collected data and the subject's information. The data acquisition and processing module 630 includes real-time waveform display, data acquisition control, and acquisition assistance functions. The software provides two acquisition modes (PWV / PWA acquisition) and sets acquisition assistance controls to ensure signal quality. Users can bypass strict detection and enter the formal acquisition stage as needed by clicking the "Cancel Acquisition Assistance (Single)" button. The parameter setting module 640... Figure 8 The software features a detailed parameter settings interface, allowing users to adjust filter parameters (high and low cutoff frequencies, filter type), laser drive current, and sampling parameters (sampling rate, sampling depth, sampling time). Parameter adjustments take effect immediately without additional confirmation. After data acquisition, the report generation module 650 supports generating temporary reports, displaying test results including PWV, mean conduction time, heart rate, and carotid-femoral artery pulse wave curves, and providing a diagnostic suggestion input area. Final data and reports are automatically saved according to preset naming rules for easy data traceability and management.

[0122] Figure 9 This is an operation flowchart of the main software interface provided in an embodiment of the present invention, specifically an operation flowchart of the main software interface of the arteriosclerosis detection system provided in an embodiment of the present invention, for reference. Figure 9 The operation process includes the following steps:

[0123] S910. After confirming that the testing equipment is in normal condition, connect the testing equipment to the host computer.

[0124] The detection equipment includes an arteriosclerosis detector and related equipment (such as optical lenses and measuring probes) that communicate via data cables or wirelessly.

[0125] S920. Enter the subject's relevant information and cf distance in the personal basic information area, and click "Confirm" to save.

[0126] S930, Click "Click to search for devices".

[0127] After clicking "Search devices," the software will automatically search for and detect the device connection status and display the connection results.

[0128] S940: After adjusting the filter parameters, laser parameters, and sampling parameters, turn on the light source.

[0129] In the detailed parameter settings interface, you can adjust the filter parameters, laser parameters, and sampling parameters.

[0130] S950: After selecting the appropriate acquisition mode, click "Start Acquisition" to start data acquisition.

[0131] Specifically, the system is divided into an acquisition assistance phase and a formal acquisition phase, displaying the pulse wave and velocity curve in real time.

[0132] S960. After the data collection is completed, enter the report page by clicking the "Generate Temporary Report and Input Diagnostic Results" button. After entering the diagnostic suggestions, click "Save Data (Including Report)" to complete the storage of the data and report.

[0133] The naming rules for report files are automatically generated (e.g., "Name-Age-CF Spacing-Time").

[0134] For example, on the operating system, after the user connects the detector to the host computer via USB / serial port, starts the software and clicks "Click to Search for Devices," the system successfully detects the device and displays "Connection Successful." In the personal basic information area, the user enters their name, gender, age, height, weight, and cf spacing. After entering the detailed parameter settings interface, the user adjusts the filter type (e.g., setting it to a low-pass filter, with the cutoff frequency set to a predetermined value), sets the laser drive current to 50mA (not exceeding 80mA), the sampling rate to 50K, and the sampling depth to 1M. The system automatically calculates the sampling time as 20 seconds. After clicking "Start Acquisition," the software first enters the acquisition assistance stage to detect signal quality; if the signal quality meets the requirements, the system enters the formal acquisition stage. After acquisition, the system automatically generates a temporary report containing key data such as PWV, mean pulse conduction time, and heart rate, and allows the user to enter preliminary diagnostic suggestions in the report. Finally, clicking "Save Data (including report)" completes the data storage.

[0135] This invention achieves stable data communication between the host computer and the testing instrument through automatic device search and status detection, reducing operational errors. The functional areas are rationally laid out, and the operation process is clear, effectively improving testing efficiency and user experience. It supports multiple acquisition modes and parameter settings to meet different testing conditions and signal quality requirements, ensuring data accuracy and reliability. It integrates temporary report generation and diagnostic input functions, enabling automatic generation of testing reports after data acquisition, facilitating subsequent analysis and archiving.

[0136] Figure 10 This is a schematic diagram of the structure of an arteriosclerosis detector provided in an embodiment of the present invention, for reference. Figure 9 The arteriosclerosis detector includes an optical fiber system 3, a lens assembly 2, and a host unit 1. The host unit 1 is connected to a host computer 4, which includes the arteriosclerosis detection system provided in the above embodiment.

[0137] Specifically, the main unit 1 integrates an infrared laser, a red laser, a silicon photonics chip, a photodetector, a power supply, and control circuitry. Externally, it can be connected to a host computer 4 via a data cable, powered by a 12V adapter, and connected to an optical fiber via an adapter. During measurement, the user fixes the optical lens in the lens assembly 2 to the top of the lens bracket using a clamp, adjusts it above the measurement point (such as the carotid artery or femoral artery measurement point), and uses the red laser to indicate the measurement position. After the system starts, optical displacement detection begins. The optical displacement detection measures the displacement of the skin at the carotid and femoral arteries caused by the pulse. Specifically, the infrared laser inside the main unit 1 emits an infrared laser to the skin at the carotid and femoral arteries of the subject. After reflection from the skin at these locations, the laser is coupled back to the silicon photonics chip. After mixing by the silicon photonics chip, the signal is converted into an electrical signal by the photodetector. The waveform of this electrical signal is the carotid pulse waveform and the femoral pulse waveform. Based on the carotid pulse waveform and the femoral pulse waveform, the displacement of the skin at the carotid artery and the femoral artery caused by the pulse can be analyzed.

[0138] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0139] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for detecting arteriosclerosis, characterized in that, include: Acquire pulse data, including carotid pulse data and femoral pulse data; Compare the length of the pulse data with the preset data length; When it is determined that the length of the pulse data is greater than the preset data length, calculate the autocorrelation curve of the pulse data within the current window; Calculate the number of peak values ​​of the curve based on the autocorrelation curve; Compare the number of peak values ​​of the curve with the preset number; When the number of peak values ​​of the curve exceeds the preset number, the formal collection of carotid pulse data and femoral pulse data begins. After preprocessing and differential processing of the carotid and femoral pulse waveforms, they are divided according to the heartbeat cycle; Obtain the maximum carotid artery signal and the maximum femoral artery signal within each heartbeat cycle; The conduction time within each heartbeat cycle is determined based on the maximum values ​​of the carotid artery signal and the femoral artery signal within each heartbeat cycle. The conduction time range is estimated based on the subject's heart rate and age. The conduction time within the specified conduction time range is retained, and the conduction time outside the specified conduction time range is discarded; Based on the distance between the carotid artery measurement point and the femoral artery measurement point, and the conduction time within each heartbeat cycle, the conduction velocity of the subject's carotid-femoral pulse wave within each heartbeat cycle is determined. The average conduction velocity is determined based on the conduction velocity of the neck and femoral pulse waves of the subject during each heartbeat cycle. The degree of arteriosclerosis is determined based on the average conduction velocity.

2. The method for detecting arteriosclerosis according to claim 1, characterized in that, After the formal collection of the carotid pulse data and the femoral pulse data begins when the number of curve peaks is greater than the preset number, the method further includes: acquiring the distance between the carotid artery measurement point and the femoral artery measurement point, the carotid pulse waveform, and the femoral pulse waveform. The heart rate of the subject is determined based on the carotid pulse waveform and the femoral pulse waveform.

3. The method for detecting arteriosclerosis according to claim 1, characterized in that, After preprocessing and differentiating the carotid artery pulse waveform and the femoral artery pulse waveform, they are divided according to the heartbeat cycle, including: The carotid pulse waveform and the femoral pulse waveform are subjected to a first filtering process; The carotid pulse waveform and the femoral pulse waveform after the first filtering process are respectively subjected to the first differentiation process. A second filtering process is performed on the carotid pulse waveform and the femoral pulse waveform after the first differentiation process; The carotid pulse waveform and the femoral pulse waveform after the second filtering process are respectively subjected to a second differential process; A third filtering process is performed on the carotid pulse waveform and the femoral pulse waveform after the second differentiation process; The carotid pulse waveform and the femoral pulse waveform after the third filtering process are divided according to the heartbeat cycle.

4. The method for detecting arteriosclerosis according to claim 1, characterized in that, The conduction time within each heartbeat cycle is determined based on the maximum carotid artery signal and the maximum femoral artery signal within each heartbeat cycle, including: The conduction time within each heartbeat cycle is determined as the difference between the maximum value of the carotid artery signal and the maximum value of the femoral artery signal within each heartbeat cycle.

5. The method for detecting arteriosclerosis according to claim 1, characterized in that, Determining the conduction velocity of the carotid-femoral pulse wave of the subject based on the distance between the carotid artery measurement point and the femoral artery measurement point and the conduction time includes: The conduction velocity of the carotid-femoral pulse wave of the subject is determined as the ratio of the distance between the carotid artery measurement point and the femoral artery measurement point to the conduction time.

6. The method for detecting arteriosclerosis according to claim 1, characterized in that, After determining the average conduction velocity based on the conduction velocity of the subject's carotid-femoral pulse wave during each heartbeat cycle, the method further includes: The average conduction time is determined based on the conduction time within each heartbeat cycle; The standard deviation of the conduction time is determined based on the conduction time and the average conduction time within each heartbeat cycle; The evaluation score is determined based on the number of heartbeat cycles remaining after excluding conduction times outside the specified conduction time range, the total number of heartbeat cycles, the average conduction time, and the standard deviation of the conduction time.

7. An arteriosclerosis detection device, characterized in that, include: The waveform processing module is used to preprocess and differentiate the carotid pulse waveform and femoral pulse waveform, and then divide them according to the heartbeat cycle. The maximum value acquisition module is used to acquire the maximum value of the carotid artery signal and the maximum value of the femoral artery signal within each heartbeat cycle; The conduction time determination module is used to determine the conduction time within each heartbeat cycle based on the maximum value of the carotid artery signal and the maximum value of the femoral artery signal within each heartbeat cycle obtained by the maximum value acquisition module; to estimate the conduction time range based on the subject's heart rate and the subject's age; to retain the conduction time within the conduction time range and to discard the conduction time outside the conduction time range; The conduction velocity determination module is used to determine the conduction velocity of the subject's carotid-femoral pulse wave in each heartbeat cycle based on the distance between the carotid artery measurement point and the femoral artery measurement point and the conduction time in each heartbeat cycle. The average conduction velocity determination module is used to determine the average conduction velocity based on the conduction velocity of the neck and femoral pulse waves of the subject in each heartbeat cycle. An arteriosclerosis degree determination module is used to determine the degree of arteriosclerosis based on the average conduction velocity determined by the average conduction velocity determination module. Before dividing the carotid pulse waveform and femoral pulse waveform according to the heartbeat cycle after preprocessing and differentiation, the method further includes: acquiring pulse data, which includes carotid pulse data and femoral pulse data; Compare the length of the pulse data with the preset data length; When it is determined that the length of the pulse data is greater than the preset data length, calculate the autocorrelation curve of the pulse data within the current window; Calculate the number of peak values ​​of the curve based on the autocorrelation curve; Compare the number of peak values ​​of the curve with the preset number; When the number of peak values ​​of the curve exceeds the preset number, the formal collection of carotid pulse data and femoral pulse data begins.

8. An arteriosclerosis detection system, characterized in that, include: The device includes a device connection module, a user information management module, a data acquisition and processing module, a parameter setting module, and a report generation module; the data acquisition and processing module includes the arteriosclerosis early warning device as described in claim 7. The device connection module communicates with the arteriosclerosis detector via an interface module. The user information management module is used to store the information of the test subjects; The data acquisition and processing module is used to acquire and process data and display waveforms in real time; The parameter setting module is used to set filter parameters, laser parameters, and sampling parameters; The report generation module is used to generate a temporary report after the data collection is completed.

9. An arteriosclerosis detection instrument, characterized in that, Includes fiber optic system, lens assembly and main unit; The host unit is connected to the host computer, which includes the arteriosclerosis detection system as described in claim 8.

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