Arteriosclerosis detection method, device and system and arteriosclerosis detector

Abstract

The invention discloses an arteriosclerosis detection method, device and system and an arteriosclerosis detector, and relates to the technical field of medical instruments.The method comprises the steps that after carotid artery pulse waveforms and femoral artery pulse waveforms are subjected to preprocessing and differential processing, division is conducted according to heartbeat cycles; acquiring a carotid artery signal maximum value and a femoral artery signal maximum value in each heartbeat cycle; determining conduction time in each heartbeat cycle according to the carotid artery signal maximum value and the femoral artery signal maximum value in each heartbeat cycle; according to the distance between the carotid artery measuring point and the femoral artery measuring point and the conduction time in each heartbeat cycle, the conduction speed of the pulse wave of the tested person in the arterial system in each heartbeat cycle is determined; determining an average conduction velocity according to the conduction velocity of the pulse wave of the testee in the arterial system in each heartbeat cycle; and determining the arteriosclerosis degree according to the average conduction velocity. The technical problem of low arteriosclerosis measurement precision in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of medical devices, and in particular to an arteriosclerosis detection method, device, system and arteriosclerosis detector. Background Art

[0002] Currently, early screening and diagnosis of arteriosclerosis primarily rely on methods such as noninvasive blood pressure measurement, ultrasound, and pulse wave velocity (PWV) analysis. Traditional noninvasive testing methods (such as blood pressure cuffs) have low pulse wave signal acquisition accuracy, making them incapable of meeting the requirements for high-resolution vascular elasticity measurements. 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] PWV is a key indicator of arterial stiffness, and carotid-femoral pulse wave velocity (PWV) is the gold standard for assessing central arterial stiffness. Therefore, accurate PWV calculation is crucial for assessing arterial stiffness. Common PWV calculations use a method that identifies characteristic points in the electrocardiogram (ECG) and arterial waveforms, or in the carotid-femoral or ankle-brachial artery waveforms, to calculate pulse wave transit time and then calculate PWV. The selection of these characteristic points significantly impacts the results; an error of 20 milliseconds can result in a deviation of approximately 1 meter per second. The most common characteristic point selection methods include the "foot" method and the second-order derivative method. While both work well with very good signal quality, they exhibit significant dispersion with lower-quality signals, making the results unreliable.

[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. In addition, existing measurement equipment is usually large in size, high in cost, difficult to operate, difficult to measure accurately, and difficult to popularize in primary medical care or home self-testing environments. Summary of the Invention

[0005] The present invention provides an arteriosclerosis detection method, device, system and arteriosclerosis detector to solve the technical problem of low arteriosclerosis measurement accuracy in the prior art.

[0006] In a first aspect, an embodiment of the present invention provides a method for detecting arteriosclerosis, comprising:

[0007] After pre-processing and differentiation of the carotid artery pulse waveform and the femoral artery pulse waveform, they are divided according to the heartbeat cycle;

[0008] Obtain the maximum value of the carotid artery signal and the maximum value of the femoral artery signal in each cardiac cycle;

[0009] Determine the conduction time in each cardiac cycle according to the maximum value of the carotid artery signal and the maximum value of the femoral artery signal in each cardiac cycle;

[0010] determining the conduction velocity of the subject's carotid-femoral pulse wave in each cardiac cycle based on the distance between the carotid artery measurement point and the femoral artery measurement point and the conduction time in each cardiac cycle;

[0011] Determining an average conduction velocity based on the conduction velocity of the subject's carotid-femoral pulse wave in each heartbeat cycle;

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

[0013] Optionally, after pre-processing and differentiation processing the carotid artery pulse waveform and the femoral artery pulse waveform, and before dividing the waveforms according to the heartbeat cycle, the method further includes:

[0014] Acquiring pulse data, wherein the pulse data includes carotid artery pulse data and femoral artery pulse data;

[0015] Comparing the length of the pulse data with a preset data length;

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

[0017] Calculating the number of curve peaks according to the autocorrelation curve;

[0018] Comparing the number of peaks of the curve with a preset number;

[0019] When the number of the curve peaks is greater than the preset number, formally starting to collect the carotid artery pulse data and the femoral artery pulse data;

[0020] Acquiring 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;

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

[0022] Optionally, after determining the conduction time in each cardiac cycle according to the maximum value of the carotid artery signal and the maximum value of the femoral artery signal in each cardiac cycle, the method further includes:

[0023] Calculating the conduction time range according to the heart rate of the subject and the age of the subject;

[0024] The conduction times within the conduction time range are retained, and the conduction times outside the conduction time range are discarded.

[0025] Optionally, after pre-processing and differentiation processing the carotid artery pulse waveform and the femoral artery pulse waveform, dividing them according to the heartbeat cycle includes:

[0026] performing a first filtering process on the carotid artery pulse waveform and the femoral artery pulse waveform;

[0027] performing a first differential process on the carotid artery pulse waveform and the femoral artery pulse waveform after the first filtering process respectively;

[0028] performing a second filtering process on the carotid artery pulse waveform and the femoral artery pulse waveform after the first differentiation process;

[0029] performing a second differentiation process on the carotid artery pulse waveform and the femoral artery pulse waveform after the second filtering process respectively;

[0030] performing a third filtering process on the carotid artery pulse waveform and the femoral artery pulse waveform after the second differentiation process;

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

[0032] Optionally, determining the conduction time in each cardiac cycle according to the maximum value of the carotid artery signal and the maximum value of the femoral artery signal in each cardiac cycle includes:

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

[0034] Optionally, determining the conduction velocity of the carotid-femoral pulse wave of the subject according to 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 in each heartbeat cycle, the method further includes:

[0037] determining an average conduction time based on the conduction time in each heartbeat cycle;

[0038] determining a standard deviation of the conduction time according to the conduction time and the average conduction time in each heartbeat cycle;

[0039] An evaluation score is determined based on the number of heartbeat cycles remaining after excluding 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.

[0040] In a second aspect, an embodiment of the present invention further provides an arteriosclerosis detection device, comprising:

[0041] A waveform processing module is used to perform pre-processing and differential processing on the carotid artery pulse waveform and the femoral artery pulse waveform, and then divide them according to the heartbeat cycle;

[0042] A maximum value acquisition module is used to obtain the maximum value of the carotid artery signal and the maximum value of the femoral artery signal in each heartbeat cycle;

[0043] a conduction time determination module, configured to determine the conduction time in each cardiac cycle based on the maximum value of the carotid artery signal and the maximum value of the femoral artery signal in each cardiac cycle acquired by the maximum value acquisition module;

[0044] a conduction velocity determination module, configured to determine the conduction velocity of the subject's carotid-femoral pulse wave in each cardiac cycle based on the distance between the carotid artery measurement point and the femoral artery measurement point and the conduction time in each cardiac cycle;

[0045] an average conduction velocity determination module, configured to determine the average conduction velocity based on the conduction velocity of the carotid-femoral pulse wave of the subject in each heartbeat cycle;

[0046] The arteriosclerosis degree determination module is used to determine the arteriosclerosis degree according to the average conduction velocity determined by the average conduction velocity determination module.

[0047] In a third aspect, an embodiment of the present invention further provides 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 is communicatively connected to the arteriosclerosis detector via the interface module;

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

[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 filtering parameters, laser parameters and sampling parameters;

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

[0053] In a fourth aspect, an embodiment of the present invention further provides an arteriosclerosis detector, comprising an optical fiber system, a lens assembly, and a host unit;

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

[0055] Embodiments of the present invention provide an arteriosclerosis detection method, device, system, and instrument. The method preprocesses and differentiates the carotid and femoral pulse waveforms, dividing them according to cardiac cycles. The method obtains the maximum carotid and femoral signal values ​​within each cardiac cycle. The method determines the conduction time within each cardiac cycle based on the maximum carotid and femoral signal values ​​within each cardiac cycle. The method determines the conduction velocity of the subject's carotid-femoral pulse wave within each cardiac cycle based on the distance between the carotid and femoral measurement points and the conduction time within each cardiac cycle. The method determines the average conduction velocity based on the conduction velocity of the subject's carotid-femoral pulse wave within each cardiac cycle. The method also determines the degree of arteriosclerosis based on the average conduction velocity. The preprocessing of the carotid and femoral pulse waveforms eliminates portions of the carotid and femoral pulse waveforms affected by respiratory and motion artifacts while retaining information representative of physiological characteristics. The method also determines the average conduction velocity based on the conduction velocity of the subject's carotid-femoral pulse wave within each cardiac cycle, and the degree of arteriosclerosis based on the average conduction velocity. The resulting measurement results are relatively stable. The reliability of the detection results can be improved, and the technical problem of low arteriosclerosis measurement accuracy in the prior art can be solved.

[0056] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0058] Figure 1 This is a flow chart of a method for detecting arteriosclerosis provided by an embodiment of the present invention;

[0059] Figure 2is a flow chart of another arteriosclerosis detection method provided by an embodiment of the present invention;

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

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

[0062] Figure 5 1 is a schematic structural diagram of an arteriosclerosis detection device provided by an embodiment of the present invention;

[0063] Figure 6 1 is a schematic structural diagram of an arteriosclerosis detection system provided by an embodiment of the present invention;

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

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

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

[0067] Figure 10 It is a structural schematic diagram of an arteriosclerosis detector provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0068] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0069] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0070] Figure 1 This is a flow chart 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 performed by an arteriosclerosis detection device. The arteriosclerosis detection device can be implemented in the form of hardware and / or software. The arteriosclerosis detection device can be configured in a processor. Figure 1 , the method comprises the following steps:

[0071] S110 , after pre-processing and differentiation processing, the carotid artery pulse waveform and the femoral artery pulse waveform are divided according to the heartbeat cycle.

[0072] It should be noted that the carotid artery pulse waveform and the femoral artery pulse waveform are electrical signal waveforms containing displacement information of the skin at the carotid artery and the skin at the femoral artery of the subject due to the pulse beating. Figure 10 The infrared laser inside the host unit 1 emits infrared laser to the skin at the carotid artery and the skin at the femoral artery of the person to be tested, and then after being reflected by the skin at the carotid artery and the skin at the femoral artery of the person to be tested, it is coupled back to the silicon photonic chip, mixed by the silicon photonic chip, and converted into the waveform of the electrical signal by the photodetector.

[0073] Among them, preprocessing is completed through filtering.

[0074] S120 , obtaining the maximum value of the carotid artery signal and the maximum value of the femoral artery signal in each cardiac cycle.

[0075] It is understandable that after pre-processing and differential processing of the carotid artery pulse waveform and the femoral artery pulse waveform, the maximum value of the carotid artery signal and the maximum value of the femoral artery signal in each heartbeat cycle are determined, and the obtained results are more credible.

[0076] S130 , determining the conduction time in each cardiac cycle according to the maximum value of the carotid artery signal and the maximum value of the femoral artery signal in each cardiac cycle.

[0077] S140. 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.

[0078] S150. Determine the average conduction velocity based on the conduction velocity of the subject's carotid-femoral pulse wave in each heartbeat cycle.

[0079] It can be understood that determining the average conduction velocity based on the conduction velocity of the subject's carotid-femoral pulse wave in each heartbeat cycle can obtain a more stable and reliable result.

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

[0081] It can be understood that since the carotid pulse waveform and femoral pulse waveform of the present application are electrical signal waveforms that contain displacement information of the skin at the carotid artery and the skin at the femoral artery of the subject due to pulse beating, the obtained carotid pulse waveform and femoral pulse waveform will be affected by respiratory and motion artifacts, thereby affecting the accuracy of arteriosclerosis measurement.

[0082] By preprocessing the carotid and femoral pulse waveforms, the present invention eliminates portions of the carotid and femoral pulse waveforms affected by respiratory and motion artifacts while retaining information that characterizes physiological characteristics. The average conduction velocity is determined based on the conduction velocity of the subject's carotid and femoral pulse waves within each cardiac cycle, and the degree of arteriosclerosis is determined based on the average conduction velocity. This results in more stable measurement results, enhancing the reliability of the test results and addressing the low accuracy of arteriosclerosis measurements in existing technologies.

[0083] Figure 2 is a flow chart of another arteriosclerosis detection method provided by an embodiment of the present invention, with reference to Figure 2 , the method comprises the following steps:

[0084] S210: Acquire pulse data, where the pulse data includes carotid artery pulse data and femoral artery pulse data.

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

[0086] S230: 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 in the current window.

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

[0088] S240: Calculate the number of curve peaks according to the autocorrelation curve.

[0089] S250: Compare the number of curve peaks with a preset number.

[0090] Different measurement results using different numbers of curve peaks can be obtained through experiments. When the score of the result evaluation of the measurement result reaches more than 90 points, the number of curve peaks at this time is set as the preset number.

[0091] It's understandable that when the number of peaks exceeds the set value, we consider the subject to be stable, with minimal environmental interference, and in a suitable test state, and formal collection can begin. If the amount of data is insufficient or the number of correlation peaks in the current window is insufficient, the judgment will not pass. New data must be received and the judgment process repeated until the requirements are met.

[0092] S260: When the number of curve peaks is greater than a preset number, formally collect the carotid artery pulse data and the femoral artery pulse data.

[0093] In this embodiment of the present invention, data quality is assessed during data collection. Carotid and femoral pulse data are not formally collected until the number of curve peaks exceeds a preset number, resulting in more reliable results. By assessing signal quality before actual measurement, only qualified data 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] It is understandable that after the carotid pulse data and femoral pulse data are collected, the data will be automatically input into the algorithm. The algorithm will extract the distance between the carotid measurement point and the femoral measurement point, the carotid pulse waveform and the femoral pulse waveform, and use the carotid pulse waveform and the femoral pulse waveform to calculate the heart rate. The heart rate will be used to search for signal landmarks, data elimination and result evaluation.

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

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

[0098] S290: After pre-processing and differentiation processing, the carotid artery pulse waveform and the femoral artery pulse waveform are divided according to the heartbeat cycle.

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

[0100] This embodiment of the present invention performs three preprocessing steps on the carotid and femoral pulse waveforms, removing portions of the signals affected by respiratory and motion artifacts while retaining information that characterizes physiological characteristics. Velocity and acceleration information are obtained through two differential processes, ensuring high confidence in the collected results in most cases.

[0101] Figure 3 This is a waveform diagram of a carotid artery pulse and a femoral artery pulse provided by an embodiment of the present invention, specifically a waveform diagram of the carotid artery pulse waveform and the femoral artery pulse waveform before and after preprocessing and differential processing. Figure 3 , Figure 3 The blue waveform is the carotid artery pulse waveform, and the red waveform is the femoral artery pulse waveform. The waveforms before preprocessing and differentiation of the carotid artery pulse waveform and the femoral artery pulse waveform are concentrated in the upper half, and the waveforms after preprocessing and differentiation of the carotid artery pulse waveform and the femoral artery pulse waveform are concentrated in the lower half. Figure 3 The green points marked on the waveform before processing are the feature points. Figure 3 At the time point where the characteristic point is located, the carotid artery signal value or the femoral artery signal value obtained from the processed waveform is the maximum value within the heartbeat cycle at the time point.

[0102] S2100 , obtaining the maximum value of the carotid artery signal and the maximum value of the femoral artery signal in each heartbeat cycle.

[0103] S2110. Determine the conduction time in each cardiac cycle according to the maximum value of the carotid artery signal and the maximum value of the femoral artery signal in each cardiac cycle.

[0104] Optionally, based on the above embodiment, step S2110 includes: determining that the conduction time in each cardiac cycle is the difference between the maximum value of the carotid artery signal and the maximum value of the femoral artery signal in each cardiac cycle.

[0105] Optionally, based on the above embodiment, after step S2110, it also includes: estimating the conduction time range based on the heart rate and age of the subject; retaining the conduction time within the conduction time range, and eliminating the conduction time outside the conduction time range.

[0106] It is understandable that after multiple preprocessing steps, most data can be processed correctly and more realistic results can be obtained. However, there may still be unavailable results due to occasional interference factors such as coughing and movement of the subject. Therefore, after preprocessing and differential processing, the conduction time limit range is calculated based on heart rate and age, and unreasonable data is eliminated within this range to obtain more reliable test results.

[0107] The embodiment of the present invention calculates the conduction time range based on the heart rate and age of the subject, retains the conduction time within the conduction time range, and eliminates the conduction time outside the conduction time range, so as to obtain a more reliable detection result.

[0108] S2120. 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.

[0109] S2130. Determine the average conduction velocity based on the conduction velocity of the subject's carotid-femoral pulse wave in each heartbeat cycle.

[0110] Optionally, based on the above embodiment, step S2130 includes: determining the conduction velocity of the subject's carotid-femoral pulse wave 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 in each heartbeat cycle; determining the standard deviation of the conduction time based on the conduction time in each heartbeat cycle and the average conduction time; and determining an evaluation score based on the number of heartbeat cycles remaining after excluding the conduction time 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 evaluation score = 0.5 [(the number of heartbeat cycles remaining after excluding the conduction times outside the conduction time range / the total number of heartbeat cycles) + (the average conduction time - the standard deviation of the conduction time) / the average conduction time].

[0113] The embodiment of the present invention evaluates the test results and provides 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 mean conduction velocity.

[0115] Figure 4 This is a distribution diagram of the conduction time 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 of the subject is 50 cm. After the original pulse waveform is preprocessed and differentiated, the maximum value of the carotid artery signal and the maximum value of the femoral artery signal in each heartbeat cycle are obtained through the characteristic points. The distribution result of a single measurement is as follows: Figure 4 As shown in the figure. The distance between the carotid and femoral artery measurement points can be combined to determine the carotid-femoral pulse wave conduction velocity, and the average conduction velocity, average conduction time, result error, and result evaluation are output. The calculated carotid-femoral pulse wave conduction velocity 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. An evaluation score of 90 or above indicates good data and reliable results; an evaluation score between 80 and 90 indicates average data and weak reference value; and an evaluation score below 80 indicates poor data and meaningless results, and re-measurement is recommended.

[0116] In summary, the embodiments of the present invention, by preprocessing the carotid and femoral pulse waveforms, can eliminate portions of the carotid and femoral pulse waveforms affected by respiratory and motion artifacts while retaining information representative of physiological characteristics. The average conduction velocity is determined based on the conduction velocity of the subject's carotid and femoral pulse waves during each cardiac 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 test results and addresses the technical issue of low arteriosclerosis measurement accuracy in existing technologies. By assessing data quality during data acquisition and only beginning to formally collect carotid and femoral pulse data when the number of curve peaks exceeds a preset number, the results are more reliable. By assessing signal quality before formal measurement, only qualified data is saved, ensuring data reliability and improving measurement efficiency. By performing three preprocessing steps on the carotid and femoral pulse waveforms, portions of the signals affected by respiratory and motion artifacts are eliminated while retaining information representative of physiological characteristics. Through double differentiation, velocity and acceleration information can be obtained, and the collected results are generally highly reliable. The conduction time range is estimated based on the subject's heart rate and age. Conduction times within this range are retained and those outside are discarded, resulting in even more reliable test results. Test results are evaluated, and individual measurement and calculation results are scored to help users understand the accuracy of the results.

[0117] Figure 5 This is a schematic diagram of the structure of an arteriosclerosis detection device provided by an embodiment of the present invention, with reference to 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 an embodiment of the present invention, the waveform processing module 510 is used to perform preprocessing and differential processing on the carotid artery pulse waveform and the femoral artery pulse waveform, and then divide them according to the cardiac cycle; the maximum value acquisition module 520 is used to obtain the maximum value of the carotid artery signal and the femoral artery signal in each cardiac cycle; the conduction time determination module 530 is used to determine the conduction time in each cardiac cycle based on the maximum value of the carotid artery signal and the femoral artery signal in each cardiac cycle obtained 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 cardiac cycle based on the distance between the carotid artery measurement point and the femoral artery measurement point and the conduction time in each cardiac 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 cardiac 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 the embodiments of the present invention can execute the arteriosclerosis detection method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method. For matters not described in detail in the embodiments of the present invention, reference can be made 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 by an embodiment of the present invention, with reference to 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-mentioned embodiment. The device connection module 610 communicates with the arteriosclerosis detector via an interface module. The user information management module 620 is used to store the subject's information. 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 filtering parameters, laser parameters, and sampling parameters. The report generation module 650 is used to generate a temporary report after the data acquisition is completed.

[0121] Figure 7This is a schematic diagram of the layout of a software main interface provided by an embodiment of the present invention. Figure 8 This is a schematic diagram of a detailed parameter setting interface provided by an embodiment of the present invention. Specifically, the arteriosclerosis detection system provided by an embodiment of the present invention is a self-developed host computer software. This software is specially designed for the arteriosclerosis detector provided by an embodiment of the present invention. 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. Among them, the interface module includes USB and serial ports. The device connection module 610 supports establishing communication with the arteriosclerosis detector through USB and serial ports, establishing communication, and automatically searching and identifying the device status. Users can use Figure 7 The device search function area shown in is triggered by the "Click to Search Device" button, and displays the status information of "Waiting for Connection", "Connection Successful" or "Connection Failed" to ensure reliable data communication. The user information management module 620 requires the user to fill in the name, gender, age, height, weight and cf distance (the distance between the carotid artery measurement point and the femoral artery measurement point) and other information before the formal collection through the personal basic information input area, and save it through the "Confirm" button to ensure that the collected data corresponds to the information of the subject. The data acquisition and processing module 630 includes real-time waveform display, data acquisition control and acquisition auxiliary functions. The software provides two acquisition modes (PWV / PWA acquisition) and sets acquisition auxiliary control to ensure signal quality. Users can bypass strict detection and enter the formal acquisition stage through the "Cancel acquisition assistance (single)" button as needed. The parameter setting module 640 is Figure 8 The detailed parameter setting interface allows users to adjust filtering parameters (high and low cutoff frequencies, filter type), laser drive current, and sampling parameters (sampling rate, sampling depth, and sampling time). Parameter adjustments take effect in real time, requiring no additional confirmation. After acquisition is complete, the report generation module 650 generates a temporary report displaying test results including PWV, mean transit time, heart rate, and carotid-femoral pulse wave curves, and provides an input area for diagnostic suggestions. Final data and reports are automatically saved according to pre-set naming conventions for easy data traceability and management.

[0122] Figure 9 This is an operation flow chart of a software main interface provided by an embodiment of the present invention, specifically an operation flow chart of the software main interface of the arteriosclerosis detection system provided by an embodiment of the present invention, reference Figure 9 , the operation process includes the following steps:

[0123] S910: After confirming that the detection device is in a normal state, connect the detection device to the host computer.

[0124] Among them, the detection equipment includes an arteriosclerosis detector and its related equipment (such as optical lenses, measuring probes) which communicate via data cables or wirelessly.

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

[0126] S930. Click “Click to search for devices”.

[0127] Among them, after clicking "Click to search for devices", the software will automatically search and detect the device connection status and display the connection results.

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

[0129] Among them, adjust the filtering parameters, laser parameters and sampling parameters in the detailed parameter setting interface.

[0130] S950. After selecting the corresponding collection mode, click "Start Collection" to start data collection.

[0131] Specifically, the system is divided into an auxiliary acquisition stage and a formal acquisition stage, and displays the pulse wave and velocity curve in real time.

[0132] S960. After the collection is completed, click the "Generate temporary report and enter the diagnosis results" button to enter the report page. After entering the diagnosis suggestions, click "Save data (including report)" to complete the storage of data and report.

[0133] Among them, the report file naming rules are automatically generated (such as "name-age-cf interval-time").

[0134] For example, on the operating system, after connecting the detector to the host computer via USB / serial port, the user launches the software and clicks "Click to Search Device." The system successfully detects the device and displays "Connection Successful." Enter your name, gender, age, height, weight, and CF spacing in the basic personal information area. After entering the detailed parameter settings interface, adjust the filter type (for example, set it to a low-pass filter with a predetermined cutoff frequency), set 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 to 20 seconds. After clicking "Start Acquisition," the software first enters the acquisition assistance phase to test signal quality. If the signal quality meets the requirements, the system enters the formal acquisition phase. After the acquisition is complete, the system automatically generates a temporary report containing key data such as PWV, mean pulse transit time, and heart rate. The user can also enter preliminary diagnostic recommendations in the report. Finally, click "Save Data (Including Report)" to complete the data storage.

[0135] This embodiment of the present invention achieves stable data communication between the host computer and the detector through automatic device search and status detection, reducing operational errors. The rational layout of each functional area and the clear operation process effectively improve detection efficiency and user experience. It supports multiple acquisition modes and parameter settings to meet different detection conditions and signal quality requirements, ensuring data accuracy and reliability. The integrated temporary report generation and diagnostic input function automatically generates test 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 by an embodiment of the present invention, with reference to Figure 9 The arteriosclerosis detector comprises 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 comprises the arteriosclerosis detection system provided by the above embodiment.

[0137] Specifically, the host unit 1 integrates an infrared laser, a red laser, a silicon photonics chip, a photodetector, a driver power supply, and a control circuit. 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 secures the optical lens in the lens assembly 2 to the top of the lens holder using a clamp, adjusts it to the measurement point (e.g., the carotid or femoral artery measurement point), and indicates the measurement position using a red laser. After the system is activated, optical displacement detection begins. This optical displacement detection measures the displacement of the patient's skin at the carotid and femoral arteries due to the pulse. Specifically, the infrared laser within the host unit 1 emits infrared laser light at the patient's skin at the carotid and femoral arteries. The infrared laser then reflects from the patient's skin at the carotid and femoral arteries and couples back to the silicon photonics chip. After frequency 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 and femoral pulse waveforms. The displacement of the skin at the carotid artery and the skin at the femoral artery due to the pulse beating can be analyzed based on the carotid artery pulse waveform and the femoral artery pulse waveform.

[0138] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0139] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A method for detecting arteriosclerosis, characterized in that: include: After pre-processing and differentiation of the carotid artery pulse waveform and the femoral artery pulse waveform, they are divided according to the heartbeat cycle; Obtain the maximum value of the carotid artery signal and the maximum value of the femoral artery signal in each cardiac cycle; Determine the conduction time in each cardiac cycle according to the maximum value of the carotid artery signal and the maximum value of the femoral artery signal in each cardiac cycle; determining the conduction velocity of the subject's carotid-femoral pulse wave in each cardiac cycle based on the distance between the carotid artery measurement point and the femoral artery measurement point and the conduction time in each cardiac cycle; Determining an average conduction velocity based on the conduction velocity of the subject's carotid-femoral pulse wave in each heartbeat cycle; The degree of arteriosclerosis is determined based on the mean conduction velocity.

2. The method for detecting arteriosclerosis according to claim 1, wherein: After pre-processing and differentiation processing of the carotid artery pulse waveform and the femoral artery pulse waveform, and before dividing the waveforms according to the heartbeat cycle, the method further includes: Acquiring pulse data, wherein the pulse data includes carotid artery pulse data and femoral artery pulse data; Comparing the length of the pulse data with a preset data length; When it is determined that the length of the pulse data is greater than the preset data length, calculating the autocorrelation curve of the pulse data in the current window; Calculating the number of curve peaks according to the autocorrelation curve; Comparing the number of peaks of the curve with a preset number; When the number of the curve peaks is greater than the preset number, formally starting to collect the carotid artery pulse data and the femoral artery pulse data; Acquiring 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; The heart rate of the subject is determined based on the carotid artery pulse waveform and the femoral artery pulse waveform.

3. The method for detecting arteriosclerosis according to claim 2, wherein: After determining the conduction time in each cardiac cycle according to the maximum value of the carotid artery signal and the maximum value of the femoral artery signal in each cardiac cycle, the method further includes: Calculating the conduction time range according to the heart rate of the subject and the age of the subject; The conduction times within the conduction time range are retained, and the conduction times outside the conduction time range are discarded.

4. The method for detecting arteriosclerosis according to claim 1, wherein: After pre-processing and differentiation processing of the carotid artery pulse waveform and the femoral artery pulse waveform, division according to the heartbeat cycle includes: performing a first filtering process on the carotid artery pulse waveform and the femoral artery pulse waveform; performing a first differential process on the carotid artery pulse waveform and the femoral artery pulse waveform after the first filtering process respectively; performing a second filtering process on the carotid artery pulse waveform and the femoral artery pulse waveform after the first differentiation process; performing a second differentiation process on the carotid artery pulse waveform and the femoral artery pulse waveform after the second filtering process respectively; performing a third filtering process on the carotid artery pulse waveform and the femoral artery pulse waveform after the second differentiation process; The carotid artery pulse waveform and the femoral artery pulse waveform after the third filtering process are divided according to heartbeat cycles.

5. The method for detecting arteriosclerosis according to claim 1 or 3, wherein: Determining the conduction time within each cardiac cycle based on the maximum carotid artery signal and the maximum femoral artery signal within each cardiac cycle includes: The conduction time in each cardiac cycle is determined as the difference between the maximum value of the carotid artery signal and the maximum value of the femoral artery signal in each cardiac cycle.

6. The method for detecting arteriosclerosis according to claim 1, wherein: Determining the conduction velocity of the carotid-femoral pulse wave of the subject according to 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.

7. The method for detecting arteriosclerosis according to claim 3, wherein: After determining the average conduction velocity according to the conduction velocity of the carotid-femoral pulse wave of the subject in each heartbeat cycle, the method further includes: determining an average conduction time based on the conduction time in each heartbeat cycle; determining a standard deviation of the conduction time according to the conduction time and the average conduction time in each heartbeat cycle; An evaluation score is determined based on the number of heartbeat cycles remaining after excluding 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.

8. An arteriosclerosis detection device, characterized in that: include: A waveform processing module is used to perform pre-processing and differential processing on the carotid artery pulse waveform and the femoral artery pulse waveform, and then divide them according to the heartbeat cycle; A maximum value acquisition module is used to obtain the maximum value of the carotid artery signal and the maximum value of the femoral artery signal in each heartbeat cycle; a conduction time determination module, configured to determine the conduction time in each cardiac cycle based on the maximum value of the carotid artery signal and the maximum value of the femoral artery signal in each cardiac cycle acquired by the maximum value acquisition module; a conduction velocity determination module, configured to determine the conduction velocity of the subject's carotid-femoral pulse wave in each cardiac cycle based on the distance between the carotid artery measurement point and the femoral artery measurement point and the conduction time in each cardiac cycle; an average conduction velocity determination module, configured to determine the average conduction velocity based on the conduction velocity of the carotid-femoral pulse wave of the subject in each heartbeat cycle; The arteriosclerosis degree determination module is used to determine the arteriosclerosis degree according to the average conduction velocity determined by the average conduction velocity determination module.

9. An arteriosclerosis detection system, characterized in that: include: Device connection module, user information management module, data acquisition and processing module, parameter setting module and report generation module; the data acquisition and processing module includes the arteriosclerosis early warning device according to claim 8; The device connection module is communicatively connected to the arteriosclerosis detector via the interface module; The user information management module is used to store the information of the test subject; 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 filtering parameters, laser parameters and sampling parameters; The report generation module is used to generate a temporary report after the collection is completed.

10. An arteriosclerosis detector, characterized in that: Including fiber optic system, lens assembly and host unit; The host unit is connected to a host computer, and the host computer includes the arteriosclerosis detection system according to claim 9.

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