A non-invasive cerebral arterial stiffness detection system

Abstract

The present invention provides a non-invasive cerebral artery stiffness detection system, including: an ultrasonic Doppler module for transmitting pulsed ultrasonic Doppler signals to the common carotid artery and the terminal of the internal carotid artery of a user, and collecting ultrasonic Doppler signals reflected from the common carotid artery and the terminal of the internal carotid artery; a signal processing module for pre-processing the two collected ultrasonic Doppler signals respectively, and obtaining two blood flow waveform data through FFT transformation; a signal analysis module for calculating the pulse wave velocity of the cervical and cerebral arteries based on the distance between the common carotid artery and the terminal of the internal carotid artery of the user using the waveform time difference of the two blood flow waveform data, and determining the cerebral artery stiffness of the user according to a preset pulse wave velocity-cerebral artery stiffness comparison table. The present invention can simply, easily and accurately measure the cervical and cerebral pulse wave velocity, evaluate the condition of cervical and cerebral arteriosclerosis, and provide effective help for the clinical diagnosis and treatment of cerebrovascular diseases by medical staff.

Description

Technical Field

[0001] The invention relates to the field of ultrasonic Doppler flow velocity measurement and medical field, and in particular to a device for realizing non-invasive cerebral vascular arteriosclerosis detection. Background Art

[0002] Cerebral vascular sclerosis is the thickening of the cerebral blood vessel walls. It is a type of arteriosclerosis and the main cause of cerebrovascular disease. It is very dangerous because it can easily cause stroke. Timely assessment of the degree of cerebral vascular sclerosis in the early stage of the disease is very important for treatment and delaying the patient's symptoms. Pulse wave velocity is the best way to assess the degree of vascular sclerosis.

[0003] Each time the heart pumps out about 70 ml of blood to the aorta, the shock wave of the pumped blood generates a pulse pressure wave on the aortic wall and is transmitted along the blood vessel wall to the peripheral blood vessels at a certain speed. This wave is called a "pulse wave", and the speed at which the pulse wave is transmitted in the artery is called "pulse wave velocity" (PWV). The pulse wave velocity can be calculated by measuring the pulse wave propagation time and distance between two arterial sites. The calculation formula is: PWV = L / t (cm / s). t is the time difference between the two waveforms, that is, the propagation time; L is the distance between the two probes, that is, the distance. The pulse wave velocity has a certain relationship with the biomechanical properties of the arterial wall (viscoelasticity), the geometric properties of the blood vessels (cavity diameter and wall thickness), and the density of the blood. Since the changes in the geometric characteristics of the blood vessels and the blood density are relatively small, the size of the pulse wave velocity can reflect the hardness of the arterial wall. Generally speaking, the faster the pulse wave velocity, the worse the elasticity of the artery, the higher the stiffness, and the harder the blood vessel wall (i.e., arteriosclerosis is developing); conversely, the slower the pulse wave velocity, the better the arterial elasticity, the lower the blood vessel stiffness, the better the compliance, the lower the risk, and the lower the incidence of acute events. Pulse wave velocity is a relatively sensitive indicator of vascular elasticity. The value of pulse wave velocity increases with age, and some chronic diseases such as hypertension, diabetes, hyperlipidemia, and obesity will also cause the value of pulse wave velocity to be higher than that of healthy people during the development process. Changes in pulse wave velocity are an overall reflection of abnormal aortic structure and function, which can well reflect the degree of large artery sclerosis and is a classic indicator for evaluating aortic sclerosis. Its determination method is simple and fast, and has good repeatability during individual follow-up, which is more suitable for large sample epidemiological surveys, physical fitness monitoring, and follow-up observations.

[0004] The main pulse wave velocity measurement technologies currently used include carotid-femoral Pulse Wave Velocity (cfPWV), brachial-ankle Pulse Wave Velocity (baPWV), carotid-aorta Pulse Wave Velocity (caPWV), carotid-radial artery Pulse Wave Velocity (crPWV), carotid-brachial artery Pulse Wave Velocity (cbPWV) measurements, etc. Carotid-femoral pulse wave velocity (cfPWV). This method is an advanced standard for measuring the degree of aortic sclerosis, but the operation method is cumbersome and is suitable for scientific research, but it is difficult to be widely used in clinical practice. Brachial-ankle pulse wave velocity (baPWV). This method is currently widely used and can reflect the degree of sclerosis of large and medium arteries, but it is limited to speculation and cannot directly reflect the degree of sclerosis of the carotid and cerebral arteries. Carotid heart pulse wave velocity (caPWV). The operation is complicated and difficult to carry out, and it can only speculate the degree of sclerosis of the carotid and cerebral arteries. Carotid radial pulse wave velocity (crPWV). The degree of sclerosis of the carotid and cerebral arteries is limited to speculation and cannot be directly reflected. Carotid brachial pulse wave velocity (cbPWV). The degree of sclerosis of the carotid and cerebral arteries is limited to speculation and cannot be directly reflected. Other carotid and cerebral pulse wave velocity (ccPWV). The linear estimation method used to measure the length of the internal carotid artery is that according to the distance measured on the body surface, the trigonometric function method is used to calculate the length of the measured part of the common carotid artery and the end of the internal carotid artery, and the estimated length is a straight-line distance. The length error is large. In addition, because the position of measuring the flow velocity at the end of the internal carotid artery is the temporal window, the offset error is large. These together lead to the error of the final calculated pulse wave velocity, and the measurement consistency is poor, which can only serve as a reference. Considering that atherosclerosis of the carotid artery and intracranial artery is the main cause of cerebrovascular disease, the currently available technical methods are not very targeted, have large measurement errors, and poor measurement consistency. They fail to effectively assess the atherosclerosis of the carotid and intracranial arteries, and therefore cannot meet the clinical needs of cerebrovascular disease diagnosis and treatment. Summary of the invention

[0005] The present invention provides a non-invasive cerebral artery stiffness detection system, which can simply, easily and accurately measure the cervical and cerebral pulse wave velocity, evaluate the status of cervical and cerebral arteriosclerosis, and provide effective assistance to medical staff in the clinical diagnosis and treatment of cerebrovascular diseases.

[0006] The present invention provides a non-invasive cerebral artery stiffness detection system, comprising:

[0007] Ultrasonic Doppler module, used to transmit pulsed ultrasonic Doppler signals to the common carotid artery and the terminal of the internal carotid artery of the user, and collect ultrasonic Doppler signals reflected back from the terminal of the common carotid artery and the terminal of the internal carotid artery;

[0008] The signal processing module is used to pre-process the two collected ultrasonic Doppler signals respectively and obtain two blood flow waveform data through FFT transformation;

[0009] The signal analysis module is used to calculate the pulse wave velocity of the carotid artery based on the distance between the terminal of the common carotid artery and the internal carotid artery of the user using the waveform time difference of two blood flow waveform data, and determine the user's cerebral artery stiffness according to a preset pulse wave velocity-cerebral artery stiffness comparison table.

[0010] Preferably, the ultrasound Doppler module comprises:

[0011] A synchronous control unit, used to perform synchronous transmission control on the ultrasonic Doppler generating unit, and control the signal collecting unit to perform synchronous signal collection;

[0012] An ultrasonic Doppler generating unit, used to generate a pulse ultrasonic Doppler signal using an ultrasonic Doppler generating device to irradiate the common carotid artery and the terminal end of the internal carotid artery of the user;

[0013] The signal acquisition unit is used to receive the ultrasonic Doppler signal reflected from the common carotid artery and the terminal end of the internal carotid artery.

[0014] Preferably, the signal processing module includes:

[0015] A digital orthogonal modem is used to perform I and Q demodulation on the collected ultrasonic Doppler signal and extract the frequency difference signal;

[0016] A wall filter is used to filter out interference signals in the frequency difference signal;

[0017] The FFT conversion unit is used to convert the frequency difference signal into blood flow waveform data.

[0018] Preferably, it also includes a body surface detection module, and the body surface detection module includes:

[0019] A body surface bracket, used to fix the inflection point of the user's mandible and adjust the position of the neck ultrasonic Doppler generating device so that the neck ultrasonic Doppler generating device is at a preset neck measurement point;

[0020] A common carotid artery length analysis unit, used to determine the length of the user's common carotid artery based on the width of the ultrasonic Doppler generating device by using the distance between the user's mandibular inflection point and a preset neck measurement point;

[0021] A body surface detection unit, used to determine the positions of multiple preset types of feature points on the user's face;

[0022] A distance analysis unit, for determining, based on a plurality of preset types of feature points on the user's face, a first longitudinal distance between the user's eye corner and the lower eye socket, a first transverse distance between the user's pupil center and the lower eye socket near the nose wing, a second longitudinal distance between the user's lower eye socket near the nose wing and the nose wing, and a third longitudinal distance between the user's nose wing and the inflection point of the mandible;

[0023] The distance determination unit is used to determine the distance between the common carotid artery and the terminal end of the internal carotid artery of the user according to the first longitudinal distance, the first transverse distance, the second longitudinal distance, the third longitudinal distance and the length of the common carotid artery of the user.

[0024] Preferably, determining the positions of a plurality of preset types of feature points on the user's face includes:

[0025] Acquire a user's facial image through a camera, determine a facial area in the facial image based on a facial recognition algorithm, and perform interception to obtain a first image;

[0026] Performing noise reduction, light filling, brightening and normalization processing on the intercepted first image to obtain a second image;

[0027] Based on a preset facial feature point recognition library, a plurality of feature points in the second image are determined by a feature point image recognition algorithm, and a type of each feature point is determined according to a comparison result in the facial feature point recognition library;

[0028] The positions of all feature points on the user's face in the second image are determined, and each feature point is labeled with a type.

[0029] Preferably, the distance analysis unit performs the following operations:

[0030] Determine the camera's shooting ratio when capturing the user's face;

[0031] Determine a first image longitudinal distance between a corner of the user's eye and a lower eye socket in the second image, and determine a first longitudinal distance between the corner of the user's eye and the lower eye socket based on the shooting ratio;

[0032] Determine the first image lateral distance between the center of the pupil of the user and the lower eye socket near the nose wing in the second image, and determine the first lateral distance between the center of the pupil of the user and the lower eye socket near the nose wing based on the shooting ratio;

[0033] Determine the second image longitudinal distance between the lower eye socket of the user near the nose wing and the nose wing in the second image, and determine the second longitudinal distance between the lower eye socket of the user near the nose wing and the nose wing based on the shooting ratio;

[0034] Determine the third image longitudinal distance between the user's nose wing and the mandibular inflection point in the second image, and determine the third longitudinal distance between the user's nose wing and the mandibular inflection point based on the shooting ratio.

[0035] Preferably, the distance determination unit performs the following operations:

[0036] According to the first longitudinal distance, a first blood vessel length is calculated using a trigonometric function calculation formula based on a preset angle coefficient;

[0037] Calculating a second blood vessel length using the Pythagorean theorem according to the first transverse distance and the second longitudinal distance;

[0038] The first blood vessel length, the second blood vessel length, the third longitudinal distance, and the length of the user's common carotid artery are added together to obtain the distance between the common carotid artery and the terminal end of the internal carotid artery of the user.

[0039] Preferably, the distance determination unit further includes a compensation coefficient determination subunit, and the compensation coefficient determination subunit performs the following operations:

[0040] According to the positions of multiple preset types of feature points on the user's face, two feature points are grouped in pairs according to a preset grouping method to obtain multiple feature point groups and determine the feature point distances between feature points in each feature point group;

[0041] Determine a feature point distance of a preset feature point group as a basic unit distance, and respectively determine the distance ratios of the feature point distances of other feature point groups to the basic unit distance;

[0042] A facial feature set of the user is established by using multiple distance ratios, and the facial feature set is matched with multiple facial feature sets in a facial feature set library preset on a cloud network for similarity, so as to obtain an angle coefficient and a compensation coefficient corresponding to the facial feature set with the highest similarity; wherein the angle coefficient and the compensation coefficient are used to determine the distance between the common carotid artery and the terminal end of the internal carotid artery of the user according to the first longitudinal distance, the first transverse distance, the second longitudinal distance, the third longitudinal distance and the length of the common carotid artery of the user;

[0043] Each face feature set in the face feature set library and its corresponding angle coefficient and compensation coefficient are determined as follows:

[0044] By collecting facial images of volunteers and analyzing the facial images, a facial feature set of the volunteers is obtained;

[0045] By collecting anatomical data of the volunteer, the relative position of the internal carotid artery of the volunteer is determined, and the relative position relationship between the internal carotid artery of the volunteer and the feature point group of the face is determined;

[0046] According to the relative position relationship between the volunteer's internal carotid artery and the facial feature point group, the angle coefficient and the compensation coefficient between the volunteer's internal carotid artery and the preset feature point group are determined.

[0047] Preferably, it also includes a frequency determination module, and the frequency determination module performs the following operations:

[0048] The user's heart beat frequency is obtained, and the validity of the two blood flow waveform data of the user is determined according to the user's heart beat frequency. When the pulsation frequency in the blood flow waveform data is inconsistent with the heart beat frequency, it is determined that the blood flow waveform data detection is incorrect, and the operator is reminded to adjust the position of the ultrasonic Doppler generating device for re-detection.

[0049] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0051] Figure 1 It is a structural schematic diagram of a system for realizing non-invasive cerebral arterial stiffness detection in an embodiment of the present invention;

[0052] Figure 2 It is a schematic diagram of the structure of an ultrasonic Doppler device in an embodiment of the present invention;

[0053] Figure 3 This is a schematic diagram of the structure of a signal processing module in an embodiment of the present invention;

[0054] Figure 4 A schematic diagram of facial feature point and distance selection in an embodiment of the present invention;

[0055] Figure 5 It is a schematic diagram of the terminal segments of the common carotid artery and the internal carotid artery in an embodiment of the present invention;

[0056] Figure 6 is a calculation analysis diagram of the length of the first blood vessel in an embodiment of the present invention;

[0057] Figure 7 A comparison diagram of the positional relationship between the facial distance selection and the first blood vessel length and the second blood vessel length in an embodiment of the present invention;

[0058] Figure 8 A schematic diagram showing the comparison between the position distribution of intracranial blood vessels and facial feature points of a user in an embodiment of the present invention;

[0059] Fig. 9is a comparative schematic diagram of the relationship between the length of the third blood vessel and the position of the mandibular inflection point in an embodiment of the present invention;

[0060] Fig.10 Schematic diagram showing the comparison between the measurement point at the end of the right internal carotid artery and the position of the user's right eye in an embodiment of the present invention;

[0061] Fig.11 Schematic diagram of the comparison between the measurement point at the end of the left internal carotid artery and the position of the user's left eye in an embodiment of the present invention;

[0062] Fig.12 A comparison diagram of the relationship between the length of the common carotid artery of a user, the length of the body surface fixing bracket, and the diameter of the Doppler flow velocity probe at the neck position in an embodiment of the present invention;

[0063] Fig.13 Schematic diagram of waveform time difference selection of blood flow waveform data in an embodiment of the present invention. DETAILED DESCRIPTION

[0064] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0065] The embodiment of the present invention provides a system for detecting non-invasive cerebral arterial stiffness. Figure 1 ,include:

[0066] Ultrasonic Doppler module 1, used to transmit pulsed ultrasonic Doppler signals to the common carotid artery and the terminal of the internal carotid artery of the user, and collect ultrasonic Doppler signals reflected back from the terminal of the common carotid artery and the terminal of the internal carotid artery;

[0067] The signal processing module 2 is used to pre-process the two collected ultrasonic Doppler signals respectively and obtain two blood flow waveform data through FFT transformation;

[0068] Signal analysis module 3 is used to calculate the pulse wave velocity of the carotid artery based on the distance between the common carotid artery and the internal carotid artery of the user using the waveform time difference of two blood flow waveform data, and determine the user's cerebral artery stiffness according to a preset pulse wave velocity-cerebral artery stiffness comparison table.

[0069] The working principle and beneficial effects of the above technical solution are as follows: the ultrasonic Doppler module 1 transmits pulsed ultrasonic Doppler signals to the common carotid artery and the terminal of the internal carotid artery of the user, and collects the ultrasonic Doppler signals reflected from the terminal of the common carotid artery and the terminal of the internal carotid artery, and then the signal processing module 2 pre-processes the two collected ultrasonic Doppler signals respectively, and obtains two blood flow waveform data through FFT transformation, and finally the signal analysis module 3 calculates the waveform time difference Δt of the two sets of data according to the different data of the two measurement points in the two blood flow waveform data, and refers to Fig.13 , the pulse wave velocity ΔL / Δt of the cervical and cerebral arteries is calculated based on the distance ΔL between the end of the user's common carotid artery and the end of the internal carotid artery using the waveform time difference Δt of two blood flow waveform data, and the user's cerebral artery stiffness is determined according to the preset pulse wave velocity-cerebral artery stiffness comparison table and displayed on the display. This makes it possible to simply, easily and accurately measure the cervical and cerebral pulse wave velocity, evaluate the condition of cervical and cerebral arteriosclerosis, and realize non-invasive detection of the user's cerebral vascular arteriosclerosis status, providing effective assistance to medical staff in the clinical diagnosis and treatment of cerebrovascular diseases.

[0070] In a preferred embodiment, the ultrasound Doppler module comprises:

[0071] A synchronous control unit, used to perform synchronous transmission control on the ultrasonic Doppler generating unit, and control the signal collecting unit to perform synchronous signal collection;

[0072] An ultrasonic Doppler generating unit, used to generate a pulse ultrasonic Doppler signal using an ultrasonic Doppler generating device to irradiate the common carotid artery and the terminal end of the internal carotid artery of the user;

[0073] The signal acquisition unit is used to receive the ultrasonic Doppler signal reflected from the common carotid artery and the terminal end of the internal carotid artery.

[0074] The working principle and beneficial effect of the above technical solution are: the ultrasonic Doppler generating unit is synchronously transmitted and controlled by the synchronous control unit, wherein the detailed structure diagram of the ultrasonic Doppler generating device can be found in Figure 2 , and control the signal acquisition unit to perform synchronous signal acquisition, and use the ultrasonic Doppler generating unit to generate pulsed ultrasonic Doppler signals to irradiate the common carotid artery and the terminal of the internal carotid artery of the user respectively, and receive the ultrasonic Doppler signals reflected from the common carotid artery and the terminal of the internal carotid artery respectively through the signal acquisition unit. The synchronous detection and signal acquisition of the terminal position of the common carotid artery and the terminal of the internal carotid artery of the user are realized, thereby ensuring that the blood flow waveform data of the two measurement points are within one heartbeat cycle, making the detection result of cerebral artery stiffness detection more accurate.

[0075] In a preferred embodiment, if Figure 3, the signal processing module includes:

[0076] A digital orthogonal modem is used to perform I and Q demodulation on the collected ultrasonic Doppler signal and extract the frequency difference signal;

[0077] A wall filter is used to filter out interference signals in the frequency difference signal;

[0078] The FFT conversion unit is used to convert the frequency difference signal into blood flow waveform data.

[0079] The working principle and beneficial effects of the above technical solution are as follows: the collected ultrasonic Doppler signal is I and Q demodulated by a digital orthogonal modem to extract the frequency difference signal, and the wall filter is used to filter out the interference signal in the frequency difference signal, and the FFT conversion unit is used to convert the frequency difference signal into blood flow waveform data, thereby realizing the pre-analysis processing of the ultrasonic Doppler signal, improving the analysis efficiency of the data and reducing the interference signal in the data.

[0080] In a preferred embodiment, a body surface detection module is also included, and the body surface detection module includes:

[0081] A body surface bracket, used to fix the inflection point of the user's mandible and adjust the position of the neck ultrasonic Doppler generating device so that the neck ultrasonic Doppler generating device is at a preset neck measurement point;

[0082] A common carotid artery length analysis unit, used to determine the length of the user's common carotid artery based on the width of the ultrasonic Doppler generating device by using the distance between the user's mandibular inflection point and a preset neck measurement point;

[0083] A body surface detection unit, used to determine the positions of multiple preset types of feature points on the user's face;

[0084] A distance analysis unit, for determining, based on a plurality of preset types of feature points on the user's face, a first longitudinal distance between the user's eye corner and the lower eye socket, a first transverse distance between the user's pupil center and the lower eye socket near the nose wing, a second longitudinal distance between the user's lower eye socket near the nose wing and the nose wing, and a third longitudinal distance between the user's nose wing and the inflection point of the mandible;

[0085] The distance determination unit is used to determine the distance between the common carotid artery and the terminal end of the internal carotid artery of the user according to the first longitudinal distance, the first transverse distance, the second longitudinal distance, the third longitudinal distance and the length of the common carotid artery of the user.

[0086] The working principle and beneficial effects of the above technical solution are as follows: fix the inflection point of the user's mandible by a body surface bracket and adjust the position of the neck ultrasonic Doppler generator so that the neck ultrasonic Doppler generator is at a preset neck measurement point; determine the length of the user's common carotid artery based on the width of the ultrasonic Doppler generator by using the distance between the inflection point of the user's mandible and the preset neck measurement point through a common carotid artery length analysis unit; determine the positions of multiple preset types of feature points on the user's face through a body surface detection unit; determine the first longitudinal distance between the user's eye corner and the lower eye socket, the first transverse distance between the user's pupil center and the lower eye socket near the nose wing, the second longitudinal distance between the user's lower eye socket near the nose wing and the nose wing, and the third longitudinal distance between the user's nose wing and the inflection point of the mandible by a distance determination unit according to the first longitudinal distance, the first transverse distance, the second longitudinal distance, the third longitudinal distance and the length of the user's common carotid artery. The length of the common carotid artery and the terminal internal carotid artery was estimated by combining physical landmarks with surface measurements and referring to the data on the relative position of the internal carotid artery in anatomy. Figure 4 , Figure 5 , the blood vessel length is estimated, thereby improving the accuracy of the blood vessel length estimation and calculating a more accurate pulse wave velocity.

[0087] In a preferred embodiment, determining the positions of multiple preset types of feature points on the user's face includes:

[0088] Acquire a user's facial image through a camera, determine a facial area in the facial image based on a facial recognition algorithm, and perform interception to obtain a first image;

[0089] Performing noise reduction, light filling, brightening and normalization processing on the intercepted first image to obtain a second image;

[0090] Based on a preset facial feature point recognition library, a plurality of feature points in the second image are determined by a feature point image recognition algorithm, and a type of each feature point is determined according to a comparison result in the facial feature point recognition library;

[0091] The positions of all feature points on the user's face in the second image are determined, and each feature point is labeled with a type.

[0092] The working principle and beneficial effects of the above technical solution are as follows: the user's facial image is obtained through a camera, and the facial area in the facial image is determined based on a facial recognition algorithm and intercepted to obtain a first image; the intercepted first image is subjected to noise reduction, light filling, brightening and normalization processing to obtain a clear and easy-to-analyze second image; based on a preset facial feature point recognition library, multiple feature points in the second image are determined through a feature point image recognition algorithm, and the type of each feature point is determined based on the comparison results in the facial feature point recognition library; the position of all feature points on the user's face in the second image is determined, and each feature point is labeled with a type. Thus, the rapid recognition and marking of multiple feature points on the user's face in the facial image is achieved.

[0093] In a preferred embodiment, if Figure 4 , the distance analysis unit performs the following operations:

[0094] Determine the camera's shooting ratio when capturing the user's face;

[0095] Determine a first image longitudinal distance between a corner of the user's eye and a lower eye socket in the second image, and determine a first longitudinal distance between the corner of the user's eye and the lower eye socket based on a shooting ratio;

[0096] Determine the first image lateral distance between the center of the pupil of the user and the lower eye socket near the nose wing in the second image, and determine the first lateral distance between the center of the pupil of the user and the lower eye socket near the nose wing based on the shooting ratio;

[0097] Determine the second image longitudinal distance between the lower eye socket of the user near the nose wing and the nose wing in the second image, and determine the second longitudinal distance between the lower eye socket of the user near the nose wing and the nose wing based on the shooting ratio;

[0098] Determine the third image longitudinal distance between the user's nose wing and the mandibular inflection point in the second image, and determine the third longitudinal distance between the user's nose wing and the mandibular inflection point based on the shooting ratio.

[0099] The working principle and beneficial effects of the above technical solution are as follows: according to the shooting ratio used by the camera when shooting the user's face, the distance between the feature points in the image is converted into the distance between the corresponding feature points on the actual human face, and then the first image longitudinal distance between the user's eye corner and the lower eye socket in the second image is determined, and the first longitudinal distance L between the user's eye corner and the lower eye socket is determined based on the shooting ratio. 1-1 ; Determine the first image lateral distance between the center of the user's pupil and the lower eye socket near the nose wing in the second image, and determine the first lateral distance L between the center of the user's pupil and the lower eye socket near the nose wing based on the shooting ratio 2-1; Determine the second image longitudinal distance between the lower eye socket of the user near the nose wing and the nose wing in the second image, and determine the second longitudinal distance L between the lower eye socket of the user near the nose wing and the nose wing based on the shooting ratio 2-2 ; Determine the third image longitudinal distance between the user's nose wing and the mandibular inflection point in the second image, and determine the third longitudinal distance L between the user's nose wing and the mandibular inflection point based on the shooting ratio 3 . Thus, according to the shooting ratio used by the camera when shooting the user's face, the distance between the feature points in the image is converted into the distance between the corresponding feature points on the actual human face, thereby reducing the adjustment requirements for the camera position.

[0100] In a preferred embodiment, if Figures 4 to 12 , the distance determination unit performs the following operations:

[0101] According to the first longitudinal distance, the first blood vessel length is calculated by using a trigonometric function calculation formula based on a preset angle coefficient;

[0102] The length of the second blood vessel is calculated using the Pythagorean theorem according to the first transverse distance and the second longitudinal distance;

[0103] The first blood vessel length, the second blood vessel length, the third longitudinal distance, and the length of the user's common carotid artery are added together to obtain the distance between the user's common carotid artery and the terminal end of the internal carotid artery.

[0104] The working principle and beneficial effects of the above technical solution are as follows: according to the first longitudinal distance L 1-1 Based on the preset angle coefficient α, the first blood vessel length L is calculated using the trigonometric function calculation formula 1 :

[0105] L 1 =L 1-1 / sinα

[0106] According to the first lateral distance L 2-1 and the second longitudinal distance L 2-2 , using the Pythagorean theorem to calculate the length of the second blood vessel L 2 :

[0107]

[0108] The length of the first blood vessel L 1 , the second blood vessel length L 2 , the third longitudinal distance L 3 (used to indicate the length of the third blood vessel) and the length of the user's common carotid artery L 4 The distance between the end of the user's common carotid artery and the end of the internal carotid artery is obtained by adding them together. 4 Obtained by the following formula:

[0109]

[0110] Where, L 4-1 L is the distance between the user's mandibular inflection point and the preset neck measurement point. 4-2 is the diameter of the Doppler flow velocity probe. Thus, the multiple blood vessels between the end of the user's common carotid artery and the internal carotid artery are calculated and analyzed respectively, and the length of each blood vessel is obtained and then added together to obtain the distance between the end of the user's common carotid artery and the internal carotid artery.

[0111] In a preferred embodiment, the distance determination unit further includes a compensation coefficient determination subunit, which performs the following operations:

[0112] According to the positions of multiple preset types of feature points on the user's face, two feature points are grouped in pairs according to a preset grouping method to obtain multiple feature point groups and determine the feature point distances between feature points in each feature point group;

[0113] Determine a feature point distance of a preset feature point group as a basic unit distance, and respectively determine the distance ratios of the feature point distances of other feature point groups to the basic unit distance;

[0114] A facial feature set of the user is established by using multiple distance ratios, and the facial feature set is matched with multiple facial feature sets in a facial feature set library preset on a cloud network for similarity, so as to obtain an angle coefficient and a compensation coefficient corresponding to the facial feature set with the highest similarity; wherein the angle coefficient and the compensation coefficient are used to determine the distance between the end of the common carotid artery and the internal carotid artery of the user according to the first longitudinal distance, the first transverse distance, the second longitudinal distance, the third longitudinal distance and the length of the common carotid artery of the user;

[0115] Each face feature set in the face feature set library and its corresponding angle coefficient and compensation coefficient are determined as follows:

[0116] By collecting facial images of volunteers and analyzing the facial images, a facial feature set of the volunteers is obtained;

[0117] By collecting anatomical data of the volunteer, the relative position of the internal carotid artery of the volunteer is determined, and the relative position relationship between the internal carotid artery of the volunteer and the feature point group of the face is determined;

[0118] According to the relative position relationship between the volunteer's internal carotid artery and the facial feature point group, the angle coefficient and the compensation coefficient between the volunteer's internal carotid artery and the preset feature point group are determined.

[0119] The working principle and beneficial effects of the above technical solution are as follows: according to the positions of multiple preset types of feature points on the user's face, two-by-two teams are formed in a preset teaming method to obtain multiple feature point groups and determine the feature point distance between the feature points in each feature point group; determine the feature point distance of one of the preset feature point groups as the basic unit distance, and respectively determine the distance ratios of the feature point distances of other feature point groups to the basic unit distance; use multiple distance ratios to establish a user's face feature set, and perform similarity matching on the face feature set with multiple face feature sets in a preset face feature set library on a cloud network to obtain the angle coefficient and compensation coefficient corresponding to the face feature set with the highest similarity. Thus, the user's face shape is matched, and the anatomical data of a volunteer corresponding to the same face shape as the user is obtained according to the matching results, and the angle coefficient and compensation coefficient between the volunteer's internal carotid artery and the preset feature point group in the volunteer's anatomical data are used as the calculation basis for the angle coefficient and compensation coefficient between the user's internal carotid artery and the preset feature point group, and the calculation result of each blood vessel length of the user is adjusted to obtain a more accurate distance between the user's common carotid artery and the terminal of the internal carotid artery. For example, the distance calculation formula between the common carotid artery and the terminal end of the internal carotid artery of the user mentioned above is:

[0120]

[0121] The angle coefficient is taken as the mean value α obtained by big data analysis in the default state. If the angle coefficient corresponding to the volunteer is β, the formula can be corrected as follows:

[0122]

[0123] In the formula, K 1 , K 2 , K 3 , K 4 The first compensation coefficient, the second compensation coefficient, the third compensation coefficient and the fourth compensation coefficient are respectively used to correct the calculation results of the first blood vessel length, the second blood vessel length, the third blood vessel length and the common carotid artery length, so as to obtain a more accurate distance between the terminal ends of the user's common carotid artery and the internal carotid artery.

[0124] The method for determining each facial feature set in the facial feature set library and its corresponding angle coefficient and compensation coefficient is as follows: by collecting facial images of volunteers and analyzing the facial images to obtain the facial feature set of the volunteer; by collecting anatomical data of the volunteer, determining the relative position of the volunteer's internal carotid artery, and determining the relative position relationship between the volunteer's internal carotid artery and the facial feature point group; according to the relative position relationship between the volunteer's internal carotid artery and the facial feature point group, determining the angle coefficient and compensation coefficient between the volunteer's internal carotid artery and the preset feature point group. Thus, the data expansion of each facial feature set in the feature set library and its corresponding angle coefficient and compensation coefficient is achieved, providing a correction coefficient for the calculation of the user's blood vessel length, making the calculation result more accurate.

[0125] In a preferred embodiment, a frequency determination module is further included, and the frequency determination module performs the following operations:

[0126] The user's heart beat frequency is obtained, and the validity of the two blood flow waveform data of the user is determined according to the user's heart beat frequency. When the pulsation frequency in the blood flow waveform data is inconsistent with the heart beat frequency, it is determined that the blood flow waveform data detection is incorrect, and the operator is reminded to adjust the position of the ultrasonic Doppler generating device for re-detection.

[0127] The working principle and beneficial effects of the above technical solution are as follows: by obtaining the user's heart beating frequency, and judging the validity of the user's two blood flow waveform data respectively according to the user's heart beating frequency, when the pulsation frequency in the blood flow waveform data is inconsistent with the heart beating frequency, it is judged that the blood flow waveform data detection is incorrect, and the operator is reminded to adjust the position of the ultrasonic Doppler generating device for re-testing, thereby preventing invalid blood flow waveform data from having an adverse effect on the cerebral artery stiffness detection results.

[0128] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A non-invasive cerebral artery stiffness detection system. It is characterized in that include: Ultrasonic Doppler module, used to transmit pulsed ultrasonic Doppler signals to the common carotid artery and the terminal of the internal carotid artery of the user, and collect ultrasonic Doppler signals reflected back from the terminal of the common carotid artery and the terminal of the internal carotid artery; The signal processing module is used to pre-process the two collected ultrasonic Doppler signals respectively and obtain two blood flow waveform data through FFT transformation; A signal analysis module, for calculating the pulse wave velocity of the carotid and cerebral arteries based on the distance between the common carotid artery and the internal carotid artery of the user using the waveform time difference of two blood flow waveform data, and determining the user's cerebral artery stiffness according to a preset pulse wave velocity-cerebral artery stiffness comparison table; It also includes a body surface detection module, which includes: A body surface bracket, used to fix the inflection point of the user's mandible and adjust the position of the neck ultrasonic Doppler generating device so that the neck ultrasonic Doppler generating device is at a preset neck measurement point; A common carotid artery length analysis unit, used to determine the length of the user's common carotid artery based on the width of the ultrasonic Doppler generating device by using the distance between the user's mandibular inflection point and a preset neck measurement point; A body surface detection unit, used to determine the positions of multiple preset types of feature points on the user's face; A distance analysis unit, for determining, based on a plurality of preset types of feature points on the user's face, a first longitudinal distance between the user's eye corner and the lower eye socket, a first transverse distance between the user's pupil center and the lower eye socket near the nose wing, a second longitudinal distance between the user's lower eye socket near the nose wing and the nose wing, and a third longitudinal distance between the user's nose wing and the inflection point of the mandible; The distance determination unit is used to determine the distance between the common carotid artery and the terminal end of the internal carotid artery of the user according to the first longitudinal distance, the first transverse distance, the second longitudinal distance, the third longitudinal distance and the length of the common carotid artery of the user.

2. A non-invasive cerebral arterial stiffness detection system according to claim 1, It is characterized in that The ultrasound Doppler module comprises: A synchronous control unit, used to perform synchronous transmission control on the ultrasonic Doppler generating unit, and control the signal collecting unit to perform synchronous signal collection; An ultrasonic Doppler generating unit, used to generate a pulse ultrasonic Doppler signal using an ultrasonic Doppler generating device to irradiate the common carotid artery and the terminal end of the internal carotid artery of the user; The signal acquisition unit is used to receive the ultrasonic Doppler signal reflected from the common carotid artery and the terminal end of the internal carotid artery.

3. A non-invasive cerebral artery stiffness detection system according to claim 1, It is characterized in that The signal processing module comprises: A digital orthogonal modem is used to perform I and Q demodulation on the collected ultrasonic Doppler signal and extract the frequency difference signal; A wall filter is used to filter out interference signals in the frequency difference signal; The FFT conversion unit is used to convert the frequency difference signal into blood flow waveform data.

4. A non-invasive cerebral artery stiffness detection system according to claim 1, It is characterized in that The step of determining the positions of a plurality of preset types of feature points on the user's face comprises: Acquire a user's facial image through a camera, determine a facial area in the facial image based on a facial recognition algorithm, and perform interception to obtain a first image; Performing noise reduction, light filling, brightening and normalization processing on the intercepted first image to obtain a second image; Based on a preset facial feature point recognition library, a plurality of feature points in the second image are determined by a feature point image recognition algorithm, and a type of each feature point is determined according to a comparison result in the facial feature point recognition library; The positions of all feature points on the user's face in the second image are determined, and each feature point is labeled with a type.

5. A non-invasive cerebral artery stiffness detection system according to claim 1, It is characterized in that The distance analysis unit performs the following operations: Determine the camera's shooting ratio when capturing the user's face; Determine a first image longitudinal distance between a corner of the user's eye and a lower eye socket in the second image, and determine a first longitudinal distance between the corner of the user's eye and the lower eye socket based on the shooting ratio; Determine the first image lateral distance between the center of the pupil of the user and the lower eye socket near the nose wing in the second image, and determine the first lateral distance between the center of the pupil of the user and the lower eye socket near the nose wing based on the shooting ratio; Determine the second image longitudinal distance between the lower eye socket of the user near the nose wing and the nose wing in the second image, and determine the second longitudinal distance between the lower eye socket of the user near the nose wing and the nose wing based on the shooting ratio; Determine the third image longitudinal distance between the user's nose wing and the mandibular inflection point in the second image, and determine the third longitudinal distance between the user's nose wing and the mandibular inflection point based on the shooting ratio.

6. A non-invasive cerebral artery stiffness detection system according to claim 1, It is characterized in that The distance determination unit performs the following operations: According to the first longitudinal distance, a first blood vessel length is calculated using a trigonometric function calculation formula based on a preset angle coefficient; Calculating a second blood vessel length using the Pythagorean theorem according to the first transverse distance and the second longitudinal distance; The first blood vessel length, the second blood vessel length, the third longitudinal distance, and the length of the user's common carotid artery are added together to obtain the distance between the common carotid artery and the terminal end of the internal carotid artery of the user.

7. The system for non-invasively detecting cerebral arterial stiffness according to claim 1, It is characterized in that The distance determination unit further includes a compensation coefficient determination subunit, which performs the following operations: According to the positions of multiple preset types of feature points on the user's face, two feature points are grouped in pairs according to a preset grouping method to obtain multiple feature point groups and determine the feature point distances between feature points in each feature point group; Determine a feature point distance of a preset feature point group as a basic unit distance, and respectively determine the distance ratios of the feature point distances of other feature point groups to the basic unit distance; A facial feature set of the user is established by using multiple distance ratios, and the facial feature set is matched with multiple facial feature sets in a facial feature set library preset on a cloud network for similarity, so as to obtain an angle coefficient and a compensation coefficient corresponding to the facial feature set with the highest similarity; wherein the angle coefficient and the compensation coefficient are used to determine the distance between the common carotid artery and the terminal end of the internal carotid artery of the user according to the first longitudinal distance, the first transverse distance, the second longitudinal distance, the third longitudinal distance and the length of the common carotid artery of the user; Each face feature set in the face feature set library and its corresponding angle coefficient and compensation coefficient are determined as follows: By collecting facial images of volunteers and analyzing the facial images, a facial feature set of the volunteers is obtained; By collecting anatomical data of the volunteer, the relative position of the internal carotid artery of the volunteer is determined, and the relative position relationship between the internal carotid artery of the volunteer and the feature point group of the face is determined; According to the relative position relationship between the volunteer's internal carotid artery and the facial feature point group, the angle coefficient and the compensation coefficient between the volunteer's internal carotid artery and the preset feature point group are determined.

8. The system for non-invasively detecting cerebral arterial stiffness according to claim 1, It is characterized in that It also includes a frequency determination module, which performs the following operations: The user's heart beat frequency is obtained, and the validity of the two blood flow waveform data of the user is determined according to the user's heart beat frequency. When the pulsation frequency in the blood flow waveform data is inconsistent with the heart beat frequency, it is determined that the blood flow waveform data detection is incorrect, and the operator is reminded to adjust the position of the ultrasonic Doppler generating device for re-detection.

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