Carotid artery detection method and device

Abstract

The invention relates to a carotid artery detection method and device, and the method comprises the steps: building different sample data severity levels for different somatosensory data, carrying out the statistics of blood oxygen detection data of a plurality of pieces of sample data, and sorting the blood oxygen detection data; determining a blood oxygen detection data range corresponding to each severity level based on the blood oxygen detection data corresponding to each severity level, constructing a severity level prediction model based on the blood oxygen detection data range, and then evaluating the carotid artery B ultrasonic image in the sample data; the severity level of the sample data is updated according to the evaluation result of the carotid artery B ultrasonic image, and the target severity level of the sample data is obtained; according to the invention, comprehensive evaluation is carried out on the neck artery of the user through multiple levels of evaluation directions, so that the method better fits the physical condition of the user, and a more accurate prediction result is obtained.

Description

Technical Field

[0001] The present application relates to the technical field of carotid artery detection, and in particular to a carotid artery detection method and device. Background Art

[0002] Carotid artery sclerosis is also known as carotid atherosclerosis, which is the manifestation of systemic atherosclerosis in the carotid artery. It usually occurs in adolescence and gradually worsens with age. It is believed to be closely related to the occurrence of ischemic stroke in the elderly. In the early stage of carotid artery sclerosis, the first manifestation is the thickening of the intima-media membrane, and then the atherosclerotic plaque is gradually formed. On this basis, intraplaque hemorrhage, plaque rupture and detachment, mural thrombosis and secondary vascular stenosis occur, causing corresponding hemodynamic changes and leading to the occurrence of ischemic cerebrovascular events.

[0003] At present, all tests related to the carotid artery need to be performed in the hospital using large medical equipment. During this process, the person concerned often needs to spend a lot of time and energy to perform the test. Therefore, this will cause most people to wait until the body has a more serious reaction before going to the hospital for examination, which will lead to the late discovery of the specific condition of the carotid artery and delay treatment. Summary of the invention

[0004] Based on this, it is necessary to provide a carotid artery detection method and device to address the problem that most people traditionally wait until the body has a more serious reaction before going to the hospital for examination, which will lead to late discovery of the specific condition of the carotid artery and delay treatment.

[0005] The present application provides a carotid artery detection method, comprising:

[0006] Acquire sample data of a plurality of samples, wherein the sample data includes a carotid artery B-ultrasound image, blood oxygen detection data, and body sensation data;

[0007] Creating a severity level of the sample data based on the somatosensory data in each sample data;

[0008] Calculate the predicted blood oxygen detection data range of the severity level of each sample data based on the blood oxygen detection data;

[0009] Build a severity level prediction model based on each sample data, each body sensor data, each blood oxygen detection data, each blood oxygen detection data range, and each severity level;

[0010] Based on the carotid artery B-ultrasound image in each sample data, the carotid artery of each sample data is evaluated to obtain a carotid artery evaluation result of each sample data, and the severity level of each sample data is updated based on the carotid artery evaluation result of each sample data;

[0011] Acquire a sample data to be tested, and acquire a carotid artery B-ultrasound image to be tested, blood oxygen detection data to be tested, and body sensation data to be tested in the sample data to be tested;

[0012] Inputting the blood oxygen detection data to be tested and the body sensory data to be tested into the prediction model of the blood oxygen saturation difference range with different severity levels, starting the prediction model of the blood oxygen saturation difference range with different severity levels, and obtaining the predicted severity level output by the prediction model of the blood oxygen saturation difference range with different severity levels;

[0013] The predicted severity level is adjusted based on the carotid artery B-ultrasound image to be tested to obtain the target severity level of the sample data to be tested.

[0014] Furthermore, the predicted blood oxygen detection data range of calculating the severity level of each sample data based on the blood oxygen detection data includes:

[0015] Select a sample of sample data;

[0016] Parsing the sample data to obtain blood oxygen detection data in the sample data, wherein the blood oxygen detection data includes a first blood oxygen saturation value and a second blood oxygen saturation value;

[0017] Calculate the difference between the first blood oxygen saturation value and the second blood oxygen saturation value to obtain a blood oxygen saturation difference;

[0018] Returning the sample data of the selected sample until the sample data of each sample has been selected once;

[0019] The blood oxygen saturation differences in all sample data are sorted in descending order to obtain a blood oxygen saturation sequence table.

[0020] Furthermore, the calculation of the predicted blood oxygen detection data range of the severity level of each sample data based on the blood oxygen detection data also includes:

[0021] Select a sample of sample data;

[0022] Parsing the sample data to obtain somatosensory data in the sample data, wherein the somatosensory data includes no sensation, dizziness, vertigo, headache, and visual impairment;

[0023] Fill the acquired body sense data into the blood oxygen saturation sequence table to obtain an updated blood oxygen saturation sequence table;

[0024] The sample data of the selected sample is returned until the sample data of each sample has been selected once, thereby obtaining a perfected blood oxygen saturation sequence table.

[0025] Furthermore, the calculation of the predicted blood oxygen detection data range of the severity level of each sample data based on the blood oxygen detection data also includes:

[0026] Create severity levels based on each somatosensory data;

[0027] Dividing the blood oxygen saturation difference data in the blood oxygen saturation sequence table based on the severity level to obtain a plurality of blood oxygen saturation difference data corresponding to each severity level;

[0028] Select a severity level;

[0029] Analyze the severity level to obtain multiple blood oxygen saturation difference data in the severity level;

[0030] The minimum value among the multiple blood oxygen saturation difference data is used as the minimum boundary value of the predicted blood oxygen saturation difference range of the severity level, and the maximum value among the multiple blood oxygen saturation difference data is used as the maximum boundary value of the predicted blood oxygen saturation difference range of the severity level, to obtain the predicted blood oxygen saturation difference range of the severity level;

[0031] Return to selecting a severity level until each severity level has been selected once.

[0032] Furthermore, the creating a severity level based on each somatosensory data includes:

[0033] The severity level of the sample data with no sensory data is defined as level one;

[0034] The severity level of the sample data with the somatosensory data of dizziness is defined as level 2;

[0035] The severity level of the sample data with the somatosensory data of dizziness is defined as level three;

[0036] The severity level of the sample data with the somatosensory data of dizziness and headache is defined as level 4;

[0037] The severity level of the sample data of the somatosensory data of headache and visual impairment is defined as five levels.

[0038] Furthermore, the constructing of a severity level prediction model based on each sample data, each body sensor data, each blood oxygen detection data, each blood oxygen detection data range, and each severity level includes:

[0039] Create severity prediction models;

[0040] The sample data of each sample, each first blood oxygen saturation value, each second blood oxygen saturation value, each blood oxygen saturation difference, the blood oxygen saturation sequence table, each updated blood oxygen saturation sequence table, the improved blood oxygen saturation sequence table, each somatosensory data, each severity level and each blood oxygen saturation difference range are used as training data to train the blood oxygen saturation difference range prediction model of different severity levels to obtain the trained severity level prediction model.

[0041] Further, based on the carotid artery B-ultrasound image in each sample data, the carotid artery of each sample data is evaluated to obtain a carotid artery evaluation result of each sample data, and the severity level of each sample data is updated based on the carotid artery evaluation result of each sample data, including:

[0042] Select a carotid artery B-ultrasound image of sample data;

[0043] Analyze the carotid artery B-ultrasound image to obtain carotid artery inner diameter data in the carotid artery B-ultrasound image;

[0044] Acquiring flow velocity data in the carotid artery;

[0045] Based on the carotid artery inner diameter data and the carotid artery flow velocity data, the carotid artery is evaluated to obtain a carotid artery evaluation result, wherein the carotid artery evaluation result includes normal, mild stenosis, moderate stenosis, moderate stenosis and complete occlusion;

[0046] The severity level of the sample data is updated based on the carotid artery assessment result to obtain an updated severity level;

[0047] The selected carotid artery B-ultrasound image of a sample data is returned until the carotid artery B-ultrasound image of each sample data has been selected once.

[0048] Furthermore, the severity level of the sample data is updated based on the carotid artery assessment result, and the updated severity level includes:

[0049] Get the severity level of the sample data;

[0050] Based on the severity level of the sample data, if the carotid artery assessment result is normal, the severity level of the sample data is reduced, and the reduced severity level of the sample data is defined as the updated severity level;

[0051] Based on the severity level of the sample data, if the carotid artery assessment result is any one of mild stenosis, moderate stenosis, moderate stenosis and complete occlusion, the severity level of the sample data is increased, and the increased severity level of the sample data is defined as the updated severity level.

[0052] The present application also provides a carotid artery detection device, comprising:

[0053] A support frame, hung on the neck of the user;

[0054] A first detection device is arranged on the support frame, the first detection device is arranged on one side of the user's neck, the first detection device is used to detect arterial data on one side of the user's neck, and the first detection device is used to obtain a carotid artery B-ultrasound image and blood oxygen detection data of the user's neck;

[0055] A second detection device is arranged on the support frame, the second detection device is arranged on the other side of the user's neck, the second detection device is used to detect the artery data on the other side of the user's neck, and the second detection device is used to obtain a carotid artery B-ultrasound image and blood oxygen detection data of the user's neck;

[0056] A processing device, arranged on the support frame, the first detection device is connected to the processing device for communication, the second detection device is connected to the processing device for communication, and the processing device is used to perform the above-mentioned carotid artery detection method;

[0057] The structure of the first detection device is the same as that of the second detection device.

[0058] Furthermore, the first detection device includes:

[0059] a first blood oxygen detection component, which is attached to the user's skin, is disposed on a side close to the user's head, and is communicatively connected with the processing device;

[0060] A second blood oxygen detection component is attached to the user's skin, the second blood oxygen detection component is arranged on a side close to the user's shoulder, and the second blood oxygen detection component is communicatively connected with the processing device;

[0061] The B-ultrasound component is arranged between the first blood oxygen detection component and the second blood oxygen detection component, and the B-ultrasound component is communicatively connected with the processing device.

[0062] The present application relates to a carotid artery detection method and device, which establishes different sample data severity levels based on different somatosensory data, then counts the blood oxygen detection data of multiple sample data and sorts the blood oxygen detection data, and then establishes the blood oxygen detection data range corresponding to each severity level based on the blood oxygen detection data corresponding to each severity level, and constructs a severity level prediction model based on this as a logical basis, and then evaluates the carotid artery B-ultrasound image in the sample data, updates the sample data severity level based on the evaluation result of the carotid artery B-ultrasound image, and obtains the target severity level of the sample data; comprehensively evaluates the user's carotid artery through multiple levels of evaluation directions, so as to better fit the user's own physical condition and obtain more accurate prediction results. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1A schematic diagram of a flow chart of a carotid artery detection method provided in one embodiment of the present application.

[0064] Figure 2 A schematic diagram of the structure of a carotid artery detection device provided in one embodiment of the present application.

[0065] Figure 3 A schematic diagram of the positional relationship between the first blood oxygen detection component and the second blood oxygen detection component in a carotid artery detection device provided in one embodiment of the present application.

[0066] Reference numerals:

[0067] 11. Support frame; 12. First detection device; 121. First blood oxygen detection element;

[0068] 122. Second blood oxygen detection component; 123. B-ultrasound component; 13. Second detection device; 14. Processing device. DETAILED DESCRIPTION

[0069] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0070] like Figure 1 As shown, in one embodiment of the present application, the carotid artery detection method includes the following S100 to S800:

[0071] S100, acquiring sample data of a plurality of samples, wherein the sample data includes a carotid artery B-ultrasound image, blood oxygen detection data, and body sensation data.

[0072] Specifically, the carotid artery B-ultrasound image includes two carotid artery B-ultrasound images, which are B-ultrasound images of two carotid arteries in the user's neck; the blood oxygen detection data includes two groups, which are blood oxygen detection data of two carotid arteries in the user's neck.

[0073] S200: Create a severity level of the sample data based on the somatosensory data in each sample data.

[0074] Specifically, the somatosensory data is the user's own feeling data, which requires the user to make an active selection or input.

[0075] S300, calculating a predicted blood oxygen detection data range of a severity level of each sample data based on the blood oxygen detection data.

[0076] Specifically, new sample data may be added in real time, and the predicted blood oxygen detection data range corresponding to the severity level may be updated through the somatosensory data and blood oxygen detection data in the newly added sample data to better reflect the accuracy of the severity level.

[0077] S400, construct a severity level prediction model based on each sample data, each body sensor data, each blood oxygen detection data, each blood oxygen detection data range, and each severity level.

[0078] S500, based on the carotid artery B-ultrasound image in each sample data, the carotid artery of each sample data is evaluated to obtain a carotid artery evaluation result of each sample data, and the severity level of each sample data is updated based on the carotid artery evaluation result of each sample data.

[0079] Specifically, the actual condition of the carotid artery as reflected by the carotid B-ultrasound image is used to conduct a specific and scientific assessment of the carotid artery, and the assessment result is used as the direction and benchmark for updating the severity level of the sample data.

[0080] S600, obtaining a sample data to be tested, and obtaining a carotid artery B-ultrasound image to be tested, blood oxygen detection data to be tested, and body sensation data to be tested in the sample data to be tested.

[0081] Specifically, the sample data to be tested refers to the real-time data detected by the neck of the current user, as well as the current body sensation data provided by the current user based on his or her own feelings.

[0082] S700, input the blood oxygen detection data to be tested and the body sensory data to be tested into the severity level prediction model, start the severity level prediction model, and obtain the predicted severity level output by the severity level prediction model.

[0083] S800, adjusting the predicted severity level based on the carotid artery B-ultrasound image to be tested to obtain a target severity level of the sample data to be tested.

[0084] In this embodiment, different sample data severity levels are established based on different somatosensory data, and then the blood oxygen detection data of multiple sample data are counted and sorted. Then, based on the blood oxygen detection data corresponding to each severity level, the blood oxygen detection data range corresponding to each severity level is established. Based on this as the logical basis, a severity level prediction model is constructed, and then the carotid artery B-ultrasound image in the sample data is evaluated. The severity level of the sample data is updated based on the evaluation result of the carotid artery B-ultrasound image to obtain the target severity level of the sample data. The user's carotid artery is comprehensively evaluated through multiple levels of evaluation directions, so as to better fit the user's own physical condition and obtain more accurate prediction results.

[0085] In one embodiment of the present application, the predicted blood oxygen detection data range of calculating the severity level of each sample data based on the blood oxygen detection data includes the following steps S301 to S305:

[0086] S301, selecting sample data of a sample.

[0087] S302, parsing the sample data to obtain blood oxygen detection data in the sample data, wherein the blood oxygen detection data includes a first blood oxygen saturation value and a second blood oxygen saturation value.

[0088] S303: Calculate the difference between the first blood oxygen saturation value and the second blood oxygen saturation value to obtain a blood oxygen saturation difference.

[0089] S304, returning the sample data of the selected sample until the sample data of each sample has been selected once.

[0090] S305: sort the blood oxygen saturation differences in all sample data in descending order to obtain a blood oxygen saturation sequence table.

[0091] Specifically, the first blood oxygen saturation value and the second blood oxygen saturation value are blood oxygen saturation values ​​detected by two detection points of the user's carotid artery. The position where the first blood oxygen saturation value is detected is closer to the user's head, and the position where the second blood oxygen saturation value is detected is closer to the user's shoulder.

[0092] If any one of the first blood oxygen saturation value and the second blood oxygen saturation value is not within the first range, an alarm message is directly sent. The first range refers to the blood oxygen saturation value range in a healthy state given in the medical instructions based on the user's physical condition.

[0093] In this embodiment, the blood oxygen saturation values ​​at two locations on the same section of the carotid artery are detected, and the difference between the blood oxygen saturation values ​​at the two locations is calculated. The size of the difference can be used to analyze whether a certain degree of hardening has occurred in the section of the carotid artery; and the differences in the blood oxygen saturation values ​​of multiple sample data are sorted.

[0094] In one embodiment of the present application, the predicted blood oxygen detection data range of calculating the severity level of each sample data based on the blood oxygen detection data further includes the following S306 to S305:

[0095] S306, selecting sample data of a sample.

[0096] S307, parsing the sample data to obtain somatosensory data in the sample data, wherein the somatosensory data includes no sensation, dizziness, vertigo, headache and visual impairment.

[0097] S308, filling the acquired body sense data into the blood oxygen saturation sequence table to obtain an updated blood oxygen saturation sequence table.

[0098] S309, returning the sample data of the selected sample, until the sample data of each sample has been selected once, and a perfected blood oxygen saturation sequence table is obtained.

[0099] Specifically, the somatosensory data specifically fed back by the user may be one or more of numbness, dizziness, vertigo, headache, and visual impairment.

[0100] In one embodiment of the present application, the predicted blood oxygen detection data range of calculating the severity level of each sample data based on the blood oxygen detection data further includes the following S310 to S315:

[0101] S310: Create a severity level based on each body sensing data.

[0102] S311, dividing the blood oxygen saturation difference data in the blood oxygen saturation sequence table based on the severity level, and obtaining a plurality of blood oxygen saturation difference data corresponding to each severity level.

[0103] S312, select a severity level.

[0104] S313, analyzing the severity level to obtain a plurality of blood oxygen saturation difference data in the severity level.

[0105] S314, taking the minimum value among the multiple blood oxygen saturation difference data as the minimum boundary value of the predicted blood oxygen saturation difference range of the severity level, and taking the maximum value among the multiple blood oxygen saturation difference data as the maximum boundary value of the predicted blood oxygen saturation difference range of the severity level, to obtain the predicted blood oxygen saturation difference range of the severity level.

[0106] S315, returning to the step of selecting a severity level until each severity level has been selected once.

[0107] Specifically, each severity level may include one or more of no sensation, dizziness, vertigo, headache and visual impairment; if the predicted blood oxygen saturation difference ranges of two adjacent severity levels have an intersection, the severity level of the sample data at the intersection of the predicted blood oxygen saturation difference ranges is determined based on the somatosensory data.

[0108] In this embodiment, more specifically, if the somatosensory data in the sample data includes headache or a more serious condition, the sample data is determined to be a severity level with a higher severity level; if the somatosensory data in the sample data does not include headache or a more serious condition, the sample data is determined to be a severity level with a lower severity level.

[0109] In one embodiment of the present application, the step of creating a severity level based on each body sensing data includes the following steps S201 to S205:

[0110] S201, defining the severity level of the sample data whose somatosensory data is no sensation as level one.

[0111] S202, defining the severity level of the sample data whose somatosensory data is dizziness as level 2.

[0112] S203, defining the severity level of the sample data whose somatosensory data is dizziness as level three.

[0113] S204, defining the severity level of the sample data of the somatosensory data of dizziness and headache as level four.

[0114] S205, defining the severity level of the sample data of the somatosensory data of headache and visual impairment as level five.

[0115] Specifically, the order of no sensation, dizziness, vertigo, headache and visual impairment is from mild to severe.

[0116] In this embodiment, the severity level is determined based on the most serious somatosensory data among the somatosensory data of the current sample data.

[0117] In one embodiment of the present application, the severity level prediction model is constructed based on each sample data, each body sensor data, each blood oxygen detection data, each blood oxygen detection data range, and each severity level, including the following S401 to S402:

[0118] S401, creating a severity level prediction model.

[0119] S402, using the sample data of each sample, each first blood oxygen saturation value, each second blood oxygen saturation value, each blood oxygen saturation difference, the blood oxygen saturation sequence table, each updated blood oxygen saturation sequence table, the improved blood oxygen saturation sequence table, each somatosensory data, each severity level and each blood oxygen saturation difference range as training data to train the blood oxygen saturation difference range prediction model of different severity levels, and obtain the trained severity level prediction model.

[0120] In one embodiment of the present application, based on the carotid artery B-ultrasound image in each sample data, the carotid artery of each sample data is evaluated to obtain a carotid artery evaluation result of each sample data, and the severity level of each sample data is updated based on the carotid artery evaluation result of each sample data, including the following S501 to S506:

[0121] S501, selecting a carotid artery B-ultrasound image of sample data.

[0122] S502, analyzing the carotid artery B-ultrasound image to obtain carotid artery inner diameter data in the carotid artery B-ultrasound image.

[0123] S503, obtaining the flow velocity data in the carotid artery.

[0124] S504, based on the carotid artery inner diameter data and the carotid artery flow velocity data, the carotid artery is evaluated to obtain a carotid artery evaluation result, wherein the carotid artery evaluation result includes normal, mild stenosis, moderate stenosis, moderate stenosis and complete occlusion.

[0125] S505, updating the severity level of the sample data based on the carotid artery assessment result to obtain an updated severity level.

[0126] S506, returning to the selection of the carotid artery B-ultrasound image of a sample data, until the carotid artery B-ultrasound image of each sample data has been selected once.

[0127] Specifically, the carotid artery inner diameter data and the carotid artery flow velocity data are obtained through B-ultrasound detection, and the carotid artery inner diameter data and the carotid artery flow velocity data can be used to objectively evaluate the location on the carotid artery. The carotid artery evaluation results include normal, mild stenosis, moderate stenosis, moderate stenosis and complete occlusion, and the carotid artery evaluation result will only be one of normal, mild stenosis, moderate stenosis, moderate stenosis and complete occlusion. And normal, mild stenosis, moderate stenosis, moderate stenosis and complete occlusion are arranged in ascending order of severity.

[0128] In this embodiment, the severity level of the sample data is updated by the severity of the carotid artery assessment result, and then the severity level of the sample data is updated by objectively accurate data, so that the evaluation of the severity level of the sample data is more accurate.

[0129] In one embodiment of the present application, the updating of the severity level of the sample data based on the carotid artery assessment result to obtain the updated severity level includes the following steps S505a to S505c:

[0130] S505a, obtaining the severity level of the sample data.

[0131] S505b: Based on the severity level of the sample data, if the carotid artery assessment result is normal, the severity level of the sample data is reduced, and the reduced severity level of the sample data is defined as the updated severity level.

[0132] S505c, based on the severity level of the sample data, if the carotid artery assessment result is any one of mild stenosis, moderate stenosis, moderate stenosis and complete occlusion, the severity level of the sample data is increased, and the increased severity level of the sample data is defined as the updated severity level.

[0133] In this embodiment, the carotid artery assessment results are mild stenosis, moderate stenosis, moderate stenosis and complete occlusion. Due to the decreasing severity level, the more severe the carotid artery assessment result is, the higher the severity level of the sample data is. For example, if the carotid artery assessment result is mild stenosis, the severity level of the sample data is increased by one severity level, and if the carotid artery assessment result is complete occlusion, the severity level of the sample data is increased by three severity levels.

[0134] like Figure 2 and Figure 3 As shown, in one embodiment of the present application, a carotid artery detection device is provided, and the carotid artery detection device includes a support frame 11, a first detection device 12, a first detection device 12 and a processing device 14.

[0135] The support frame 11 is hung on the neck of the user.

[0136] The first detection device 12 is disposed on the support frame 11, and the first detection device 12 is disposed on one side of the user's neck. The first detection device 12 is used to detect arterial data on one side of the user's neck, and the first detection device 12 is used to obtain a carotid artery B-ultrasound image and blood oxygen detection data of the user's neck.

[0137] The second detection device 13 is arranged on the support frame 11, and the second detection device 13 is arranged on the other side of the user's neck. The second detection device 13 is used to detect the artery data on the other side of the user's neck, and the second detection device 13 is used to obtain the carotid artery B-ultrasound image and blood oxygen detection data of the user's neck.

[0138] The processing device 14 is disposed on the support frame 11 , the first detection device 12 is communicatively connected to the processing device 14 , the second detection device 13 is communicatively connected to the processing device 14 , and the processing device 14 is used to execute the carotid artery detection method as described above.

[0139] The structure of the first detection device 12 is the same as that of the second detection device 13 .

[0140] Specifically, the support frame 11 is configured as a circular ring, and the support frame 11 can also be configured as a C-shape; the first detection device 12 and the second detection device 13 are both fixedly connected to the support frame 11 , and the processing device 14 is also fixedly connected to the support frame 11 .

[0141] In this embodiment, the carotid artery data on one side of the user's neck is detected by the first detection device 12, and the first detection device 12 sends the detected data to the processing device 14 for data processing and analysis, so as to obtain a preliminary prediction result of the carotid artery; and the carotid artery data on the other side of the user's neck is detected by the first detection device 12, and the second detection device 13 sends the detected data to the processing device 14 for data processing and analysis, so as to obtain a preliminary prediction result of the carotid artery; the support frame 11 is hung or sleeved on the user's neck, and the support frame 11 is also used to fix the positions of the first detection device 12 and the second detection device 13 to improve the stability of the use of the first detection device 12 and the second detection device 13.

[0142] like Figure 3 As shown, in one embodiment of the present application, the first detection device 12 includes a first blood oxygen detection component 121, a second blood oxygen detection component 122 and a B-ultrasound component 123.

[0143] The first blood oxygen detection component 121 is in contact with the user's skin. The first blood oxygen detection component 121 is disposed on a side close to the user's head. The first blood oxygen detection component 121 is communicatively connected with the processing device 14 .

[0144] The second blood oxygen detection component 122 is in contact with the user's skin. The second blood oxygen detection component 122 is arranged on a side close to the user's shoulder. The second blood oxygen detection component 122 is communicatively connected with the processing device 14 .

[0145] The B-ultrasound component 123 is disposed between the first blood oxygen detection component 121 and the second blood oxygen detection component 122 , and the B-ultrasound component 123 is communicatively connected with the processing device 14 .

[0146] Specifically, the B-ultrasound component 123 is a miniature B-ultrasound detector, which can automatically detect the B-ultrasound information of the corresponding position. The structure of the first blood oxygen detection component 121 is the same as that of the second blood oxygen detection component 122. The first blood oxygen detection component 121 and the first blood oxygen detection component 121 are both blood oxygen detectors, and the first blood oxygen detection component 121 and the first blood oxygen detection component 121 are respectively arranged at the top and bottom of the B-ultrasound component 123.

[0147] In this embodiment, the B-ultrasound device 123, the first blood oxygen detection device 121 and the second blood oxygen detection device 122 are all placed close to the user's carotid artery, and the B-ultrasound device 123 is located between the first blood oxygen detection device 121 and the second blood oxygen detection device 122, so that the data obtained by the B-ultrasound device 123, the first blood oxygen detection device 121 and the second blood oxygen detection device 122 are relatively stable and accurate, and the accuracy of the processing device 14 in judging the carotid artery data is further improved.

[0148] The technical features of the above-described embodiments may be arbitrarily combined, and the execution order of the method steps is not limited. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0149] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for detecting a carotid artery, characterized in that: The carotid artery detection method comprises: Acquire sample data of a plurality of samples, wherein the sample data includes a carotid artery B-ultrasound image, blood oxygen detection data, and body sensation data; Creating a severity level of the sample data based on the somatosensory data in each sample data; Calculate the predicted blood oxygen detection data range of the severity level of each sample data based on the blood oxygen detection data; Build a severity level prediction model based on each sample data, each body sensor data, each blood oxygen detection data, each blood oxygen detection data range, and each severity level; Based on the carotid artery B-ultrasound image in each sample data, the carotid artery of each sample data is evaluated to obtain a carotid artery evaluation result of each sample data, and the severity level of each sample data is updated based on the carotid artery evaluation result of each sample data; Acquire a sample data to be tested, and acquire a carotid artery B-ultrasound image to be tested, blood oxygen detection data to be tested, and body sensation data to be tested in the sample data to be tested; Inputting the blood oxygen detection data to be tested and the body sensory data to be tested into the severity level prediction model, starting the severity level prediction model, and obtaining the predicted severity level output by the severity level prediction model; The predicted severity level is adjusted based on the carotid artery B-ultrasound image to be tested to obtain the target severity level of the sample data to be tested.

2. The carotid artery detection method according to claim 1, characterized in that: The predicted blood oxygen detection data range of calculating the severity level of each sample data based on the blood oxygen detection data includes: Select a sample of sample data; Parsing the sample data to obtain blood oxygen detection data in the sample data, wherein the blood oxygen detection data includes a first blood oxygen saturation value and a second blood oxygen saturation value; Calculate the difference between the first blood oxygen saturation value and the second blood oxygen saturation value to obtain a blood oxygen saturation difference; Returning the sample data of the selected sample until the sample data of each sample has been selected once; The blood oxygen saturation differences in all sample data are sorted in descending order to obtain a blood oxygen saturation sequence table.

3. The method for detecting the carotid artery according to claim 2, characterized in that: The predicted blood oxygen detection data range for calculating the severity level of each sample data based on the blood oxygen detection data also includes: Select a sample of sample data; Parsing the sample data to obtain somatosensory data in the sample data, wherein the somatosensory data includes no sensation, dizziness, vertigo, headache, and visual impairment; Fill the acquired body sense data into the blood oxygen saturation sequence table to obtain an updated blood oxygen saturation sequence table; The sample data of the selected sample is returned until the sample data of each sample has been selected once, thereby obtaining a perfected blood oxygen saturation sequence table.

4. The method for detecting the carotid artery according to claim 3, characterized in that: The predicted blood oxygen detection data range for calculating the severity level of each sample data based on the blood oxygen detection data also includes: Create severity levels based on each somatosensory data; Dividing the blood oxygen saturation difference data in the blood oxygen saturation sequence table based on the severity level to obtain a plurality of blood oxygen saturation difference data corresponding to each severity level; Select a severity level; Analyze the severity level to obtain multiple blood oxygen saturation difference data in the severity level; The minimum value among the multiple blood oxygen saturation difference data is used as the minimum boundary value of the predicted blood oxygen saturation difference range of the severity level, and the maximum value among the multiple blood oxygen saturation difference data is used as the maximum boundary value of the predicted blood oxygen saturation difference range of the severity level, to obtain the predicted blood oxygen saturation difference range of the severity level; Return to selecting a severity level until each severity level has been selected once.

5. The method for detecting the carotid artery according to claim 4, characterized in that: The creation of severity levels based on each somatosensory data includes: The severity level of the sample data with no sensory data is defined as level one; The severity level of the sample data with the somatosensory data of dizziness is defined as level 2; The severity level of the sample data with the somatosensory data of dizziness is defined as level three; The severity level of the sample data with the somatosensory data of dizziness and headache is defined as level 4; The severity level of the sample data of the somatosensory data of headache and visual impairment is defined as five levels.

6. The method for detecting the carotid artery according to claim 5, characterized in that: The severity level prediction model is constructed based on each sample data, each body sensor data, each blood oxygen detection data, each blood oxygen detection data range, and each severity level, including: Create severity prediction models; The sample data of each sample, each first blood oxygen saturation value, each second blood oxygen saturation value, each blood oxygen saturation difference, the blood oxygen saturation sequence table, each updated blood oxygen saturation sequence table, the improved blood oxygen saturation sequence table, each somatosensory data, each severity level and each blood oxygen saturation difference range are used as training data to train the blood oxygen saturation difference range prediction model of different severity levels to obtain the trained severity level prediction model.

7. The method for detecting the carotid artery according to claim 6, characterized in that: The method of evaluating the carotid artery of each sample data based on the carotid artery B-ultrasound image in each sample data to obtain a carotid artery evaluation result of each sample data, and updating the severity level of each sample data based on the carotid artery evaluation result of each sample data includes: Select a carotid artery B-ultrasound image of sample data; Analyze the carotid artery B-ultrasound image to obtain carotid artery inner diameter data in the carotid artery B-ultrasound image; Acquiring flow velocity data in the carotid artery; Based on the carotid artery inner diameter data and the carotid artery flow velocity data, the carotid artery is evaluated to obtain a carotid artery evaluation result, wherein the carotid artery evaluation result includes normal, mild stenosis, moderate stenosis, moderate stenosis and complete occlusion; The severity level of the sample data is updated based on the carotid artery assessment result to obtain an updated severity level; The selected carotid artery B-ultrasound image of a sample data is returned until the carotid artery B-ultrasound image of each sample data has been selected once.

8. The method for detecting the carotid artery according to claim 7, characterized in that: The severity level of the sample data is updated based on the carotid artery assessment result, and the updated severity level includes: Get the severity level of the sample data; Based on the severity level of the sample data, if the carotid artery assessment result is normal, the severity level of the sample data is reduced, and the reduced severity level of the sample data is defined as the updated severity level; Based on the severity level of the sample data, if the carotid artery assessment result is any one of mild stenosis, moderate stenosis, moderate stenosis and complete occlusion, the severity level of the sample data is increased, and the increased severity level of the sample data is defined as the updated severity level.

9. A carotid artery detection device, characterized in that: The carotid artery detection device comprises: A support frame, hung on the neck of the user; A first detection device is arranged on the support frame, the first detection device is arranged on one side of the user's neck, the first detection device is used to detect arterial data on one side of the user's neck, and the first detection device is used to obtain a carotid artery B-ultrasound image and blood oxygen detection data of the user's neck; A second detection device is arranged on the support frame, the second detection device is arranged on the other side of the user's neck, the second detection device is used to detect the artery data on the other side of the user's neck, and the second detection device is used to obtain a carotid artery B-ultrasound image and blood oxygen detection data of the user's neck; a processing device, arranged on the support frame, the first detection device being communicatively connected to the processing device, the second detection device being communicatively connected to the processing device, the processing device being used to execute the carotid artery detection method according to any one of claims 1 to 8; The structure of the first detection device is the same as that of the second detection device.

10. The carotid artery detection device according to claim 9, characterized in that: The first detection device comprises: a first blood oxygen detection component, which is attached to the user's skin, is disposed on a side close to the user's head, and is communicatively connected with the processing device; A second blood oxygen detection component is attached to the user's skin, the second blood oxygen detection component is arranged on a side close to the user's shoulder, and the second blood oxygen detection component is communicatively connected with the processing device; The B-ultrasound component is arranged between the first blood oxygen detection component and the second blood oxygen detection component, and the B-ultrasound component is communicatively connected with the processing device.

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