Obstructive sleep apnea crowd detection system and method
By using a front-end measurement system and an AI computing platform detection system in the screening of obstructive sleep apnea population, the problem of high screening costs and inability to be promoted on a large scale in the existing technology is solved, and simpler and more efficient screening tools and methods are realized.
Patent Information
- Application Number
- CN202411858355.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-17
- Publication Date
- 2025-03-14
AI Technical Summary
In the prior art, screening for obstructive sleep apnea population is expensive and cannot achieve screening for large-scale or large-scale populations.
The detection system including a front-end measurement system and an AI computing platform is adopted to obtain anthropometric feature data through the body index measurement component, and the AI computing platform is used to conduct risk assessment based on the AI feature data set of obstructive sleep apnea.
Screening and testing of obstructive sleep apnea populations is achieved, reducing costs, and providing a simpler and more efficient screening tool and method.
Smart Images
Figure CN119606323A_ABST
Abstract
Description
Technical Field
[0001] The present application belongs to the technical field of AI screening and analysis of people with obstructive sleep apnea, and specifically relates to a system and method for detecting people with obstructive sleep apnea. Background Art
[0002] Sleep apnea hypopnea syndrome includes: 1. Central type (CSAS) 2. Obstructive type (OSAS) 3. Mixed type (MSAS). In recent years, with the continuous progress of various testing methods, in-depth and comprehensive research on obstructive type (OSAS) has found that this disease is complex, changeable and ubiquitous. Therefore, obstructive type (OSAS) cannot be generally regarded as an annoying sound that affects the rest of others in social life, but a clinical disease that needs to be carefully examined. It can cause many serious complications. Since the anatomical part of snoring is closely related to the ear, nose and throat, many patients often seek treatment from the otolaryngology department first.
[0003] In the prior art, polysomnography (PSG) is the most important examination for diagnosing sleep snoring (sleep apnea-hypopnea syndrome). Through the continuous monitoring of breathing, arterial oxygen saturation, electroencephalogram, electrocardiogram, heart rate and other indicators at night, it is possible to understand whether the snorer has respiratory arrest, the number of pauses, the duration of the pauses, the lowest arterial oxygen value when the pauses occur, and the degree of impact on physical health. It is the internationally recognized gold standard for diagnosing sleep apnea-hypopnea syndrome. Polysomnography is the most commonly used sleep monitoring method, the most important examination for diagnosing snoring, and the internationally recognized gold standard for diagnosing sleep apnea-hypopnea syndrome.
[0004] like Figure 6 As shown, wearing various sensors on the body during sleep at night will have an adverse effect on sleep itself. The cost of testing is also very high, requiring comprehensive analysis of various equipment and data. Moreover, only single-person testing can be performed, and it is impossible to promote it on a large scale, let alone achieve large-scale screening of the population. How to conduct obstructive sleep apnea screening in the population in a more convenient and simple way is a problem to be solved. Summary of the invention
[0005] In this application, the inventor proposes an obstructive sleep apnea population detection system, which can realize the screening and detection of obstructive sleep apnea population based on the front-end measurement system and AI computing platform, and greatly reduce the cost of screening obstructive sleep apnea population in the prior art.
[0006] The technical solution to the technical problem solved in the present application is an obstructive sleep apnea population detection system, comprising a front-end measurement system and an AI computing platform; the front-end measurement system comprises a body index measurement component and an access data terminal; the AI computing platform comprises an AI computing module and an obstructive sleep apnea AI feature data set; the body index measurement component is signal-connected to the access data terminal; the AI computing platform is signal-connected to the access data terminal; the tester obtains anthropometric feature data through measurement by the body index measurement component; the body index measurement component sends the anthropometric feature data to the access data terminal; the access data terminal sends the anthropometric feature data to the AI computing platform; the AI computing platform obtains an obstructive sleep apnea risk assessment report based on the obstructive sleep apnea AI feature data set and the anthropometric feature data; the anthropometric feature data includes height, weight, waist circumference, neck circumference, and hip circumference.
[0007] The general demographic characteristic data of the tester is input through the access data terminal, and the general demographic characteristic data includes age and gender.
[0008] The AI server includes one or more algorithm modules, including a machine learning algorithm module, a deep learning module, and a logistic regression calculation module; the obstructive sleep apnea AI feature data set corresponds to the algorithm module, and the obstructive sleep apnea AI feature data set is obtained by training with anthropometric feature data of known obstructive sleep apnea patients combined with patients or non-patients.
[0009] The AI server is connected to the access data terminal via a network.
[0010] The body index measurement component includes a height measurement module, a weight measurement module, a waist measurement module, a neck measurement module, and a hip measurement module.
[0011] The technical solution to the technical problem solved by the present application can also be a method for detecting a population with obstructive sleep apnea, which collects data of the testers, wherein the data of the testers include general demographic characteristic data and anthropometric characteristic data; the demographic characteristic data and anthropometric characteristic data are input into an AI computing platform; the AI computing platform calculates according to the obstructive sleep apnea AI feature data set and the anthropometric characteristic data to obtain an obstructive sleep apnea risk assessment report; the anthropometric characteristic data include height, weight, waist circumference, neck circumference, and hip circumference.
[0012] The general demographic characteristics data include age and gender.
[0013] The AI computing platform includes one or more algorithm modules, including a machine learning algorithm module, a deep learning module, and a logistic regression computing module; the obstructive sleep apnea AI feature data set corresponds to the algorithm module, and the obstructive sleep apnea AI feature data set is obtained by training with anthropometric feature data of known obstructive sleep apnea patients combined with patients or non-patients.
[0014] The anthropometric characteristic data is input by the examiner.
[0015] The anthropometric characteristic data is obtained by the tester through measurement using a set body index measurement component, and the body index measurement component sends the measured data to the AI computing platform.
[0016] One of the technical effects of the above technical solution is that based on the front-end measurement system and AI computing platform, it can realize the screening and detection of people with obstructive sleep apnea, which greatly reduces the cost of screening people with obstructive sleep apnea in the prior art.
[0017] One of the technical effects of the above technical solution is that the body index measurement component obtains anthropometric characteristic data including height, weight, waist circumference, neck circumference, and hip circumference. These data are obtained without the aid of complex wearable sensors, making the detection more comfortable and able to be completed without contact.
[0018] One of the technical effects of the above technical solution is that the height measurement module, weight measurement module, waist measurement module, neck measurement module, and hip measurement module can obtain corresponding data.
[0019] One of the technical effects of the above technical solution is: based on demographic characteristic data and anthropometric characteristic data, it provides a simpler and more efficient screening tool and method.
[0020] One of the technical effects of the above technical solution is that basic data such as age, gender, height, weight, waist circumference, neck circumference and hip circumference are relatively easy to obtain.
[0021] One of the technical effects of the above technical solution is that the algorithm module includes multiple algorithms and has good adaptability.
[0022] One of the technical effects of the above technical solution is that the AI recognition and training sample collection components are connected through the network, which can improve the data collection ability of the AI server. The more data there is, the better the adaptability of the trained model and data set. BRIEF DESCRIPTION OF THE DRAWINGS
[0023] Figure 1 is a schematic block diagram of an obstructive sleep apnea crowd detection system;
[0024] Figure 2is a schematic block diagram of a method for detecting a population with obstructive sleep apnea;
[0025] Figure 3 is a schematic block diagram of an obstructive sleep apnea crowd detection system;
[0026] Figure 4 is a schematic diagram of body index measurement;
[0027] Figure 5 It is a schematic diagram of the interface for detecting people with obstructive sleep apnea;
[0028] Figure 6 It is a schematic diagram of a device for detecting people with obstructive sleep apnea in the prior art. DETAILED DESCRIPTION
[0029] The content of this application is further described in detail below in conjunction with the accompanying drawings. It should be noted that the following is a description of the preferred embodiments of the present invention and does not constitute any limitation to the present invention. The description of the preferred embodiments of the present invention is only an illustration of the general principles of the present invention. The numbers such as "first", "second" and "A" and "B" involved in the present invention are only for the convenience of explanation and do not represent the order relationship in time or space. The combination of letters and numbers "TA", "TB" and "H" involved in the present invention are only for the convenience of explanation, and the specific meaning is determined by the specific words referred to.
[0030] like Figure 1 A system for detecting people with obstructive sleep apnea includes a front-end measurement system and an AI computing platform; the front-end measurement system includes a body index measurement component and an access data terminal; the AI computing platform includes an AI computing module and an obstructive sleep apnea AI feature data set; the body index measurement component is connected to the access data terminal by signal; the AI computing platform is connected to the access data terminal by signal; the tester obtains anthropometric feature data through measurement by the body index measurement component; the body index measurement component sends the anthropometric feature data to the access data terminal; the access data terminal sends the anthropometric feature data to the AI computing platform; the AI computing platform calculates according to the obstructive sleep apnea AI feature data set and the anthropometric feature data to obtain an obstructive sleep apnea risk assessment report; the anthropometric feature data includes height, weight, waist circumference, neck circumference, and hip circumference.
[0031] The general demographic characteristic data of the tester is input through the access data terminal, and the general demographic characteristic data includes age and gender.
[0032] The AI server includes one or more algorithm modules, including a machine learning algorithm module, a deep learning module, and a logistic regression calculation module; the obstructive sleep apnea AI feature data set corresponds to the algorithm module, and the obstructive sleep apnea AI feature data set is obtained by training with anthropometric feature data of known obstructive sleep apnea patients combined with patients or non-patients.
[0033] The AI server is connected to the access data terminal via a network.
[0034] like Figure 4 The body index measurement component includes a height measurement module, a weight measurement module, a waist measurement module, a neck measurement module, and a hip measurement module.
[0035] like Figure 2 A method for detecting a population with obstructive sleep apnea, collecting data of a detector, wherein the detector data includes general demographic characteristic data and anthropometric characteristic data; inputting the demographic characteristic data and anthropometric characteristic data into an AI computing platform; the AI computing platform calculates according to the obstructive sleep apnea AI feature data set and the anthropometric characteristic data to obtain an obstructive sleep apnea risk assessment report; the anthropometric characteristic data includes height, weight, waist circumference, neck circumference, and hip circumference.
[0036] The general demographic characteristics data include age and gender.
[0037] The AI computing platform includes one or more algorithm modules, including a machine learning algorithm module, a deep learning module, and a logistic regression computing module; the obstructive sleep apnea AI feature data set corresponds to the algorithm module, and the obstructive sleep apnea AI feature data set is obtained by training with anthropometric feature data of known obstructive sleep apnea patients combined with patients or non-patients.
[0038] The anthropometric characteristic data is input by the examiner.
[0039] The anthropometric characteristic data is obtained by the tester through measurement using a set body index measurement component, and the body index measurement component sends the measured data to the AI computing platform.
[0040] like Figure 3 , a system for detecting people with obstructive sleep apnea, wherein the general demographic characteristic data includes age and gender. The physical characteristic parameters include polysomnography PSG data. Figure 3The AI server is connected to the sample collection component through a network. The demographic data, anthropometric data and PSG data are transmitted to the terminal data collector through the Internet of Things, and then transmitted to the AI server through the local area network. The server performs calculations and stores the data in the database.
[0041] like Figure 5 , is one of the software operation interfaces mentioned above. It can be seen that the input standard AI training input data also includes a variety of obesity indicators. The body parameter data is converted into standard AI training input data, where the AI training input data includes: This is to be completed after the review is completed.
[0042] Although the present invention is illustrated and described according to the preferred embodiment and several alternatives, the invention is not limited by the specific description in this specification. Other additional replacement or equivalent components can also be used to practice the present invention.
Claims
1. An obstructive sleep apnea crowd detection system, characterized in that: It includes a front-end measurement system and an AI computing platform; the front-end measurement system includes a body index measurement component and an access data terminal; the AI computing platform includes an AI computing module and an obstructive sleep apnea AI feature data set; The body index measurement component is connected to the data access terminal signal; The AI computing platform is connected to the data terminal by signal; The tester obtains anthropometric characteristic data through the body index measurement component; the body index measurement component sends the anthropometric characteristic data to the access data terminal; The access data terminal sends the anthropometric feature data to the AI computing platform; The AI computing platform calculates the obstructive sleep apnea AI feature data set and the anthropometric feature data to obtain an obstructive sleep apnea risk assessment report; The anthropometric characteristic data include height, weight, waist circumference, neck circumference and hip circumference.
2. The obstructive sleep apnea crowd detection system according to claim 1, characterized in that: The general demographic characteristic data of the tester is input through the access data terminal, and the general demographic characteristic data includes age and gender.
3. The obstructive sleep apnea crowd detection system according to claim 1, characterized in that: The AI server includes one or more algorithm modules, including a machine learning algorithm module, a deep learning module, and a logistic regression calculation module; the obstructive sleep apnea AI feature data set corresponds to the algorithm module, and the obstructive sleep apnea AI feature data set is obtained by training with anthropometric feature data of known obstructive sleep apnea patients combined with patients or non-patients.
4. The obstructive sleep apnea crowd detection system according to claim 1, characterized in that: The AI server is connected to the access data terminal via a network.
5. The obstructive sleep apnea crowd detection system according to claim 1, characterized in that: The body index measurement component includes a height measurement module, a weight measurement module, a waist measurement module, a neck measurement module, and a hip measurement module.
6. A method for detecting people with obstructive sleep apnea, characterized in that: Collecting test subject data, wherein the test subject data includes general demographic characteristic data and anthropometric characteristic data; Inputting the demographic characteristic data and anthropometric characteristic data into an AI computing platform; The AI computing platform calculates the obstructive sleep apnea AI feature data set and the anthropometric feature data to obtain an obstructive sleep apnea risk assessment report; The anthropometric characteristic data include height, weight, waist circumference, neck circumference and hip circumference.
7. The method for detecting people with obstructive sleep apnea according to claim 1, characterized in that: The general demographic characteristics data include age and gender.
8. The method for detecting people with obstructive sleep apnea according to claim 1, characterized in that: The AI computing platform includes one or more algorithm modules, including a machine learning algorithm module, a deep learning module, and a logistic regression computing module; the obstructive sleep apnea AI feature data set corresponds to the algorithm module, and the obstructive sleep apnea AI feature data set is obtained by training with anthropometric feature data of known obstructive sleep apnea patients combined with patients or non-patients.
9. The method for detecting people with obstructive sleep apnea according to claim 1, characterized in that: The anthropometric characteristic data is input by the examiner.
10. The method for detecting people with obstructive sleep apnea according to claim 1, characterized in that: The anthropometric characteristic data is obtained by the tester through measurement using a set body index measurement component, and the body index measurement component sends the measured data to the AI computing platform.