Method and system for diagnosing sleep disorder

Abstract

Provided are a method and system for diagnosing a sleep disorder. The method for diagnosing a sleep disorder, according to some embodiments, may comprise the steps of: acquiring computational fluid dynamics (CFD) input data about the upper airway of a subject; deriving flow variable data about an airflow of the upper airway by performing CFD simulation using the CFD input data; generating a breathing diagram on the basis of the flow variable data; and determining a sleep disorder of the subject by analyzing the breathing diagram. According to the method, the subject's sleep disorder can be accurately diagnosed without polysomnography.

Description

Sleep Disorder Diagnosis Method and System

[0001] The present application claims priority based on Korean Patent Application No. 10-2025-0009309 filed on January 22, 2025, and all contents disclosed in the specification and drawings of said application are incorporated by reference into the present application.

[0002] The present disclosure relates to a technique for diagnosing sleep disorders (e.g., obstructive sleep apnea).

[0003] This study was supported by the 2020 Seoul Metropolitan Boramae Hospital Intensive Research Grant and the 2024 National Research Foundation of Korea Mid-Career Researcher Support Program (2022R1A2C1093364). The research project titles are Basic Research for the Development of Treatment Methods for Obstructive Sleep Apnea and Research on Optimal Thrombin Injection Methods for the Treatment of Femoral Artery Pseudoaneurysm, respectively.

[0004] The upper airway, a part of the respiratory system extending from the nasal cavity to the larynx, serves as a pathway for airflow during breathing, and obstruction or blockage caused by internal or external abnormalities in the upper airway is known to cause sleep disorders. For example, partial blockage within the upper airway is known to cause frequent snoring, while complete blockage is known to cause obstructive sleep apnea (OSA).

[0005] Obstructive sleep apnea disrupts deep sleep, causing various inconveniences in daily activities such as excessive daytime sleepiness, fatigue, morning headaches, impaired memory and judgment, and personality changes; it can also place a burden on the heart and lungs due to reduced oxygen levels. Furthermore, obstructive sleep apnea may act as a potential risk factor for cardiovascular disease, hypertension, or unexpected death.

[0006] Currently, obstructive sleep apnea is diagnosed based on the subject's Apnea-Hypopnea Index (AHI). The AHI is a key indicator used in clinical practice to assess the severity of obstructive sleep apnea and sleep hypopnea. However, measuring the AHI requires the subject to undergo polysomnography (PSG) at a medical institution; this test is not only time-consuming and costly but also causes significant inconvenience to the subject.

[0007] The technical problem to be solved through some embodiments of the present disclosure is to provide an indicator, a method, and a system capable of accurately diagnosing a sleep disorder (e.g., obstructive sleep apnea) in a subject.

[0008] The technical problems of the present disclosure are not limited to those mentioned above, and other unmentioned technical problems will be clearly understood by a person skilled in the art of the present disclosure from the description below.

[0009] A method for diagnosing a sleep disorder according to some embodiments of the present disclosure for solving the aforementioned technical problem may include a method performed by at least one processor, comprising: a step of acquiring Computational Fluid Dynamics (CFD) input data regarding the upper airway of a subject; a step of deriving fluid variable data regarding the airflow of the upper airway by performing a CFD simulation using the CFD input data; a step of generating a breathing diagram based on the fluid variable data, wherein the breathing diagram includes a graph representing changes in fluid variable values ​​according to a breathing cycle; and a step of determining the sleep disorder of the subject by analyzing the breathing diagram.

[0010] In some embodiments, the flow variable data may be data recording the values ​​of the flow variable of the minimum cross-sectional area portion of the upper airway.

[0011] In some embodiments, the flow variable data may be data recording the values ​​of flow variables of a specific part of the upper respiratory tract. In this case, the specific part may correspond to at least a portion of the section of the upper respiratory tract including the velopharynx and the oropharynx.

[0012] In some embodiments, the flow variable data includes values ​​of pressure and flow rate, and the graph may represent changes in the values ​​of pressure and flow rate according to the breathing cycle.

[0013] In some embodiments, the CFD input data includes information regarding a 3D grid model of the upper respiratory tract, and the step of acquiring the CFD input data may include: acquiring a CT (Computed Tomography) image set of the subject; generating a 3D model of the upper respiratory tract from the CT image set; and meshing the 3D model to generate the 3D grid model.

[0014] In some embodiments, the step of generating the breathing diagram includes the step of generating the breathing diagram by dividing the breathing cycle into a plurality of segments, wherein the plurality of segments may include: a first segment from the breathing start point to the maximum inspiratory rate point; a second segment from the maximum inspiratory rate point to the maximum expiratory rate point; a third segment from the maximum expiratory rate point to the rest start point; and a fourth segment from the rest start point to the breathing end point.

[0015] In some embodiments, the step of generating the breathing diagram includes the step of generating the breathing diagram by dividing the breathing cycle into a plurality of sections, wherein the plurality of sections may include: a first section from the exhalation start point to the exhalation rate maximum point; a second section from the exhalation rate maximum point to the inhalation rate maximum point; a third section from the inhalation rate maximum point to the rest start point; and a fourth section from the rest start point to the exhalation start point.

[0016] In some embodiments, the step of determining the sleep disorder of the subject may include the step of determining that the subject has a sleep disorder if the area of ​​the region formed by the graph is greater than or equal to a threshold.

[0017] In some embodiments, the threshold may be determined based on a breathing diagram of an overweight person whose body mass index is above a reference value.

[0018] In some embodiments, the step of determining the sleep disorder of the subject may include determining the sleep disorder based on the shape of the area formed by the graph.

[0019] In some embodiments, the step of determining the sleep disorder based on the shape of the region may include determining that the sleep disorder exists in the subject when the asymmetry of the shape is greater than or equal to a threshold.

[0020] In some embodiments, the step of determining the sleep disorder of the subject includes determining the sleep disorder based on the morphological characteristics of the exhalation-related section in the graph, and the morphological characteristics may include at least one of the length of the exhalation-related section and the size of the area occupied by the exhalation-related section.

[0021] In some embodiments, the step of determining the sleep disorder of the subject may include determining that the sleep disorder exists in the subject if the morphological irregularity of the exhalation-related section in the graph is greater than or equal to a threshold.

[0022] In some embodiments, the step of determining the sleep disorder of the subject includes determining the sleep disorder based on the morphological characteristics of the inhalation-related section in the graph, and the morphological characteristics may include at least one of the length of the inhalation-related section, the morphological irregularity, and the size of the area occupied by the inhalation-related section.

[0023] In some embodiments, the step of determining the sleep disorder of the subject includes determining the sleep disorder based on the morphological characteristics of the rest-related section in the graph, and the morphological characteristics may include at least one of the length of the rest-related section, the morphological irregularity, and the size of the area occupied by the rest-related section.

[0024] In some embodiments, the step of determining the sleep disorder of the subject may include determining whether the subject has obstructive sleep apnea.

[0025] A sleep disorder diagnosis system according to some embodiments of the present disclosure for solving the technical problem described above comprises: one or more processors; and a memory for storing a computer program executed by said one or more processors, wherein the computer program may include instructions for: acquiring Computational Fluid Dynamics (CFD) input data regarding the upper airway of a subject; deriving fluid variable data for the airflow of said upper airway by performing a CFD simulation using said CFD input data; generating a breathing diagram based on said fluid variable data—said breathing diagram includes a graph representing changes in fluid variable values ​​according to a breathing cycle—; and analyzing said breathing diagram to determine the sleep disorder of said subject.

[0026] A computer program according to some embodiments of the present disclosure for solving the aforementioned technical problem may be stored on a computer-readable recording medium to execute the steps of: acquiring Computational Fluid Dynamics (CFD) input data regarding the upper airway of a subject, coupled with a computer processor; deriving fluid variable data regarding the airflow of the upper airway by performing a CFD simulation using the CFD input data; generating a breathing diagram based on the fluid variable data, wherein the breathing diagram includes a graph representing changes in fluid variable values ​​according to a breathing cycle; and determining a sleep disorder of the subject by analyzing the breathing diagram.

[0027] According to some embodiments of the present disclosure, flow variable data regarding the airflow in the upper respiratory tract can be derived by performing a Computational Fluid Dynamics (CFD) simulation using CFD input data regarding the upper respiratory tract of a subject. Then, a breathing diagram is generated based on the said flow variable data, and the subject's sleep disorder can be determined by analyzing the said breathing diagram. Since this breathing diagram clearly shows the subject's breathing patterns and breathing characteristics, the subject's sleep disorder can be accurately diagnosed (determined) by analyzing it without undergoing polysomnography.

[0028] The effects according to the technical concept of the present disclosure are not limited to those mentioned above, and other unmentioned effects will be clearly understood by a person skilled in the art from the description below.

[0029] FIG. 1 is an exemplary drawing for explaining the operation of a sleep disorder diagnosis system according to some embodiments of the present disclosure at the system level.

[0030] FIG. 2 is an exemplary drawing for further explaining the operation of a sleep disorder diagnosis system according to some embodiments of the present disclosure.

[0031] FIG. 3 is an exemplary flowchart illustrating a method for diagnosing sleep disorders according to some embodiments of the present disclosure.

[0032] FIGS. 4 and 5 are exemplary drawings for illustrating a method of generating a 3D grid model of the upper respiratory tract according to some embodiments of the present disclosure.

[0033] FIG. 6 is an exemplary drawing showing the shape of the upper airway and the location of the minimum cross-sectional area portion that may be referenced in some embodiments of the present disclosure.

[0034] Figure 7 illustrates a comparison of pressure distribution in the upper airways of patients with obstructive sleep apnea, normal individuals, and overweight individuals.

[0035] FIG. 8 is an exemplary drawing for illustrating a method for generating a breathing diagram according to some embodiments of the present disclosure.

[0036] FIGS. 9 and FIGS. 10 are exemplary drawings for illustrating a method for generating a breathing diagram according to some other embodiments of the present disclosure.

[0037] FIGS. 11a and FIGS. 11b are exemplary drawings for illustrating a method for generating a breathing diagram according to some other embodiments of the present disclosure.

[0038] FIG. 12 is an exemplary drawing for explaining a method for determining sleep disorders according to some embodiments of the present disclosure.

[0039] FIGS. 13a to 13c are exemplary drawings for explaining the results of experiments conducted by the inventors of the present disclosure.

[0040] FIG. 14 illustrates an exemplary computing device capable of implementing a sleep disorder diagnosis system according to some embodiments of the present disclosure.

[0041] Hereinafter, various embodiments of the present disclosure will be described in detail with reference to the attached drawings. The advantages and features of the present disclosure and the methods for achieving them will become clear by referring to the embodiments described below in detail together with the attached drawings. However, the technical concept of the present disclosure is not limited to the following embodiments but can be implemented in various different forms. The following embodiments are provided merely to complete the technical concept of the present disclosure and to fully inform those skilled in the art of the scope of the present disclosure, and the technical concept of the present disclosure is defined only by the scope of the claims.

[0042] In describing the various embodiments of the present disclosure, if it is determined that a detailed description of related known configurations or functions could obscure the essence of the present disclosure, such detailed description is omitted.

[0043] Unless otherwise defined, terms used in the following embodiments (including technical and scientific terms) may be used in a meaning commonly understood by those skilled in the art to which this disclosure pertains, but this may vary depending on the intent of those skilled in the art, case law, the emergence of new technology, etc. The terms used in this disclosure are for describing the embodiments and are not intended to limit the scope of this disclosure.

[0044] In the following embodiments, singular expressions include plural concepts unless the context clearly specifies them as singular. Additionally, plural expressions include singular concepts unless the context clearly specifies them as plural.

[0045] In addition, terms such as first, second, A, B, (a), (b), etc. used in the following embodiments are used merely to distinguish one component from another, and the essence, order, or sequence of the said component is not limited by such terms.

[0046] The components described by reference to terms such as part or unit, module, block, ~or, ~er, etc. used in the following embodiments, and the functional blocks illustrated in the drawings may be implemented in the form of software, hardware, or a combination thereof. Software may be, for example, machine code, firmware, embedded code, and application software. Additionally, hardware may include, for example, electrical circuits, electronic circuits, processors, computers, integrated circuits, integrated circuit cores, passive components, or a combination thereof.

[0047] Hereinafter, various embodiments of the present disclosure will be described in detail with reference to the attached drawings.

[0048] FIG. 1 is an exemplary drawing for explaining the operation of a sleep disorder diagnosis system (10) according to some embodiments of the present disclosure at the system level.

[0049] As illustrated in FIG. 1, the sleep disorder diagnosis system (10) is a computing device / system equipped with a function to diagnose sleep disorders of various subjects. For example, the sleep disorder diagnosis system (10) can diagnose a subject's sleep disorder based on CFD (Computational Fluid Dynamics) input data (11) regarding the upper airway, and output a diagnosis result (12) through this.

[0050] CFD input data (11) refers to data used for CFD simulation of air flow (flow) in the upper airway. The CFD input data (11) may include information regarding, for example, a 3D (3-dimensonal) grid model (or geometric / shape information) of the upper airway, initial conditions (e.g., initial values ​​of variables related to the fluid (i.e., air), boundary conditions (e.g., conditions of the fluid inlet and outlet points), simulation settings (e.g., types of governing equations, types of turbulence models, number of time steps, etc.), but the scope of the present disclosure is not limited thereto.

[0051] The CFD input data (11) may be named as ‘diagnostic basic data’, ‘subject data’, or ‘CFD simulation data’ depending on the case.

[0052] The diagnostic result (12) may include various diagnostic information regarding the subject's sleep disorder without limitation. Examples of such diagnostic information may include whether a sleep disorder exists, the type of sleep disorder, the intensity (degree) of the sleep disorder, and the likelihood of the sleep disorder occurring, but the scope of the present disclosure is not limited thereto. For instance, the diagnostic result (12) may further include information such as guidelines for improving / treating the sleep disorder and the cause of the sleep disorder.

[0053] Sleep disorders can encompass various disorder symptoms that may occur during sleep due to, for example, abnormalities (or characteristics) of the upper airway. Examples of such disorder symptoms include obstructive sleep apnea (OSA), but the scope of this disclosure is not limited thereto. For instance, the scope of sleep disorders may also include disorder symptoms such as sleep hypopnea.

[0054] FIG. 2 is an exemplary drawing for further explaining the operation of a sleep disorder diagnosis system (10) according to some embodiments of the present disclosure.

[0055] As illustrated in FIG. 2, the sleep disorder diagnosis system (10) can derive a diagnosis result (12) of the subject's sleep disorder through CFD simulation (21) and breathing diagram analysis (22).

[0056] Specifically, the sleep disorder diagnosis system (10) can derive flow variable data (23) for air flow in the upper respiratory tract by performing a CFD simulation (21) using CFD input data (11) regarding the upper respiratory tract. For example, the sleep disorder diagnosis system (10) can simulate air flow in the upper respiratory tract by calculating the values ​​of flow variables according to each time step on a 3D grid model of the upper respiratory tract using a governing equation and a Shear Stress Transport (SST) turbulence model, and as a result, flow variable data (23) can be derived. For example, the continuity equation and the Reynolds-Averaged Navier-Stokes (RANS) equation may be used as governing equations, but the scope of the present disclosure is not limited thereto. Those skilled in the art will already be familiar with the concept and operating principles of CFD simulation, so a detailed explanation thereof will be omitted.

[0057] Flow variable data (23) may include values ​​of flow variables (e.g., values ​​at each time step) related to air flow in the upper airway (or CFD simulation (21)). Examples of flow variables include pressure, flow rate, flow velocity, etc., but the scope of the present disclosure is not limited thereto.

[0058] Next, the sleep disorder diagnosis system (10) can generate a breathing diagram (24) based on fluid variable data (23). Here, the breathing diagram (24) is a diagram showing the change in fluid variable values ​​(or the relationship between fluid variables) according to the breathing cycle, and may include a graph (e.g., a graph in the form of a closed curve) that visually represents the change in fluid variable values ​​(or the relationship between fluid variables). That is, the graph of the breathing diagram (24) can be understood as visually representing the relationship between fluid variables. For an example of the breathing diagram (24), refer to FIGS. 10 to 12, etc.

[0059] Next, the sleep disorder diagnosis system (10) can derive a diagnosis result (12) of the subject's sleep disorder through the analysis step (22) of the breathing diagram (24). Since this breathing diagram (24) clearly shows the subject's breathing characteristics and breathing patterns, using it allows the subject's sleep disorder (e.g., obstructive sleep apnea) to be accurately diagnosed (determined) without undergoing polysomnography (PSG). A specific method for diagnosing (determining) the subject's sleep disorder will be explained in detail later with reference to the drawings from Fig. 3 onwards.

[0060] The detailed operations of the sleep disorder diagnosis system (10) will also be explained in detail later with reference to the drawings from Fig. 3 and below.

[0061] In some embodiments, the sleep disorder diagnosis system (10) may provide diagnosis services to multiple users. For example, the sleep disorder diagnosis system (10) may receive CFD input data (e.g., 11) regarding the upper respiratory tract of the user from the user's terminal, diagnose the user's sleep disorder based on this, and provide (transmit) the diagnosis result (e.g., 12) to the user terminal. Alternatively, the sleep disorder diagnosis system (10) may receive basic data (e.g., a CT (Computed Tomography) image set of a body part including the upper respiratory tract) for generating CFD input data from the user terminal, and generate CFD input data based on this to diagnose the user's sleep disorder. In some cases, the sleep disorder diagnosis system (10) may provide such diagnosis services to multiple medical institutions.

[0062] The sleep disorder diagnosis system (10) described above may be implemented with at least one computing device. For example, all functions of the sleep disorder diagnosis system (10) may be implemented in a single computing device, or the first function of the sleep disorder diagnosis system (10) may be implemented in a first computing device and the second function may be implemented in a second computing device. Alternatively, specific functions of the sleep disorder diagnosis system (10) may be implemented in multiple computing devices.

[0063] A computing device may include any device equipped with computing (processing) functions, and for an example of such a device, refer to FIG. 14. Since a computing device is an assembly of various components (e.g., memory, processor, etc.) that interact, it may be referred to as a 'computing system' depending on the case. Of course, the term computing system may also encompass the concept of an assembly of multiple computing devices that interact.

[0064] Up to now, the operation of a sleep disorder diagnosis system (10) according to some embodiments of the present disclosure has been schematically described with reference to FIGS. 1 and 2. Hereinafter, various methods that can be performed in the sleep disorder diagnosis system (10) described above will be described with reference to FIGS. 3 and subsequent drawings.

[0065] For the sake of convenience of understanding, the following description will continue under the assumption that all steps / operations of the methods described below are performed in the sleep disorder diagnosis system (10, e.g., at least one processor) described above. Therefore, if the subject of a specific step / operation is omitted, the corresponding step / operation can be understood as being performed by the sleep disorder diagnosis system (10). However, in an actual environment, some steps / operations of the methods described below may be performed on a different computing device.

[0066] For convenience of explanation, the sleep disorder diagnosis system (10) will be abbreviated as 'system' below.

[0067] FIG. 3 is an exemplary flowchart illustrating a method for diagnosing sleep disorders according to some embodiments of the present disclosure. However, this is merely an exemplary embodiment for achieving the purpose of the present disclosure, and it is understood that some steps may be added or deleted as necessary.

[0068] As illustrated in FIG. 3, the sleep disorder diagnosis method according to the embodiments may begin at step S31 of acquiring CFD input data regarding the upper respiratory tract of a subject. As described above, the CFD input data may include information regarding a 3D grid model (or geometric / shape information) of the upper respiratory tract, initial conditions, boundary conditions, simulation settings, etc.

[0069] In some embodiments, a 3D grid model of the upper airway may be generated from a CT image set of a subject (i.e., a CT image set of a body part including the upper airway). For example, as illustrated in FIG. 4, the system (10) may generate a 3D model (44) of the upper airway from a CT image set (e.g., 41, 42, 43) through a 3D reconstruction process. FIG. 4 illustrates CT images of a patient with obstructive sleep apnea, a normal person, and an overweight person as examples. Then, the system (10) may mesh the 3D model (44) to generate a 3D grid model of the upper airway. For an example of such a 3D grid model, refer to FIG. 5.

[0070] Referring again to Fig. 3, the explanation will be provided.

[0071] In step S32, flow variable data for the airflow in the upper respiratory tract is derived by performing a CFD simulation using the CFD input data. As described above, the flow variable data may include values ​​of flow variables (e.g., values ​​at each time step) related to the airflow in the upper respiratory tract (i.e., airflow according to the breathing cycle), and examples of flow variables include pressure, flow rate, and flow velocity.

[0072] In some embodiments, the system (10) may generate flow variable data by recording the values ​​of flow variables in the smallest cross-sectional area (SMA) portion of the upper airway. Alternatively, the system (10) may generate flow variable data by recording the values ​​of flow variables in at least a portion of the upper airway segment including the velopharynx and oropharynx (e.g., the velopharynx, the oropharynx, or the area between the velopharynx and the oropharynx). This can be understood as being due to the fact that obstructive sleep apnea is caused by partial or complete blockage of the upper airway, and that such blockage occurs mainly in the velopharynx and oropharynx, which are the areas with the smallest cross-sectional area in the upper airway. That is, using the flow variable data of the relevant area allows for an accurate diagnosis of the subject's obstructive sleep apnea. Refer to FIG. 6 for the location of the smallest cross-sectional area portion of the upper airway. In FIG. 6, 'plane 1', 'plane 2', and 'plane 3' refer to the vicinity of the nasopharynx, oropharynx, and hypopharynx, respectively, and plane 2 corresponds to the portion with the minimum cross-sectional area. Also, in FIG. 6, 'A Inlet ', 'A Outlet ' represents the air inlet and air outlet points, respectively, 'TL' represents the total length of the upper airway, and 'L1', 'L2', and 'L3' represent the lengths of the corresponding upper airway segments, respectively.

[0073] Additionally, in some embodiments, the system (10) may generate flow variable data by recording pressure and flow rate values ​​in specific parts of the upper airway. This is because if there is an abnormality in the upper airway, the fluctuation patterns of pressure and flow rate differ significantly, and through this, sleep disorders (e.g., obstructive sleep apnea) can be accurately diagnosed. For example, referring to FIG. 7, it can be seen that the pressure distribution (71) inside the upper airway of a patient with obstructive sleep apnea is significantly different from that of a normal person (see 72) and also significantly different from that of an overweight person (see 73). Therefore, by analyzing the pressure and flow rate values, the subject's sleep disorder can be accurately diagnosed. In FIG. 7, 'plane 1', 'plane 2', and 'plane 3' refer to the nasopharynx, oropharynx, and hypopharynx, respectively, as in FIG. 6.

[0074] Referring again to Fig. 3, the explanation will be provided.

[0075] In step S33, a breathing diagram is generated based on the fluid variable data. As described above, the breathing diagram (24) is a diagram representing the change in fluid variable values ​​(or the relationship between fluid variables) according to the breathing cycle, and may include a graph (e.g., a closed curve graph, etc.) that visually represents the change in fluid variable values ​​(or the relationship between fluid variables).

[0076] The specific method for generating a breathing diagram may vary depending on the example.

[0077] In some embodiments, as illustrated in FIG. 8, the breathing cycle may be divided into an inhalation phase (81), an exhalation phase (82), and a resting phase (83). That is, the system (10) can generate a graph of a breathing diagram by visualizing flow variable data according to the three phases (81 to 83) described above for the breathing cycle. FIG. 8 illustrates a change in flow velocity at an outlet point according to the breathing cycle.

[0078] In some other embodiments, as illustrated in FIG. 9, the breathing cycle may be divided into a first section (S1), a second section (S2), a third section (S3), and a fourth section (S4). Specifically, the system (10) may set the first section (S1) from the breathing (inhalation) start point (91) to the inhalation speed maximum point (92), the second section (S2) from the inhalation speed maximum point (92) to the exhalation speed maximum point (93), the third section (S3) from the exhalation speed maximum point (93) to the rest start point (94), and the fourth section (S4) from the rest start point (94) to the breathing (rest) end point. Then, the system (10) may generate a graph of a breathing diagram according to the set sections (S1 to S4). For an example of the breathing diagram thus generated, refer to FIG. 10. For reference, Figure 10 assumes that the breathing diagram is a pressure (P)-flow (Q) diagram. Also, 'BP' indicated within the area formed by the graph in Figure 10 (which may hereinafter be referred to as the 'graph area') represents breathing power, which can be understood as representing the concept of the force (or work / energy) required for breathing mechanically.

[0079] In some other embodiments, the breathing cycle may be divided into first to fourth sections in a manner different from the preceding embodiments. Specifically, the system (10) may set the first section from the exhalation start point to the exhalation rate maximum point, the second section from the exhalation rate maximum point to the inhalation rate maximum point, the third section from the inhalation rate maximum point to the rest start point, and the fourth section from the rest start point to the exhalation start point. For example, as illustrated in FIG. 11a, the system (10) may create these four sections by modifying the flow variable data by adding the flow rate value at the minimum pressure point (94) in the flow variable data to the flow rate value within the section (see 93) from the minimum pressure point (94) to the maximum pressure point (95). Then, the system (10) may generate a graph (92) of the breathing diagram according to the set sections (S1 to S4) (i.e., generate a graph (92) of the breathing diagram using the modified flow variable data). In such cases, a breathing diagram that better illustrates the subject's breathing patterns and breathing characteristics can be generated (see 93 and 96, and 91 and 92). For other examples of such generated breathing diagrams, refer to Fig. 11b.

[0080] Referring again to Fig. 3, the explanation will be provided.

[0081] In step S34, the subject's sleep disorder is determined by analyzing the breathing diagram. For example, the system (10) can determine whether the subject has a sleep disorder (e.g., obstructive sleep apnea), the type of sleep disorder, the intensity (degree) of the sleep disorder, and the likelihood of the sleep disorder occurring by analyzing the breathing diagram.

[0082] The specific method for determining sleep disorders may vary depending on the example.

[0083] In some embodiments, a subject's sleep disorder may be determined based on the area (size) of the graph region of the breathing diagram. For example, referring to FIG. 12, the system (10) may determine (determine) that the subject has a sleep disorder (e.g., obstructive sleep apnea) if the area of ​​the region (122) formed by the graph (121) is greater than or equal to a threshold. Conversely, the system (10) may determine that the subject does not have a sleep disorder. Here, determining that a sleep disorder exists may encompass the concept of determining that there is a high probability of the sleep disorder occurring. That is, the system (10) may determine that the larger the area of ​​the graph region (122), the higher the probability of the sleep disorder occurring, and the smaller the area, the lower the probability of occurrence. This is because a large area of ​​the graph region (122) means that there is a large variability in pressure and flow rate according to the breathing cycle, which means that the subject's breathing is significantly unstable and requires a significant amount of force (or energy / work) for each breath. In some cases, the system (10) may determine the intensity (degree) of the sleep disorder based on the area of ​​the graph region (122) (e.g., determining that the greater the area, the greater the intensity of the sleep disorder). This technical content may also be applied to the following embodiments.

[0084] In the preceding embodiments, the threshold may be determined in various ways. For example, the threshold may be determined based on the breathing diagram (i.e., the area of ​​the graph region) of an overweight person whose Body Mass Index (BMI) and / or weight is above a reference value. This is because the area of ​​the graph region appearing in the breathing diagram of an overweight person is generally larger than that of a normal person and smaller than that of a patient with obstructive sleep apnea, so setting the threshold based on this area allows for accurate determination of the presence of a sleep disorder. As another example, the threshold may be determined based on the breathing diagram of a patient with a sleep disorder (e.g., a patient with obstructive sleep apnea) (e.g., determining the threshold based on the minimum area of ​​the graph region appearing in the breathing diagram of a patient with a sleep disorder, the area of ​​the bottom 10%, etc.). As yet another example, the threshold may be determined based on the breathing diagram of a normal person (e.g., determining the threshold based on the maximum area of ​​the graph region appearing in the breathing diagram of a normal person, the area of ​​the top 10%, etc.). As yet another example, the threshold may be determined based on various combinations of the examples described above. In some cases, the threshold may be set according to the type of subject. Here, the type of subject may be classified based on demographic characteristics (e.g., gender, age group, etc.), medical history (e.g., history of sleep disorders), medication history, physical examination results (e.g., body mass index, weight, height, etc.), but the scope of the present disclosure is not limited thereto. Such technical details regarding the threshold may also be applied to the following embodiments.

[0085] In some other embodiments, a subject's sleep disorder may be determined based on the shape of the graph area of ​​the breathing diagram. For example, referring again to FIG. 12, the system (10) may determine (determine) that the subject has a sleep disorder (e.g., obstructive sleep apnea) if the asymmetry (or irregularity) of the shape of the graph area (122) is above a threshold. Conversely, the system (10) may determine that the subject does not have a sleep disorder. In other words, the system (10) may determine that the more asymmetric (or irregular) the shape of the graph area (122) is, the higher the probability of a sleep disorder occurring, and the more symmetric it is, the lower the probability of occurrence. This is because an asymmetric shape of the graph area (122) means that the values ​​of pressure and flow rate change irregularly according to the breathing cycle.

[0086] In some other embodiments, a subject's sleep disorder may be determined based on the morphological characteristics of the exhalation-related section (i.e., the graph section corresponding to the exhalation-related section of the breathing cycle) in the graph of the breathing diagram. Here, the morphological characteristics may include the length of the section, the size of the area occupied by the section, and the morphological irregularity of the section, but the scope of the present disclosure is not limited thereto. For example, referring again to FIG. 12, the first section (S1) of the graph (121) is the section from the exhalation start point to the exhalation velocity maximum point (i.e., FIG. 12 is a breathing diagram generated in the same way as FIG. 11b), and the system (10) may determine a subject's sleep disorder based on the length of the first section (S1), the size of the area occupied by the first section (S1) (123, e.g., a rectangular area as in FIG. 12), and the irregularity of the first section (S1). For example, the system (10) may determine (determine) that the subject has a sleep disorder (e.g., obstructive sleep apnea) if the length of the first section (S1) is greater than a threshold, the irregular shape is greater than a threshold, or the size of the corresponding area (123) is greater than a threshold. This is because a long length or irregular shape of the first section (S1) means that significant force is required during the exhalation phase, which implies that the subject is making excessive breathing effort during the exhalation phase due to airway resistance / abnormalities (e.g., partial blockage, complete blockage, etc.).

[0087] In some other embodiments, a sleep disorder of a subject can be determined based on the morphological characteristics of the inspiratory-related section (i.e., the graph section corresponding to the inspiratory-related section of the breathing cycle) in the graph of the breathing diagram. For example, referring again to FIG. 12, the second section (S2) of the graph (121) is the section from the point of maximum expiratory velocity to the point of maximum inspiratory velocity, and the system (10) can determine a sleep disorder of a subject based on the length of the second section (S2), the size of the area occupied by the second section (S2), the morphological irregularity of the second section (S2), etc. For instance, the system (10) can determine (determine) that a sleep disorder (e.g., obstructive sleep apnea) exists in the subject if the length of the second section (S2) is greater than or equal to a threshold, the morphological irregularity is greater than or equal to a threshold, or the size of the area occupied by the second section (S2) is greater than or equal to a threshold. This is because a long length or irregular shape of the second segment (S2) implies that significant force is required during the inspiratory phase, which means that the subject is exerting excessive respiratory effort (i.e., inspiratory effort) during the inspiratory phase due to airway resistance / abnormalities (e.g., partial obstruction, complete obstruction, etc.).

[0088] In some other embodiments, a subject's sleep disorder can be determined based on the morphological characteristics of the rest-related section (i.e., the graph section corresponding to the rest-related section of the breathing cycle) in the graph of the breathing diagram. For example, referring again to FIG. 12, the third section (S3) and the fourth section (S4) of the graph (121) are the section from the point of maximum inhalation speed to the point of rest and the section from the point of rest to the point of exhalation, respectively, and the system (10) can determine the subject's sleep disorder based on the length of the third section (S3) and / or the fourth section (S4), the size of the area occupied by the third section (S3) and / or the fourth section (S4), the morphological irregularity of the third section (S3) and / or the fourth section (S4), etc. For example, the system (10) may determine (determine) that the subject has a sleep disorder (e.g., obstructive sleep apnea) if the length of the third section (S3) and / or the fourth section (S4) is less than a threshold, the morphological irregularity is greater than or equal to a threshold, or the size of the area occupied by the third section (S3) and / or the fourth section (S4) is less than a threshold. This is because the length of the third section (S3) and / or the fourth section (S4) being long or having an irregular shape means that the resting phase is unstable and the subject's breathing pattern is less comfortable.

[0089] In some other embodiments, the subject's sleep disorder may be determined based on various combinations of the embodiments described above.

[0090] Up to this point, a method for diagnosing sleep disorders according to several embodiments of the present disclosure has been described with reference to FIGS. 3 to 12. As described above, by performing a CFD simulation using CFD input data regarding the upper respiratory tract of a subject, flow variable data regarding the airflow of the upper respiratory tract can be derived. Then, a breathing diagram is generated based on the said flow variable data, and the subject's sleep disorder can be determined by analyzing the said breathing diagram. Since such a breathing diagram clearly shows the subject's breathing pattern and breathing characteristics, the subject's sleep disorder can be accurately diagnosed (determined) by analyzing it without undergoing a polysomnography.

[0091] Hereinafter, the results of experiments conducted by the inventors of the present disclosure will be briefly described with reference to FIGS. 13a to 13c.

[0092] The inventors conducted an experiment to verify the validity of the sleep disorder diagnosis method described above (hereinafter referred to as the "proposed method"). Specifically, according to the proposed method, the inventors included a group of 9 patients with obstructive sleep apnea, a group of 9 normal individuals, and a group of 9 overweight individuals (provided that the overweight group had a body mass index of 30 kg / m²). 2 Breathing diagrams were generated for a group of people with abnormalities, and the average area of ​​the graph region in the breathing diagrams was calculated. The generated breathing diagrams are shown in FIGS. 13a to 13c, and the average area of ​​the graph region is listed in Table 1 below. FIGS. 13a to 13c show breathing diagrams for a group of patients with obstructive sleep apnea, a normal group, and an overweight group, respectively.

[0093] Group of patients with obstructive sleep apnea, normal group, overweight group, respiratory rate (N·m / s) 32.6×10 -3 24.9×10 -3 26.0×10 -3

[0094] Referring to FIGS. 13a to 13c and Table 1, it was found that the graph area of ​​the breathing diagram for the obstructive sleep apnea patient group was much larger than that of the normal and overweight groups. Additionally, it was found that the shape of the graph area of ​​the breathing diagram for the obstructive sleep apnea patient group was more asymmetrical and irregular compared to the normal and overweight groups. Through this, it can be confirmed that the proposed method can accurately diagnose sleep disorders including obstructive sleep apnea. Up to this point, the results of the experiments conducted by the inventors have been briefly described with reference to FIGS. 13a to 13c. Below, with reference to FIG. 14, an exemplary computing device (140) capable of implementing the system (10) described above will be described.

[0095] FIG. 14 is an exemplary hardware configuration diagram showing a computing device (140).

[0096] As illustrated in FIG. 14, a computing device (140) may include one or more processors (141), a bus (143), a communication interface (144), a memory (142) for loading a computer program (146) executed by the processor (141), and a storage (145) for storing the computer program (146). However, FIG. 14 illustrates only the components related to the embodiments of the present disclosure. Therefore, a person skilled in the art to which the present disclosure belongs will understand that other general-purpose components may be included in addition to the components (141 to 146) illustrated in FIG. 14. That is, the computing device (140) may include various additional components in addition to the components (141 to 146) illustrated in FIG. 14. Furthermore, depending on the case, the computing device (140) may be configured in a form in which some of the components (141 to 146) illustrated in FIG. 14 are omitted. Below, each component of the computing device (140) is described.

[0097] The processor (141) can control the overall operation of each component of the computing device (140). The processor (141) may be configured to include at least one of a CPU (Central Processing Unit), MPU (Micro Processor Unit), MCU (Micro Controller Unit), GPU (Graphic Processing Unit), TPU (Tensor Processing Unit), NPU (Neural Processing Unit), or any form of processor well known in the art of the present disclosure. Additionally, the processor (141) may perform operations on at least one application or program to execute specific steps / operations / methods. The computing device (140) may have one or more processors.

[0098] Next, the memory (142) may store various data, commands and / or information. The memory (142) may load a computer program (146) from storage (145) to execute specific steps / operations / methods. The memory (142) may be implemented as volatile memory such as RAM, but the technical scope of the present disclosure is not limited thereto.

[0099] Next, the bus (143) can provide communication functions between components of the computing device (140). The bus (143) can be implemented as various types of buses, such as an address bus, a data bus, and a control bus.

[0100] Next, the communication interface (144) may support wired and wireless internet communication of the computing device (140). Additionally, the communication interface (144) may support various communication methods other than internet communication. To this end, the communication interface (144) may be configured to include a communication module well known in the art of the present disclosure.

[0101] Next, the storage (145) may store one or more computer programs (146) non-temporarily. The storage (145) may be configured to include non-volatile memory such as ROM (Read Only Memory), EPROM (Erasable Programmable ROM), EEPROM (Electrically Erasable Programmable ROM), flash memory, a hard disk, a removable disk, or any form of computer-readable recording medium well known in the art to which this disclosure belongs.

[0102] Next, the computer program (146) may include instructions that cause the processor (141) to perform specific steps / actions / methods when loaded into memory (142). That is, the processor (141) can perform specific steps / actions / methods by executing the instructions loaded into memory (142).

[0103] For example, the computer program (146) may include instructions for the operation of acquiring CFD input data regarding the upper respiratory tract of a subject, the operation of deriving flow variable data regarding the airflow of the upper respiratory tract by performing a CFD simulation using the CFD input data, the operation of generating a breathing diagram based on the flow variable data, and the operation of determining the subject's sleep disorder by analyzing the breathing diagram.

[0104] As another example, a computer program (146) may include instructions to perform at least some of the steps / actions / methods described with reference to FIGS. 1 through 13.

[0105] As illustrated, a system (10) according to some embodiments of the present disclosure can be implemented through a computing device (140).

[0106] Meanwhile, in some embodiments, the computing device (140) illustrated in FIG. 14 may refer to a virtual machine implemented based on cloud technology. For example, the computing device (140) may be a virtual machine running on one or more physical servers included in a server farm. In this case, at least some of the processor (141), memory (142), and storage (145) illustrated in FIG. 14 may be virtual hardware, and the communication interface (144) may also be implemented as a virtualized networking element such as a virtual switch.

[0107] Up to now, with reference to FIG. 14, an exemplary computing device (140) capable of implementing a system (10) according to some embodiments of the present disclosure has been described.

[0108] Various embodiments of the present disclosure and effects according to those embodiments have been described with reference to FIGS. 1 to 14. The effects according to the technical concept of the present disclosure are not limited to those mentioned above, and other unmentioned effects will be clearly understood by a person skilled in the art from the description below.

[0109] Furthermore, just because the above embodiments describe a plurality of components being combined into one or operating in combination, the technical concept of the present disclosure is not necessarily limited to these embodiments. That is, within the scope of the purpose of the technical concept of the present disclosure, all such components may be selectively combined into one or more combinations to operate.

[0110] The technical concept of the present disclosure described above may be implemented as computer-readable code on a computer-readable recording medium. A computer program stored on a computer-readable recording medium may be transmitted to another computing device via a network such as the Internet and installed on said computing device, thereby being used on said computing device.

[0111] Although operations are depicted in a specific order in the drawings, it should not be understood that the operations must necessarily be executed in the specific order depicted or in a sequential order, or that all depicted operations must be executed to obtain the desired result. In certain situations, multitasking and parallel processing may be advantageous. Although various embodiments of the present disclosure have been described above with reference to the attached drawings, those skilled in the art will understand that the technical concept of the present disclosure may be implemented in other specific forms without altering the technical concept or essential features thereof. Therefore, the embodiments described above should be understood as illustrative in all respects and not restrictive. The scope of protection of the present disclosure shall be interpreted by the claims below, and all technical concepts within the equivalent scope shall be interpreted as being included within the scope of rights of the technical concept defined by the present disclosure.

Claims

1. A method performed by at least one processor, A step of acquiring CFD (Computational Fluid Dynamics) input data regarding the upper airway of a subject; A step of deriving flow variable data for the airflow of the upper airway by performing a CFD simulation using the above CFD input data; A step of generating a breathing diagram based on the above fluid variable data - the breathing diagram includes a graph showing changes in fluid variable values ​​according to the breathing cycle -; and A step comprising analyzing the above breathing diagram to determine the sleep disorder of the subject, Methods for diagnosing sleep disorders.

2. In Paragraph 1, The above flow variable data is data that records the values ​​of the flow variables of the minimum cross-sectional area portion of the above upper airway, Methods for diagnosing sleep disorders.

3. In Paragraph 1, The above flow variable data is data that records the values ​​of the flow variables of a specific part of the upper respiratory tract, The aforementioned specific portion corresponds to at least a part of the segment of the upper respiratory tract including the velopharynx and the oropharynx, Methods for diagnosing sleep disorders.

4. In Paragraph 1, The above flow variable data includes pressure and flow rate values, and The above graph represents the change in the values ​​of the pressure and the flow rate according to the breathing cycle, Methods for diagnosing sleep disorders.

5. In Paragraph 1, The above CFD input data includes information regarding the 3D grid model of the upper respiratory tract, and The step of acquiring the above CFD input data is, A step of acquiring a CT (Computed Tomography) image set of the above-mentioned subject; A step of generating a 3D model of the upper respiratory tract from the above CT image set; and A step comprising meshing the above 3D model to generate the above 3D grid model, Methods for diagnosing sleep disorders.

6. In Paragraph 1, The step of generating the above breathing diagram is, The method includes the step of generating the breathing diagram by dividing the breathing cycle into multiple sections, and The above multiple sections are: The first section from the point where breathing begins to the point of maximum inspiratory speed; The second section from the maximum inhalation speed point to the maximum exhalation speed point mentioned above; The third section from the maximum exhalation point to the rest start point; and Including the fourth section from the rest start point to the breathing end point mentioned above, Methods for diagnosing sleep disorders.

7. In Paragraph 1, The step of generating the above breathing diagram is, The method includes the step of generating the breathing diagram by dividing the breathing cycle into multiple sections, and The above multiple sections are: The first section from the exhalation start point to the exhalation maximum point; The second section from the maximum exhalation point to the maximum inhalation point mentioned above; The third section from the maximum intake point to the rest start point; and Including the fourth section from the above rest start point to the above exhalation start point, Methods for diagnosing sleep disorders.

8. In Paragraph 1, The step of determining the sleep disorder of the above-mentioned subject is, A step comprising determining that the subject has a sleep disorder when the area of ​​the region formed by the above graph is greater than or equal to a threshold value, Methods for diagnosing sleep disorders.

9. In Paragraph 8, The above threshold is determined based on the breathing diagram of an overweight person whose body mass index is above the reference value, Methods for diagnosing sleep disorders.

10. In Paragraph 1, The step of determining the sleep disorder of the above-mentioned subject is, A step comprising determining the sleep disorder based on the shape of the region formed by the above graph, Methods for diagnosing sleep disorders.

11. In Paragraph 10, The step of determining the sleep disorder based on the shape of the above region is, A step comprising determining that the sleep disorder exists in the subject when the asymmetry of the above shape is greater than or equal to a threshold value, Methods for diagnosing sleep disorders.

12. In Paragraph 1, The step of determining the sleep disorder of the above-mentioned subject is, It includes a step of determining the sleep disorder based on the morphological characteristics of the exhalation-related section in the above graph, and The above morphological characteristics include at least one of the length of the above exhalation-related section and the size of the area occupied by the above exhalation-related section. Methods for diagnosing sleep disorders.

13. In Paragraph 1, The step of determining the sleep disorder of the above-mentioned subject is, A step comprising determining that the sleep disorder exists in the subject when the morphological irregularity of the exhalation-related section in the above graph is above a threshold value, Methods for diagnosing sleep disorders.

14. In Paragraph 1, The step of determining the sleep disorder of the above-mentioned subject is, It includes a step of determining the sleep disorder based on the morphological characteristics of the inhalation-related section in the above graph, and The above morphological characteristics include at least one of the length of the intake-related section, morphological irregularity, and the size of the area occupied by the intake-related section. Methods for diagnosing sleep disorders.

15. In Paragraph 1, The step of determining the sleep disorder of the above-mentioned subject is, It includes a step of determining the sleep disorder based on the morphological characteristics of the rest-related section in the above graph, and The above morphological characteristics include at least one of the length of the rest-related section, the morphological irregularity, and the size of the area occupied by the rest-related section. Methods for diagnosing sleep disorders.

16. In Paragraph 1, The step of determining the sleep disorder of the above-mentioned subject is, A method comprising the step of determining whether obstructive sleep apnea is present in the subject. Methods for diagnosing sleep disorders.

17. One or more processors; and It includes memory for storing computer programs executed by one or more of the above processors, and The above computer program is: An action of acquiring CFD (Computational Fluid Dynamics) input data regarding the subject's upper airway; The operation of deriving flow variable data for the airflow of the upper airway by performing a CFD simulation using the above CFD input data; The operation of generating a breathing diagram based on the above fluid variable data - the breathing diagram includes a graph representing changes in fluid variable values ​​according to the breathing cycle -; and Instructions for an operation to determine a sleep disorder of the subject by analyzing the above breathing diagram, Sleep disorder diagnosis system.

18. In Paragraph 17, The above flow variable data is data that records the values ​​of the flow variables of the minimum cross-sectional area portion of the above upper airway, Sleep disorder diagnosis system.

19. In Paragraph 17, The above flow variable data includes pressure and flow rate values, and The above graph represents the change in the values ​​of the pressure and the flow rate according to the breathing cycle, Sleep disorder diagnosis system.

20. Combined with the computer processor, A step of acquiring CFD (Computational Fluid Dynamics) input data regarding the upper airway of a subject; A step of deriving flow variable data for the airflow of the upper airway by performing a CFD simulation using the above CFD input data; A step of generating a breathing diagram based on the above fluid variable data - the breathing diagram includes a graph showing changes in fluid variable values ​​according to the breathing cycle -; and In order to execute the step of determining the sleep disorder of the subject by analyzing the above breathing diagram, stored in a computer-readable recording medium, Computer program.

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