A method for diagnosing obstructive sleep apnea-hypopnea syndrome

Abstract

The present invention discloses a method for diagnosing obstructive sleep apnea-hypopnea syndrome, which relates to the technical field of medical diagnosis. The method comprises the following steps: collecting an airflow curve and a blood oxygen saturation curve of a patient during sleep, traversing and retrieving all respiratory events from the airflow curve, finding a corresponding search window from the blood oxygen saturation curve according to the time of occurrence of each respiratory event, calculating the area of ​​the search window for each apnea event, summing the areas of all closed areas to obtain an area sum, dividing the area sum by the total sleep time, obtaining an index value of a sleep apnea-specific hypoxia load, and obtaining the patient's apnea symptom level based on the index value and an index threshold. The method has the advantages of being simple, fast and highly accurate in indicators, and can be well applied to the screening and diagnosis of obstructive sleep apnea-hypopnea syndrome.

Description

Technical Field

[0001] The present invention relates to the technical field of medical diagnosis, in particular to a method for diagnosing obstructive sleep apnea-hypopnea syndrome. Background Art

[0002] Obstructive sleep apnea-hypopnea syndrome (OSA) is a common chronic sleep disorder characterized by frequent upper airway obstruction, decreased respiratory rate, intermittent hypoxia, and recurrent arousals during sleep. Despite improvements in diagnostic techniques, the rate of missed diagnosis remains high. Polysomnography (PSG) is often used as the standard for OSA diagnosis, but it is time-consuming, labor-intensive, and expensive. Early practical diagnostic models used minimum oxygen saturation and other parameters, such as lipid accumulation product levels and the triglyceride-glucose index, in combination with age, sex, body mass index, neck-waist circumference, glucose, insulin, and apolipoprotein B levels. The STOP-BANG Questionnaire (SBQ) can identify undiagnosed OSA and is applicable in clinical practice. However, these diagnostic methods have low sensitivity and specificity. Furthermore, the use of questionnaires and other tools alone to diagnose OSA in adults is inaccurate. Therefore, more effective tools with higher sensitivity and specificity are urgently needed.

[0003] Patent application publication number CN108778118A discloses a sleep apnea monitoring system that utilizes the user's biological signals related to the measured parts and impedance tomography information related to biological tissue characteristics to monitor the user's sleep state. The system can be produced as a portable device, thereby allowing measurements to be performed at home in a natural sleep state. However, this method requires the generation of impedance tomography information, and the processing process is relatively cumbersome.

[0004] To this end, the present invention provides a method for diagnosing obstructive sleep apnea-hypopnea syndrome. Summary of the Invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art. To this end, the present invention provides a method for diagnosing obstructive sleep apnea-hypopnea syndrome. The method has the advantages of simple indicators, rapidity, and high accuracy, and can be well applied to the screening and diagnosis of OSA.

[0006] To achieve the above object, a method for diagnosing obstructive sleep apnea-hypopnea syndrome is proposed, comprising the following steps:

[0007] Step 1: Collect the patient's airflow curve and blood oxygen saturation curve during sleep;

[0008] Step 2: Traverse and retrieve all respiratory events from the airflow curve;

[0009] Step 3: Find the corresponding search window from the blood oxygen saturation curve according to the time when each respiratory event occurs;

[0010] Step 4: Calculate the area of ​​the search window corresponding to each apnea event, accumulate the areas of all closed areas, and obtain the area sum;

[0011] Step 5: Divide the area sum by the total sleep time to obtain the index value of sleep apnea-specific hypoxic load;

[0012] Step 6: Pre-set indicator thresholds for each symptom level, and obtain the patient's apnea symptom level based on the indicator value and the indicator threshold;

[0013] Among them, the abbreviation of sleep apnea-specific hypoxic burden is SASHB, which is the abbreviation of sleep apnea-specific hypoxic burden;

[0014] The airflow curve refers to a curve obtained by connecting the real-time airflow generated by the patient's breathing in a sleeping state in chronological order, with the horizontal axis of the airflow curve being time and the vertical axis being airflow;

[0015] The blood oxygen saturation curve refers to a curve obtained by connecting the real-time blood oxygen saturation of the patient in a sleeping state in chronological order, wherein the horizontal axis of the blood oxygen saturation curve is time and the vertical axis is blood oxygen saturation;

[0016] The step of traversing and retrieving all respiratory events from the airflow curve comprises the following steps:

[0017] Step 11: For each second in the airflow curve, the maximum value of the airflow magnitude before that second in the airflow curve is taken as the maximum airflow magnitude of that second;

[0018] Step 12: Traverse the airflow curve second by second until the airflow is lower than the corresponding maximum airflow. The time point at which the airflow is multiplied by the preset ratio threshold is used as the event start time. Continue traversing after the event start time until the airflow is higher than the corresponding maximum airflow again. The time point at which the airflow is multiplied by the preset ratio threshold is used as the event end time.

[0019] If the duration from the event start time to the event end time is greater than the preset duration threshold, the time period from the event start time to the event end time is called a respiratory event; otherwise, execute step 13;

[0020] Step 13: Starting from the end time of the event in step 12, restart the traversal and repeat step 12 until all respiratory events are found;

[0021] The method of finding the corresponding search window from the blood oxygen saturation curve according to the time when each respiratory event occurs is:

[0022] Starting from the initial time of each respiratory event, the blood oxygen saturation curve is traversed. The first time node where the blood oxygen saturation decreases is used as the search initial time. The blood oxygen saturation corresponding to the search initial time is used as the baseline level. A horizontal line parallel to the horizontal axis is drawn at the position where the vertical axis is the baseline level as the baseline.

[0023] Continue traversing the blood oxygen saturation curve from the initial search time until the blood oxygen saturation returns to the baseline level, and use the current time point as the search end time;

[0024] The closed interval formed by the curve segment from the search initial time to the search end time of the blood oxygen saturation curve and the baseline is used as the search window;

[0025] The area is marked as S, then S can be expressed as: S = ∑ i s i , where i is the number of the respiratory event, s i is the area of ​​the search window corresponding to the i-th respiratory event;

[0026] Since the unit of the area of ​​each search window can be expressed as min×%, where min is the unit of the base of the search window (the search window can be approximately regarded as a triangle, and the horizontal axis is the base), and % is the unit of the height of the search window (i.e., the unit of SPO2), and the unit of total sleep time can be expressed as h, the unit of the sleep apnea-specific hypoxia burden SASHB is %min / h;

[0027] Symptom levels include none, mild, moderate, and severe.

[0028] The method further comprises:

[0029] According to the pre-calibrated response time difference between apnea and decreased blood oxygen saturation, the search window is shifted to obtain an updated search window, and the starting point and end point of the updated search window are shifted to the left by the length of the response time difference;

[0030] Aligning the updated search window with the time interval of the apnea event in the airflow curve based on the highest point of the curve;

[0031] The aligned updated search window and the time interval of the apnea event in the airflow curve are compared, and the unaligned portions of the two are removed to obtain a final search window. The search window is replaced with the final search window to re-obtain the index value of the sleep apnea-specific hypoxia load.

[0032] The method further comprises:

[0033] The response time difference between apnea and oxygen desaturation was calculated by comparing the time point when the apnea event started or ended with the time point when the blood oxygen saturation began to decrease or recover.

[0034] An electronic device is proposed, comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0035] The processor executes the above-mentioned method for diagnosing obstructive sleep apnea-hypopnea syndrome by calling the computer program stored in the memory.

[0036] A computer-readable storage medium is provided, on which a rewritable computer program is stored.

[0037] When the computer program is executed on a computer device, the computer device is caused to execute the above-mentioned method for diagnosing obstructive sleep apnea-hypopnea syndrome.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] The present invention obtains SASHB (sleep apnea-specific hypoxia burden) by counting the total area of ​​the search window formed by the blood oxygen saturation curve when all apnea or hypopnea events occur during the patient's sleep, and then dividing the total area by the statistical time; the obtained SASHB has high accuracy, and the SASHB is highly correlated with the apnea-hypopnea index and the oxygen saturation index, which shows that SASHB has high clinical application value. Compared with the traditional diagnostic method of obstructive sleep apnea-hypopnea syndrome, SASHB is simple, fast and highly accurate, and can be well applied to the screening and diagnosis of obstructive sleep apnea-hypopnea syndrome. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 This is a flow chart of a method for diagnosing obstructive sleep apnea-hypopnea syndrome in Example 1 of the present invention;

[0041] Figure 2 Schematic diagram of the principle for calculating the index value of sleep apnea-specific hypoxia burden (SASHB);

[0042] Figure 3 This is the ROC curve comparing SASHB with other indicators for the diagnosis and severity classification of OSA. DETAILED DESCRIPTION

[0043] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0044] Example 1

[0045] like Figure 1 A method for diagnosing obstructive sleep apnea-hypopnea syndrome comprises the following steps:

[0046] Step 1: Collect the patient's airflow curve and blood oxygen saturation curve during sleep;

[0047] Step 2: Traverse and retrieve all respiratory events from the airflow curve;

[0048] Step 3: Find the corresponding search window from the blood oxygen saturation curve according to the time when each respiratory event occurs;

[0049] Step 4: Calculate the area of ​​the search window for each apnea event, and add up the areas of all enclosed regions to obtain the area sum;

[0050] Step 5: Divide the area sum by the total sleep time to obtain the index value of sleep apnea-specific hypoxic load;

[0051] Step 6: Pre-set indicator thresholds for each symptom level, and obtain the patient's apnea symptom level based on the indicator value and the indicator threshold;

[0052] It should be noted that the sleep apnea-specific hypoxic burden is abbreviated as SASHB, which is the abbreviation of sleep apnea-specific hypoxic burden. For the sake of convenience, all SASHB mentioned below are abbreviations of sleep apnea-specific hypoxic burden.

[0053] The airflow curve refers to a curve obtained by connecting the real-time airflow volume generated by the patient's breathing in a sleeping state in chronological order, with the horizontal axis of the airflow curve being time and the vertical axis being airflow volume. It is understood that the real-time airflow volume can be collected in real time by a respiratory airflow sensor.

[0054] The blood oxygen saturation curve refers to a curve obtained by connecting the real-time blood oxygen saturation of the patient in a sleeping state in chronological order, wherein the horizontal axis of the blood oxygen saturation curve is time and the vertical axis is blood oxygen saturation. It is understood that the real-time blood oxygen saturation (SPO2) can be collected in real time by a smart wearable device;

[0055] like Figure 2The diagram shows the principle of calculating the index value of sleep apnea-specific hypoxia burden (SASHB). The curve in part A of the figure is the airflow curve, with the horizontal axis representing time (unit: seconds) and the vertical axis representing the airflow size. An airflow greater than 0 indicates exhalation, and an airflow less than 0 indicates inspiration. The curve in part B is the blood oxygen saturation curve, with the horizontal axis representing time and the vertical axis representing blood oxygen saturation.

[0056] Furthermore, traversing and retrieving all respiratory events from the airflow curve includes the following steps:

[0057] Step 11: For each second in the airflow curve, the maximum value of the airflow magnitude before that second in the airflow curve is taken as the maximum airflow magnitude of that second;

[0058] Step 12: Traverse the airflow curve second by second until the airflow is lower than the corresponding maximum airflow. The time point at which the airflow is multiplied by the preset ratio threshold is used as the event start time. Continue traversing after the event start time until the airflow is higher than the corresponding maximum airflow again. The time point at which the airflow is multiplied by the preset ratio threshold is used as the event end time.

[0059] If the duration from the event start time to the event end time is greater than the preset duration threshold, the time period from the event start time to the event end time is called a respiratory event; otherwise, execute step 13;

[0060] Note: Apnea events are defined as airflow amplitude decreases ≥90% and lasts ≥10 seconds; hypopnea events are defined as airflow amplitude decreases ≥30% and lasts ≥10 seconds, accompanied by a SPO2 decrease ≥3% or micro-arousal. Therefore, generally, the ratio threshold is set to 90% and the duration threshold is set to 10 seconds.

[0061] Step 13: Starting from the end time of the event in step 12, restart the traversal and repeat step 12 until all respiratory events are found;

[0062] An example such as Figure 2 As shown in the respiratory event in the curve of part A, in this respiratory event, it can be considered that the patient's ventilation has dropped sharply for a period of time and the duration has exceeded 10 seconds, thus causing an apnea event or a hypopnea event;

[0063] Furthermore, the method of finding the corresponding search window from the blood oxygen saturation curve according to the time when each respiratory event occurs is:

[0064] Because when a respiratory event occurs, the oxygen inhaled before the patient's blood oxygen saturation is suspended is not consumed in time, and thus does not immediately show a significant drop, there is a time difference between the drop in blood oxygen saturation and the occurrence of a respiratory event.

[0065] Starting from the initial time of each respiratory event, the blood oxygen saturation curve is traversed. The first time node where the blood oxygen saturation decreases is used as the search initial time. The blood oxygen saturation corresponding to the search initial time is used as the baseline level. A horizontal line parallel to the horizontal axis is drawn at the position where the vertical axis is the baseline level as the baseline.

[0066] Continue traversing the blood oxygen saturation curve from the initial search time until the blood oxygen saturation returns to the baseline level, and use the current time point as the search end time;

[0067] The closed interval formed by the curve segment from the search initial time to the search end time of the blood oxygen saturation curve and the baseline is used as the search window;

[0068] It is understandable that due to intermittent hypoxia during apnea and hypopnea events, SPO2 decreases, and when the respiratory event ends, the SPO2 trend returns to normal, and a "similar triangle" is formed by the descending and ascending trend lines and the baseline. Figure 2 As shown in the curve diagram in part B, the baseline level of the search window in the figure is the blood oxygen saturation at the initial time of the search. The curve of the descending process and the curve of the subsequent ascending process and the baseline together form a "similar triangle";

[0069] Furthermore, the method of calculating the area of ​​the search window for each apnea event may be an integral calculation or calculation using computing software such as MATLAB, which will not be described in detail in this application;

[0070] Preferably, the area and symbol are S, then S can be expressed as: S = ∑ i si, where i is the number of the respiratory event and si is the area of ​​the search window for the i-th respiratory event;

[0071] Since the unit of the area of ​​each search window can be expressed as min×%, where min is the unit of the base of the search window (the search window can be approximately regarded as a triangle, and the horizontal axis is the base), and % is the unit of the height of the search window (i.e., the unit of SPO2), and the unit of total sleep time can be expressed as h, the unit of the sleep apnea-specific hypoxic load is %min / h; an example is: if there are 8 minutes of sleep per hour and the SPO2 decreases by 5% compared to the baseline, the calculated SASHB index value is: 5%×8(min / h)=40%min / h.

[0072] Preferably, the symptom levels include none, mild, moderate and severe; verified by a large amount of experimental data, the threshold values ​​of the indexes for each symptom level can be set to: 0, 19.64, 40.96 and 69.40, respectively, that is, the SASHB index value is 0-19.64 for no symptoms, 19.64-40.96 for mild, 40.96-69.40 for moderate, and 69.40 or above for severe;

[0073] In an optional embodiment, the method further includes:

[0074] According to the pre-calibrated response time difference between apnea and decreased blood oxygen saturation, the search window is shifted to obtain an updated search window, and the starting point and end point of the updated search window are shifted to the left by the length of the response time difference;

[0075] Aligning the updated search window with the time interval of the apnea event in the airflow curve based on the highest point of the curve;

[0076] The aligned updated search window and the time interval of the apnea event in the airflow curve are compared, and the unaligned portions of the two are removed to obtain a final search window. The search window is replaced with the final search window to re-obtain the index value of the sleep apnea-specific hypoxia load.

[0077] In this way, in this embodiment, the updated search window can be aligned with the time interval of the apnea event in the airflow curve based on the highest point of the curve, thereby avoiding errors caused by the delay between apnea and the decrease in blood oxygen concentration.

[0078] In an optional embodiment, the method further includes:

[0079] The response time difference between apnea and oxygen desaturation was calculated by comparing the time point when the apnea event started or ended with the time point when the blood oxygen saturation began to decrease or recover.

[0080] It should be further noted that in order to verify the effectiveness of the sleep apnea-specific hypoxia load proposed in this application, this application also provides the following experimental data for support:

[0081] 1. Research subjects

[0082] Patients with suspected obstructive sleep apnea who visited the sleep center of the same hospital from January 2019 to July 2021 constituted the experimental cohort, and patients with unrelated obstructive sleep apnea admitted from August 2021 to July 2024 served as the validation cohort;

[0083] A total of 2303 subjects were included, including 1200 in the experimental group (no OSA: 344; mild OSA: 225; moderate OSA: 237; severe OSA: 394) and 1103 in the validation group (no OSA: 309; mild OSA: 223; moderate OSA: 214; severe OSA: 357).

[0084] Table 1 shows the clinical characteristics of the subjects, including the various clinical and physiological characteristics of each patient in the experimental group and the validation group;

[0085] Table 1 Clinical characteristics of the subjects

[0086]

[0087]

[0088] 1. Inclusion criteria:

[0089] 1) Aged 18 or above;

[0090] 2) Obvious snoring;

[0091] 3) Subjects who signed the informed consent.

[0092] 2. Exclusion criteria:

[0093] 1) Previous treatment for obstructive sleep apnea;

[0094] 2) chronic diseases, such as chronic kidney disease, mental illness, or hyperparathyroidism;

[0095] 3) mental disorders or malignant tumors;

[0096] 4) Under 18 years old;

[0097] 5) receiving systemic steroid therapy or hormone replacement therapy;

[0098] 6) missing clinical data;

[0099] 7) Patients with sleep disorders other than OSA, such as upper airway resistance syndrome, restless legs syndrome, or narcolepsy, were also excluded.

[0100] 3. Other comparative indicators of comparative experiments:

[0101] Neck circumference NC, waist circumference WC and hip circumference HC, neck-to-height ratio NHR, waist-to-hip ratio WHR, Epworth Sleepiness Scale ESS, minimum blood oxygen saturation LSPO2 and average blood oxygen saturation MSPO2, awakening reaction index ArI and oxygen saturation index ODI.

[0102] 4. Experimental setup:

[0103] All-night sleep data was recorded by two commonly used standardized PSG systems (Alice-5, Alice-6). Relevant sleep parameters were calculated by those skilled in the art. Apnea was defined as a complete cessation of airflow for at least 10 seconds; hypopnea was defined as a 30% decrease in airflow for more than 10 seconds, or a decrease of less than 30%, but accompanied by a decrease in SPO2 of at least 3%, or a micro-arousal. The apnea-hypopnea index (AHI) refers to the number of apnea and hypopnea events per hour of sleep. None, mild, moderate, and severe OSA were defined as AHI < 5, 5 ≤ AHI < 15, 15 ≤ AHI < 30, and AHI ≥ 30.

[0104] 5. Research Methods

[0105] A model for predicting OSA was established for SASHB and other indicators by logistic regression in the experimental group and validated by drawing receiver operating characteristic (ROC) curves in the validation group.

[0106] 6. Statistical analysis methods:

[0107] All statistical analyses were performed using SPSS version 26.0 or MATLAB. Continuous variables with a normal distribution were expressed as mean ± standard deviation, and skewed data were expressed as median (interquartile range). Categorical variables were expressed as percentages. The Pearson test was used to test the correlation between variables and SASHB. Receiver operating characteristic (ROC) curves were plotted to investigate the accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and positive and negative likelihood ratios of SASHB and other indicators in the diagnosis and severity classification of OSA. A P value < 0.05 indicated statistically significant differences.

[0108] 7. Experimental results:

[0109] Pearson correlation analysis in the validation group showed that SASHB was significantly correlated with anthropometric indicators such as neck circumference NC (r = 0.374, P < 0.001), neck-to-height ratio NHR (r = 0.347, P < 0.001), waist circumference WC (r = 0.386, P < 0.001), hip circumference HC (r = 0.284, P < 0.001), AHI (r = 0.823, P < 0.001), and lowest blood oxygen saturation LSPO2 (r = -0. 636, P < 0.001), mean blood oxygen saturation MSPO2 (r = -0.548, P < 0.001), the proportion of time with arterial oxygen saturation < 90% to total sleep time (r = 0.697, P < 0.001); arousal reactivity index (ArI) (r = 0.304, P < 0.001); oxygen saturation index (ODI) (r = 0.805, P < 0.001); and ESS score (r = 0.212, P < 0.001). The strongest positive correlation was between SASHB and AHI (r = 0.823, P < 0.001).

[0110] like Figure 3 As shown in the figure, the ROC curves of SASHB and other indicators for the diagnosis and severity classification of OSA are shown. The four curves are ROC curves of the prediction accuracy of SASHB and other comparative indicators in the population without OSA, mild OSA, moderate OSA and severe OSA respectively; Table 2 is based on Figure 3 The more specific ROC curve analysis results of each indicator are analyzed;

[0111] Table 2 ROC curve analysis results of single variable prediction of OSA in the validation group

[0112]

[0113]

[0114] from Figure 3As shown in Table 2, when the area under the receiver operating characteristic (ROC) curve for each indicator's optimal cutoff point was compared with that of the SASHB, the SASHB was the most accurate in both the experimental and validation groups. In the experimental group, the SASHB distinguished OSA from non-OSA with a sensitivity / specificity of 89.5% / 87.7% (AUC: 0.948, 95% CI 0.934-0.962). In the validation group (AUC: 0.931, 95% CI 0.913-0.949), the sensitivity / specificity reached 85.7% / 90.1%. We then explored whether the SASHB could predict mild, moderate, and severe OSA. Participants in the validation group were categorized as mild OSA (SASHB = 19.64), moderate OSA (SASHB = 40.96), and severe OSA (SASHB = 69.40). The diagnostic limits of OSA are mild (AUC: 0.878, 0.847-0.908), moderate (AUC: 0.938, 0.912-0.965) and severe (AUC: 0.959, 0.942-0.976).

[0115] 8. Experimental Conclusion

[0116] SASHB had the highest accuracy, followed by sleep indices related to blood oxygenation; anthropometric indices (NC, NHR, WC, HC, and WHR) performed poorly. SASHB distinguished OSA from non-OSA subjects and predicted the severity of OSA. SASHB outperformed the commonly used physical indices of neck circumference NC, waist circumference WC, and hip circumference HC, lowest blood oxygen saturation LSPO2, and mean blood oxygen saturation MSPO2. SASHB was highly correlated with the apnea-hypopnea index and oxygen saturation index, indicating that SASHB has a high clinical value. In particular, the participants in the cohort had a lower degree of obesity {body mass index BMI: 25.6 (23.4-27.8)}, a relatively high mean pulse oxygen saturation MSPO2 {96 (94-96)}, and a low proportion of time with arterial oxygen saturation <90% of the total sleep time {0.56 (0.02-4.50)}. Therefore, SASHB is a more sensitive marker of hypoxemia in non-obese, non-severely desaturated OSA patients than previously used hypoxemia parameters such as ODI and the proportion of time with arterial oxygen saturation <90% to total sleep time (CT90).

[0117] SASHB is an effective method for diagnosing and grading OSA in patients, superior to conventional indicators. If the algorithm for calculating SASHB is embedded in a portable oximeter (Type 3 or Type 4 sleep device), this work will be beneficial for screening suspected OSA patients, as this process can save medical costs and spare patients the inconvenience of testing.

[0118] Example 2

[0119] The present application also provides an electronic device. The electronic device may include one or more processors and one or more memories. The memories may store computer-readable code that, when executed by the one or more processors, may perform the method for diagnosing obstructive sleep apnea-hypopnea syndrome as described above.

[0120] The methods or apparatuses according to the embodiments of this application can also be implemented using the architecture of the electronic device disclosed herein. The electronic device may include a bus, one or more CPUs, ROM, RAM, a communication port connected to a network, input / output components, a hard disk, and the like. A storage device in the electronic device, such as a ROM or hard disk, may store the method for diagnosing obstructive sleep apnea-hypopnea syndrome provided herein.

[0121] Furthermore, the electronic device may further include a user interface. Of course, the architecture disclosed in this application is merely exemplary, and when implementing different devices, one or more components in the electronic device disclosed in this application may be omitted according to actual needs.

[0122] Example 3

[0123] A computer-readable storage medium according to an embodiment of the present application. Computer-readable instructions are stored on the computer-readable storage medium. When the computer-readable instructions are executed by a processor, the method for diagnosing obstructive sleep apnea-hypopnea syndrome according to the embodiment of the present application described above can be executed. The computer-readable storage medium includes, but is not limited to, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and cache memory. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0124] In addition, according to embodiments of the present application, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, the present application provides a non-transitory machine-readable storage medium storing machine-readable instructions capable of being executed by a processor to execute instructions corresponding to the steps of the method provided in the present application. When the computer program is executed by a central processing unit (CPU), the above-mentioned functions defined in the method of the present application are performed.

[0125] The methods, apparatuses, and devices of the present application may be implemented in many ways. For example, the methods, apparatuses, and devices of the present application may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above order of steps for the method is for illustration only, and the steps of the method of the present application are not limited to the order specifically described above unless otherwise specified. In addition, in some embodiments, the present application may also be implemented as programs recorded in a recording medium, which include machine-readable instructions for implementing the methods according to the present application. Therefore, the present application also covers recording media that store programs for executing the methods according to the present application.

[0126] In addition, the parts of the above technical solutions provided in the embodiments of the present application that are consistent with the implementation principles of the corresponding technical solutions in the prior art are not described in detail to avoid excessive redundancy.

[0127] The above-described specific embodiments further illustrate the objectives, technical solutions, and beneficial effects of the present invention. It should be understood that the above description is merely a specific embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

[0128] The above preset parameters or preset thresholds are all set by those skilled in the art according to actual conditions or obtained through large amounts of data simulation.

[0129] The above embodiments are only used to illustrate the technical method of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A method for diagnosing obstructive sleep apnea-hypopnea syndrome, wherein the method is applied to the screening of obstructive sleep apnea-hypopnea syndrome and is performed by an electronic device, characterized in that: The following steps are involved: Step 1: Collect the patient's airflow curve and blood oxygen saturation curve during sleep; Step 2: Traverse and retrieve all respiratory events from the airflow curve; Step 3: Find the corresponding search window from the blood oxygen saturation curve according to the time when each respiratory event occurs. The method of finding the corresponding search window from the blood oxygen saturation curve according to the time when each respiratory event occurs is: Starting from the initial time of each respiratory event, the blood oxygen saturation curve is traversed. The first time node where the blood oxygen saturation decreases is used as the search initial time, and the blood oxygen saturation corresponding to the search initial time is used as the baseline level. Continue traversing the blood oxygen saturation curve from the initial search time until the blood oxygen saturation returns to the baseline level, and use the current time point as the search end time; The closed interval formed by the curve segment of the blood oxygen saturation curve from the search start time to the search end time and the baseline is used as the search window; according to the pre-calibrated response time difference between apnea and blood oxygen saturation drop, the search window is shifted to obtain an updated search window, and the starting point and end point of the updated search window are shifted to the left by the time length of the response time difference; Aligning the updated search window with the time interval of the apnea event in the airflow curve based on the highest point of the curve; Comparing the aligned updated search window with the time interval of the apnea event in the airflow curve, removing the misaligned portions of the updated search window and obtaining a final search window, and replacing the search window with the final search window to re-obtain an index value of sleep apnea-specific hypoxia load; Step 4: Calculate the area of ​​the search window for each apnea event, and add up the areas of all enclosed regions to obtain the area sum; Step 5: Divide the area sum by the total sleep time to obtain the index value of sleep apnea-specific hypoxic load; Step 6: Pre-set indicator thresholds for each symptom level, and obtain the patient's apnea symptom level based on the indicator value and the indicator threshold.

2. The method for diagnosing obstructive sleep apnea-hypopnea syndrome according to claim 1, wherein: The step of traversing and retrieving all respiratory events from the airflow curve comprises the following steps: Step 11: For each second in the airflow curve, the maximum value of the airflow magnitude before that second in the airflow curve is taken as the maximum airflow magnitude of that second; Step 12: Traverse the airflow curve second by second until the airflow is lower than the corresponding maximum airflow. The time point at which the airflow is multiplied by the preset ratio threshold is used as the event start time. Continue traversing after the event start time until the airflow is higher than the corresponding maximum airflow again. The time point at which the airflow is multiplied by the preset ratio threshold is used as the event end time. If the duration from the event start time to the event end time is greater than the preset duration threshold, the time period from the event start time to the event end time is called a respiratory event; otherwise, execute step 13; Step 13: Starting from the event end time of step 12, restart the traversal and repeat step 12 until all respiratory events are found.

3. The method for diagnosing obstructive sleep apnea-hypopnea syndrome according to claim 2, wherein: The baseline is constructed as follows: Draw a horizontal line parallel to the horizontal axis at the position where the vertical axis is the baseline as the baseline.

4. The method for diagnosing obstructive sleep apnea-hypopnea syndrome according to claim 3, wherein: The airflow curve refers to a curve obtained by connecting the real-time airflow volume generated by the patient's breathing in a sleeping state in chronological order. The horizontal axis of the airflow curve is time, and the vertical axis is airflow volume.

5. The method for diagnosing obstructive sleep apnea-hypopnea syndrome according to claim 4, characterized in that: The blood oxygen saturation curve refers to a curve obtained by connecting the real-time blood oxygen saturation of the patient in a sleeping state in chronological order. The horizontal axis of the blood oxygen saturation curve is time, and the vertical axis is blood oxygen saturation.

6. The method for diagnosing obstructive sleep apnea-hypopnea syndrome according to claim 5, characterized in that: The method further comprises: The response time difference between apnea and oxygen desaturation was calculated by comparing the time point when the apnea event started or ended with the time point when the blood oxygen saturation began to decrease or recover.

7. An electronic device, characterized in that: include: A processor and a memory, wherein: The memory stores a computer program that can be called by the processor; The processor executes the method for diagnosing obstructive sleep apnea-hypopnea syndrome according to any one of claims 1 to 6 in the background by calling the computer program stored in the memory.

8. A computer-readable storage medium, characterized in that A rewritable computer program is stored thereon; When the computer program is executed on a computer device, the computer device is caused to execute the method for diagnosing obstructive sleep apnea-hypopnea syndrome according to any one of claims 1 to 6 in the background.

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