Sleep breathing event monitoring method and device, terminal and storage medium

Abstract

The invention relates to the technical field of medical equipment, in particular to a sleep breathing event monitoring method and device, a terminal and a storage medium. The method comprises the following steps: acquiring a first blood oxygen saturation signal when a user actively suffocuses, and calculating an average blood oxygen decline slope according to the first blood oxygen saturation signal; in the sleep monitoring process, physiological signals of the user are continuously collected, and the physiological signals at least comprise a second blood oxygen saturation degree signal and a respiratory effort related signal; the second blood oxygen saturation degree signal is monitored in real time, and when a blood oxygen decline event is detected, the blood oxygen decline slope of the blood oxygen decline event is calculated; the blood oxygen decline rate is compared with the average blood oxygen decline rate, and in combination with the respiratory effort related signal, whether a sleep breathing event occurs is determined. The problems that in the prior art, monitoring equipment is complex, cost is high, comfort and compliance are reduced, and monitoring accuracy is low can be solved.

Description

Technical Field

[0001] This invention relates to the field of medical device technology, and in particular to a method, apparatus, terminal, and storage medium for monitoring sleep breathing events. Background Technology

[0002] Sleep apnea syndrome (SAS) is a common sleep disorder in clinical practice. If it is not effectively intervened in the long term, it may lead to a variety of complications such as cardiovascular disease and cognitive decline, which seriously affect the health and quality of life of patients. Therefore, timely and accurate monitoring and diagnosis are crucial for disease prevention and control.

[0003] Currently, the core technical solutions for sleep apnea monitoring are mainly divided into two categories: one is polysomnography (PSG) monitoring, which is the gold standard for diagnosis and requires patients to wear multiple sensors such as EEG, EMG, EMG, nasal airflow, and chest and abdominal movement in a professional sleep laboratory; the other is home sleep apnea monitoring (HST) devices, which, in order to simplify the process and adapt to home scenarios, usually reduce the types of sensors and retain only monitoring modules that are directly related to breathing, such as blood oxygen and nasal airflow.

[0004] Existing technologies have significant limitations: PSG monitoring procedures are cumbersome, uncomfortable to wear, and costly. Furthermore, the laboratory environment can easily lead to the "first night effect" in patients, affecting diagnostic accuracy. Here, the first night effect refers to the phenomenon that sleep quality may change when a person spends their first night in an unfamiliar environment due to environmental changes; it is a common physiological and psychological reaction. Although HST devices are more portable, their algorithms mostly rely on fixed thresholds based on population statistics to judge respiratory events. However, individual physiological differences are significant, and fixed thresholds can easily lead to overdiagnosis or missed diagnosis, making it difficult to meet the needs of personalized and accurate monitoring. Summary of the Invention

[0005] This invention provides a method, device, terminal, and storage medium for monitoring sleep breathing events, in order to solve the problems of complex monitoring equipment, reduced comfort and compliance, and low monitoring accuracy in the prior art.

[0006] In a first aspect, embodiments of the present invention provide a method for monitoring sleep breathing events, comprising: Acquire the first blood oxygen saturation signal when the user actively holds their breath, and calculate the average blood oxygen saturation decrease slope based on the first blood oxygen saturation signal; During sleep monitoring, the user's physiological signals are continuously collected, including at least: a second blood oxygen saturation signal and a respiratory effort-related signal; The second blood oxygen saturation signal is monitored in real time, and when a blood oxygen decrease event is detected, the blood oxygen decrease slope of the blood oxygen decrease event is calculated; The slope of the decrease in blood oxygen is compared with the average slope of the decrease in blood oxygen, and combined with the respiratory effort-related signals, to determine whether a sleep apnea event has occurred.

[0007] In one possible implementation, acquiring the first blood oxygen saturation signal when the user actively holds their breath, and calculating the average blood oxygen saturation decline slope based on the first blood oxygen saturation signal, includes: Acquire the first blood oxygen saturation signal, start time, and end time of each breath-hold during multiple voluntary breath-holding sessions by the user; Based on the first blood oxygen saturation signal, the start time, and the end time, calculate the duration of each breath-holding and the magnitude of the decrease in blood oxygen. Calculate the average duration and average decrease in blood oxygen based on the duration of each breath-hold and the decrease in blood oxygen level. The average blood oxygen saturation rate is calculated based on the average duration and the average decrease in blood oxygen saturation.

[0008] In one possible implementation, the duration and magnitude of blood oxygen drop for each breath-holding session are calculated based on the first blood oxygen saturation signal, the start time, and the end time, including: Calculate the duration of each breath-holding based on the start time and the end time; The decrease in blood oxygen saturation is calculated based on the first blood oxygen saturation signal corresponding to the start time and the lowest first blood oxygen saturation signal during the corresponding breath-holding period.

[0009] In one possible implementation, the breathing effort-related signal includes: a snoring signal; The slope of the decrease in blood oxygen saturation is compared with the average slope of the decrease in blood oxygen saturation, and combined with the respiratory effort-related signals, to determine whether a sleep apnea event has occurred, including: If the slope of the decrease in blood oxygen is less than the average slope of the decrease in blood oxygen, and / or if the snoring signal is detected during the blood oxygen decrease event, then a hypoventilation event is determined to have occurred; otherwise, an apnea event is determined to have occurred.

[0010] In one possible implementation, the breathing effort-related signals include: snoring signals and / or electromyographic signals; After comparing the slope of the decrease in blood oxygen with the average slope of the decrease in blood oxygen, and combining this with the respiratory effort-related signal to determine whether a sleep apnea event has occurred, the method further includes: If the snoring signal and / or the enhanced electromyographic signal are detected throughout the entire process from the beginning to the end of the blood oxygenation decrease event, then the event is determined to be an obstructive event. If no increase in snoring signal or electromyographic signal is detected during the entire process from the beginning to the end of the blood oxygenation decrease event, the event is determined to be a central event. If, during the period from the beginning to the end of the blood oxygenation decrease event, the snoring signal or the enhanced electromyographic signal is detected for a portion of the time, the event is determined to be a mixed-type event.

[0011] In one possible implementation, the step of detecting a decrease in blood oxygenation includes: When the second blood oxygen saturation signal is detected to begin to decrease from a stable blood oxygen saturation value, and the decrease is greater than or equal to a preset threshold, a blood oxygen decrease event is determined to have been detected.

[0012] In one possible implementation, before calculating the slope of the blood oxygen decrease event when a blood oxygen decrease event is detected, the method further includes: The time corresponding to the start of the decrease in blood oxygen saturation is determined based on the first blood oxygen saturation signal; The difference between the time when the blood oxygen saturation begins to decrease and the time when the user begins to hold their breath each time is used to obtain the blood oxygen saturation decrease delay time. When a blood oxygenation decrease event is detected, the slope of the blood oxygenation decrease event is calculated, including: When a blood oxygen drop event is detected, the blood oxygen drop end time and blood oxygen saturation value corresponding to the last sampling point before the second blood oxygen saturation signal recovers in the blood oxygen drop event are determined. The end time of the blood oxygen decrease event is obtained based on the difference between the end time of the blood oxygen decrease and the delay time of the blood oxygen saturation decrease. The start time of the blood oxygen saturation decrease event is obtained based on the difference between the time corresponding to the stable blood oxygen saturation value and the delay time of the decrease in blood oxygen saturation. according to Calculate the slope of the oxygenation decrease in the aforementioned oxygenation decrease event; in, This represents the slope of the blood oxygen decrease event. This represents the blood oxygen saturation value corresponding to the last sampling point before the second blood oxygen saturation signal recovers. Indicates a stable blood oxygen saturation value. Indicates the end time of the blood oxygenation drop event. This indicates the start time of the event that caused a drop in blood oxygen levels.

[0013] Secondly, embodiments of the present invention provide a device for monitoring sleep breathing events, comprising: The data acquisition module is used to acquire the first blood oxygen saturation signal when the user actively holds their breath, and to calculate the average blood oxygen saturation decline slope based on the first blood oxygen saturation signal. The data acquisition module is also used to continuously collect the user's physiological signals during sleep monitoring, including at least: a second blood oxygen saturation signal and a respiratory effort-related signal; The processing module is used to monitor the second blood oxygen saturation signal in real time, and when a blood oxygen decrease event is detected, calculate the blood oxygen decrease slope of the blood oxygen decrease event; The processing module is further configured to compare the blood oxygen saturation drop slope with the average blood oxygen saturation drop slope, and combine the respiratory effort-related signals to determine whether a sleep apnea event has occurred.

[0014] Thirdly, embodiments of the present invention provide a terminal including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the sleep breathing event monitoring method as described in the first aspect or any possible implementation of the first aspect.

[0015] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the sleep breathing event monitoring method as described in the first aspect or any possible implementation thereof.

[0016] This invention provides a method, device, terminal, and storage medium for monitoring sleep apnea events. The method involves acquiring a first blood oxygen saturation signal when a user actively holds their breath and calculating an average blood oxygen saturation decline slope based on this signal. During sleep monitoring, the method continuously collects the user's physiological signals, including at least a second blood oxygen saturation signal and a respiratory effort-related signal. The second blood oxygen saturation signal is monitored in real time. When a blood oxygen saturation decline event is detected, the blood oxygen decline slope of that event is calculated. The blood oxygen decline slope is compared with the average blood oxygen decline slope, and combined with the respiratory effort-related signal, to determine whether a sleep apnea event has occurred. This invention establishes a unique respiratory event discrimination baseline by calibrating personalized physiological parameters for the user, such as calibrating the average blood oxygen saturation decline slope. Therefore, during subsequent sleep monitoring, only a small number of simple sensors are needed to accurately identify whether a sleep apnea event has occurred, solving the problems of complex monitoring equipment, reduced comfort and compliance, and low monitoring accuracy in existing technologies. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating the implementation of the sleep breathing event monitoring method provided in this embodiment of the invention; Figure 2 This is a schematic diagram of the sleep breathing event monitoring device provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the terminal provided in an embodiment of the present invention. Detailed Implementation

[0019] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of the invention. However, those skilled in the art will understand that the invention can be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods are omitted so as not to obscure the description of the invention with unnecessary detail.

[0020] To make the objectives, technical solutions, and advantages of the present invention clearer, specific embodiments will be described below in conjunction with the accompanying drawings.

[0021] Figure 1 A flowchart illustrating the implementation of a sleep apnea event monitoring method according to an embodiment of the present invention is described in detail below: Step 101: Obtain the first blood oxygen saturation signal when the user actively holds their breath, and calculate the average blood oxygen saturation decline slope based on the first blood oxygen saturation signal.

[0022] In existing technologies for sleep apnea monitoring, PSG or HST is used for signal acquisition. Respiratory events are identified based on fixed thresholds in population statistics (e.g., a drop in blood oxygen ≥3% or 4% lasting for at least 10 seconds is considered a hypoventilation or apnea event). However, there are significant differences in the physiological responses to respiratory events among individuals (e.g., differences in lung volume, circulatory efficiency, basal metabolic rate, and hemoglobin content). Fixed thresholds may lead to overdiagnosis in individuals with sensitive physiological responses or underdiagnosis in individuals with dulled physiological responses, affecting the accuracy of monitoring.

[0023] Therefore, this embodiment provides a sleep breathing event monitoring method based on individualized physiological calibration. This method establishes a unique breathing event discrimination baseline by calibrating the user's personalized physiological parameters before the test, thereby enabling more accurate breathing event identification and classification using simplified sensors in subsequent formal monitoring, and achieving more convenient home sleep breathing monitoring.

[0024] Optionally, during the personalized parameter calibration phase, after the user puts on the device, they can perform 2-3 active breath-holding sessions according to system prompts, such as voice and visual guidance via a mobile app, and use an input unit (such as a handheld button) to mark the start and end times, simultaneously record the first blood oxygen saturation signal, and calculate key individualized parameters.

[0025] Optionally, during this personalized parameter calibration phase, the device worn by the user can verify the stability and validity of each breath-holding data, eliminate invalid data, and ensure the reliability of the collected data.

[0026] In one embodiment, acquiring a first blood oxygen saturation signal when the user actively holds their breath, and calculating the average blood oxygen saturation decline slope based on the first blood oxygen saturation signal, may include: Acquire the first blood oxygen saturation signal, start time, and end time of each breath-hold during multiple voluntary breath-holding sessions by the user; Based on the first blood oxygen saturation signal, start time, and end time, calculate the duration of each breath-holding and the magnitude of blood oxygen drop; Calculate the average duration and average decrease in blood oxygen based on the duration of each breath-hold and the magnitude of the decrease in blood oxygen. The average blood oxygen saturation rate is calculated based on the average duration and the average rate of decrease in blood oxygen saturation.

[0027] Optionally, the start and end times of each breath-holding session initiated by the user can be received and marked as follows: , .

[0028] It should be noted that the "first" in the first blood oxygen saturation signal here is set to distinguish it from the blood oxygen saturation signal collected during actual sleep monitoring, and is not for sorting or any other purpose.

[0029] Optionally, the slope of the average blood oxygen decrease can be calculated based on the average duration and the average decrease in blood oxygen, which may include: The quotient of the average decrease in blood oxygen saturation divided by the average duration is used as the slope of the average decrease in blood oxygen saturation.

[0030] Optionally, based on the first blood oxygen saturation signal, start time, and end time, calculate the duration of each breath-holding and the magnitude of blood oxygen decrease, including: Calculate the duration of each breath-holding session based on the start and end times; The decrease in blood oxygen saturation is calculated based on the first blood oxygen saturation signal corresponding to the start time and the lowest first blood oxygen saturation signal during the corresponding breath-holding period.

[0031] Optionally, the time corresponding to the start of the decline in blood oxygen saturation can also be determined based on the first blood oxygen saturation signal. For example, in the first blood oxygen saturation signal curve, find the inflection point where the slope changes from positive to negative or the starting point of the continuous decline, and take the time corresponding to this inflection point or starting point as the time corresponding to the start of the decline in blood oxygen saturation. Calculate the difference between this time and the start time of each time the user holds their breath, and take this difference as the delay time of the decline in blood oxygen saturation.

[0032] In one embodiment, based on the aforementioned active breath-holding physiological parameter calibration method, an automated physiological parameter calibration strategy that does not require manual intervention by the user is set up. Standardized breath-holding physiological parameters are calibrated using a voice and visual synchronous guidance method built into the user-worn device. For example, a voice prompt might say: "Maintain natural breathing for 2 minutes, hold your breath for 15 seconds." The user follows the voice prompt to hold their breath. Simultaneously, an AI-assisted marking function is added. By analyzing the abrupt changes in the first blood oxygen saturation signal and the activity state of electromyography signals in real time, the start and end times of breath-holding are automatically identified, replacing manual marking, reducing operational errors, and improving calibration accuracy.

[0033] The abrupt change point of the first blood oxygen saturation signal can be identified based on the physiological characteristics that blood oxygen is stable at the beginning of breath-holding and stops decreasing and begins to rise at the end.

[0034] Optionally, passive physiological load calibration can also be performed. Combining active physiological parameter calibration with passive physiological load calibration can form more comprehensive individualized baseline parameters.

[0035] Passive physiological load calibration: With the user in a sleeping position, the nostrils are completely blocked with a nasal plug or other device to simulate upper respiratory tract obstruction. The start and end times of nasal obstruction, the time of decrease in blood oxygen saturation, and the magnitude of the decrease in blood oxygen saturation are then recorded. Based on the recorded data, the passively calibrated blood oxygen saturation decrease slope is calculated.

[0036] Based on the obtained actively calibrated average blood oxygen saturation decline slope and passively calibrated blood oxygen saturation decline slope, and combined with the user's age, BMI, baseline blood oxygen level (e.g., average blood oxygen saturation level after 5 minutes of rest before sleep) and estimated hemoglobin concentration (derived from the infrared / red light absorption ratio of blood oxygen signal), a comprehensive average blood oxygen saturation decline slope is calculated. This allows the baseline to simultaneously adapt to the physiological limits of active breath-holding and passive pathological states, thus solving the problem of inaccurate thresholds caused by individual differences.

[0037] Optionally, when calculating the overall average blood oxygen saturation decline slope, a composite benchmark model can be constructed. For example, a lightweight machine learning algorithm can be used to train and generate a personalized comprehensive discrimination baseline. The model parameters can be optimized through cross-validation to obtain the target composite benchmark model. The actively calibrated average blood oxygen saturation decline slope and the passively calibrated blood oxygen saturation decline slope are input, and the overall average blood oxygen saturation decline slope is output by combining the user's age, BMI, baseline blood oxygen level and hemoglobin concentration estimates.

[0038] Step 102: During sleep monitoring, continuously collect the user's physiological signals, which include at least: second blood oxygen saturation signal and respiratory effort related signal.

[0039] It should be noted that the "second" in the second blood oxygen saturation signal here is set to distinguish it from the blood oxygen saturation signal collected in the personalized parameter calibration stage in step 101, and is not for sorting or other purposes.

[0040] Breathing effort-related signals are commonly found in the medical field, especially in respiratory physiology and sleep apnea research. They refer to physiological signals generated in the upper airway (such as the pharynx) during breathing that are related to respiratory muscle contraction / breathing drive, or physical signals that reflect respiratory movements and are often used to assess the degree of breathing effort.

[0041] In this embodiment, respiratory effort-related signals include: nasal airflow signals, snoring signals and / or electromyographic signals, chest and abdominal movements, chest and abdominal cavity contours, and changes in volume.

[0042] During sleep monitoring, the system continuously collects the user's physiological signals throughout the night.

[0043] Optionally, the device for acquiring blood oxygen saturation signals can be a finger clip or wrist probe; nasal airflow signals can be acquired using a nasal airflow sensor, which can be a thermal or pressure sensor; snoring signals can be acquired using a snoring sensor, which can be a microphone; and electromyography (EMG) signals can be acquired using an EMG sensor. The EMG sensor acquires activity signals of respiratory-related muscles through electrodes, which may include: mandibular EMG, sternocleidomastoid muscle, diaphragm, external intercostal muscles, internal intercostal muscles, abdominal muscles, etc. Sensors that directly detect chest and abdominal respiratory movements may include: chest and abdominal motion sensors (such as accelerometers) and non-contact detectors (such as radar or nighttime infrared motion detectors).

[0044] Step 103: Monitor the second blood oxygen saturation signal in real time. When a blood oxygen decrease event is detected, calculate the blood oxygen decrease slope of the blood oxygen decrease event.

[0045] Based on the second blood oxygen saturation signal continuously collected in step 102, real-time analysis is performed to promptly detect events of decreased blood oxygenation.

[0046] In one embodiment, the step of detecting a decrease in blood oxygenation may include: When a second blood oxygen saturation signal is detected to begin decreasing from a stable blood oxygen saturation value, and the decrease is greater than or equal to a preset threshold, a blood oxygen decrease event is determined to have been detected.

[0047] Optionally, the preset threshold can be set based on experience, such as 3% or 4%. In this embodiment, the value of the preset threshold is not limited. That is, when the second blood oxygen saturation signal is detected to decrease from a stable blood oxygen saturation value, and the decrease is greater than or equal to 3%, a blood oxygen decrease event is determined to have been detected, and the blood oxygen decrease event is tracked until the blood oxygen saturation begins to recover. Then, a valid blood oxygen decrease event is recorded, and the type of sleep apnea event can be determined based on the valid blood oxygen decrease event.

[0048] In one embodiment, when a blood oxygenation decrease event is detected, calculating the slope of the blood oxygenation decrease event may include: When a blood oxygenation drop event is detected, determine the end time of the blood oxygenation drop and the blood oxygen saturation value corresponding to the last sampling point before the second blood oxygen saturation signal recovers in the blood oxygenation drop event; The end time of the blood oxygen decline event is obtained based on the difference between the end time of blood oxygen decline and the delay time of blood oxygen saturation decline. The start time of the blood oxygen saturation drop event is obtained by the difference between the time corresponding to the stable blood oxygen saturation value and the delay time of the blood oxygen saturation drop. according to Calculate the slope of blood oxygenation decrease during blood oxygenation events; in, The slope of the decrease in blood oxygenation indicates the event of decreased blood oxygenation. This represents the oxygen saturation value at the last sampling point before the second oxygen saturation signal recovers. Indicates a stable blood oxygen saturation value. Indicates the end time of the blood oxygenation drop event. This indicates the start time of the event that caused a drop in blood oxygen levels.

[0049] Optionally, during the real-time monitoring of the second blood oxygen saturation signal, the quality of the second blood oxygen saturation signal can also be assessed to determine whether the signal is affected by motion artifacts.

[0050] Signal quality assessment can employ methods based on multi-feature fusion and support vector machines. Once motion artifact interference is identified, it can be addressed through filtering and other methods. Details are as follows: Signal quality assessment method: First, design relevant experiments to obtain signal data under different conditions, and have experts label the signal quality levels as "good," "medium," and "poor." Then, extract features that reflect interference information, such as the time-domain and frequency-domain features of the signal. Use a grid search method to optimize the parameters of the support vector machine classification model, and use the features and their combinations as input parameters to classify the signal quality through the support vector machine classifier.

[0051] The handling method for signals affected by motion artifacts: If the signal quality level is "poor", the signal can be directly removed; if the signal quality level is "medium", motion artifacts (artifacts: abnormal data caused by signal interference) can be suppressed based on generalized combined morphological filtering to obtain more accurate physiological parameters such as blood oxygen saturation.

[0052] Step 104: Compare the slope of blood oxygen saturation decrease with the average slope of blood oxygen saturation decrease, and combine this with respiratory effort-related signals to determine whether a sleep apnea event has occurred.

[0053] In one embodiment, the distinction between apnea and hypoventilation is as follows: Comparing the slope of the oxygen saturation drop with the mean slope of the oxygen saturation drop, and combining this with respiratory effort-related signals, can help determine whether a sleep apnea event has occurred. This can include: If the rate of decrease in blood oxygen saturation is less than the average rate of decrease in blood oxygen saturation, and / or if snoring is detected during a blood oxygen saturation decrease event, a hypoventilation event is determined to have occurred; otherwise, an apnea event is determined to have occurred.

[0054] For example, in a sleep apnea event, the slope of the decrease in blood oxygen is greater than or equal to the average slope of the decrease in blood oxygen.

[0055] In one embodiment, nasal airflow signals can also be used to assist in the judgment. For example, when the amplitude of the nasal airflow signal decreases by more than 90%, the criterion of sleep apnea can be strengthened.

[0056] In one embodiment, the specific type of the event can be further determined: If snoring and / or increased electromyographic signals are detected throughout the entire process from the beginning to the end of a blood oxygenation decrease event, the event is determined to be an obstructive event. If no snoring or increased electromyographic signals are detected from the beginning to the end of a blood oxygenation decrease event, the event is determined to be a central event. If snoring or increased electromyographic signals are detected for a portion of the time during the period from the beginning to the end of a blood oxygenation decrease event, the event is determined to be a mixed event.

[0057] It should be noted that when determining central nervous system events, snoring signals and electromyographic signals may not be detected. Instead, respiratory movements may be detected, such as using chest and abdominal motion sensors, radar, or nighttime infrared motion detectors that can directly detect respiratory movements of the chest and abdomen.

[0058] This invention provides a method for monitoring sleep apnea events. The method involves acquiring a first blood oxygen saturation signal when the user actively holds their breath and calculating the average blood oxygen saturation decline slope based on this signal. During sleep monitoring, the method continuously collects the user's physiological signals, including at least a second blood oxygen saturation signal and respiratory effort-related signals. The second blood oxygen saturation signal is monitored in real time. When a blood oxygen saturation decline event is detected, the blood oxygen decline slope of the event is calculated. The blood oxygen decline slope is compared with the average blood oxygen decline slope, and combined with the respiratory effort-related signals, to determine whether a sleep apnea event has occurred. In this invention, by pre-calibrating personalized bioparameters, a unique physiological response baseline is established for each user, such as a determined average blood oxygen saturation decline slope. This makes subsequent event identification more closely reflect the user's actual physiological state, reducing misjudgments and missed judgments due to individual differences and improving the clinical accuracy of the monitoring results.

[0059] In this embodiment of the invention, only measurements from a few easily wearable sensors, such as blood oxygen saturation signals, nasal airflow, and snoring / electromyography, are required to determine sleep apnea events, greatly improving user comfort and enhancing the user experience during monitoring. Furthermore, while maintaining accuracy, it reduces the requirements for hardware complexity, facilitating wider adoption and wider application.

[0060] In this embodiment of the invention, the morphological characteristics (slope) of blood oxygen decline and the upper airway effort signal (snoring / electromyography) are cleverly combined to distinguish between apnea and hypoventilation, and further to automatically classify obstructive, central and mixed events, providing more clinically valuable diagnostic information.

[0061] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0062] The following are device embodiments of the present invention. For details not described in detail, please refer to the corresponding method embodiments described above.

[0063] Figure 2 A schematic diagram of a sleep breathing event monitoring device according to an embodiment of the present invention is shown. For ease of explanation, only the parts related to the embodiment of the present invention are shown, and are described in detail below: like Figure 2As shown, the sleep breathing event monitoring device includes a data acquisition module 21 and a processing module 22.

[0064] The data acquisition module 21 is used to acquire the first blood oxygen saturation signal when the user actively holds his breath, and to calculate the average blood oxygen saturation decline slope based on the first blood oxygen saturation signal. The data acquisition module 21 is also used to continuously collect the user's physiological signals during sleep monitoring. The physiological signals include at least: second blood oxygen saturation signal and respiratory effort related signal. Processing module 22 is used to monitor the second blood oxygen saturation signal in real time, and when a blood oxygen decrease event is detected, calculate the blood oxygen decrease slope of the blood oxygen decrease event; The processing module 22 is also used to compare the slope of blood oxygen saturation decrease with the average slope of blood oxygen saturation decrease, and combine it with respiratory effort-related signals to determine whether a sleep apnea event has occurred.

[0065] In one possible implementation, the data acquisition module 21 acquires the first blood oxygen saturation signal when the user actively holds their breath, and when calculating the average blood oxygen saturation decline slope based on the first blood oxygen saturation signal, it is used for: Acquire the first blood oxygen saturation signal, start time, and end time of each breath-hold during multiple voluntary breath-holding sessions by the user; Based on the first blood oxygen saturation signal, start time, and end time, calculate the duration of each breath-holding and the magnitude of blood oxygen drop; Calculate the average duration and average decrease in blood oxygen based on the duration of each breath-hold and the magnitude of the decrease in blood oxygen. The average blood oxygen saturation rate is calculated based on the average duration and the average rate of decrease in blood oxygen saturation.

[0066] In one possible implementation, when the data acquisition module 21 calculates the duration of each breath-holding and the magnitude of the decrease in blood oxygen based on the first blood oxygen saturation signal, the start time, and the end time, it is used for: Calculate the duration of each breath-holding session based on the start and end times; The decrease in blood oxygen saturation is calculated based on the first blood oxygen saturation signal corresponding to the start time and the lowest first blood oxygen saturation signal during the corresponding breath-holding period.

[0067] In one possible implementation, breathing effort-related signals include: snoring signals; Processing module 22 compares the slope of the decrease in blood oxygen saturation with the average slope of the decrease in blood oxygen saturation, and combines this with respiratory effort-related signals to determine whether a sleep apnea event has occurred. This is used for: If the rate of decrease in blood oxygen saturation is less than the average rate of decrease in blood oxygen saturation, and / or if snoring is detected during a blood oxygen saturation decrease event, a hypoventilation event is determined to have occurred; otherwise, an apnea event is determined to have occurred.

[0068] In one possible implementation, the breathing effort-related signals include: snoring signals and / or electromyographic signals; Processing module 22 is also used for: If snoring and / or increased electromyographic signals are detected throughout the entire process from the beginning to the end of a blood oxygenation decrease event, the event is determined to be an obstructive event. If no snoring or increased electromyographic signals are detected from the beginning to the end of a blood oxygenation decrease event, the event is determined to be a central event. If snoring or increased electromyographic signals are detected for a portion of the time during the period from the beginning to the end of a blood oxygenation decrease event, the event is determined to be a mixed event.

[0069] In one possible implementation, when processing module 22 detects a blood oxygenation decrease event, it is used to: When a second blood oxygen saturation signal is detected to begin decreasing from a stable blood oxygen saturation value, and the decrease is greater than or equal to a preset threshold, a blood oxygen decrease event is determined to have been detected.

[0070] In one possible implementation, before calculating the slope of the blood oxygen decrease event when processing module 22 detects a blood oxygen decrease event, it is also used for: Determine the time at which blood oxygen saturation begins to decrease based on the first blood oxygen saturation signal; The difference between the time when blood oxygen saturation begins to decrease and the time when the user begins to hold their breath each time is used to obtain the blood oxygen saturation decrease delay time. When a blood oxygen saturation drop event is detected, the processing module 22 calculates the blood oxygen saturation drop slope for the event, and uses it for: When a blood oxygenation drop event is detected, determine the end time of the blood oxygenation drop and the blood oxygen saturation value corresponding to the last sampling point before the second blood oxygen saturation signal recovers in the blood oxygenation drop event; The end time of the blood oxygen decline event is obtained based on the difference between the end time of blood oxygen decline and the delay time of blood oxygen saturation decline. The start time of the blood oxygen saturation drop event is obtained by the difference between the time corresponding to the stable blood oxygen saturation value and the delay time of the blood oxygen saturation drop. according to Calculate the slope of blood oxygenation decrease during blood oxygenation events; in, The slope of the decrease in blood oxygenation indicates the event of decreased blood oxygenation. This represents the oxygen saturation value at the last sampling point before the second oxygen saturation signal recovers. Indicates a stable blood oxygen saturation value. Indicates the end time of the blood oxygenation drop event. This indicates the start time of the event that caused a drop in blood oxygen levels.

[0071] The above embodiments provide a device for monitoring sleep apnea events. A data acquisition module acquires a first blood oxygen saturation signal when the user actively holds their breath and calculates the average blood oxygen saturation decline slope based on this signal. During sleep monitoring, the data acquisition module continuously collects the user's physiological signals, including at least a second blood oxygen saturation signal and respiratory effort-related signals. A processing module monitors the second blood oxygen saturation signal in real time. When a blood oxygen saturation decline event is detected, the processing module calculates the blood oxygen saturation decline slope of the event. The processing module compares the blood oxygen saturation decline slope with the average blood oxygen saturation decline slope and, in conjunction with the respiratory effort-related signals, determines whether a sleep apnea event has occurred. In this embodiment, through pre-emptive personalized bioparameter calibration, a unique physiological response baseline is established for each user, i.e., a determined average blood oxygen saturation decline slope. This makes subsequent event identification more closely match the user's true physiological state, reducing misjudgments and missed judgments due to individual differences and improving the clinical accuracy of the monitoring results.

[0072] In this embodiment of the invention, only measurements from a few easily worn sensors, such as blood oxygen saturation signals, nasal airflow, and snoring / electromyography, are required to determine sleep breathing events, greatly improving device comfort and user compliance, making it ideal for home sleep monitoring scenarios. Furthermore, while maintaining accuracy, it reduces the requirements for hardware complexity, facilitating widespread adoption.

[0073] In this embodiment of the invention, the morphological characteristics (slope) of blood oxygen decline and the upper airway effort signal (snoring / electromyography) are cleverly combined to distinguish between apnea and hypoventilation, and further to automatically classify obstructive, central and mixed events, providing more clinically valuable diagnostic information.

[0074] Figure 3 This is a schematic diagram of a terminal provided in an embodiment of the present invention. The terminal can be a device that is conveniently worn by the user. Figure 3 As shown, the terminal 3 in this embodiment includes: a processor 30, a memory 31, and a computer program 32 stored in the memory 31 and executable on the processor 30. When the processor 30 executes the computer program 32, it implements the steps in the various sleep breathing event monitoring method embodiments described above, for example... Figure 1 Steps 101 to 104 are shown. Alternatively, when processor 30 executes computer program 32, it implements the functions of each module / unit in the above-described device embodiments, for example... Figure 2 The functions of each module / unit are shown.

[0075] For example, computer program 32 can be divided into one or more modules / units, one or more of which are stored in memory 31 and executed by processor 30 to complete the present invention. One or more modules / units can be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of computer program 32 in terminal 3. For example, computer program 32 can be divided into... Figure 2 The modules / units shown are shown.

[0076] Terminal 3 may include, but is not limited to, processor 30 and memory 31. Those skilled in the art will understand that... Figure 3 This is merely an example of terminal 3 and does not constitute a limitation on terminal 3. It may include more or fewer components than shown, or combine certain components, or different components. For example, the terminal may also include input / output devices, network access devices, buses, etc.

[0077] The processor 30 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0078] The memory 31 can be an internal storage unit of the terminal 3, such as a hard disk or RAM of the terminal 3. The memory 31 can also be an external storage device of the terminal 3, such as a plug-in hard disk, Smart MediaCard (SMC), Secure Digital (SD) card, or Flash Card equipped on the terminal 3. Furthermore, the memory 31 can include both internal and external storage units of the terminal 3. The memory 31 is used to store computer programs and other programs and data required by the terminal. The memory 31 can also be used to temporarily store data that has been output or will be output.

[0079] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0080] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0081] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0082] In the embodiments provided by this invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0083] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0084] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0085] If integrated modules / units are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various sleep breathing event monitoring method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0086] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method of sleep disordered breathing event monitoring, the method comprising: The method comprises the following steps: acquiring a first blood oxygen saturation signal when a user actively holds breath, and calculating an average blood oxygen descent slope according to the first blood oxygen saturation signal; acquiring a physiological signal of the user during sleep monitoring, the physiological signal comprising at least a second blood oxygen saturation signal and a respiration effort related signal; monitoring the second blood oxygen saturation signal in real time, and calculating a blood oxygen descent slope of a blood oxygen descent event when the blood oxygen descent event is detected; comparing the blood oxygen descent slope with the average blood oxygen descent slope, and determining whether a sleep respiration event occurs in combination with the respiration effort related signal.

2. The method of sleep disordered breathing event monitoring of claim 1, wherein, The method comprises the following steps: acquiring a first blood oxygen saturation signal when a user actively holds breath, and calculating an average blood oxygen descent slope according to the first blood oxygen saturation signal; acquiring a first blood oxygen saturation signal, a start time and an end time of each time of active breath holding of the user; calculating a duration and a blood oxygen descent amplitude of each time of breath holding according to the first blood oxygen saturation signal, the start time and the end time; calculating an average duration and an average blood oxygen descent amplitude according to the duration and the blood oxygen descent amplitude of each time of breath holding; 3. The method of sleep disordered breathing event monitoring of claim 2, wherein, calculating an average blood oxygen descent slope according to the average duration and the average blood oxygen descent amplitude. The method comprises the following steps: acquiring a first blood oxygen saturation signal, a start time and an end time of each time of active breath holding of the user; 4. The method of sleep disordered breathing event monitoring according to any one of claims 1-3, wherein, calculating a duration of each time of breath holding according to the start time and the end time; calculating a blood oxygen descent amplitude according to the first blood oxygen saturation signal corresponding to the start time and the lowest first blood oxygen saturation signal during the corresponding breath holding. The respiration effort related signal comprises a snoring sound signal; 5. The method of sleep disordered breathing event monitoring according to any one of claims 1-3, wherein, The method comprises the following steps: if the blood oxygen descent slope is smaller than the average blood oxygen descent slope, and / or the snoring sound signal is monitored during the blood oxygen descent event, a low ventilation event is determined to occur, otherwise a respiratory pause event is determined to occur. The respiration effort related signal comprises a snoring sound signal and / or an electromyography signal; The method comprises the following steps: if the snoring sound signal and / or the electromyography signal are enhanced during the start to the end of the blood oxygen descent event, the event is determined to be an obstructive event; 6. The method of sleep disordered breathing event monitoring according to any one of claims 1-3, wherein, if the snoring sound signal and the electromyography signal are not enhanced during the start to the end of the blood oxygen descent event, the event is determined to be a central event; if the snoring sound signal or the electromyography signal are enhanced during part of the start to the end of the blood oxygen descent event, the event is determined to be a mixed event. The step of detecting a blood oxygen descent event comprises the following steps: when it is detected that the second blood oxygen saturation signal starts to descend from a stable blood oxygen saturation value, and the descent amplitude is greater than or equal to a preset threshold, it is determined that a blood oxygen descent event is detected.

7. The method of sleep disordered breathing event monitoring of claim 6, wherein, Before the blood oxygen descent slope of the blood oxygen descent event is calculated when the blood oxygen descent event is detected, the method further comprises: determining a time corresponding to the start of the decrease in blood oxygen saturation according to the first blood oxygen saturation signal; obtaining a blood oxygen saturation descent delay time by subtracting the time corresponding to the start of the decrease in blood oxygen saturation from the start time of each breath-holding of the user; When the blood oxygen descent event is detected, the blood oxygen descent slope of the blood oxygen descent event is calculated, comprising: When the blood oxygen descent event is detected, determining a blood oxygen descent end time and a blood oxygen saturation value corresponding to the last sampling point before the second blood oxygen saturation signal rises in the blood oxygen descent event; obtaining an end time of the blood oxygen descent event based on the difference between the blood oxygen descent end time and the blood oxygen saturation descent delay time; obtaining a start time of the blood oxygen descent event based on the difference between the time corresponding to the stable blood oxygen saturation value and the blood oxygen saturation descent delay time; According to computing an oxygen desaturation slope for the oxygen desaturation event; wherein, represents a blood oxygen decrease slope of the blood oxygen decrease event, represents a blood oxygen saturation value corresponding to a last sampling point before the second blood oxygen saturation signal rebounds, represents a stable blood oxygen saturation value, represents an end time of the blood oxygen decrease event, represents a start time of the blood oxygen decrease event.

8. An apparatus for sleep disordered breathing event monitoring, the apparatus comprising: comprising: a data acquisition module configured to acquire a first blood oxygen saturation signal when the user actively holds breath, and calculate an average blood oxygen descent slope based on the first blood oxygen saturation signal; The data acquisition module is further configured to continuously acquire physiological signals of the user during sleep monitoring, wherein the physiological signals at least include a second blood oxygen saturation signal and a respiration effort related signal; a processing module configured to monitor the second blood oxygen saturation signal in real time, and calculate a blood oxygen descent slope of a blood oxygen descent event when the blood oxygen descent event is detected; The processing module is further configured to compare the blood oxygen descent slope with the average blood oxygen descent slope, and determine whether a sleep respiratory event occurs in combination with the respiration effort related signal. 9.A terminal, comprising a memory for storing a computer program and a processor for invoking and running the computer program stored in the memory, characterized in that, The processor executes the computer program to realize the steps of the method for monitoring a sleep respiratory event according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1-9. The computer program is executed by the processor to realize the steps of the method for monitoring a sleep respiratory event according to any one of claims 1 to 7.

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