CN111657948B

CN111657948B - Sleep breathing state detection method, device and equipment

Info

Publication number: CN111657948B
Application number: CN202010447273.6A
Authority: CN
Inventors: 罗强
Original assignee: Shenzhen Vvfly Electronics Co ltd
Current assignee: Shenzhen Vvfly Electronics Co ltd
Priority date: 2020-05-25
Filing date: 2020-05-25
Publication date: 2024-04-05
Anticipated expiration: 2040-05-25
Also published as: CN111657948A

Abstract

The application is applicable to the technical field of medical treatment, and provides a sleep breathing state detection method, which comprises the following steps: monitoring throat vibration signals of an object to be detected; determining a respiratory motion track curve according to the throat vibration signal; and determining the sleep breathing state of the object to be detected based on the breathing motion track curve and a preset detection rule. According to the scheme, the sleep breathing state of the object to be detected can be determined by monitoring vibration data of the upper respiratory airway and the throat of the human body during sleep, so that the obstructive sleep apnea and hypopnea syndrome OSAHS breathing event can be detected in real time. The medical cost of detection is lower, is applicable to the detection at home, and the interference factor is reduced in the normal sleep state of the object to be detected in the detection process.

Description

Sleep breathing state detection method, device and equipment

Technical Field

The application belongs to the technical field of medical treatment, and particularly relates to a sleep breathing state detection method, device and equipment.

Background

With the increasing pace of modern life and work, dietary changes and natural age increase, obstructive sleep apnea and hypopnea syndrome (Obstructive Sleep Apnea and Hypopnea Syndrome, OSAHS) are becoming more common in the adult population. The detection of the sleeping respiratory status can be used for detecting whether the OSAHS exists, the traditional detection method of the OSAHS is to detect Polysomnography (PSG) in a specific laboratory or a sleeping medical center of a hospital, but the PSG needs professional medical staff to operate, a plurality of interference factors exist when a detected object is in a strange sleeping environment, the detected medical cost is high, and the universality of the use of the public is low. .

Disclosure of Invention

The embodiment of the application provides a method, a device and equipment for detecting sleep breathing state, which can solve the problems of higher medical cost and lower universality of public use.

In a first aspect, an embodiment of the present application provides a method for detecting a sleep respiratory state, including:

monitoring throat vibration signals of an object to be detected;

determining a respiratory motion track curve according to the throat vibration signal;

and determining the sleep breathing state of the object to be detected based on the breathing motion track curve and a preset detection rule.

Further, the determining the sleep respiratory state of the object to be detected based on the respiratory motion track curve and a preset detection rule includes:

acquiring sound wave data of the object to be detected, and determining snore event identification according to the sound wave data;

if the fluctuation amplitude of the respiratory motion track curve is smaller than a first threshold and larger than a second threshold within a preset time period, and the snore event mark is a target mark, judging that the sleep respiratory state of the object to be detected is a first state, wherein the first state represents that an obstructive sleep respiratory hypopnea event occurs.

and if the fluctuation amplitude of the respiratory motion track curve is smaller than a second threshold value within a preset time period, judging that the sleep respiratory state of the object to be detected is a second state, wherein the second state represents an obstructive sleep apnea event.

if the fluctuation amplitude of the respiratory motion track curve is larger than a first threshold value in a preset time period, judging that the sleep respiratory state of the object to be detected is a third state, wherein the third state indicates that no obstructive sleep apnea and low ventilation event occurs.

Further, the determining a respiratory motion trajectory curve according to the throat vibration signal includes:

filtering the throat vibration signal to obtain a target throat vibration signal;

and generating a respiratory motion track curve according to the target throat vibration signal.

Further, the throat vibration signal comprises a Z-axis throat vibration signal acquired by a triaxial acceleration sensor.

Further, the method further comprises the following steps:

the electrical stimulation module is controlled to perform an electrical stimulation operation.

In a second aspect, an embodiment of the present application provides a device for detecting a sleep breathing state, including:

the monitoring unit is used for monitoring throat vibration signals of the object to be detected;

a first determining unit for determining a respiratory motion trajectory curve according to the throat vibration signal;

and the second determining unit is used for determining the sleep breathing state of the object to be detected based on the breathing motion track curve and a preset detection rule.

Further, the second determining unit is specifically configured to:

and if the fluctuation amplitude of the respiratory motion track curve is smaller than a second threshold value within a preset time period, judging that the sleep respiratory state of the object to be detected is a second state, wherein the second state represents that an obstructive sleep apnea event occurs.

Further, the second determining unit is specifically configured to:

Further, the first determining unit is specifically configured to:

Further, the sleep breathing state detection device further includes:

and the control unit is used for controlling the electric stimulation module to execute electric stimulation operation.

In a third aspect, an embodiment of the present application provides a sleep-breathing state detection device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the sleep-breathing state detection method according to the first aspect when executing the computer program.

In a fourth aspect, embodiments of the present application provide a computer readable storage medium storing a computer program, where the computer program is executed by a processor to implement the method for detecting a sleep apnea state according to the first aspect.

In the embodiment of the application, the throat vibration signal of the object to be detected is monitored; determining a respiratory motion track curve according to the throat vibration signal; and determining the sleep breathing state of the object to be detected based on the breathing motion track curve and a preset detection rule. According to the scheme, the sleep breathing state of the object to be detected can be determined by monitoring the vibration data of the upper respiratory airway and the throat of the human body during sleep, so that the OSAHS breathing event can be detected in real time. The medical cost of detection is lower, is applicable to at home detection, has promoted the universality that masses used to the testing process is under waiting to detect the normal sleep state of object, has reduced interference factor.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required for the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

Fig. 1 is a schematic flow chart of a method for detecting sleep breathing state according to a first embodiment of the present application;

fig. 2 is a schematic flowchart of refinement of S102 in a method for detecting sleep breathing state according to a first embodiment of the present application;

fig. 3 is a schematic flowchart of refinement of S103 in a method for detecting sleep breathing state according to the first embodiment of the present application;

fig. 4 is a schematic flow chart of a method for detecting sleep breathing state according to a first embodiment of the present application;

fig. 5 is a schematic diagram of a sleep breathing state detection device according to a second embodiment of the present application;

fig. 6 is a schematic diagram of a sleep breathing state detection apparatus according to a third embodiment of the present application.

Detailed Description

In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system configurations, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.

It should be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

It should also be understood that the term "and/or" as used in this specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.

As used in this specification and the appended claims, the term "if" may be interpreted as "when..once" or "in response to a determination" or "in response to detection" depending on the context. Similarly, the phrase "if a determination" or "if a [ described condition or event ] is detected" may be interpreted in the context of meaning "upon determination" or "in response to determination" or "upon detection of a [ described condition or event ]" or "in response to detection of a [ described condition or event ]".

In addition, in the description of the present application and the appended claims, the terms "first," "second," "third," and the like are used merely to distinguish between descriptions and are not to be construed as indicating or implying relative importance.

Reference in the specification to "one embodiment" or "some embodiments" or the like means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," and the like in the specification are not necessarily all referring to the same embodiment, but mean "one or more but not all embodiments" unless expressly specified otherwise. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless expressly specified otherwise.

Referring to fig. 1, fig. 1 is a schematic flowchart of a method for detecting sleep breathing status according to a first embodiment of the present application. An execution subject of a sleep-breathing state detection method in this embodiment is a device having a sleep-breathing state detection function. The method for detecting sleep breathing state as shown in fig. 1 may include:

s101: monitoring a throat vibration signal of a subject to be detected.

Obstructive sleep apnea and hypopnea syndrome (Obstructive Sleep Apnea and Hypopnea Syndrome, OSAHS) is a condition in which upper respiratory airways are repeatedly blocked during sleep, resulting in hypopnea and thus apnea, often accompanied by hypoxia, decreased blood oxygen saturation, increased heart rate, snoring, daytime sleepiness, and the like. Thus, it is possible to detect whether OSAHS has occurred by monitoring sleep respiratory conditions.

In this embodiment, the device with the function of detecting the sleep breathing state includes a data acquisition module, where the data acquisition module includes an acceleration sensor, and the acceleration sensor is used to acquire a throat vibration signal of the subject to be detected. The acceleration sensor can be positioned on a firmware connected with the device, and the firmware is stuck on the inner side of the mandibular angle of the neck or the superficial part of the hypoglossal nerve when in use. The firmware can be a patch electrode, and the patch electrode can be connected with the equipment through a magnetic electrode button when in use.

The device can detect throat vibration signals of the object to be detected through the data acquisition module, wherein the throat vibration signals of the object to be detected are muscle tissue vibration signals at the throat of the upper airway of the object to be detected.

S102: and determining a respiratory motion track curve according to the throat vibration signal.

The device determines a respiratory motion trajectory profile from the throat vibration signal. Wherein the respiratory motion track curve identifies the change condition of the respiratory state of the object to be detected in the sleep state. The equipment can process the vibration signal of the throat part to obtain a respiratory motion track curve.

Further, in order to accurately acquire the respiratory motion track curve and thus detect the sleep respiratory state of the object to be detected, S102 may include S1021 to S1022, as shown in fig. 2, S1021 to S1022 are specifically as follows:

s1021: and filtering the throat vibration signal to obtain a target throat vibration signal.

The device filters the throat vibration signal to obtain a target throat vibration signal. The processing procedure can be as follows:

first, a moving average smoothing process of 100 data points is performed on the throat vibration signal Z (t), and a moving average formula is:

F(t)＝Z’ _t ＝(Z _t-1 +Z _t-2 +Z _t-3 +…+Z _t-n )/n

wherein Z is _t Is the vibration signal of the throat part at the moment t, Z' _t For the throat vibration signal at time t after the moving average, n=100, the time series data F (t) is obtained after the moving average smoothing process.

Secondly, performing first-order difference on the time series data F (t) obtained after the moving average smoothing treatment, wherein a first-order difference formula is as follows:

D(t)＝ΔF _t ＝F _t+1 -F _t

wherein DeltaF _t And obtaining time series data D (t) after the first order difference as the throat vibration signal at the moment t after the first order difference.

Finally, the time series data D (t) is subjected to moving average smoothing processing of 100 data points again, and the formula is as follows:

R(t)＝D’ _t ＝(D _t-1 +D _t-2 +D _t-3 +…+D _t-n )/n

wherein R (t) is a target throat vibration signal, n=100.

Further, the throat vibration signal comprises a Z-axis throat vibration signal acquired by a triaxial acceleration sensor. In this embodiment, the throat vibration signal may be collected by a triaxial acceleration sensor, and the triaxial acceleration sensor may collect an X-axis throat vibration signal, a Y-axis throat vibration signal, and a Z-axis throat vibration signal, and the sampling rate may be set to 200Hz. The device determines the target throat vibration signal primarily from the Z-axis throat vibration signal.

S1022: and generating a respiratory motion track curve according to the target throat vibration signal.

The device generates a respiratory motion track curve according to the target throat vibration signal, acquires a waveform diagram of the target throat vibration signal, and takes the waveform of the target throat vibration signal as the respiratory motion track curve.

S103: and determining the sleep breathing state of the object to be detected based on the breathing motion track curve and a preset detection rule.

The method comprises the steps that detection rules are preset in equipment, the preset detection rules are used for determining sleep breathing states of an object to be detected according to a breathing motion track curve, and the sleep breathing states are used for identifying whether OSAHS occurs currently or not. The preset detection rule may include a condition that OSAHS occurs and a condition that OSAHS does not occur. If the respiratory motion track curve meets the condition of OSAHS occurrence, determining that the sleeping respiratory state of the object to be detected is marked to occur OSAHS; if the respiratory motion track curve meets the condition that OSAHS does not occur, determining that the sleeping respiratory state of the object to be detected is marked that OSAHS does not occur.

Further, in order to accurately determine the sleep breathing state, S103 may include S1031 to S1032, as shown in fig. 3, S1031 to S1032 are specifically as follows:

s1031: and acquiring sound wave data of the object to be detected, and determining snore event identification according to the sound wave data.

The device acquires sound wave data of an object to be detected, and determines a snore event identifier according to the sound wave data, wherein the snore event identifier can identify whether a snore event occurs. In one embodiment, the device may perform high-pass filtering on the acoustic data of the object to be detected to extract snore event information, and determine a snore event identifier based on the snore event information, where a cut-off frequency of the high-pass filtering may be 30Hz; in another embodiment, the device may determine the sound wave data of the object to be detected based on a pre-trained snore machine learning model, so as to obtain the snore event identifier.

S1032: if the fluctuation amplitude of the respiratory motion track curve is smaller than a first threshold and larger than a second threshold within a preset time period, and the snore event mark is a target mark, judging that the sleep respiratory state of the object to be detected is a first state, wherein the first state represents that an obstructive sleep respiratory hypopnea event occurs.

In this embodiment, a condition corresponding to the fluctuation range of the respiratory motion track curve in a preset time period is set in a preset detection rule. The device firstly obtains the fluctuation amplitude of the respiratory motion track curve in a preset time period, wherein the device can represent the fluctuation amplitude of the respiratory motion track curve through the area size of the enclosing area of the respiratory motion track curve and the zero axis in the preset time period. The calculation process of the area size of the enclosing area in the preset time period is as follows: calculating the area A (x) of the enclosing region of the respiratory motion track curve R (t) and the zero axis in the preset time period by approximate summation, wherein when the preset time period is 5 seconds, the summation formula is as follows:

where N is the data length of the respiratory motion trajectory curve R (t), Δt is constantly equal to 1.

The method comprises the steps of pre-storing a first threshold value and a second threshold value in the device, wherein the first threshold value and the second threshold value are used for measuring fluctuation amplitude of a respiratory motion track curve in a preset time period. The first threshold and the second threshold may be preset by the device, and the first threshold and the second threshold may also be updated in real time.

The device can set a reference fluctuation amplitude, wherein the reference fluctuation amplitude is a respiratory motion track curve in a normal respiratory state, and the normal respiratory state is a state in which OSAHS does not occur and other interference factors do not exist. The first coefficient and the second coefficient are preset in the device, the device determines the first threshold value based on the first coefficient and the reference fluctuation amplitude, and the device determines the second threshold value based on the second coefficient and the reference fluctuation amplitude. For example, the first coefficient is 0.4 and the second coefficient is 0.1, the device multiplies the first coefficient 0.4 by the reference fluctuation amplitude to determine a first threshold, and the device multiplies the second coefficient 0.1 by the reference fluctuation amplitude to determine a second threshold.

The device may also adjust the reference fluctuation amplitude in real time according to the state of the object to be detected, i.e. the reference fluctuation amplitude is dynamic, so that the first threshold value and the second threshold value may be updated in real time according to the reference fluctuation amplitude. The setting rule of the dynamic reference fluctuation amplitude can be as follows: firstly, respiratory motion data is initialized and set after equipment is started, the average value of fluctuation amplitudes of three continuous preset time periods is obtained by the equipment to serve as a reference fluctuation amplitude, the fluctuation amplitudes of the three continuous preset time periods must meet the condition that the fluctuation amplitude difference is within 10%, and the initial reference fluctuation amplitude is set. If the fluctuation amplitude of the respiratory motion track curve in three continuous preset time periods is increased to more than 90% of the initial reference fluctuation amplitude, and the fluctuation amplitude difference in the three continuous preset time periods is within 10%, the average value of the fluctuation amplitudes in the three continuous preset time periods is used as the new reference fluctuation amplitude, otherwise, the reference fluctuation amplitude is not updated. And after each OSAHS respiratory event occurs and ends, updating the reference fluctuation amplitude in real time, and setting the dynamic reference fluctuation amplitude. The dynamic reference fluctuation amplitude multiplied by the first coefficient and the second coefficient are respectively used as a dynamic first threshold value and a dynamic second threshold value.

If the fluctuation amplitude of the respiratory motion track curve is smaller than a first threshold value and larger than a second threshold value within a preset time period, and the snore event is identified as a target identifier, judging that the sleep respiratory state of the object to be detected is a first state, wherein the first state represents an obstructive sleep respiratory hypopnea event. Wherein the target indicia is indicative of occurrence of a snoring event.

Further, S103 may include S1033, where S1033 is specifically as follows:

s1033: and if the fluctuation amplitude of the respiratory motion track curve is smaller than a second threshold value within a preset time period, judging that the sleep respiratory state of the object to be detected is a second state, wherein the second state represents that an obstructive sleep apnea event occurs.

In this embodiment, if the fluctuation amplitude of the respiratory motion trajectory curve is smaller than the second threshold value in the preset time period, the sleep respiratory state of the object to be detected is determined to be the second state, and the second state indicates that the obstructive sleep apnea event occurs. The details of this embodiment may be referred to the description of S1032, and will not be described herein.

Further, S103 may include S1034, S1034 being specifically as follows:

s1034: if the fluctuation amplitude of the respiratory motion track curve is larger than a first threshold value in a preset time period, judging that the sleep respiratory state of the object to be detected is a third state, wherein the third state indicates that no obstructive sleep apnea and low ventilation event occurs.

In this embodiment, if the fluctuation range of the respiratory motion trajectory curve is greater than the first threshold in the preset time period, the sleep respiratory state of the object to be detected is determined to be a third state, where the third state indicates that no obstructive sleep apnea and hypopnea event occurs. The details of this embodiment may be referred to the description of S1032, and will not be described herein.

As shown in fig. 4, S1031 to S1032, S1033, S1034 are alternatively executed relationships, and are three cases included in the present embodiment S103.

Further, after determining that the sleep breathing state of the subject to be detected is the first state and the second state, the method further includes: the electrical stimulation module is controlled to perform an electrical stimulation operation. The device determines that the sleep breathing state of the object to be detected is a first state and a second state, and the device can send a control signal to the electric stimulation module to control the electric stimulation module to execute electric stimulation operation. The electric stimulation operation is forward and reverse pulse voltage stimulation, the stimulation frequency is 53Hz, the pulse width is 60 microseconds, the voltage level is 20-32V, the time duration of each stimulation is 4 seconds, and a user can firstly measure self-withstand voltage and preset the maximum stimulation voltage level before using the device. If the sleep respiratory state of the object to be detected is still the first state or the second state after the one-time submandibular percutaneous electrical stimulation is finished, and is not obviously improved, the electrical stimulation module can be automatically started and continue to execute the electrical stimulation operation at a higher voltage level until the sleep respiratory state of the object to be detected is changed into a third state, namely the OSAHS respiratory event is terminated, and the voltage level of the terminated OSAHS respiratory event is automatically constant to be the voltage level of the subsequent electrical stimulation.

The electrical stimulation module may control the patch electrode to perform an electrical stimulation operation. The whole shape of the patch electrode can be designed according to cashew shape, so as to be beneficial to being tightly attached to the neck of a human body under the jaw, two disc electrodes are symmetrically distributed on the left side and the right side of the medical adhesive tape adhesive surface, and the back surface of the disc electrode (namely the back surface of the medical adhesive tape adhesive surface) is a magnetic electrode button. The specific positions of the patch electrode adhered to the neck submaxillary part are as follows: the inner side of the mandibular angle is about (1+ -0.011) cm, the outer average of the chin midline is (0.3+ -0.022) cm, and the vertical distance is (1.5+ -0.023) cm from the mandibular edge.

In the embodiment of the application, the throat vibration signal of the object to be detected is monitored; determining a respiratory motion track curve according to the throat vibration signal; and determining the sleep breathing state of the object to be detected based on the breathing motion track curve and a preset detection rule. According to the scheme, the sleep breathing state of the object to be detected can be determined by monitoring the vibration data of the upper respiratory airway and the throat of the human body during sleep, so that the OSAHS breathing event can be detected in real time. The medical cost of detection is lower, is applicable to home detection, and has no other interference factors in the normal sleep state of the object to be detected in the detection process, thereby improving the accuracy of the detection result.

It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic of each process, and should not limit the implementation process of the embodiment of the present application in any way.

Referring to fig. 5, fig. 5 is a schematic diagram of a sleep-breathing state detection device according to a second embodiment of the present application. The units included are used to perform the steps in the corresponding embodiments of fig. 1-4. Refer specifically to the related descriptions in the respective embodiments of fig. 1 to 5. For convenience of explanation, only the portions related to the present embodiment are shown. Referring to fig. 5, the sleep-breathing state detection device 5 includes:

a monitoring unit 510 for monitoring a throat vibration signal of a subject to be detected;

a first determining unit 520 for determining a respiratory motion trajectory curve according to the throat vibration signal;

a second determining unit 530, configured to determine a sleep respiratory state of the object to be detected based on the respiratory motion trajectory curve and a preset detection rule.

Further, the second determining unit 530 is specifically configured to:

Further, the second determining unit 520 is specifically configured to:

Further, the sleep-breathing state detection device 5 further includes:

Fig. 6 is a schematic diagram of a sleep breathing state detection apparatus according to a third embodiment of the present application. As shown in fig. 6, the detection apparatus 6 of the sleep-breathing state of this embodiment includes: a processor 60, a memory 61 and a computer program 62 stored in said memory 61 and executable on said processor 60, for example a sleep breathing state detection program. The processor 60, when executing the computer program 62, implements the steps of the above-described embodiments of the method for detecting sleep apnea states, such as steps 101 to 103 shown in fig. 1. Alternatively, the processor 60, when executing the computer program 62, performs the functions of the modules/units of the apparatus embodiments described above, such as the functions of the modules 510-530 shown in fig. 5.

By way of example, the computer program 62 may be partitioned into one or more modules/units that are stored in the memory 61 and executed by the processor 60 to complete the present application. The one or more modules/units may be a series of computer program instruction segments capable of performing a specific function for describing the execution of the computer program 62 in the sleep disordered breathing detection device 6. For example, the computer program 62 may be divided into a monitoring unit, a first determining unit, a second determining unit, each unit functioning specifically as follows:

The sleep breathing state detection device may include, but is not limited to, a processor 60, a memory 61. It will be appreciated by those skilled in the art that fig. 6 is merely an example of a sleep-breathing state detection device 6 and is not intended to be limiting, and that a sleep-breathing state detection device 6 may include more or less components than illustrated, or may be combined with certain components, or different components, e.g., the sleep-breathing state detection device may also include an input-output device, a network access device, a bus, etc.

The processor 60 may be a central processing unit (Central Processing Unit, CPU), other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

The memory 61 may be an internal storage unit of the sleep-breathing state detection device 6, such as a hard disk or a memory of the sleep-breathing state detection device 6. The memory 61 may be an external storage device of the sleep-breathing state detection device 6, for example, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card) or the like provided on the sleep-breathing state detection device 6. Further, the sleep-breathing state detection device 6 may also include both an internal storage unit and an external storage device of the sleep-breathing state detection device 6. The memory 61 is used for storing the computer program and other programs and data required for the detection device of sleep apnea status. The memory 61 may also be used for temporarily storing data that has been output or is to be output.

It should be noted that, because the content of information interaction and execution process between the above devices/units is based on the same concept as the method embodiment of the present application, specific functions and technical effects thereof may be referred to in the method embodiment section, and will not be described herein again.

It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.

The embodiment of the application also provides a network device, which comprises: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, which when executed by the processor performs the steps of any of the various method embodiments described above.

Embodiments of the present application also provide a computer readable storage medium storing a computer program which, when executed by a processor, implements steps that may implement the various method embodiments described above.

Embodiments of the present application provide a computer program product which, when run on a mobile terminal, causes the mobile terminal to perform steps that may be performed in the various method embodiments described above.

The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present application implements all or part of the flow of the method of the above embodiments, and may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, where the computer program, when executed by a processor, may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include at least: any entity or device capable of carrying computer program code to a photographing device/terminal apparatus, recording medium, computer Memory, read-Only Memory (ROM), random access Memory (RAM, random Access Memory), electrical carrier signals, telecommunications signals, and software distribution media. Such as a U-disk, removable hard disk, magnetic or optical disk, etc. In some jurisdictions, computer readable media may not be electrical carrier signals and telecommunications signals in accordance with legislation and patent practice.

In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

In the embodiments provided in the present application, it should be understood that the disclosed apparatus/network device and method may be implemented in other manners. For example, the apparatus/network device embodiments described above are merely illustrative, e.g., the division of the modules or units is merely a logical functional division, and there may be additional divisions in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.

The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

The above embodiments are only for illustrating the technical solution of the present application, and are not limiting; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application.

Claims

1. A sleep disordered breathing detection device, comprising:

the second determining unit is used for determining the sleep breathing state of the object to be detected based on the breathing motion track curve and a preset detection rule;

the second determining unit is specifically configured to:

if the fluctuation amplitude of the respiratory motion track curve is smaller than a first threshold value and larger than a second threshold value within a preset time period, and the snore event mark is a target mark, judging that the sleep respiratory state of the object to be detected is a first state, wherein the first state represents that an obstructive sleep respiratory hypopnea event occurs;

a first threshold value and a second threshold value are preset, and the first threshold value and the second threshold value are used for measuring fluctuation amplitude of a respiratory motion track curve in a preset time period;

a first coefficient and a second coefficient are preset, a first threshold value is determined based on the first coefficient and the reference fluctuation amplitude, and a second threshold value is determined based on the second coefficient and the reference fluctuation amplitude;

the reference fluctuation amplitude is dynamic, and the first threshold value and the second threshold value are updated in real time according to the reference fluctuation amplitude;

the setting rule of the dynamic reference fluctuation amplitude is as follows:

initializing and setting respiratory motion data, and acquiring the average value of fluctuation amplitudes of three continuous preset time periods as the reference fluctuation amplitude, wherein the fluctuation amplitudes of the three continuous preset time periods must meet the fluctuation amplitude difference within 10%, and the initial reference fluctuation amplitude is set; if the fluctuation amplitude of the respiratory motion track curve of three continuous preset time periods rises to more than 90% of the initial reference fluctuation amplitude, and the fluctuation amplitude difference of the three continuous preset time periods is within 10%, the average value of the fluctuation amplitudes of the three continuous preset time periods is used as the new reference fluctuation amplitude, otherwise, the reference fluctuation amplitude is not updated and set.

2. The sleep-breathing state detection apparatus according to claim 1, wherein the determining the sleep-breathing state of the subject to be detected based on the respiratory motion trajectory curve and a preset detection rule comprises:

3. The sleep-breathing state detection apparatus according to claim 1, wherein the determining the sleep-breathing state of the subject to be detected based on the respiratory motion trajectory curve and a preset detection rule comprises:

4. The sleep disordered breathing detection device of claim 1, wherein said determining a respiratory motion profile from said throat vibration signal includes:

5. The sleep disordered breathing detection device of claim 4 wherein said throat vibration signal includes a Z-axis throat vibration signal acquired by a tri-axis acceleration sensor.

6. The sleep disordered breathing detection device of claim 2, further comprising:

7. A sleep breathing state detection apparatus comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor when executing the computer program implements the steps of:

monitoring throat vibration signals of an object to be detected;

determining the sleep breathing state of the object to be detected based on the breathing motion track curve and a preset detection rule;

the determining the sleep respiratory state of the object to be detected based on the respiratory motion track curve and a preset detection rule comprises the following steps:

the setting rule of the dynamic reference fluctuation amplitude is as follows:

8. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor performs the steps of:

monitoring throat vibration signals of an object to be detected;

the setting rule of the dynamic reference fluctuation amplitude is as follows: