CN115910351A

CN115910351A - Method, system and device for sleep periodicity detection quantification and auxiliary intervention

Info

Publication number: CN115910351A
Application number: CN202310195991.2A
Authority: CN
Inventors: 何将
Original assignee: Anhui Xingchen Zhiyue Technology Co ltd
Current assignee: Anhui Xingchen Zhiyue Technology Co ltd
Priority date: 2023-03-03
Filing date: 2023-03-03
Publication date: 2023-04-04
Anticipated expiration: 2043-03-03
Also published as: CN115910351B

Abstract

The invention provides a method for sleep periodic detection quantification and auxiliary intervention, which comprises the following steps: acquiring physiological state data and environmental state data of a user in a sleeping process, and performing signal data preprocessing, time frame processing and time frame state feature analysis to generate physiological state features and environmental state features; analyzing the physiological state characteristics by sleep state, time sequence components and periodicity quantization, extracting sleep periodicity indexes, and generating sleep periodicity quantization daily reports; and repeating the steps, continuously monitoring and tracking and analyzing the sleeping process of the user, evaluating the influence of the sleeping environment on the sleeping periodicity, extracting an optimal sleeping periodicity environment scheme, dynamically optimizing and adjusting the sleeping environment, and generating a sleeping periodicity quantitative report. The invention can realize real-time or off-line analysis and quantification and auxiliary intervention on the sleep periodicity of the user, and improve the sleep periodicity and sleep quality of the user.

Description

Method, system and device for sleep periodic detection quantification and auxiliary intervention

Technical Field

The invention relates to the field of sleep periodic detection quantification and auxiliary intervention, in particular to a sleep periodic detection quantification and auxiliary intervention method, system and device.

Background

Sleep is the basic physiological requirement and the basic guarantee of life and health of people, and the sleep time accounts for about one third of the life time of people. With the rapid development of social economy, the pressure of work, families, economy, society and the like is continuously increased, the sleeping problems of people are increasingly highlighted and raised, and the risk of diseases related to physiology and psychology is also continuously increased. According to the guidelines of the American society for sleep medicine, sleep stages of humans include a wake period, a non-rapid eye movement sleep period NREM (light sleep 1, light sleep 2, deep sleep), and a rapid eye movement sleep period REM. Good sleep is a cyclic process with good periodicity, with NREM and REM alternating, once a cycle and back and forth, typically for healthy adults each cycle lasts 90-120 minutes and goes through 4-5 cycles per night.

The sleep periodic intensity is different from a sleep circadian rhythm and a sleep time phase staging, the sleep circadian rhythm is widely defined as a 24-hour large-scale behavior rule of multi-day long-term sleep and wake-up, falling asleep and getting up, and the sleep time phase staging is defined as a small-cycle condition that light sleep, deep sleep and rapid eye movement sleep alternate with each other in the one-time sleep process; the sleep cycle strength is a comprehensive measure of the continuous change level of the sleep normal cycle alternating mode in the continuous change of the sleep state of the user and whether the user has the ability to maintain the normal alternating continuous change of the normal sleep mode, and is an indispensable index in the sleep practice evaluation.

At present, no matter in clinical diagnosis and treatment or health management, no clear method exists at home and abroad for accurately evaluating and quantifying the sleep periodicity strength of a user, the sleep quality and the sleep efficiency are difficult to be further scientifically judged and analyzed, and meanwhile, no data is used as a hand grip to realize auxiliary intervention on the sleep periodicity of the user, namely scientific evaluation and auxiliary intervention on the sleep baseline period change strength, the baseline period change trend and the change mode rationality of the sleep state of the user.

For example, patent document CN115525081A discloses an adaptive adjustment system and control method for indoor environment of building, wherein a data processing module is used for integrating posture information of a user and environmental acquisition information of a current period, determining instant photo-thermal comfort feeling of the user, generating a corresponding adjustment instruction, and sending the adjustment instruction to a corresponding execution module. CN114839890A discloses a sleep environment control system, a sleep state detection device, which is used for detecting physiological data of a user, where the physiological data includes body movement data, heart rate data, and respiration data; the analysis module performs the following calculation according to the detection data of the detection module: (1) Judging whether the user gets into bed or not and whether the user has a sleep desire or a sleep trend; (2) judging the sleep cycle; (3) Judging whether the time of falling asleep is consistent with the rhythm improvement plan; (4) Judging whether the environmental data is consistent with the preset environmental data; (5) Judging the conformity of the sleep, awakening and rhythm of the user in the month, week and day with the big data standard plan; but does not teach how to calculate the sleep period. It is also seen that the prior art only proposes a fuzzy assumption, and how to calculate the sleep cycle, how to scientifically measure the influence of environmental factors on the sleep cycle, how to accurately control environmental changes, and the like all have blind areas.

Therefore, the prior art needs to be improved to accurately quantify the sleep cycle variation and efficiently improve the sleep experience of the user.

Disclosure of Invention

Aiming at the defects and the improvement requirements of the existing method, the invention aims to provide a sleep periodicity detection quantification and auxiliary intervention method, which realizes scientific quantification of the sleep periodicity of a user by continuously acquiring, analyzing and extracting the characteristics of physiological state data and environmental state data of the user, further analyzes the correlation influence of sleep environment factors on the sleep periodicity, extracts an optimal sleep periodicity environment optimization scheme, realizes dynamic optimization and adjustment of the sleep environment, improves the sleep periodicity and the sleep quality of the user and assists the relevant health management of the user. The invention also provides a system for sleep cycle detection quantification and auxiliary intervention, which is used for realizing the method. The invention also provides a device for sleep periodic detection quantification and auxiliary intervention, which is used for realizing the system.

According to the purpose of the invention, the invention provides a sleep periodicity detection quantification and auxiliary intervention method, which comprises the following steps:

acquiring physiological state data and environmental state data of a user in a sleeping process, and performing signal data preprocessing and time frame processing to obtain physiological state information and environmental state information;

analyzing the physiological state information and the environmental state information by time frame state characteristics to generate physiological state characteristics and environmental state characteristics;

analyzing the physiological state characteristics by sleep state, time sequence component analysis and periodicity quantitative analysis, evaluating the sleep baseline periodic variation intensity, baseline periodic variation trend and variation mode rationality of the sleep state of the user, extracting sleep periodicity indexes, and generating a sleep periodicity quantitative daily report;

and repeating the steps, continuously monitoring and tracking and analyzing the sleeping process of the user, evaluating the influence of the sleeping environment on the sleeping periodicity, extracting an optimal sleeping periodicity environment scheme, dynamically optimizing and adjusting the sleeping environment, and generating a sleeping periodicity quantitative report.

Preferably, the physiological state data at least comprises electroencephalogram signal data, electrocardiosignal data, respiratory signal data, blood oxygen signal data and body temperature signal data; the physiological state information at least comprises electroencephalogram state information, electrocardio state information, respiration state information, blood oxygen state information and body temperature state information.

Preferably, the environmental state data at least comprises illumination signal data, spectrum signal data, air pressure signal data, temperature signal data, humidity signal data, microparticle signal data, noise signal data, oxygen concentration signal data, carbon dioxide concentration signal data and formaldehyde concentration signal data; the environmental state information at least comprises illumination state information, spectrum state information, air pressure state information, temperature state information, humidity state information, microparticle state information, noise state information, oxygen concentration state information, carbon dioxide concentration state information and formaldehyde concentration state information.

Preferably, the signal data preprocessing comprises at least a/D conversion, resampling, artifact removal, noise reduction, notching, band-pass filtering, de-nulling, re-referencing, and smoothing.

Preferably, the time frame processing is to perform sliding segmentation of a preset framing duration window on the signal data by a preset framing step length according to the sampling rate of the signal.

Preferably, the time frame state feature analysis at least comprises numerical feature analysis, envelope feature analysis, power spectrum feature analysis, entropy feature analysis, fractal feature analysis and complexity feature analysis.

Preferably, the physiological status features at least include electroencephalogram signal features, electrocardio signal features, respiration signal features, blood oxygen signal features, and body temperature signal features.

Preferably, the environmental status characteristics at least include the environmental status index mean characteristic sequence, an illumination signal characteristic, a spectrum signal characteristic, an air pressure signal characteristic, a temperature signal characteristic, a humidity signal characteristic, a microparticle signal characteristic, a noise signal characteristic, an oxygen concentration signal characteristic, a carbon dioxide concentration signal characteristic and a formaldehyde concentration signal characteristic; the environment state index mean characteristic sequence is composed of state signal mean values of different information types in the environment state information, and at least comprises a light illumination mean value, a spectrum fusion mean value, an air pressure mean value, a temperature mean value, a humidity mean value, a microparticle mean value, a noise mean value, an oxygen concentration mean value, a carbon dioxide concentration mean value and a formaldehyde concentration mean value.

Preferably, the steps of analyzing the physiological status characteristics by sleep status, analyzing time sequence components and analyzing periodicity quantification, evaluating the sleep baseline periodic variation intensity, baseline periodic variation trend and variation mode rationality of the sleep status of the user, extracting sleep periodicity indexes and generating sleep periodicity quantification daily reports further specifically include:

analyzing the sleep state of the physiological state characteristics, identifying the sleep state characteristic time phase and the sleep state level of the user, generating a sleep state characteristic curve of the user, and extracting a sleep duration period state characteristic curve;

and performing time sequence component analysis and periodic quantitative analysis on the state characteristic curve of the sleep duration, extracting the sleep periodicity index, and generating the sleep periodicity quantitative daily report.

More preferably, the sleep state characteristic time phases include a wake period time phase, a rapid eye movement sleep period time phase, a non-rapid eye movement light sleep period time phase and a non-rapid eye movement deep sleep period time phase.

Preferably, the value division method of the sleep state level comprises the following steps:

dividing each sleep state characteristic time phase into horizontal grades in different value ranges, wherein the horizontal grades are continuous positive integer sequences:

1) The state characteristic level of the time phase in the waking period is taken as

In which>

Is a positive integer and->

；

2) The state characteristic level of the time phase in the rapid eye movement sleep period is taken as

In which

Is a positive integer and->

；

3) The state characteristic level value of the time phase of the non-rapid eye movement shallow sleep period is

In which>

Is a positive integer and->

；

4) The state characteristic level of the time phase in the non-rapid eye movement deep sleep period is taken as

Wherein->

Is a positive integer and->

。

Preferably, the method for extracting the sleep duration state characteristic curve comprises the following steps:

1) Acquiring the physiological state characteristics according to time sequence of a time frame, identifying the time phase of the sleep state characteristics of the current frame and determining the value of the sleep state level;

2) Obtaining all sleep state levels of all time frames to generate a sleep state level curve;

3) According to a data smoothing method, carrying out data smoothing on the sleep state horizontal curve to generate a sleep state characteristic curve;

4) And based on the sleep state characteristic curve, intercepting the sleep state characteristic curve by taking the first non-waking period time phase frame as the beginning and the last non-waking period time phase as the end to obtain the sleep duration state characteristic curve.

Preferably, the data smoothing method at least comprises moving average, mean value filtering, SG filtering, low-pass filtering and kalman filtering.

More preferably, the time series component analysis includes at least additive time series component analysis and multiplicative time series component analysis.

Preferably, the method for calculating the sleep periodicity index comprises the following steps:

1) Acquiring a state characteristic curve of the sleep duration;

2) Judging whether the time sequence characteristic of the sleep duration state characteristic curve is an additive time sequence or a multiplicative time sequence, and selecting corresponding additive time sequence component analysis or multiplicative time sequence component analysis;

3) Performing corresponding time sequence component analysis on the sleep duration state characteristic curve to obtain a sleep duration state time sequence period component and a sleep duration state time sequence residual error component, and calculating to obtain sleep periodicity strength;

4) Extracting a sleep periodicity factor coefficient of the sleep duration state characteristic curve;

5) And calculating the product of the sleep periodicity intensity and the sleep periodicity factor coefficient to generate the sleep periodicity index.

Preferably, the calculation method of the sleep cycle strength comprises the following steps:

1) If the sleep duration state characteristic curve is an additive time sequence, the calculation formula is as follows:

；

wherein, the first and the second end of the pipe are connected with each other,

is the sleep cycle intensity and->

，/>

For the purpose of determining a variance function, is>

The sleep duration state time sequence period component and the sleep duration state time sequence residual component are respectively;

2) If the sleep duration state characteristic curve is a multiplicative time sequence, the calculation formula is as follows:

；

wherein the content of the first and second substances,

is the sleep cycle intensity and->

，/>

For the purpose of a variance function>

The sleep duration state time sequence period component and the sleep duration state time sequence residual component are respectively.

Preferably, the calculation formula of the sleep periodicity factor coefficient is as follows:

；

is a sleep periodicity factor coefficient and>

，/>

a signal averaging period cycle time that is a fraction of the state timing period of the sleep duration period, <' >>

The maximum sleep cycle time and the minimum sleep cycle time of the normal healthy population corresponding to the age group of the user are respectively.

Preferably, the sleep cycle quantitative daily report at least comprises a sleep cycle analysis summary, the sleep cycle index, the sleep state level curve, the sleep duration state characteristic curve and the environment state index mean characteristic sequence.

Preferably, the step of repeating the above steps, continuously monitoring and tracking and analyzing the sleep process of the user, evaluating the influence of the sleep environment on the sleep periodicity, extracting the optimal sleep periodicity environment scheme and dynamically optimizing and adjusting the sleep environment, and generating the sleep periodicity quantitative report further specifically includes:

continuously acquiring, monitoring and tracking and analyzing the physiological state data and the environmental state data of the user for multiple days to obtain an environmental state index mean value characteristic sequence curve and a sleep periodicity index curve;

calculating the correlation characteristics of the environment state index mean value characteristic sequence curve and the sleep periodicity index curve to obtain the sleep periodicity environment influence factor sequence, and extracting the optimal sleep periodicity environment scheme;

generating a sleep environment optimization adjustment scheme according to the optimal sleep periodic environment scheme by combining the current environment state information;

according to the sleep environment optimization adjustment scheme, connecting sleep environment regulation and control equipment to dynamically optimize and adjust the sleep environment;

and generating the sleep periodic quantitative report according to the environment state index mean characteristic sequence curve, the sleep periodic index curve, the sleep periodic environment influence factor sequence and the optimal sleep periodic environment scheme.

Preferably, the sleep periodicity quantitative report at least comprises a sleep periodicity analysis summary, a sleep periodicity adjustment scheme, the environment state index mean characteristic sequence curve, the sleep periodicity index curve, the sleep periodicity environment influence factor sequence and the optimal sleep periodicity environment scheme.

Preferably, the sleep cycle environmental influence factor sequence at least comprises an ambient light source illumination correlation index, an ambient light source spectrum correlation index, an ambient air pressure correlation index, an ambient temperature correlation index, an ambient humidity correlation index, an ambient microparticle correlation index, an ambient noise correlation index, an ambient oxygen concentration correlation index, an ambient carbon dioxide concentration correlation index and an ambient formaldehyde concentration correlation index; the optimal sleep periodic environment scheme at least comprises an environment light source illumination guide parameter, an environment light source spectrum guide parameter, an environment air pressure guide parameter, an environment temperature guide parameter, an environment humidity guide parameter, an environment microparticle guide parameter, an environment noise guide parameter, an environment oxygen concentration guide parameter, an environment carbon dioxide concentration guide parameter and an environment formaldehyde concentration guide parameter; the sleep environment optimization adjustment scheme at least comprises an ambient light source illumination execution parameter, an ambient light source spectrum execution parameter, an ambient air pressure execution parameter, an ambient temperature execution parameter, an ambient humidity execution parameter, an ambient microparticle execution parameter, an ambient noise execution parameter, an ambient oxygen concentration execution parameter, an ambient carbon dioxide concentration execution parameter and an ambient formaldehyde concentration execution parameter; the sleep environment regulation and control equipment at least comprises ambient light source illumination regulation and control equipment, ambient light source spectrum regulation and control equipment, ambient air pressure regulation and control equipment, ambient temperature regulation and control equipment, ambient humidity regulation and control equipment, ambient microparticle regulation and control equipment, ambient noise regulation and control equipment, ambient oxygen concentration regulation and control equipment, ambient carbon dioxide concentration regulation and control equipment and ambient formaldehyde concentration regulation and control equipment.

Preferably, the method for extracting the sleep cycle environmental impact factor sequence comprises the following steps:

1) Continuously monitoring and tracking and analyzing the physiological state data and the environmental state data of the user, and calculating to obtain a daily environmental state index mean characteristic sequence and a daily sleep periodicity index;

2) According to the date time sequence, calculating to obtain an environment state index mean value characteristic sequence curve and a sleep periodicity index curve corresponding to all dates;

3) Sequentially calculating the relevance characteristics of one type of environment state index mean value curve in the environment state index mean value characteristic sequence curve and the sleep periodicity index curve to generate a sleep periodicity environment index mean value relevance matrix;

4) And performing coefficient harmonic on the correlation coefficient indexes of the sleep environment state information of different information types in the sleep periodic environment index mean value correlation matrix according to different information types of the sleep environment state information to generate a sleep periodic environment influence factor sequence.

Preferably, the method for extracting the optimal sleep cycle environment scheme comprises the following steps:

1) Continuously monitoring, tracking and analyzing the physiological state data and the environmental state data of the user, and calculating to obtain daily environmental state index mean characteristic sequence and daily sleep periodicity index;

3) Judging a preset sleep periodicity index threshold value based on the sleep periodicity index curve, screening corresponding dates of which the sleep periodicity index curve exceeds the preset sleep periodicity index threshold value, and generating an optimal sleep periodicity index date set;

4) Judging whether the optimal sleep periodicity index date set is an empty set or not, if so, performing descending order arrangement on the sleep periodicity indexes of the optimal sleep periodicity index curve and screening the number of preset heads to generate the optimal sleep periodicity index date set;

5) Extracting the environmental state index mean value characteristics of the corresponding date from the environmental state index mean value characteristic sequence curve according to the date of the optimal sleep periodicity index date set to generate an optimal sleep environment state index mean value set;

6) And according to different information types of the sleep environment state information, performing environment state index fusion processing on the optimal sleep environment state index mean value set to generate an optimal sleep periodic environment scheme.

Preferably, the calculation manner of the environment state index fusion processing at least includes mean processing, normal weighting processing, incremental weighting processing and decremental weighting processing.

According to the purpose of the present invention, the present invention provides a system for sleep cycle detection quantification and auxiliary intervention, which comprises the following modules:

the state acquisition processing module is used for acquiring physiological state data and environmental state data of a user in a sleeping process, and performing signal data preprocessing and time frame processing to obtain physiological state information and environmental state information;

the time frame characteristic analysis module is used for carrying out time frame state characteristic analysis on the physiological state information and the environmental state information to generate physiological state characteristics and environmental state characteristics;

the periodicity quantitative analysis module is used for performing sleep state analysis, time sequence component analysis and periodicity quantitative analysis on the physiological state characteristics, evaluating the sleep baseline periodic variation intensity, baseline periodic variation trend and variation mode rationality of the sleep state of the user, extracting a sleep periodicity index and generating a sleep periodicity quantitative daily report;

the continuous tracking analysis module is used for continuously monitoring and tracking analyzing the sleeping process of the user, evaluating the influence of the sleeping environment on the sleeping periodicity, extracting an optimal sleeping periodicity environment scheme and generating a sleeping periodicity quantitative report;

the environment auxiliary regulation and control module is used for generating a sleep environment optimization and adjustment scheme and dynamically optimizing and adjusting the sleep environment according to the optimal sleep periodic environment scheme and by combining the current environment state information;

and the data management center module is used for carrying out unified storage and operation management on all process data of the system.

Preferably, the state collection processing module comprises the following functional units:

the physiological state monitoring unit is used for acquiring the physiological state data of the user in the sleeping process; the physiological state data at least comprises electroencephalogram signal data, electrocardio signal data, respiration signal data, blood oxygen signal data and body temperature signal data;

the environment state monitoring unit is used for collecting the environment state data of the user in the sleeping process; the environmental state data at least comprises illumination signal data, spectrum signal data, air pressure signal data, temperature signal data, humidity signal data, microparticle signal data, noise signal data, oxygen concentration signal data, carbon dioxide concentration signal data and formaldehyde concentration signal data;

the signal preprocessing unit is used for preprocessing the physiological state data and the environmental state data; the signal data preprocessing at least comprises A/D conversion, resampling, artifact removing, noise reduction, notching, band-pass filtering, invalidation removing, re-reference and smoothing processing;

the data time frame processing unit is used for carrying out time frame processing on the physiological state data and the environmental state data; and the time frame processing is to perform sliding segmentation of a preset framing duration window on the signal data by preset framing step length according to the sampling rate of the signal.

More preferably, the time frame feature analysis module comprises the following functional units:

the numerical characteristic analysis unit is used for carrying out numerical characteristic analysis on the physiological state information and the environmental state information;

the envelope characteristic analysis unit is used for carrying out envelope characteristic analysis on the physiological state information and the environmental state information;

the power spectrum characteristic analysis unit is used for carrying out power spectrum characteristic analysis on the physiological state information and the environmental state information;

the entropy characteristic analysis unit is used for carrying out entropy characteristic analysis on the physiological state information and the environment state information;

a fractal feature analysis unit, configured to perform fractal feature analysis on the physiological state information and the environmental state information;

the complexity characteristic analysis unit is used for carrying out complexity characteristic analysis on the physiological state information and the environmental state information;

the physiological characteristic integration unit is used for integrating and generating the physiological state characteristic; the physiological state characteristics at least comprise an electroencephalogram signal characteristic, an electrocardiosignal characteristic, a respiration signal characteristic, a blood oxygen signal characteristic and a body temperature signal characteristic;

the environment characteristic integration unit is used for integrating and generating the environment state characteristic; the environment state characteristics at least comprise an environment state index mean characteristic sequence, an illumination signal characteristic, a spectrum signal characteristic, an air pressure signal characteristic, a temperature signal characteristic, a humidity signal characteristic, a microparticle signal characteristic, a noise signal characteristic, an oxygen concentration signal characteristic, a carbon dioxide concentration signal characteristic and a formaldehyde concentration signal characteristic.

More preferably, the periodic quantitative analysis module comprises the following functional units:

the sleep state identification unit is used for analyzing the sleep state of the physiological state characteristics, identifying the sleep state characteristic time phase and the sleep state level of the user, generating a sleep state characteristic curve of the user and extracting a sleep duration state characteristic curve;

the period quantitative analysis unit is used for carrying out time sequence component analysis and periodicity quantitative analysis on the sleep duration state characteristic curve and extracting the sleep periodicity index; the time sequence component analysis at least comprises additive time sequence component analysis and multiplicative time sequence component analysis;

the quantitative daily report generating unit is used for generating the sleep periodic quantitative daily report; the sleep cycle quantitative daily report at least comprises a sleep cycle analysis summary, the sleep cycle index, the sleep state level curve, the sleep duration state characteristic curve and the environment state index mean value characteristic sequence.

More preferably, the continuous tracking analysis module comprises the following functional units:

the tracking quantitative analysis unit is used for continuously acquiring, monitoring and tracking and analyzing the physiological state data and the environmental state data of the user for multiple days to obtain an environmental state index mean value characteristic sequence curve and a sleep periodicity index curve;

the environment influence analysis unit is used for calculating the correlation characteristics of the environment state index mean value characteristic sequence curve and the sleep periodicity index curve to obtain the sleep periodicity environment influence factor sequence; the sleep periodic environmental influence factor sequence at least comprises an ambient light source illumination correlation index, an ambient light source spectrum correlation index, an ambient air pressure correlation index, an ambient temperature correlation index, an ambient humidity correlation index, an ambient microparticle correlation index, an ambient noise correlation index, an ambient oxygen concentration correlation index, an ambient carbon dioxide concentration correlation index and an ambient formaldehyde concentration correlation index;

an optimal environment extraction unit for extracting the optimal sleep cycle environment scheme; the optimal sleep periodic environment scheme at least comprises an ambient light source illumination guide parameter, an ambient light source spectrum guide parameter, an ambient air pressure guide parameter, an ambient temperature guide parameter, an ambient humidity guide parameter, an ambient microparticle guide parameter, an ambient noise guide parameter, an ambient oxygen concentration guide parameter, an ambient carbon dioxide concentration guide parameter and an ambient formaldehyde concentration guide parameter;

a quantitative report generating unit, configured to generate the sleep periodicity quantitative report according to the environment state index mean characteristic sequence curve, the sleep periodicity index curve, the sleep periodicity environmental impact factor sequence, and the optimal sleep periodicity environmental scheme; the sleep cycle quantitative report at least comprises a sleep cycle analysis summary, a sleep cycle adjustment scheme, the environment state index mean characteristic sequence curve, the sleep cycle index curve, the sleep cycle environment influence factor sequence and the optimal sleep cycle environment scheme.

More preferably, the environment-assisted regulation and control module comprises the following functional units:

an environment scheme generating unit, configured to generate the sleep environment optimization adjustment scheme according to the optimal sleep periodic environment scheme in combination with the current environment state information; the sleep environment optimization adjustment scheme at least comprises an ambient light source illumination execution parameter, an ambient light source spectrum execution parameter, an ambient air pressure execution parameter, an ambient temperature execution parameter, an ambient humidity execution parameter, an ambient microparticle execution parameter, an ambient noise execution parameter, an ambient oxygen concentration execution parameter, an ambient carbon dioxide concentration execution parameter and an ambient formaldehyde concentration execution parameter;

the environment dynamic regulation and control unit is used for connecting the sleep environment regulation and control equipment according to the sleep environment optimization and regulation scheme and dynamically optimizing and regulating and controlling the sleep environment of the user; the sleep environment regulation and control equipment at least comprises ambient light source illumination regulation and control equipment, ambient light source spectrum regulation and control equipment, ambient air pressure regulation and control equipment, ambient temperature regulation and control equipment, ambient humidity regulation and control equipment, ambient microparticle regulation and control equipment, ambient noise regulation and control equipment, ambient oxygen concentration regulation and control equipment, ambient carbon dioxide concentration regulation and control equipment and ambient formaldehyde concentration regulation and control equipment.

According to an objective of the present invention, the present invention provides a device for sleep cycle detection quantification and auxiliary intervention, comprising the following modules:

the state acquisition and processing module is used for acquiring physiological state data and environmental state data of a user in a sleeping process, and performing signal data preprocessing and time frame processing to obtain physiological state information and environmental state information;

the period characteristic analysis module is used for carrying out time frame state characteristic analysis on the physiological state information and the environmental state information to generate physiological state characteristics and environmental state characteristics; analyzing the physiological state characteristics by sleep state, time sequence component analysis and periodicity quantitative analysis, evaluating the sleep baseline periodic variation intensity, baseline periodic variation trend and variation mode rationality of the sleep state of the user, extracting sleep periodicity indexes, and generating a sleep periodicity quantitative daily report;

the continuous tracking analysis module is used for continuously monitoring and tracking analyzing the sleeping process of the user, evaluating the influence of the sleeping environment on the sleeping periodicity, extracting the optimal sleeping periodicity environment scheme and generating a sleeping periodicity quantitative report;

the environment auxiliary regulation and control module is used for generating a sleep environment optimization and adjustment scheme and carrying out dynamic optimization and adjustment on a sleep environment according to the optimal sleep periodic environment scheme and by combining the current environment state information;

the data visualization module is used for visually displaying all process data, the physiological state data, the environment state data, the sleep periodic quantitative daily report, the sleep periodic quantitative report, the optimal sleep periodic environment scheme and the sleep environment optimization adjustment scheme of the device;

and the data management center module is used for uniformly storing and operating and managing all process data of the device.

The method, the system and the device for sleep periodic detection quantification and auxiliary intervention have the characteristics of real-time analysis and off-line analysis, and meet the requirements of the sleep periodic real-time or off-line analysis quantification and auxiliary intervention of a user by acquiring, analyzing and extracting the physiological sign data and the environmental factor data of the user. Due to the difference of different environments of different users, a plurality of incidence relations of sleep are fully excavated by utilizing the dynamic analysis and adjustment to realize scientific quantification of the sleep periodicity of the users, the difference of different individuals can be brought into the adjustment process, and the system is a system for continuous learning and iteration. And extracting an optimal environment optimization scheme based on analysis of the correlation influence of the sleep environment factors on the sleep periodicity. The invention can enable or cooperate with other sleep-related products and services to be deployed in various human living environments, thereby improving the sleep periodicity and the sleep quality of the user and assisting the related health management of the user.

Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.

Drawings

The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the example serve to explain the principles of the invention and not to limit the invention.

FIG. 1 is a flowchart illustrating a method for sleep cycle detection quantification and assisted intervention according to an embodiment of the present invention;

FIG. 2 is a block diagram of a system for sleep cycle detection quantification and assisted intervention according to an embodiment of the present invention;

fig. 3 is a schematic diagram illustrating a module structure of an apparatus for sleep cycle detection quantification and assisted intervention according to an embodiment of the present invention.

Detailed Description

In order to more clearly illustrate the object and technical solution of the present invention, the present invention will be further described with reference to the accompanying drawings in the embodiments of the present application. It should be apparent that the embodiments described below are only a part of the embodiments of the present invention, and not all of them. Other embodiments, which can be derived by one of ordinary skill in the art from the embodiments of the present invention without inventive faculty, are within the scope of the present invention. It should be noted that the embodiments and features of the embodiments in the present application may be arbitrarily combined with each other without conflict.

As shown in fig. 1, a method for sleep cycle detection quantification and assisted intervention provided by an embodiment of the present invention includes the following steps:

p100: and acquiring physiological state data and environmental state data of a user in a sleeping process, and performing signal data preprocessing and time frame processing to obtain physiological state information and environmental state information.

In this embodiment, the physiological state data at least includes electroencephalogram signal data, electrocardiograph signal data, respiration signal data, blood oxygen signal data, and body temperature signal data; the physiological state information at least comprises electroencephalogram state information, electrocardio state information, respiration state information, blood oxygen state information and body temperature state information.

In this embodiment, the environmental state data at least includes illumination signal data, spectrum signal data, barometric signal data, temperature signal data, humidity signal data, microparticle signal data, noise signal data, oxygen concentration signal data, carbon dioxide concentration signal data, and formaldehyde concentration signal data; the environmental status information includes at least illumination status information, spectral status information, barometric status information, temperature status information, humidity status information, microparticle status information, noise status information, oxygen concentration status information, carbon dioxide concentration status information, and formaldehyde concentration status information.

In this embodiment, the signal data preprocessing at least includes a/D conversion, resampling, artifact removal, noise reduction, notching, bandpass filtering, invalidation removal, re-referencing, and smoothing. The data preprocessing of the physiological state data mainly comprises the steps of carrying out artifact removal, wavelet noise reduction, 50hz notch and 0.1-45hz band-pass filtering on electroencephalogram signals and electrocardiosignals; and performing artifact, wavelet noise reduction, 50hz notch and 0.01-5hz band-pass filtering on the respiration signal, the blood oxygen signal and the body temperature signal. The data preprocessing of the environmental state data mainly comprises A/D conversion, artifact removal and wavelet noise reduction.

In this embodiment, the time frame processing is to perform sliding segmentation of a preset framing duration window on the signal data by using a preset framing step length according to a sampling rate of the signal, where the preset framing duration and the preset framing step length are both 10 seconds, that is, non-overlapping window sliding segmentation.

P200: and analyzing the physiological state information and the environmental state information according to the time frame state characteristics to generate physiological state characteristics and environmental state characteristics.

In this embodiment, the time frame state feature analysis at least includes numerical feature analysis, envelope feature analysis, power spectrum feature analysis, entropy feature analysis, fractal feature analysis, and complexity feature analysis.

In the embodiment, the physiological state information is subjected to time frame state feature analysis to generate physiological state features; the physiological state characteristics at least comprise an electroencephalogram signal characteristic, an electrocardiosignal characteristic, a respiration signal characteristic, a blood oxygen signal characteristic and a body temperature signal characteristic.

In the embodiment, time frame state feature analysis is performed on the environmental state information to generate environmental state features; the environmental state characteristics at least comprise an environmental state index mean value characteristic sequence, an illumination signal characteristic, a spectrum signal characteristic, an air pressure signal characteristic, a temperature signal characteristic, a humidity signal characteristic, a microparticle signal characteristic, a noise signal characteristic, an oxygen concentration signal characteristic, a carbon dioxide concentration signal characteristic and a formaldehyde concentration signal characteristic; the environment state index mean characteristic sequence is composed of state signal mean values of different information types in the environment state information, and at least comprises an illumination mean value, a spectrum fusion mean value, an air pressure mean value, a temperature mean value, a humidity mean value, a microparticle mean value, a noise mean value, an oxygen concentration mean value, a carbon dioxide concentration mean value and a formaldehyde concentration mean value.

P300: and analyzing the physiological state characteristics by sleep state analysis, time sequence component analysis and periodicity quantification analysis, evaluating the sleep baseline periodic variation intensity, baseline periodic variation trend and variation mode rationality of the sleep state of the user, extracting sleep periodicity indexes, and generating a sleep periodicity quantification daily report.

Firstly, sleep state analysis is carried out on physiological state characteristics, a sleep state characteristic time phase and a sleep state level of a user are identified, a sleep state characteristic curve of the user is generated, and a sleep duration state characteristic curve is extracted.

In the embodiment, physiological state characteristics are subjected to fusion analysis according to an AASM sleep stage rule, a sleep behavior analysis principle and a sleep stage deep learning model to obtain a sleep state characteristic time phase and a sleep state level of a user under all time frames; and secondly, generating a sleep state characteristic curve of the user and extracting a sleep duration state characteristic curve.

In this embodiment, the sleep state characteristic time phases include a waking period time phase, a rapid eye movement sleep period time phase, a non-rapid light eye movement sleep period time phase, and a non-rapid deep eye movement sleep period time phase.

In this embodiment, the value division method of the sleep state level divides each sleep state characteristic time phase into level levels of different value ranges, where the level levels are continuous positive integer sequences:

；

；

3) The characteristic level of the state of the time phase in the non-rapid eye movement shallow sleep period is taken as

；

；

In this embodiment, the method for extracting the state characteristic curve of the sleep duration period includes:

1) Acquiring physiological state characteristics according to time sequence of a time frame, identifying the time phase of the sleep state characteristics of the current frame and determining the value of the sleep state level;

3) According to the data smoothing method, performing data smoothing (moving average) on the sleep state horizontal curve to generate a sleep state characteristic curve;

4) And based on the sleep state characteristic curve, intercepting the sleep state characteristic curve by taking the first non-waking period time phase frame as the beginning and the last non-waking period time phase as the end to obtain a sleep duration state characteristic curve.

And secondly, performing time sequence component analysis and periodic quantitative analysis on the sleep duration state characteristic curve, evaluating the sleep baseline periodic variation intensity, baseline periodic variation trend and variation mode rationality of the sleep state of the user, extracting a sleep periodicity index, and generating a sleep periodic quantitative daily report.

In this embodiment, first, the characteristic of the sleep duration state characteristic curve is determined and time sequence component analysis is completed, where the time sequence component analysis at least includes additive time sequence component analysis and multiplicative time sequence component analysis; and secondly, completing the calculation of the sleep periodicity index and the generation of a quantitative daily report.

In this embodiment, the method for calculating the sleep periodicity index includes:

1) Acquiring a sleep duration state characteristic curve;

4) Extracting a sleep periodicity factor coefficient of a state characteristic curve of a sleep duration period;

5) And calculating the product of the sleep periodicity strength and the sleep periodicity factor coefficient to generate a sleep periodicity index.

In this embodiment, the method for calculating the sleep cycle strength includes:

1) If the state characteristic curve of the sleep duration is an additive time sequence, the calculation formula is as follows:

；

wherein the content of the first and second substances,

is the sleep cycle intensity and->

，/>

For the purpose of a variance function>

Respectively a sleep duration state time sequence period component and a sleep duration state time sequence residual error component;

；

is a sleep period intensity and>

，/>

for the purpose of determining a variance function, is>

Respectively a sleep duration state timing cycle component and a sleep duration state timing residual component.

In this embodiment, the formula for calculating the sleep periodicity factor coefficient is as follows:

；

is a sleep periodicity factor coefficient and>

，/>

signal averaging period cycle times that are components of a state timing period of sleep duration period, <' > based upon a signal averaging period>

The maximum sleep cycle time and the minimum sleep cycle time of normal healthy people corresponding to the user age group are respectively.

In this embodiment, the sleep cycle quantitative daily report at least includes a sleep cycle analysis summary, a sleep cycle index, a sleep state level curve, a sleep duration state characteristic curve, and an environment state index mean value characteristic sequence.

P400: and repeating the steps, continuously monitoring and tracking and analyzing the sleeping process of the user, evaluating the influence of the sleeping environment on the sleeping periodicity, extracting the optimal sleeping periodicity environment scheme, dynamically optimizing and adjusting the sleeping environment, and generating a sleeping periodicity quantitative report.

The method comprises the steps of firstly, continuously collecting, monitoring and tracking and analyzing physiological state data and environmental state data of a user for multiple days to obtain an environmental state index mean value characteristic sequence curve and a sleep periodicity index curve.

And secondly, calculating the correlation characteristics of the environment state index mean value characteristic sequence curve and the sleep periodicity index curve to obtain a sleep periodicity environment influence factor sequence, and extracting an optimal sleep periodicity environment scheme.

In this embodiment, the sleep cycle environmental impact factor sequence at least includes an ambient light source illumination correlation index, an ambient light source spectrum correlation index, an ambient air pressure correlation index, an ambient temperature correlation index, an ambient humidity correlation index, an ambient microparticle correlation index, an ambient noise correlation index, an ambient oxygen concentration correlation index, an ambient carbon dioxide concentration correlation index, and an ambient formaldehyde concentration correlation index. The extraction method of the sleep periodic environment influence factor sequence comprises the following steps:

1) Continuously monitoring and tracking and analyzing physiological state data and environmental state data of a user, and calculating to obtain a daily environmental state index mean characteristic sequence and a sleep periodicity index;

3) Sequentially calculating the relevance characteristics of an environment state index mean value curve of one type in the environment state index mean value characteristic sequence curve and a sleep periodicity index curve to generate a sleep periodicity environment index mean value relevance matrix;

4) And according to different information types of the sleep environment state information, performing coefficient reconciliation on the correlation coefficient indexes of the sleep environment state information of different information types in the sleep periodic environment index mean correlation matrix to generate a sleep periodic environment influence factor sequence.

And thirdly, generating a sleep environment optimization adjustment scheme according to the optimal sleep periodic environment scheme and by combining the current environment state information.

In this embodiment, the optimal sleep cycle environment scheme at least includes an ambient light source illumination guide parameter, an ambient light source spectrum guide parameter, an ambient air pressure guide parameter, an ambient temperature guide parameter, an ambient humidity guide parameter, an ambient microparticle guide parameter, an ambient noise guide parameter, an ambient oxygen concentration guide parameter, an ambient carbon dioxide concentration guide parameter, and an ambient formaldehyde concentration guide parameter. The extraction method of the optimal sleep periodic environment scheme comprises the following steps:

1) Continuously monitoring, tracking and analyzing physiological state data and environmental state data of a user, and calculating to obtain a daily environmental state index mean value characteristic sequence and a sleep periodicity index;

4) Judging whether the optimal sleep periodicity index date set is an empty set or not, if so, performing descending order arrangement on the sleep periodicity indexes of the optimal sleep periodicity index curve and screening the number of preset heads to generate an optimal sleep periodicity index date set;

5) Extracting the environmental state index mean value characteristics of corresponding dates from the environmental state index mean value characteristic sequence curve according to the dates of the optimal sleep periodicity index date set to generate an optimal sleep environmental state index mean value set;

In this embodiment, the calculation manner of the environment state index fusion processing at least includes an average processing, a normal weighting processing, an incremental weighting processing, and a decremental weighting processing.

In this embodiment, the sleep environment optimization adjustment scheme at least includes an ambient light source illumination execution parameter, an ambient light source spectrum execution parameter, an ambient air pressure execution parameter, an ambient temperature execution parameter, an ambient humidity execution parameter, an ambient microparticle execution parameter, an ambient noise execution parameter, an ambient oxygen concentration execution parameter, an ambient carbon dioxide concentration execution parameter, and an ambient formaldehyde concentration execution parameter.

And fourthly, connecting the sleep environment regulation and control equipment according to the sleep environment optimization and adjustment scheme to dynamically optimize and adjust the sleep environment.

In this embodiment, the sleep environment control device at least includes an ambient light source illumination control device, an ambient light source spectrum control device, an ambient air pressure control device, an ambient temperature control device, an ambient humidity control device, an ambient microparticle control device, an ambient noise control device, an ambient oxygen concentration control device, an ambient carbon dioxide concentration control device, and an ambient formaldehyde concentration control device.

And fifthly, generating a sleep periodic quantitative report according to the environment state index mean value characteristic sequence curve, the sleep periodic index curve, the sleep periodic environment influence factor sequence and the optimal sleep periodic environment scheme.

In this embodiment, the sleep cycle quantitative report at least includes a sleep cycle analysis summary, a sleep cycle adjustment scheme, an environment status index mean characteristic sequence curve, a sleep cycle index curve, a sleep cycle environment influence factor sequence, and an optimal sleep cycle environment scheme.

As shown in fig. 2, the system for sleep cycle detection quantification and assisted intervention according to the embodiment of the present invention is configured to perform the above-mentioned method steps. The system comprises the following modules:

the state acquisition and processing module S100 is used for acquiring physiological state data and environmental state data of a user in a sleeping process, and performing signal data preprocessing and time frame processing to obtain physiological state information and environmental state information;

the time frame characteristic analysis module S200 is used for performing time frame state characteristic analysis on the physiological state information and the environmental state information to generate physiological state characteristics and environmental state characteristics;

the periodic quantitative analysis module S300 is used for performing sleep state analysis, time sequence component analysis and periodic quantitative analysis on physiological state characteristics, evaluating the sleep baseline periodic variation intensity, baseline periodic variation trend and variation mode rationality of the sleep state of a user, extracting a sleep periodicity index and generating a sleep periodic quantitative daily report;

the continuous tracking analysis module S400 is used for continuously monitoring and tracking and analyzing the sleeping process of the user, evaluating the influence of the sleeping environment on the sleeping periodicity, extracting the optimal sleeping periodicity environment scheme and generating a sleeping periodicity quantitative report;

the environment auxiliary regulation and control module S500 is used for generating a sleep environment optimization and adjustment scheme and dynamically optimizing and adjusting the sleep environment according to the optimal sleep periodic environment scheme and by combining the current environment state information;

and the data management center module S600 is used for performing unified storage and operation management on all process data of the system.

In this embodiment, the state acquisition processing module S100 includes the following functional units:

a physiological status monitoring unit S110, configured to collect physiological status data of a user in a sleep process; the physiological state data at least comprises electroencephalogram signal data, electrocardio signal data, respiration signal data, blood oxygen signal data and body temperature signal data;

the environmental state monitoring unit S120 is used for collecting environmental state data of a user in a sleeping process; the environmental state data at least comprises illumination signal data, spectrum signal data, air pressure signal data, temperature signal data, humidity signal data, microparticle signal data, noise signal data, oxygen concentration signal data, carbon dioxide concentration signal data and formaldehyde concentration signal data;

a signal preprocessing unit S130 for performing signal data preprocessing on the physiological state data and the environmental state data; the signal data preprocessing at least comprises A/D conversion, resampling, artifact removal, noise reduction, notch trapping, band-pass filtering, invalidation removal, re-reference and smoothing processing;

a data time frame processing unit S140, configured to perform time frame processing on the physiological status data and the environmental status data; the time frame processing is to perform sliding segmentation of a preset framing duration window on the signal data by preset framing step length according to the sampling rate of the signal.

In this embodiment, the time frame feature analysis module S200 includes the following functional units:

a numerical characteristic analysis unit S210, configured to perform numerical characteristic analysis on the physiological state information and the environmental state information;

the envelope characteristic analysis unit S220 is used for carrying out envelope characteristic analysis on the physiological state information and the environmental state information;

a power spectrum characteristic analysis unit S230, configured to perform power spectrum characteristic analysis on the physiological state information and the environmental state information;

an entropy feature analysis unit S240, configured to perform entropy feature analysis on the physiological state information and the environmental state information;

a fractal feature analysis unit S250, configured to perform fractal feature analysis on the physiological state information and the environmental state information;

the complexity characteristic analysis unit S260 is used for carrying out complexity characteristic analysis on the physiological state information and the environmental state information;

a physiological characteristic integration unit S270 for integrating and generating physiological status characteristics; the physiological state characteristics at least comprise electroencephalogram signal characteristics, electrocardio signal characteristics, respiration signal characteristics, blood oxygen signal characteristics and body temperature signal characteristics;

an environment feature integration unit S280 for integrating and generating environment status features; the environmental state characteristics at least comprise an environmental state index mean value characteristic sequence, an illumination signal characteristic, a spectrum signal characteristic, an air pressure signal characteristic, a temperature signal characteristic, a humidity signal characteristic, a microparticle signal characteristic, a noise signal characteristic, an oxygen concentration signal characteristic, a carbon dioxide concentration signal characteristic and a formaldehyde concentration signal characteristic.

In this embodiment, the periodic quantitative analysis module S300 includes the following functional units:

the sleep state identification unit S310 is used for analyzing the sleep state according to the physiological state characteristics, identifying the sleep state characteristic time phase and the sleep state level of the user, generating a sleep state characteristic curve of the user and extracting a sleep duration state characteristic curve;

the period quantitative analysis unit S320 is configured to perform time sequence component analysis and periodicity quantitative analysis on the sleep duration state characteristic curve, and extract a sleep periodicity index; the time sequence component analysis at least comprises additive time sequence component analysis and multiplicative time sequence component analysis;

a quantization daily report generation unit S330, configured to generate a sleep periodic quantization daily report; the sleep cycle quantitative daily report at least comprises a sleep cycle analysis summary, a sleep cycle index, a sleep state level curve, a sleep duration state characteristic curve and an environment state index mean value characteristic sequence.

In this embodiment, the continuous tracking analysis module S400 includes the following functional units:

the tracking quantitative analysis unit S410 is used for continuously acquiring, monitoring and tracking and analyzing the physiological state data and the environmental state data of the user for multiple days to obtain an environmental state index mean value characteristic sequence curve and a sleep periodicity index curve;

the environment influence analysis unit S420 is configured to calculate correlation characteristics of the environment state index mean characteristic sequence curve and the sleep periodicity index curve to obtain a sleep periodicity environment influence factor sequence; the sleep periodic environmental influence factor sequence at least comprises an ambient light source illumination correlation index, an ambient light source spectrum correlation index, an ambient air pressure correlation index, an ambient temperature correlation index, an ambient humidity correlation index, an ambient microparticle correlation index, an ambient noise correlation index, an ambient oxygen concentration correlation index, an ambient carbon dioxide concentration correlation index and an ambient formaldehyde concentration correlation index;

an optimal environment extraction unit S430 for extracting an optimal sleep periodic environment scenario; the optimal sleep periodic environment scheme at least comprises an environment light source illumination guide parameter, an environment light source spectrum guide parameter, an environment air pressure guide parameter, an environment temperature guide parameter, an environment humidity guide parameter, an environment microparticle guide parameter, an environment noise guide parameter, an environment oxygen concentration guide parameter, an environment carbon dioxide concentration guide parameter and an environment formaldehyde concentration guide parameter;

the quantitative report generating unit S440 is configured to generate a sleep periodic quantitative report according to the environmental status index mean characteristic sequence curve, the sleep periodic index curve, the sleep periodic environmental influence factor sequence, and the optimal sleep periodic environmental scheme; the sleep periodicity quantitative report at least comprises a sleep periodicity analysis summary, a sleep periodicity adjustment scheme, an environment state index mean characteristic sequence curve, a sleep periodicity index curve, a sleep periodicity environment influence factor sequence and an optimal sleep periodicity environment scheme.

In this embodiment, the environment auxiliary regulation and control module S500 includes the following functional units:

an environment scheme generating unit S510, configured to generate a sleep environment optimization adjustment scheme according to the optimal sleep periodic environment scheme and by combining the current environment state information; the sleep environment optimization adjustment scheme at least comprises an ambient light source illumination execution parameter, an ambient light source spectrum execution parameter, an ambient air pressure execution parameter, an ambient temperature execution parameter, an ambient humidity execution parameter, an ambient microparticle execution parameter, an ambient noise execution parameter, an ambient oxygen concentration execution parameter, an ambient carbon dioxide concentration execution parameter and an ambient formaldehyde concentration execution parameter;

the environment dynamic regulation and control unit S520 is used for connecting the sleep environment regulation and control equipment according to the sleep environment optimization and adjustment scheme and dynamically optimizing and controlling the sleep environment of the user; the sleep environment regulation and control equipment at least comprises environment light source illumination regulation and control equipment, environment light source spectrum regulation and control equipment, environment air pressure regulation and control equipment, environment temperature regulation and control equipment, environment humidity regulation and control equipment, environment microparticle regulation and control equipment, environment noise regulation and control equipment, environment oxygen concentration regulation and control equipment, environment carbon dioxide concentration regulation and control equipment and environment formaldehyde concentration regulation and control equipment.

As shown in fig. 3, an embodiment of the present invention provides a sleep cycle detection quantization and auxiliary intervention apparatus, which includes the following modules:

the state acquisition and processing module M100 is used for acquiring physiological state data and environmental state data of a user in a sleep process, and performing signal data preprocessing and time frame processing to obtain physiological state information and environmental state information;

the period characteristic analysis module M200 is used for performing time frame state characteristic analysis on the physiological state information and the environmental state information to generate physiological state characteristics and environmental state characteristics; analyzing sleep state, time sequence components and periodicity quantification analysis on physiological state characteristics, evaluating sleep baseline periodic variation intensity, baseline periodic variation trend and variation mode rationality of the sleep state of a user, extracting sleep periodicity indexes, and generating a sleep periodicity quantification daily report;

the continuous tracking analysis module M300 is used for continuously monitoring and tracking and analyzing the sleeping process of the user, evaluating the influence of the sleeping environment on the sleeping periodicity, extracting the optimal sleeping periodicity environment scheme and generating a sleeping periodicity quantitative report;

the environment auxiliary regulation and control module M400 is used for generating a sleep environment optimization and adjustment scheme and carrying out dynamic optimization and adjustment on the sleep environment according to the optimal sleep periodic environment scheme and by combining the current environment state information;

the data visualization module M500 is used for visually displaying all process data, physiological state data, environmental state data, sleep periodic quantitative daily reports, sleep periodic quantitative reports, optimal sleep periodic environmental schemes and sleep environmental optimization adjustment schemes of the device;

and the data management center module M600 is used for performing unified storage and operation management on all process data of the device.

Said means are configured for performing the respective steps of the method clock of fig. 1, and are not described in detail herein.

The invention also provides a programmable processor of various types (FPGA, ASIC or other integrated circuit) for running a program, wherein the program performs the steps of the above embodiments when running.

The invention also provides a corresponding computer device, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the steps in the above embodiments are implemented when the memory executes the program.

Although the embodiments of the present invention have been described above, the above description is only for the purpose of understanding the present invention, and is not intended to limit the present invention. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims. Therefore, the scope of the present invention should be determined by the following claims.

Claims

1. A method for sleep cycle detection quantification and auxiliary intervention is characterized by comprising the following steps:

analyzing the physiological state characteristics by sleep state analysis, time sequence component analysis and periodicity quantification analysis, evaluating the sleep baseline periodic variation intensity, baseline periodic variation trend and variation mode rationality of the sleep state of the user, extracting sleep periodicity indexes, and generating a sleep periodicity quantification daily report;

2. The method of claim 1, wherein the physiological state data includes at least one of electroencephalograph signal data, electrocardiograph signal data, respiration signal data, blood oxygen signal data, and body temperature signal data; the physiological state information comprises at least one item of electroencephalogram state information, electrocardio state information, respiration state information, blood oxygen state information and body temperature state information.

3. The method of claim 2, wherein: the environmental state data comprises at least one of illumination signal data, spectrum signal data, air pressure signal data, temperature signal data, humidity signal data, microparticle signal data, noise signal data, oxygen concentration signal data, carbon dioxide concentration signal data and formaldehyde concentration signal data; the environmental status information includes at least one of illumination status information, spectral status information, barometric status information, temperature status information, humidity status information, microparticle status information, noise status information, oxygen concentration status information, carbon dioxide concentration status information, and formaldehyde concentration status information.

4. The method of claim 1, wherein: the signal data preprocessing at least comprises A/D conversion, resampling, artifact removing, noise reduction, notching, band-pass filtering, invalidation removing, re-reference and smoothing processing.

5. The method of claim 1, wherein: and the time frame processing is to perform sliding segmentation of a preset framing duration window on the signal data by preset framing step length according to the sampling rate of the signal.

6. The method of claim 1, wherein: the time frame state feature analysis comprises at least one of numerical value feature analysis, envelope feature analysis, power spectrum feature analysis, entropy feature analysis, fractal feature analysis and complexity feature analysis.

7. The method of claim 1, wherein: the physiological state characteristics comprise at least one of electroencephalogram signal characteristics, electrocardio signal characteristics, respiration signal characteristics, blood oxygen signal characteristics and body temperature signal characteristics.

8. The method of claim 7, wherein: the environment state characteristics comprise at least one of an environment state index mean value characteristic sequence, an illumination signal characteristic, a spectrum signal characteristic, an air pressure signal characteristic, a temperature signal characteristic, a humidity signal characteristic, a microparticle signal characteristic, a noise signal characteristic, an oxygen concentration signal characteristic, a carbon dioxide concentration signal characteristic and a formaldehyde concentration signal characteristic; the environment state index mean value characteristic sequence is composed of state signal mean values of different information types in the environment state information, and comprises at least one of a light intensity mean value, a spectrum fusion mean value, an air pressure mean value, a temperature mean value, a humidity mean value, a microparticle mean value, a noise mean value, an oxygen concentration mean value, a carbon dioxide concentration mean value and a formaldehyde concentration mean value.

9. The method according to any one of claims 1 to 8, wherein the step of performing sleep state analysis, time sequence component analysis and periodicity quantitative analysis on the physiological status characteristics, evaluating sleep baseline periodicity variation intensity, baseline periodicity variation trend and variation pattern rationality of the sleep state of the user, extracting a sleep periodicity index, and generating a sleep periodicity quantitative diary further specifically comprises:

analyzing the sleep state of the physiological state characteristics, identifying the sleep state characteristic time phase and the sleep state level of the user, generating a sleep state characteristic curve of the user, and extracting a sleep duration state characteristic curve;

10. The method of claim 9, wherein: the sleep state characteristic time phases comprise a waking period time phase, a rapid eye movement sleep period time phase, a non-rapid eye movement light sleep period time phase and a non-rapid eye movement deep sleep period time phase.

11. The method of claim 10, wherein the sleep state level is partitioned by:

Wherein->

Is a positive integer and->

；

In which>

Is a positive integer and->

；

In which>

Is a positive integer and->

；

Wherein->

Is a positive integer and->

。

12. The method of claim 9, wherein the sleep duration state characteristic is extracted by:

4) And based on the sleep state characteristic curve, intercepting the sleep state characteristic curve by taking the first non-awake period time phase frame as the beginning and the last non-awake period time phase as the end to obtain the sleep duration state characteristic curve.

13. The method of claim 12, wherein: the data smoothing processing method comprises at least one of moving average, mean value filtering, SG filtering, low-pass filtering and Kalman filtering.

14. The method of claim 9, wherein: the time series component analysis includes at least additive time series component analysis and multiplicative time series component analysis.

15. The method of claim 9, wherein: the sleep periodicity index calculation method comprises the following steps:

1) Acquiring a state characteristic curve of the sleep duration;

3) Performing corresponding time sequence component analysis on the sleep duration state characteristic curve to obtain a sleep duration state time sequence period component and a sleep duration state time sequence residual error component, and calculating to obtain sleep periodicity intensity;

5) And calculating the product of the sleep periodicity strength and the sleep periodicity factor coefficient to generate the sleep periodicity index.

16. The method of claim 15, wherein the sleep periodicity strength is calculated as follows:

；

is a sleep period intensity and>

，/>

for the purpose of determining a variance function, is>

；

wherein the content of the first and second substances,

is the sleep cycle intensity and->

，/>

For the purpose of a variance function>

17. The method of claim 15 or 16, wherein the sleep periodicity factor coefficient is calculated as follows:

；

wherein the content of the first and second substances,

is a sleep cycle factor coefficient and>

，/>

18. The method of claim 1, wherein: the sleep cycle quantitative daily report comprises at least one of a sleep cycle analysis summary, the sleep cycle index, a sleep state level curve, a sleep duration state characteristic curve and an environment state index mean value characteristic sequence.

19. The method of claim 1, wherein: the step of repeating the above steps, performing continuous monitoring and tracking analysis on the sleep process of the user, evaluating the influence of the sleep environment on the sleep periodicity, extracting the optimal sleep periodicity environment scheme and performing dynamic optimization adjustment on the sleep environment, and generating the sleep periodicity quantitative report further specifically includes:

calculating the correlation characteristics of the environment state index mean value characteristic sequence curve and the sleep periodicity index curve to obtain a sleep periodicity environment influence factor sequence, and extracting the optimal sleep periodicity environment scheme;

20. The method of claim 19, wherein: the sleep cycle quantitative report includes at least one of a sleep cycle analysis summary, a sleep cycle adjustment scheme, the environment state index mean characteristic sequence curve, the sleep cycle index curve, the sleep cycle environment influence factor sequence, and the optimal sleep cycle environment scheme.

21. The method of claim 19, wherein: the sleep periodic environment influence factor sequence comprises an environment light source illumination correlation index, an environment light source spectrum correlation index, an environment air pressure correlation index, an environment temperature correlation index, an environment humidity correlation index, an environment microparticle correlation index, an environment noise correlation index, an environment oxygen concentration correlation index, an environment carbon dioxide concentration correlation index and an environment formaldehyde concentration correlation index; the optimal sleep periodic environment scheme at least comprises at least one of an ambient light source illumination guide parameter, an ambient light source spectrum guide parameter, an ambient air pressure guide parameter, an ambient temperature guide parameter, an ambient humidity guide parameter, an ambient microparticle guide parameter, an ambient noise guide parameter, an ambient oxygen concentration guide parameter, an ambient carbon dioxide concentration guide parameter and an ambient formaldehyde concentration guide parameter;

the sleep environment optimization adjustment scheme comprises at least one of an ambient light source illumination execution parameter, an ambient light source spectrum execution parameter, an ambient air pressure execution parameter, an ambient temperature execution parameter, an ambient humidity execution parameter, an ambient microparticle execution parameter, an ambient noise execution parameter, an ambient oxygen concentration execution parameter, an ambient carbon dioxide concentration execution parameter and an ambient formaldehyde concentration execution parameter;

the sleep environment regulation and control equipment comprises an environment light source illumination regulation and control equipment, an environment light source spectrum regulation and control equipment, an environment air pressure regulation and control equipment, an environment temperature regulation and control equipment, an environment humidity regulation and control equipment, an environment microparticle regulation and control equipment, an environment noise regulation and control equipment, an environment oxygen concentration regulation and control equipment, an environment carbon dioxide concentration regulation and control equipment and an environment formaldehyde concentration regulation and control equipment

At least one item.

22. The method of claim 19, wherein the method for extracting the sleep cycle environmental impact factor sequence comprises:

4) And performing coefficient reconciliation on the correlation coefficient indexes of the sleep environment state information of different information types in the sleep periodic environment index mean value correlation matrix according to different information types of the sleep environment state information to generate the sleep periodic environment influence factor sequence.

23. The method as claimed in claim 19, wherein the extracting method of the optimal sleep cycle environmental scheme comprises:

4) Judging whether the optimal sleep periodicity index date set is an empty set or not, if so, performing descending order on the sleep periodicity indexes of an optimal sleep periodicity index curve and screening the number of preset heads to generate the optimal sleep periodicity index date set;

24. The method of claim 23, wherein: the calculation mode of the environment state index fusion processing comprises at least one of mean processing, normal weighting processing, incremental weighting processing and decremental weighting processing.

25. A system for sleep cycle detection quantification and auxiliary intervention is characterized by comprising the following modules:

the periodicity quantitative analysis module is used for carrying out sleep state analysis, time sequence component analysis and periodicity quantitative analysis on the physiological state characteristics, evaluating the sleep baseline periodic variation intensity, baseline periodic variation trend and variation mode rationality of the sleep state of the user, extracting a sleep periodicity index and generating a sleep periodicity quantitative daily report;

and the data management center module is used for uniformly storing and operating and managing all process data of the system.

26. The system of claim 25, wherein the state acquisition processing module comprises the following functional units:

the physiological state monitoring unit is used for acquiring the physiological state data of the user in the sleeping process; the physiological state data comprises at least one item of electroencephalogram signal data, electrocardio signal data, respiration signal data, blood oxygen signal data and body temperature signal data;

the environment state monitoring unit is used for collecting the environment state data of the user in the sleeping process; the environmental state data comprises at least one of illumination signal data, spectrum signal data, air pressure signal data, temperature signal data, humidity signal data, microparticle signal data, noise signal data, oxygen concentration signal data, carbon dioxide concentration signal data and formaldehyde concentration signal data;

a signal preprocessing unit for preprocessing the physiological status data and the environmental status data; the signal data preprocessing at least comprises A/D conversion, resampling, artifact removing, noise reduction, notching, band-pass filtering, invalidation removing, re-reference and smoothing processing;

27. The system of claim 25, wherein the time frame feature analysis module comprises the functional units of:

the physiological characteristic integration unit is used for integrating and generating the physiological state characteristic; the physiological state characteristics comprise at least one of electroencephalogram signal characteristics, electrocardio signal characteristics, respiration signal characteristics, blood oxygen signal characteristics and body temperature signal characteristics;

the environment characteristic integration unit is used for integrating and generating the environment state characteristic; the environment state characteristics comprise at least one of environment state index mean value characteristic sequence, illumination signal characteristics, spectrum signal characteristics, air pressure signal characteristics, temperature signal characteristics, humidity signal characteristics, microparticle signal characteristics, noise signal characteristics, oxygen concentration signal characteristics, carbon dioxide concentration signal characteristics and formaldehyde concentration signal characteristics.

28. The system of claim 25, wherein the periodic quantitative analysis module comprises the functional units of:

the quantitative daily report generating unit is used for generating the sleep periodic quantitative daily report; the sleep cycle quantitative daily report comprises at least one of a sleep cycle analysis summary, the sleep cycle index, a sleep state level curve, the sleep duration state characteristic curve and an environment state index mean value characteristic sequence.

29. The system of claim 25, wherein the continuous trace analysis module comprises the following functional units:

the environment influence analysis unit is used for calculating the correlation characteristics of the environment state index mean value characteristic sequence curve and the sleep periodicity index curve to obtain a sleep periodicity environment influence factor sequence; the sleep periodic environment influence factor sequence comprises at least one of an environment light source illumination relevance index, an environment light source spectrum relevance index, an environment air pressure relevance index, an environment temperature relevance index, an environment humidity relevance index, an environment microparticle relevance index, an environment noise relevance index, an environment oxygen concentration relevance index, an environment carbon dioxide concentration relevance index and an environment formaldehyde concentration relevance index;

an optimal environment extraction unit for extracting the optimal sleep cycle environment scheme; the optimal sleep periodic environment scheme comprises at least one of an ambient light source illumination guide parameter, an ambient light source spectrum guide parameter, an ambient air pressure guide parameter, an ambient temperature guide parameter, an ambient humidity guide parameter, an ambient microparticle guide parameter, an ambient noise guide parameter, an ambient oxygen concentration guide parameter, an ambient carbon dioxide concentration guide parameter and an ambient formaldehyde concentration guide parameter;

a quantitative report generating unit, configured to generate the sleep periodicity quantitative report according to the environment state index mean characteristic sequence curve, the sleep periodicity index curve, the sleep periodicity environmental impact factor sequence, and the optimal sleep periodicity environmental scheme; the sleep cycle quantitative report includes at least one of a sleep cycle analysis summary, a sleep cycle adjustment scheme, the environment state index mean characteristic sequence curve, the sleep cycle index curve, the sleep cycle environment influence factor sequence, and the optimal sleep cycle environment scheme.

30. The system of any one of claims 25-29, wherein the environmentally assisted regulatory module comprises the following functional units:

an environment scheme generating unit, configured to generate the sleep environment optimization adjustment scheme according to the optimal sleep periodic environment scheme in combination with the current environment state information; the sleep environment optimization adjustment scheme comprises at least one of an environment light source illumination execution parameter, an environment light source spectrum execution parameter, an environment air pressure execution parameter, an environment temperature execution parameter, an environment humidity execution parameter, an environment microparticle execution parameter, an environment noise execution parameter, an environment oxygen concentration execution parameter, an environment carbon dioxide concentration execution parameter and an environment formaldehyde concentration execution parameter;

the environment dynamic regulation and control unit is used for connecting the sleep environment regulation and control equipment according to the sleep environment optimization and regulation scheme and dynamically optimizing and regulating and controlling the sleep environment of the user; the sleep environment regulation and control equipment comprises at least one of environment light source illumination regulation and control equipment, environment light source spectrum regulation and control equipment, environment air pressure regulation and control equipment, environment temperature regulation and control equipment, environment humidity regulation and control equipment, environment micro-particle regulation and control equipment, environment noise regulation and control equipment, environment oxygen concentration regulation and control equipment, environment carbon dioxide concentration regulation and control equipment and environment formaldehyde concentration regulation and control equipment.

31. The utility model provides a device of sleep periodicity detection quantization and supplementary intervention which characterized in that includes following module: