CN115862877A

CN115862877A - Method, system and device for sleep sustainability detection quantification and assisted intervention

Info

Publication number: CN115862877A
Application number: CN202310194079.5A
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: 2023-03-28
Anticipated expiration: 2043-03-03
Also published as: CN115862877B

Abstract

The invention provides a sleep sustainability detection quantification and auxiliary intervention method, which comprises the following steps: acquiring and monitoring physiological state signals and environmental state signals of a user in a sleeping process, processing the signals and analyzing the characteristics to generate physiological state characteristics and environmental state characteristics; performing sleep state analysis, time sequence component analysis and sustainability quantitative analysis on the physiological state characteristics, extracting a sleep sustainability index, and generating a sleep sustainability quantitative daily report; and repeating the steps, continuously monitoring and tracking and analyzing the sleep process of the user, evaluating the influence of the sleep environment on the sleep sustainability, extracting an optimal sleep sustainability environment scheme, dynamically optimizing and adjusting the sleep environment, and generating a sleep sustainability quantitative report. The invention can enable or cooperate with other sleep related products and services to be deployed in living environments such as bedrooms, dormitories and sickrooms, improves the sleep sustainability, continuity and sleep experience of users, and assists in health management of users.

Description

Method, system and device for sleep sustainability detection quantification and assisted intervention

Technical Field

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

Background

Sleep is the basic guarantee of human life processes such as physical energy and energy recovery, body growth and restoration, negative emotion elimination and the like. The difficulty in falling asleep for a long time, the easy arousal and multi-arousal of sleep, the poor sleep maintenance capability or sustainability, the poor sleep quality, especially the excessive sleep arousal or sleep interruption severely limit the work ability, the object execution and the emotional expression of people in the daytime, seriously affect the normal learning, work and life of people, and even lead to serious nervous system diseases and psychologic diseases and the rehabilitation process of related diseases.

The sleep sustainability or sustainability is distinguished from the sleep circadian rhythm, which is broadly defined as a 24-hour large-scale behavior law of multi-day long-term sleep and wake-up, sleep-in and wake-up, and the sleep phase staging, which is defined as a small-cycle condition in which shallow sleep, deep sleep and rapid eye movement sleep alternate with each other in one-time sleep; the sleep sustainability or sustainability is a comprehensive measure of the level of sleep interruption and the level of continuous change of the normal sleep mode in the continuous change of the sleep state of the user, and whether the user has the ability to maintain the normal sleep mode without continuous change of arousals, and is an indispensable index in the evaluation of sleep practice.

However, in the clinical diagnosis and treatment of sleep medicine and the health management of sleep behaviors, only the sleep circadian rhythm and the sleep phase stage are primarily analyzed and evaluated at home and abroad, the sleep interruption level, the normal continuous change level of sleep and the sleep maintenance capability of a user in the continuous change of the sleep state are not analyzed, evaluated and intervened, namely a scientific quantitative analysis method and an auxiliary intervention method for the sleep sustainability are lacked, data, evidence and plan guidance cannot be provided for the sleep health management or the sleep medicine clinic, and the sleep interruption event is identified and reduced, so that the sleep sustainability, the continuity and the sleep experience of the user are ensured.

For example, in the prior art, patent document CN104434068A discloses that the sleep stages of a human body are monitored by using body temperature and heart rate information, and the sleep quality of the human body is judged, but the decomposition of different sleep stages is discrete, the continuity is lacked, and the quantization error is large, so that the fine granularity of subsequent control is not high; only comprehensive analysis is mentioned for the environmental impact, but no specific embodiment is given. 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 user and environmental acquisition information of the same period, determining instant photo-thermal comfort feeling of user, generating corresponding adjustment instruction, and sending the adjustment instruction to corresponding execution module. It is also seen that the prior art only proposes a fuzzy assumption, how to use physiological detection information in the sleep regulation process and how to scientifically and accurately control all the physiological detection information in the sleep regulation process have a blind zone, and no proposal is proposed for completing subsequent regulation by utilizing the influence of a sleep sustainability index and an observation environment on the index.

Thus, the prior art remains to be improved to accurately quantify sleep sustainability, improving the user sleep experience.

Disclosure of Invention

In view of the above-mentioned drawbacks of the prior art methods and the need for improvement, it is an object of the present invention to provide

A method for detecting and quantifying sleep sustainability and assisting intervention is characterized in that physiological state signals and environment state signals of a user are collected and analyzed, feature extraction and feature analysis are carried out, scientific quantification of sleep sustainability of the user is achieved, correlation influence of sleep environment factors on the sleep sustainability is further analyzed, an optimal sleep sustainability environment optimization scheme is extracted, dynamic optimization and adjustment of a sleep environment are achieved, sleep sustainability and sleep quality of the user are improved, and related health management of the user is assisted. The invention also provides a system for sleep sustainability detection quantification and auxiliary intervention, which is used for realizing the method. The invention also provides a device for sleep sustainability detection quantification and auxiliary intervention, which is used for realizing the system.

According to an object of the present invention, the present invention provides a method for sleep sustainability detection quantification and assisted intervention, comprising the following steps:

acquiring and monitoring physiological state signals and environmental state signals of a user in a sleeping process, processing the signals and analyzing the characteristics to generate physiological state characteristics and environmental state characteristics;

performing sleep state analysis, time sequence component analysis and sustainability quantitative analysis on the physiological state characteristics, evaluating the sleep interruption level, the sleep mode continuous change level and the sleep maintenance capability of the sleep state of the user, extracting a sleep sustainability index, and generating a sleep sustainability quantitative diary;

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 sustainability, extracting the optimal sleeping sustainability environment scheme, dynamically optimizing and adjusting the sleeping environment, and generating a sleeping sustainability quantitative report.

Preferably, the step of collecting, monitoring, signal processing, and feature analyzing the physiological status signal and the environmental status signal of the user during the sleep process, and generating the physiological status feature and the environmental status feature further specifically includes:

collecting and monitoring the sleeping process of the user to generate the physiological state signal and the environmental state signal;

performing signal data preprocessing and time frame processing on the physiological state signal and the environmental state signal to obtain physiological state information and environmental state information;

and performing time frame characteristic analysis on the physiological state information and the environmental state information to generate the physiological state characteristics and the environmental state characteristics.

Preferably, the physiological state signal at least comprises electroencephalogram signal data, electrocardio 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.

Preferably, the environmental status signal at least comprises 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 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 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 value characteristic sequence is composed of state signal mean values of different information types in the environment state information and at least comprises 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.

Preferably, the step of performing sleep state analysis, time sequence component analysis and quantitative sustainability analysis on the physiological state features, evaluating sleep interruption level, sleep mode continuous change level and sleep maintenance capability of the user sleep state, extracting a sleep sustainability index, and generating a quantitative sleep sustainability diary further specifically includes:

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;

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

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

, wherein />

Is a positive integer and->

；

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

, wherein

Is a positive integer and->

；

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

, wherein />

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 processing 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 sustainability 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, a sleep duration state time sequence trend component and a sleep duration state time sequence residual error component, and calculating to obtain sleep sustainability strength;

4) Performing arousal analysis on the state characteristic curve of the sleep duration to extract a sleep sustainability factor coefficient;

5) Calculating a product of the sleep sustainability strength and the sleep sustainability factor coefficient, generating the sleep sustainability index.

More preferably, the sleep sustainability strength is calculated as follows:

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

；

wherein ,

is sleep sustainability intensity and->

，/>

In order to solve the function of the variance,

respectively for the sleep to continueA period state timing period component, the sleep duration state timing trend component, and the sleep duration state timing residual component;

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

；

wherein ,

is sleep sustainability intensity and->

，/>

In order to solve the function of the variance,

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

More preferably, the formula for calculating the sleep sustainability factor coefficient is as follows:

；

wherein ,

for sleep stationarity factor coefficient>

Is the number of waking period phases in the sleep duration state characteristic curve>

For the total number of all phases in the sleep duration state characteristic curve, based on the comparison of the time intervals and the time intervals>

Correction of the factor and ≥ for the physiological criterion relevant to the age group of the user>

。

More preferably, the sleep sustainability quantification diary includes at least a sleep sustainability analysis summary, the sleep sustainability index, the sleep state level curve, the sleep duration state characteristic curve, the environmental 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 sustainability, extracting the optimal sleep sustainability environment scheme and dynamically optimizing and adjusting the sleep environment, and generating the sleep sustainability quantification report further specifically includes:

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

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

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

according to the sleep environment optimization adjustment scheme, a sleep environment regulation device is connected to perform dynamic optimization adjustment on the sleep environment;

generating the sleep sustainability quantification report according to the environment state index mean characteristic sequence curve, the sleep sustainability index curve, the sleep sustainability environment impact factor sequence, and the optimal sleep sustainability environment scenario.

More preferably, the sleep sustainability quantification report includes at least a sleep sustainability analysis summary, a sleep sustainability adjustment profile, the environmental state indicator mean characteristic sequence curve, the sleep sustainability index curve, the sleep sustainability environmental impact factor sequence, and the optimal sleep sustainability environmental profile.

More preferably, the sleep sustainability environmental impact factor sequence comprises at least an ambient light source illumination correlation index, an ambient light source spectral 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 sustainability 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; 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 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.

Preferably, the method for extracting the sleep sustainability environment influence factor sequence comprises the following steps:

1) Continuously monitoring and tracking and analyzing the physiological state signal and the environmental state signal of the user, and calculating and obtaining the daily environmental state index mean characteristic sequence and the sleep sustainability index;

2) According to a date time sequence, calculating to obtain an environment state index mean value characteristic sequence curve and a sleep sustainability 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 sustainability index curve to generate a sleep sustainability environment index mean value relevance matrix;

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

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

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

4) Judging whether the optimal sleep sustainability index date set is an empty set or not, if so, performing descending arrangement on the sleep sustainability indexes of the optimal sleep sustainability index curve and screening the number of preset heads to generate the optimal sleep sustainability 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 sustainability 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 sustainability 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 an object of the present invention, the present invention provides a system for sleep sustainability detection quantification and assisted intervention, comprising the following modules:

the signal acquisition processing module is used for acquiring physiological state signals and environmental state signals 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 characteristic analysis on the physiological state information and the environmental state information to generate physiological state characteristics and environmental state characteristics;

the sustainability quantification module is used for performing sleep state analysis, time sequence component analysis and sustainability quantification analysis on the physiological state characteristics, evaluating the sleep interruption level, the sleep mode continuous change level and the sleep maintenance capability of the sleep state of the user, extracting a sleep sustainability index and generating a sleep sustainability quantification daily report;

the tracking quantitative analysis module 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 sustainability, extracting an optimal sleeping sustainability environment scheme and generating a sleeping sustainability 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 sustainability environment scheme and by combining the current environment state information;

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

Preferably, the signal acquisition processing module comprises the following functional units:

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

the environment state monitoring unit is used for acquiring the environment state signal of the user in the sleeping process; the environment state signals at least comprise 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 signal data of the physiological state signal and the environmental state signal; 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 signal and the environmental state signal; and 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 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 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 at least comprise the 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 sustainability quantification 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;

a sustainability quantification unit, configured to perform timing component analysis and sustainability quantification analysis on the sleep duration state characteristic curve, and extract the sleep sustainability 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 sustainability quantitative daily report; the sleep sustainability quantification diary includes at least a sleep sustainability analysis summary, the sleep sustainability index, the sleep state level curve, the sleep duration state characteristic curve, the environmental state indicator mean characteristic sequence.

More preferably, the tracking quantification 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 signal and the environmental state signal of the user for multiple days to obtain an environmental state index mean value characteristic sequence curve and a sleep sustainability 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 sustainability index curve to obtain the sleep sustainability environment influence factor sequence; the sleep sustainability environmental impact 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 sustainability environment scenario; the optimal sleep sustainability 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 generation unit, configured to generate the sleep sustainability quantitative report according to the environment state index mean characteristic series curve, the sleep sustainability index curve, the sleep sustainability environment influence factor series, and the optimal sleep sustainability environment scenario; the sleep sustainability quantification report includes at least a sleep sustainability analysis summary, a sleep sustainability adjustment scheme, the environmental state index mean characteristic sequence curve, the sleep sustainability index curve, the sleep sustainability environmental impact factor sequence, and the optimal sleep sustainability environmental profile.

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 sustainability 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 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.

According to an aspect of the present invention, the present invention provides a sleep sustainability detection quantification and intervention assistance device, which includes the following modules:

the signal acquisition and processing module is used for acquiring physiological state signals and environmental state signals 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 sustainability quantification module is used for carrying out time frame characteristic analysis on the physiological state information and the environmental state information to generate physiological state characteristics and environmental state characteristics; performing sleep state analysis, time sequence component analysis and sustainability quantitative analysis on the physiological state characteristics, evaluating the sleep interruption level, the sleep mode continuous change level and the sleep maintenance capability of the sleep state of the user, extracting a sleep sustainability index, and generating a sleep sustainability quantitative daily report;

the tracking and quantitative analysis module 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 sustainability, extracting an optimal sleeping sustainability environment scheme and generating a sleeping sustainability quantitative report;

a data visualization module for visually presenting all process data of the device, the physiological state signal, the environmental state signal, the sleep sustainability quantification daily report, the sleep sustainability quantification report, the optimal sleep sustainability environmental profile, and the sleep environment optimization adjustment profile;

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

The method, the system and the device for sleep sustainability detection quantification and auxiliary intervention can realize real-time or off-line analysis quantification and auxiliary intervention of user sleep sustainability, collect and analyze physiological state signals and environmental state signals of users, extract and analyze characteristics, fully excavate a plurality of incidence relations of sleep by utilizing dynamic analysis to realize scientific quantification of the user sleep sustainability due to the difference of different environments of different users, extract an optimal sleep sustainability environment optimization scheme and realize dynamic optimization and adjustment of sleep environment based on analysis of the incidence influence of sleep environment factors on the sleep sustainability. The invention can enable or cooperate with other sleep related products and services to be deployed in various human living environments such as bedrooms, dormitories and wards, improves the sleep sustainability, continuity and sleep experience of users, and assists the related health management of the users.

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 the 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 sustainability quantification and assisted intervention, according to an embodiment of the present invention;

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

FIG. 3 is a block diagram of an embodiment of a sleep sustainability quantification and intervention assistance device.

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, the method for sleep sustainability detection quantification and assisted intervention provided by the embodiment of the present invention comprises the following steps:

p100: and acquiring and monitoring the physiological state signal and the environmental state signal of the user in the sleeping process, processing the signal and analyzing the characteristic to generate the physiological state characteristic and the environmental state characteristic.

The first step is to collect and monitor the sleeping process of a user to generate a physiological state signal and an environmental state signal.

In the embodiment, physiological sign changes of a user in the sleeping process are collected and monitored through physiological monitoring equipment to generate a physiological state signal; the physiological state signal at least comprises electroencephalogram signal data, electrocardio signal data, respiration signal data, blood oxygen signal data and body temperature signal data.

In the embodiment, the environmental factor change of the user in the sleeping process is collected and monitored through the environmental monitoring equipment to generate an environmental state signal; the environmental status signal 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.

And secondly, performing signal data preprocessing and time frame processing on the physiological state signal and the environmental state signal to obtain physiological state information and environmental state information.

In this embodiment, the physiological status signal and the environmental status signal are analyzed and processed respectively according to the attribute of the signal, so as to obtain the physiological status information and the environmental status information. 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; 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 includes at least a/D conversion, resampling, artifact removal, noise reduction, notching, bandpass filtering, invalidation, re-referencing, and smoothing. The data preprocessing of the physiological state signals 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; artifact, wavelet noise reduction, 50hz notch and 0.01-5hz band pass filtering are performed on the respiration signal, the blood oxygen signal and the body temperature signal. The data preprocessing of the environment state signal 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 sliding segmentation of the window.

And thirdly, performing time frame characteristic analysis on the physiological state information and the environmental state information to generate physiological state characteristics and environmental state characteristics.

In this embodiment, the time frame 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, time frame characteristic analysis is performed on physiological state information to generate physiological state 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.

In the embodiment, time frame characteristic analysis is performed on the environmental state information to generate environmental state characteristics; the environment state characteristics at least comprise 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 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.

P200: and performing sleep state analysis, time sequence component analysis and sustainability quantitative analysis on the physiological state characteristics, evaluating the sleep interruption level, the sleep mode continuous change level and the sleep maintenance capability of the sleep state of the user, extracting a sleep sustainability index, and generating a sleep sustainability quantitative diary.

The method comprises the steps of firstly, carrying out sleep state analysis on physiological state characteristics, identifying a sleep state characteristic time phase and a sleep state level of a user, generating a sleep state characteristic curve of the user, and extracting a sleep duration state characteristic curve.

In the embodiment, physiological state characteristics are subjected to fusion analysis according to an AASM sleep staging rule, a sleep behavior analysis principle and a sleep staging 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:

；

；

；

；

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, carrying out 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-awake period time phase frame as the beginning and the last non-awake period time phase as the end to obtain a sleep duration state characteristic curve.

And secondly, performing time sequence component analysis and sustainability quantitative analysis on the sleep duration state characteristic curve, evaluating the sleep interruption level, the sleep mode continuous change level and the sleep maintenance capability of the sleep state of the user, extracting a sleep sustainability index and generating a sleep sustainability quantitative daily report.

In this embodiment, the characteristic of the state characteristic curve of the sleep duration is determined and time sequence component analysis is completed, wherein the time sequence component analysis at least comprises additive time sequence component analysis and multiplicative time sequence component analysis; second, the calculation of the sleep sustainability index and the generation of a quantitative daily report are completed.

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

1) Acquiring a sleep duration state characteristic curve;

4) Performing arousal analysis on the state characteristic curve of the sleep duration, and extracting a sleep sustainability factor coefficient;

5) A product of the sleep sustainability strength and the sleep sustainability factor coefficient is calculated to generate a sleep sustainability index.

In this embodiment, the method for calculating the sleep sustainability strength includes the following steps:

；

wherein ,

is sleep sustainability intensity and->

，/>

In order to solve the function of the variance,

respectively a sleep duration state time sequence period component, a sleep duration state time sequence trend component and a sleep duration state time sequence residual error component;

；

wherein ,

is sleep sustainability intensity and->

，/>

In order to solve the function of the variance,

respectively a sleep duration state time sequence period component, a sleep duration state time sequence trend component and a sleep duration state time sequence residual component.

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

；

wherein ,

for sleep stationarity factor coefficient>

Is the number of waking phase phases in the sleep duration state characteristic curve>

For the total number of all phases in the sleep duration state characteristic curve>

。

In this embodiment, the sleep sustainability quantification diary includes at least a sleep sustainability analysis summary, a sleep sustainability index, a sleep state level curve, a sleep duration state characteristic curve, and an environmental state index mean characteristic sequence.

P300: and repeating the steps, continuously monitoring and tracking and analyzing the sleep process of the user, evaluating the influence of the sleep environment on the sleep sustainability, extracting an optimal sleep sustainability environment scheme, dynamically optimizing and adjusting the sleep environment, and generating a sleep sustainability quantitative report.

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

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

In this embodiment, the sleep sustainability 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 sustainability environment influence factor sequence comprises the following steps:

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

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

3) Sequentially calculating the relevance characteristics of an environment state index mean value curve and a sleep sustainability index curve of one type in the environment state index mean value characteristic sequence curve to generate a sleep sustainability 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 sustainability environment index mean correlation matrix to generate a sleep sustainability environment influence factor sequence.

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

In this embodiment, the optimal sleep sustainability environment scheme at least includes an ambient light source illumination guidance parameter, an ambient light source spectrum guidance parameter, an ambient air pressure guidance parameter, an ambient temperature guidance parameter, an ambient humidity guidance parameter, an ambient microparticle guidance parameter, an ambient noise guidance parameter, an ambient oxygen concentration guidance parameter, an ambient carbon dioxide concentration guidance parameter, and an ambient formaldehyde concentration guidance parameter. A method of extracting an optimal sleep sustainability environment profile, comprising:

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

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

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

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

In this embodiment, the sleep sustainability quantification report includes at least a sleep sustainability analysis summary, a sleep sustainability adjustment scheme, an environmental state index mean characteristic curve, a sleep sustainability index curve, a sleep sustainability environmental impact factor sequence, and an optimal sleep sustainability environmental scenario.

As shown in FIG. 2, embodiments of the present invention provide a system for sleep sustainability detection quantification and assisted intervention, which is configured to perform the above-mentioned method steps. The system comprises the following modules:

the signal acquisition and processing module S100 is used for acquiring physiological state signals and environmental state signals 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 characteristic analysis on the physiological state information and the environmental state information to generate physiological state characteristics and environmental state characteristics;

a sustainability quantification module S300, configured to perform sleep state analysis, timing component analysis and sustainability quantification analysis on the physiological state features, evaluate a sleep interruption level, a sleep mode continuous change level and a sleep maintenance capability of the user sleep state, extract a sleep sustainability index, and generate a sleep sustainability quantification daily report;

the tracking quantitative analysis module S400 is used for continuously monitoring and tracking and analyzing the sleep process of the user, evaluating the influence of the sleep environment on the sleep sustainability, extracting an optimal sleep sustainability environment scheme and generating a sleep sustainability 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 sustainability environment scheme by combining the current environment state information;

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

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

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

the environment state monitoring unit S120 is used for collecting an environment state signal of a user in a sleeping process; the environment state signal 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 signal and the environmental state signal; 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, for performing time frame processing on the physiological status signal and the environmental status signal; 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.

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 sustainability quantification 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;

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

a quantitative daily report generation unit S330 for generating a sleep sustainability quantitative daily report; the sleep sustainability quantification diary includes at least a sleep sustainability analysis summary, a sleep sustainability index, a sleep state level curve, a sleep duration state characteristic curve, an environmental state index mean characteristic sequence.

In this embodiment, the tracking quantization 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 signal and the environmental state signal of the user for multiple days to obtain an environmental state index mean value characteristic sequence curve and a sleep sustainability index curve;

the environment influence analysis unit S420 is used for calculating the correlation characteristics of the environment state index mean value characteristic sequence curve and the sleep sustainability index curve to obtain a sleep sustainability environment influence factor sequence; the sleep sustainability environment influence factor sequence at least comprises 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 S430 for extracting an optimal sleep sustainability environment scenario; the optimal sleep sustainability 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;

a quantitative report generation unit S440, configured to generate a sleep sustainability quantitative report according to the environment state index mean characteristic sequence curve, the sleep sustainability index curve, the sleep sustainability environment influence factor sequence, and the optimal sleep sustainability environment scheme; the sleep sustainability quantification report includes at least a sleep sustainability analysis summary, a sleep sustainability adjustment scheme, an environmental state index mean value signature curve, a sleep sustainability index curve, a sleep sustainability environmental impact factor sequence, and an optimal sleep sustainability environmental profile.

In this embodiment, the environment-assisted 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 sustainability 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 sustainability detection quantification and assistance intervention device, which includes the following modules:

the signal acquisition and processing module M100 is used for acquiring a physiological state signal and an environmental state signal 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 sustainability quantification module M200 is used for performing time frame characteristic analysis on the physiological state information and the environmental state information to generate physiological state characteristics and environmental state characteristics; performing sleep state analysis, time sequence component analysis and sustainability quantitative analysis on physiological state characteristics, evaluating sleep interruption level, sleep mode continuous change level and sleep maintenance capability of a user sleep state, extracting sleep sustainability index, and generating a sleep sustainability quantitative daily report;

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

the environment auxiliary regulation and control module M400 is used for generating a sleep environment optimization and adjustment scheme and dynamically optimizing and adjusting the sleep environment according to the optimal sleep sustainability environment scheme by combining the current environment state information;

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

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

The apparatus is configured to perform the steps of the method of fig. 1, and will not be described herein.

The invention also provides various programmable processors (FPGA, ASIC or other integrated circuits) for running programs, wherein the steps in the above embodiments are performed when the programs are run.

The invention also provides corresponding computer equipment, which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the steps in the embodiment are realized when the memory executes the program.

Although the embodiments of the present invention have been described above, the above description is only for the convenience 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 sustainability detection quantification and assisted intervention, comprising the steps of:

performing sleep state analysis, time sequence component analysis and sustainability quantitative analysis on the physiological state characteristics, evaluating the sleep interruption level, the sleep mode continuous change level and the sleep maintenance capability of the sleep state of the user, extracting a sleep sustainability index, and generating a sleep sustainability quantitative daily report;

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

2. The method of claim 1, wherein the step of collecting, monitoring, signal processing, and analyzing the physiological status signal and the environmental status signal of the user during the sleep process to generate the physiological status signature and the environmental status signature further comprises:

3. The method of claim 2, wherein: the physiological state signals comprise electroencephalogram signal data, electrocardio signal data, respiration signal data, blood oxygen signal data and body temperature signal data; the physiological state information at least comprises at least one item of electroencephalogram state information, electrocardio state information, respiration state information, blood oxygen state information and body temperature state information.

4. The method of claim 2, wherein: the environment state signal 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 at least one of 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.

5. The method of claim 2, 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.

6. The method of claim 2, wherein: and 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.

7. The method of claim 2, wherein: the time frame characteristic analysis comprises at least one of numerical value characteristic analysis, envelope characteristic analysis, power spectrum characteristic analysis, entropy characteristic analysis, fractal characteristic analysis and complexity characteristic analysis.

8. The method of claim 1 or 2, 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.

9. The method of claim 1 or 2, 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 environment state information and at least 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.

10. The method of claim 1 or 2, wherein: the step of performing sleep state analysis, time sequence component analysis and quantitative sustainability analysis on the physiological state features, evaluating a sleep interruption level, a sleep mode continuous change level and a sleep maintenance capability of the sleep state of the user, extracting a sleep sustainability index, and generating a sleep sustainability quantitative daily report further specifically includes:

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;

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

, wherein />

Is a positive integer and->

；

, wherein />

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

, wherein />

Is a positive integer and->

；

4) Non-rapid eye movement deep sleep time phase stateThe sign level is taken as

, wherein />

Is a positive integer and->

。

12. The method of claim 10, wherein the sleep duration state characteristic is extracted by:

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 10, 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 1 or 2, wherein: the sleep sustainability index calculation method comprises the following steps:

1) Acquiring a sleep duration state characteristic curve;

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

；

wherein ,

is sleep sustainability intensity and->

，/>

For the purpose of a variance function>

Respectively the sleep duration state time sequence periodA period component, the sleep duration state timing trend component, and the sleep duration state timing residual component;

；

wherein ,

is sleep sustainability intensity and->

，/>

For the purpose of a variance function>

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

；

wherein ,

for sleep stationarity factor coefficient>

。

18. The method of claim 1, wherein: the sleep sustainability quantification diary includes at least a sleep sustainability analysis summary, the sleep sustainability index, a sleep state level curve, a sleep duration state characteristic curve, an environmental state index mean 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 sustainability, extracting an optimal sleep sustainability environment scheme and performing dynamic optimization adjustment on the sleep environment, and generating a sleep sustainability quantitative report further specifically includes:

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

generating a sleep environment optimization adjustment scheme according to the optimal sleep sustainability environment scheme by combining with 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;

20. The method of claim 19, wherein: the sleep sustainability quantification report includes at least one of a sleep sustainability analysis summary, a sleep sustainability adjustment scheme, the environmental state index mean characteristic sequence curve, the sleep sustainability index curve, the sleep sustainability environmental impact factor sequence, and the optimal sleep sustainability environmental profile.

21. The method of claim 19, wherein: the sleep sustainability environmental impact factor sequence 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 sustainability 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 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 sleep environment regulation and control equipment at least 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 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.

22. The method of claim 19, wherein the method of extracting the sequence of sleep sustainability environmental impact factors comprises:

23. The method of claim 19, wherein the method of extracting the optimal sleep sustainability environment scenario comprises:

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 sustainability detection quantification and assisted intervention, comprising:

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

the physiological state monitoring unit is used for acquiring the physiological state signal of the user in the sleeping process; the physiological state signal 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 acquiring the environment state signal of the user in the sleeping process; the environmental status signal 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;

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, the sustainability quantification module comprises the functional units of:

the quantitative daily report generating unit is used for generating the sleep sustainability quantitative daily report; the sleep sustainability quantification diary includes at least a sleep sustainability analysis summary, the sleep sustainability index, a sleep state level curve, the sleep duration state characteristic curve, an environmental state index mean characteristic sequence.

29. The system of claim 25, wherein the tracking quantification analysis module comprises the functional units of:

the tracking quantitative analysis unit is used for continuously acquiring, monitoring and tracking and analyzing the physiological state signal and the environmental state signal of the user for multiple days to obtain an environmental state index mean value characteristic sequence curve and the sleep sustainability 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 sustainability index curve to obtain the sleep sustainability environment influence factor sequence; the sleep sustainability environmental impact factor sequence comprises at least one of an ambient light source illumination correlation index, an ambient light source spectral 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 sustainability environment scenario; the optimal sleep sustainability 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;

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 sustainability environment scheme in combination with the current environment state information; 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 environment dynamic regulation and control unit 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 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 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.

31. A device for sleep sustainability detection quantification and assisted intervention is characterized by comprising the following modules:

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