CN115910351B

CN115910351B - Method, system and device for sleep periodic detection quantification and auxiliary intervention

Info

Publication number: CN115910351B
Application number: CN202310195991.2A
Authority: CN
Inventors: 何将
Original assignee: Anhui Xingchen Zhiyue Technology Co ltd
Current assignee: Anhui Xingchen Zhiyue Technology Co ltd
Priority date: 2023-03-03
Filing date: 2023-03-03
Publication date: 2023-07-07
Anticipated expiration: 2043-03-03
Also published as: CN115910351A

Abstract

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

Description

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

Technical Field

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

Background

Sleep is a basic physiological requirement and life health basic guarantee of a person, and the sleep time accounts for about one third of the life time of the person. With the rapid development of socioeconomic performance, various pressures of work, home, economy, society and the like are continuously increased, and sleeping problems of people are increasingly prominent and rising, and the risks of diseases related to physiology and psychology are also continuously increased. According to the guidelines of the american sleep medical society, the sleep stages of humans include a awake stage, a non-fast eye movement sleep stage NREM (light sleep stage 1, light sleep stage 2, deep sleep stage), a fast eye movement sleep stage REM. Good sleep is a cyclic process and has good periodicity, with NREM and REM alternating once for a cycle and back and forth, typically for healthy adults each cycle lasts 90-120 minutes, going through 4-5 cycles per night.

The periodic intensity of sleep is different from the circadian rhythm of sleep and the phase stage of sleep, the circadian rhythm of sleep is widely defined as 24-hour large-scale behavior rules of long-term sleep and wake, falling asleep and getting up, and the phase stage of sleep is defined as the small period condition of alternating light sleep, deep sleep and rapid eye movement sleep in the process of one sleep; the sleep periodic intensity is a comprehensive measure of the sleep normal periodic alternating mode continuous change level in the sleep state continuous change of the user and whether the user has the ability to maintain the normal sleep mode normal alternating continuous change, and is an index which is most indispensable in sleep practice evaluation.

At present, no clear method is available at home and abroad for accurately evaluating and quantifying the sleep periodic intensity of a user in clinical diagnosis and treatment or health management, the sleep quality and the sleep efficiency are difficult to further and scientifically judge and analyze, and meanwhile, no data basis is used as a gripper for realizing auxiliary intervention on the sleep periodicity of the user, namely scientific evaluation and auxiliary intervention on the sleep baseline periodic change intensity, the baseline periodic change trend and the change mode rationality of the sleep state of the user.

For example, patent document CN115525081a discloses a self-adaptive adjusting system and a control method for indoor environment of building, wherein a data processing module is used for integrating body posture information of a user and environment collection information of the same time period, judging instant photo-thermal comfort feeling of the user, generating corresponding adjusting instructions and sending the corresponding adjusting instructions to a corresponding executing module, however, the above scheme only refers to comprehensive use in general, does not carry out deep analysis on sleeping process, and does not propose an implementation mode. CN114839890a discloses a sleep environment control system, a sleep state detection device for detecting physiological data of a user, the physiological data including body movement data, heart rate data, breathing data; the analysis module performs the following calculation according to the detection data of the detection module: (1) Judging whether a user gets on bed, has a sleep wish or trend; (2) judging the sleep period; (3) Judging whether the sleep time accords with the rhythm improvement plan or not; (4) Judging whether the environmental data is consistent with preset environmental data or not; (5) Judging the compliance of the sleep, wake-up and rhythm of the user month, week and day with the big data standard plan; but does not teach how to calculate the sleep cycle. The above also shows that only fuzzy assumption is provided in the prior art, and blind areas exist on how to calculate the sleep period, how to scientifically measure the influence of environmental factors on the sleep period, how to accurately control the environmental change and the like.

Accordingly, the prior art is to be improved to accurately quantify sleep cycle variations and to efficiently improve the user sleep experience.

Disclosure of Invention

Aiming at the defects and improvement demands of the existing method, the invention aims to provide a sleep periodicity detection quantification and auxiliary intervention method, which realizes scientific quantification of sleep periodicity of a user by continuously collecting and analyzing physiological state data and environmental state data of the user and extracting features, further analyzes the associated influence of sleep environment factors on the sleep periodicity, extracts an optimal sleep periodicity environment optimization scheme, realizes dynamic optimization adjustment of the sleep environment, improves the sleep periodicity and sleep quality of the user, and assists related health management of the user. The invention also provides a system for detecting, quantifying and assisting in intervening the sleep periodicity, which is used for realizing the method. The invention also provides a device for detecting, quantifying and assisting in intervention of the sleep periodicity, which is used for realizing the system.

According to the purpose of the invention, the invention provides a method for detecting and quantifying sleep periodicity and assisting intervention, which comprises the following steps:

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

Performing time frame state characteristic analysis on the physiological state information and the environmental state information to generate physiological state characteristics and environmental state characteristics;

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

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

More preferably, the physiological state data at least comprises brain electrical signal data, electrocardiosignal data, respiratory signal data, blood oxygen signal data and body temperature signal data; the physiological state information at least comprises brain electrical state information, electrocardio state information, respiratory state information, blood oxygen state information and body temperature state information.

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

More preferably, the signal data preprocessing includes at least a/D conversion, resampling, de-artifacting, noise reduction, notch, bandpass filtering, de-invalidation, re-referencing, and smoothing.

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

More preferably, the time frame state feature analysis includes at least numerical feature analysis, envelope feature analysis, power spectrum feature analysis, entropy feature analysis, fractal feature analysis, and complexity feature analysis.

More preferably, the physiological state features include at least an electroencephalogram signal feature, an electrocardiographic signal feature, a respiratory signal feature, an oximetry signal feature, and a body temperature signal feature.

More preferably, the environmental state characteristics at least include the environmental state index mean characteristic sequence, an illumination signal characteristic, a spectrum signal characteristic, a barometric 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 illuminance mean value, a spectrum fusion mean value, an air pressure mean value, a temperature mean value, a humidity mean value, a micro-particle mean value, a noise mean value, an oxygen concentration mean value, a carbon dioxide concentration mean value and a formaldehyde concentration mean value.

More preferably, the step of performing sleep state analysis, time sequence component analysis and periodicity quantitative analysis on the physiological state features, evaluating the sleep baseline periodic variation intensity, the baseline periodic variation trend and the variation pattern rationality of the sleep state of the user, extracting the sleep periodic index, and generating the sleep periodic quantitative daily report further specifically includes:

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

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

More preferably, the sleep state characteristic phase includes a awake phase, a rapid eye movement sleep phase, a non-rapid eye movement light sleep phase, and a non-rapid eye movement deep sleep phase.

More preferably, the sleep state level value dividing method is as follows:

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

1) The state characteristic level of the phase of the awake period is taken as the value

Wherein->

Is a positive integer and->

；

2) The state characteristic level of the phase of the rapid eye movement sleep stage is as follows

Wherein

Is a positive integer and->

；

3) The state characteristic level of the phase of the non-rapid eye movement light sleep stage is taken as the value

Wherein->

Is a positive integer and->

；

4) The state characteristic level of the phase of the non-rapid eye movement deep sleep stage is taken as the value

Wherein->

Is a positive integer and->

。

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

1) Acquiring the physiological state characteristics according to a time frame time sequence, identifying 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, and generating 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) Based on the sleep state characteristic curve, taking the first non-awake phase time phase frame as a start and the last non-awake phase time phase as an end, intercepting the sleep state characteristic curve to obtain the sleep duration state characteristic curve.

More preferably, the data smoothing method at least comprises moving average, mean value filtering, SG filtering, low-pass filtering and Kalman filtering.

More preferably, the timing component analysis includes at least an additive timing component analysis and a multiplicative timing component analysis.

More preferably, the method for calculating sleep periodicity index includes:

1) Acquiring the sleep duration state characteristic curve;

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) Analyzing the corresponding time sequence component of the sleep duration state characteristic curve to obtain a sleep duration state time sequence period component and a sleep duration state time sequence residual component, and calculating to obtain sleep periodic intensity;

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

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

More preferably, the method for calculating the sleep periodic intensity comprises the following steps:

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

；

wherein,,

is sleep periodic intensity and->

，/>

For variance function >

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

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

；

wherein,,

is sleep periodic intensity and->

，/>

For variance function>

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

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

；

wherein,,

is a sleep periodic factor coefficient and +>

，/>

Signal average cycle time for the sleep duration state timing cycle component, +.>

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

More preferably, the sleep cycle quantification daily report at least comprises a sleep cycle analysis summary, the sleep cycle index, the sleep state level curve, the sleep duration state characteristic curve and the environmental state index average characteristic sequence.

More preferably, the steps are repeated, the sleep process of the user is continuously monitored and tracked and analyzed, the influence of the sleep environment on the sleep periodicity is evaluated, the optimal sleep periodicity environment scheme is extracted, the sleep environment is dynamically optimized and adjusted, and the step of generating the sleep periodicity quantized report further specifically comprises:

Continuously collecting, monitoring and tracking and analyzing the physiological state data and the environmental state data of the user for a plurality of continuous days to obtain the environmental state index mean value characteristic sequence curve and the sleep periodic index curve;

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

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

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

and generating the sleep periodicity quantitative report according to the environment state index mean value characteristic sequence curve, the sleep periodicity index curve, the sleep periodicity environmental impact factor sequence and the optimal sleep periodicity environmental scheme.

More preferably, the sleep periodicity quantitative report at least comprises a sleep periodicity analysis summary, a sleep periodicity adjustment scheme, the environmental status index mean characteristic sequence curve, the sleep periodicity index curve, the sleep periodicity environmental impact factor sequence and the optimal sleep periodicity environmental scheme.

More preferably, the sleep periodic 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 micro-particle correlation index, an ambient noise correlation index, an ambient oxygen concentration correlation index, an ambient carbon dioxide concentration correlation index, and an ambient formaldehyde concentration correlation index; the optimal sleep periodic environment scheme at least comprises an ambient light source illumination guiding parameter, an ambient light source spectrum guiding parameter, an ambient air pressure guiding parameter, an ambient temperature guiding parameter, an ambient humidity guiding parameter, an ambient micro-particle guiding parameter, an ambient noise guiding parameter, an ambient oxygen concentration guiding parameter, an ambient carbon dioxide concentration guiding parameter and an ambient formaldehyde concentration guiding parameter; the sleep environment optimization adjustment scheme at least 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 micro-particle 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 an environment light source illumination regulation and control equipment, an environment light source spectrum regulation and control equipment, an environment air pressure regulation and control equipment, an environment temperature regulation and control equipment, an environment humidity regulation and control equipment, an environment micro-particle regulation and control equipment, an environment noise regulation and control equipment, an environment oxygen concentration regulation and control equipment, an environment carbon dioxide concentration regulation and control equipment and an environment formaldehyde concentration regulation and control equipment.

More preferably, the method for extracting the sleep periodic environment influence factor sequence comprises the following steps:

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

2) According to the time sequence of the date, calculating to obtain the average value characteristic sequence curve of the environmental state indexes and the sleep periodic index curve corresponding to all the dates;

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

4) According to different information types of the sleep environment state information, carrying out coefficient reconciliation on associated coefficient indexes of the sleep environment state information of different information types in the sleep periodic environment index mean value associated matrix, and generating the sleep periodic environment influence factor sequence.

More preferably, the method for extracting the optimal sleep periodic environment scheme comprises the following steps:

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

4) Judging whether the optimal sleep periodic index date set is an empty set, if so, arranging the sleep periodic indexes of the optimal sleep periodic index curve in a descending order, screening the number of preset heads, and generating the optimal sleep periodic index date set;

5) Extracting environmental state index mean features of corresponding dates from the environmental state index mean feature sequence curve according to the date of the optimal sleep periodic index date set, and generating an optimal sleep environmental state index mean set;

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

More preferably, the calculation mode of the environmental state index fusion processing at least comprises mean value processing, normal weighting processing, increasing weighting processing and decreasing weighting processing.

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

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

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

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

the continuous tracking 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 periodicity, extracting the optimal sleeping periodicity environment scheme and generating a sleeping periodicity quantized report;

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

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

More preferably, the state acquisition processing module comprises the following functional units:

the physiological state monitoring unit is used for collecting the physiological state data of the sleeping process of the user; the physiological state data at least comprise brain electrical signal data, electrocardiosignal data, respiratory signal data, blood oxygen signal data and body temperature signal data;

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

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

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

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

the numerical feature analysis unit is used for performing numerical feature analysis on the physiological state information and the environment state information;

an envelope feature analysis unit for performing envelope feature 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 environment state information;

the entropy feature analysis unit is used for performing entropy feature analysis on the physiological state information and the environment state information;

the fractal characteristic analysis unit is used for carrying out fractal characteristic 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 environment state information;

a physiological characteristic integration unit, configured to integrate and generate the physiological status characteristic; the physiological state characteristics at least comprise brain electrical signal characteristics, electrocardiosignal characteristics, respiratory 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 characteristics; the environmental state characteristics at least comprise the environmental state index mean value characteristic sequence, illumination signal characteristics, spectrum signal characteristics, air pressure signal characteristics, temperature signal characteristics, humidity signal characteristics, micro-particle signal characteristics, noise signal characteristics, oxygen concentration signal characteristics, carbon dioxide concentration signal characteristics and formaldehyde concentration signal characteristics.

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

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

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

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

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

the tracking quantitative analysis unit is used for continuously collecting, monitoring and tracking and analyzing the physiological state data and the environmental state data of the user for a plurality of days to obtain the environmental state index mean value characteristic sequence curve and the sleep periodic 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 periodic index curve to obtain the sleep periodic environment influence factor sequence; the sleep periodic environment influence factor sequence at least comprises an environment light source illumination correlation index, an environment light source spectrum correlation index, an environment air pressure correlation index, an environment temperature correlation index, an environment humidity correlation index, an environment micro-particle correlation index, an environment noise correlation index, an environment oxygen concentration correlation index, an environment carbon dioxide concentration correlation index and an environment formaldehyde concentration correlation index;

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

The quantitative report generation unit is used for generating the sleep periodic quantitative report according to the environmental state index mean value characteristic sequence curve, the sleep periodic index curve, the sleep periodic environment influence factor sequence and the optimal sleep periodic environment scheme; the sleep periodicity quantitative report at least comprises a sleep periodicity analysis summary, a sleep periodicity adjustment scheme, the environmental state index mean value characteristic sequence curve, the sleep periodicity index curve, the sleep periodicity environmental impact factor sequence and the optimal sleep periodicity environmental scheme.

More preferably, the environment auxiliary regulation module comprises the following functional units:

the environment scheme generating unit is used for generating the sleep environment optimization adjustment scheme according to the optimal sleep periodic environment scheme and combining the current environment state information; the sleep environment optimization adjustment scheme at least 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 micro-particle execution parameter, an environment noise execution parameter, an environment oxygen concentration execution parameter, an environment carbon dioxide concentration execution parameter and an environment formaldehyde concentration execution parameter;

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

According to the purpose of the invention, the invention provides a device for detecting, quantifying and assisting in intervening sleep periodicity, which comprises the following modules:

the periodic characteristic analysis module is used for carrying out time frame state characteristic analysis on the physiological state information and the environment state information to generate physiological state characteristics and environment state characteristics; performing sleep state analysis, time sequence component analysis and periodic quantitative analysis on the physiological state characteristics, evaluating the sleep baseline periodic variation intensity, baseline periodic variation trend and variation pattern rationality of the sleep state of the user, extracting a sleep periodic index, and generating a sleep periodic quantitative daily report;

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

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

The method, the system and the device for the sleep periodic detection quantification and the auxiliary intervention provided by the invention have the characteristics of real-time analysis and offline analysis, and the analysis quantification and the auxiliary intervention of the sleep periodic real-time or offline analysis of the user are satisfied by collecting, analyzing and extracting the physiological sign data and the environmental factor data of the user. Due to the difference of different environments of different users, the dynamic analysis is utilized to adjust a plurality of association relations fully mined for sleeping so as to realize scientific quantification of the sleeping periodicity of the users, and the difference of different individuals can be incorporated into the adjustment process, so that the system is a continuous learning and iteration system. And extracting an optimal environment optimization scheme based on analysis of the association influence of sleep environment factors on sleep periodicity. The invention can enable or cooperate with other sleep related products and services to be deployed in various living environments, thereby improving the sleep periodicity and sleep quality of the user and assisting the related health management of the user.

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

Drawings

The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate and do not limit the invention.

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

FIG. 2 is a schematic diagram illustrating a system for quantifying sleep cycle detection and assisting in intervention according to an embodiment of the present invention;

fig. 3 is a schematic diagram of a module configuration of a device for detecting and quantifying sleep periodicity and assisting in intervention according to an embodiment of the present invention.

Detailed Description

In order to more clearly illustrate the objects and technical solutions of the present invention, the present invention will be further described below with reference to the accompanying drawings in the embodiments of the present invention. It will be apparent that the embodiments described below are only some, but not all, embodiments of the invention. Other embodiments, which are derived from the embodiments of the invention by a person skilled in the art without creative efforts, shall fall within the protection scope of the invention. It should be noted that, in the case of no conflict, the embodiments and features in the embodiments may be arbitrarily combined with each other.

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

p100: physiological state data and environmental state data of a user in the sleeping process are collected, and signal data preprocessing and time frame processing are carried out to obtain physiological state information and environmental state information.

In this embodiment, the physiological state data at least includes brain electrical signal data, electrocardiographic signal data, respiration signal data, blood oxygen signal data, and body temperature signal data; the physiological state information at least includes brain electrical state information, electrocardiographic state information, respiratory state information, blood oxygen state information, and body temperature state information.

In this embodiment, the environmental status data at least includes illumination signal data, spectrum signal data, air pressure signal data, temperature signal data, humidity signal data, microparticle signal data, noise signal data, oxygen concentration signal data, carbon dioxide concentration signal data, and formaldehyde concentration signal data; the environmental status information includes at least illumination status information, spectrum status information, air pressure 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, notch, band pass filtering, invalidation removal, re-referencing, and smoothing. The data preprocessing of the physiological state data mainly comprises the steps of removing artifacts, reducing noise by wavelet, carrying out 50hz notch and carrying out 0.1-45hz band-pass filtering on the electroencephalogram signals and the electrocardiosignals; artifact, wavelet noise reduction, 50hz notch and 0.01-5hz band pass filtering are performed on the respiratory signal, blood oxygen signal and body temperature signal. The data preprocessing of the environmental state data is mainly a/D conversion, de-artifacting, wavelet noise reduction.

In this embodiment, the time frame processing is to perform sliding segmentation of a window of a preset framing duration on signal data with a preset framing step according to a sampling rate of the signal, where the preset framing duration and the preset framing step are both 10 seconds, that is, there is no overlapping window sliding cutting.

P200: and carrying out time frame state characteristic analysis on the physiological state information and the environment state information to generate physiological state characteristics and environment state characteristics.

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

In this embodiment, time frame status feature analysis is performed on physiological status information to generate physiological status features; the physiological state features include at least an electroencephalogram signal feature, an electrocardiographic signal feature, a respiratory signal feature, a blood oxygen signal feature, and a body temperature signal feature.

In this embodiment, time frame status feature analysis is performed on the environmental status information to generate environmental 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, a barometric 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 environment state information and at least comprises an illuminance mean value, a spectrum fusion mean value, an air pressure mean value, a temperature mean value, a humidity mean value, a micro-particle mean value, a noise mean value, an oxygen concentration mean value, a carbon dioxide concentration mean value and a formaldehyde concentration mean value.

P300: and carrying out sleep state analysis, time sequence component analysis and periodic quantitative analysis on the physiological state characteristics, evaluating the sleep baseline periodic change intensity, the baseline periodic change trend and the change mode rationality of the sleep state of the user, extracting the sleep periodic index, and generating the sleep periodic quantitative daily report.

Firstly, carrying out sleep state analysis on physiological state characteristics, identifying sleep state characteristic time phases and sleep state levels of a user, generating a sleep state characteristic curve of the user, and extracting a sleep duration state characteristic curve.

In the embodiment, firstly, the physiological state characteristics are subjected to fusion analysis according to an AASM sleep stage rule and a sleep behavior analysis principle and a sleep stage deep learning model to obtain a sleep state characteristic time phase and a sleep state level of a user under all time frames; 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 phases include a awake phase, a rapid eye movement sleep phase, a non-rapid eye movement light sleep phase, and a non-rapid eye movement deep sleep phase.

In this embodiment, the method for dividing the sleep state level into level classes in different value ranges according to the feature time phase of each sleep state, where the level classes are a continuous positive integer sequence:

；

；

；

；

In this embodiment, the method for extracting the sleep duration state characteristic curve is as follows:

1) Acquiring physiological state characteristics according to a time frame time sequence, identifying sleep state characteristic time phases of a current frame and determining the value of a 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) Based on the sleep state characteristic curve, the sleep state characteristic curve is intercepted by taking the first non-awake time phase frame as the beginning and the last non-awake time phase as the ending, so as to obtain the sleep duration state characteristic curve.

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

In this embodiment, firstly, determining the characteristics of the sleep duration state characteristic curve and completing the time sequence component analysis, wherein the time sequence component analysis at least comprises additive time sequence component analysis and multiplicative time sequence component analysis; secondly, the calculation of the sleep periodicity index and the generation of the quantized daily report are completed.

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

1) Acquiring a sleep duration state characteristic curve;

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

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

In this embodiment, the method for calculating the sleep periodic intensity is as follows:

；

wherein,,

is sleep periodic intensity and->

，/>

For variance function>

A sleep duration state timing period component and a sleep duration state timing residual component, respectively;

；

Wherein,,

is sleep periodic intensity and->

，/>

For variance function>

In this embodiment, a calculation formula of the sleep periodicity factor coefficient is as follows:

；

wherein,,

is a sleep periodic factor coefficient and +>

，/>

Signal average cycle time for sleep duration state timing cycle component, +.>

In this embodiment, the sleep cycle quantization daily report at least includes a sleep cycle analysis summary, a sleep cycle index, a sleep state level curve, a sleep duration state characteristic curve, and an environmental state index average characteristic sequence.

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

The first step, continuously collecting, monitoring and tracking analysis are carried out on physiological state data and environmental state data of a user for a plurality of days, and an environmental state index mean value characteristic sequence curve and a sleep periodicity index curve are obtained.

Calculating the correlation characteristics of the environment state index mean characteristic sequence curve and the sleep periodic index curve to obtain a sleep periodic environment influence factor sequence, and extracting an optimal sleep periodic environment scheme.

In this embodiment, the sleep periodic 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 micro-particle correlation index, an ambient noise correlation index, an ambient oxygen concentration correlation index, an ambient carbon dioxide concentration correlation index, and an ambient formaldehyde concentration correlation index. The extraction method of the sleep periodic environment influence factor sequence comprises the following steps:

1) Continuously monitoring and tracking analysis of physiological state data and environmental state data of a user, and calculating to obtain an average value characteristic sequence of environmental state indexes and a sleep periodic index every day;

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

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

4) According to different information types of the sleep environment state information, carrying out coefficient reconciliation on associated coefficient indexes of the sleep environment state information of different information types in the sleep periodic environment index mean value associated matrix to generate a sleep periodic environment influence factor sequence.

Thirdly, according to the optimal sleep periodic environment scheme, combining the current environment state information to generate a sleep environment optimization adjustment scheme.

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

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

4) Judging whether the optimal sleep periodic index date set is an empty set, if so, arranging sleep periodic indexes of an optimal sleep periodic index curve in a descending order, screening the number of preset heads, and generating the optimal sleep periodic index date set;

5) According to the date of the optimal sleep periodic index date set, extracting the environmental state index mean characteristic of the corresponding date from the environmental state index mean characteristic sequence curve to generate an optimal sleep environmental state index mean set;

In this embodiment, the calculation method of the environmental status indicator fusion process at least includes a mean value process, a normal weighting process, an increasing weighting process and a decreasing 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 micro-particle 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 sleep environment regulating equipment according to a sleep environment optimizing and adjusting scheme, and dynamically optimizing and adjusting the sleep environment.

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

Fifthly, generating a sleep periodicity quantized report according to the environment state index mean value characteristic sequence curve, the sleep periodicity index curve, the sleep periodicity environment influence factor sequence and the optimal sleep periodicity environment scheme.

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

As shown in fig. 2, a system for quantifying sleep periodicity detection and assisting in intervention is provided according to an embodiment of the present invention, and is configured to perform the above-described method steps. The system comprises the following modules:

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

the time frame feature analysis module S200 is configured to perform time frame state feature analysis on the physiological state information and the environmental state information, and generate a physiological state feature and an environmental state feature;

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

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

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

The data management center module S600 is configured to perform unified storage and operation management on all process data of the system.

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

a physiological state monitoring unit S110, configured to collect physiological state data of a sleep process of a user; the physiological state data at least comprises brain electrical signal data, electrocardiosignal data, respiratory signal data, blood oxygen signal data and body temperature signal data;

an environmental state monitoring unit S120, configured to collect environmental state data of a sleeping process of a user; the environmental state data at least comprises illumination signal data, spectrum signal data, air pressure signal data, temperature signal data, humidity signal data, microparticle signal data, noise signal data, oxygen concentration signal data, carbon dioxide concentration signal data and formaldehyde concentration signal data;

a signal preprocessing unit S130, configured to perform signal data preprocessing on physiological status data and environmental status data; the signal data preprocessing at least comprises A/D conversion, resampling, artifact removal, noise reduction, notch, band-pass filtering, invalidation removal, re-referencing and smoothing processing;

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

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

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

an envelope feature analysis unit S220, configured to perform envelope feature analysis on the physiological state information and the environmental state information;

the power spectrum characteristic analysis unit S230 is used for performing 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;

the fractal feature analysis unit S250 is used for carrying out fractal feature analysis on the physiological state information and the environmental state information;

a complexity characteristic analysis unit S260, configured to perform complexity characteristic analysis on the physiological state information and the environmental state information;

a physiological characteristic integration unit S270, configured to integrate and generate physiological status characteristics; the physiological state characteristics at least comprise brain electrical signal characteristics, electrocardiosignal characteristics, respiratory signal characteristics, blood oxygen signal characteristics and body temperature signal characteristics;

an environmental feature integration unit S280, configured to integrate and generate environmental status features; the environmental state features include at least an environmental state index mean feature sequence, an illumination signal feature, a spectrum signal feature, a barometric pressure signal feature, a temperature signal feature, a humidity signal feature, a microparticle signal feature, a noise signal feature, an oxygen concentration signal feature, a carbon dioxide concentration signal feature, and a formaldehyde concentration signal feature.

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

a sleep state identification unit S310, configured to identify a sleep state characteristic time phase and a sleep state level of a user according to sleep state analysis on physiological state characteristics, generate a sleep state characteristic curve of the user, and extract a sleep duration state characteristic curve;

the period quantitative analysis unit S320 is used for carrying out time sequence component analysis and period quantitative analysis on the sleep duration state characteristic curve and extracting a sleep period index; the time sequence component analysis at least comprises an additive time sequence component analysis and a multiplicative time sequence component analysis;

a quantized daily report generating unit S330, configured to generate a sleep periodic quantized daily report; the sleep periodic quantitative daily report at least comprises a sleep periodic analysis summary, a sleep periodic index, a sleep state level curve, a sleep duration state characteristic curve and an environment state index average characteristic sequence.

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

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

The environmental impact analysis unit S420 is used for calculating the correlation characteristics of the environmental state index mean characteristic sequence curve and the sleep periodic index curve to obtain a sleep periodic environmental impact factor sequence; the sleep periodic environment influence factor sequence at least comprises an environment light source illumination correlation index, an environment light source spectrum correlation index, an environment air pressure correlation index, an environment temperature correlation index, an environment humidity correlation index, an environment micro-particle correlation index, an environment noise correlation index, an environment oxygen concentration correlation index, an environment carbon dioxide concentration correlation index and an environment formaldehyde concentration correlation index;

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

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

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

the environmental scheme generating unit S510 is configured to generate a sleep environment optimization adjustment scheme according to an optimal sleep periodic environmental scheme in combination with current environmental status information; the sleep environment optimization adjustment scheme at least 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 micro-particle execution parameter, an environment noise execution parameter, an environment oxygen concentration execution parameter, an environment carbon dioxide concentration execution parameter and an environment formaldehyde concentration execution parameter;

the environment dynamic regulation and control unit S520 is used for connecting sleep environment regulation and control equipment according to a sleep environment optimization and adjustment scheme to dynamically optimize and control the sleep environment of a user; the sleep environment regulating device at least comprises an environment light source illumination regulating device, an environment light source spectrum regulating device, an environment air pressure regulating device, an environment temperature regulating device, an environment humidity regulating device, an environment micro-particle regulating device, an environment noise regulating device, an environment oxygen concentration regulating device, an environment carbon dioxide concentration regulating device and an environment formaldehyde concentration regulating device.

As shown in fig. 3, a device for detecting, quantifying and assisting in intervention of sleep periodicity provided by an embodiment of the present invention includes the following modules:

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

the periodic characteristic analysis module M200 is used for carrying out time frame state characteristic analysis on the physiological state information and the environmental state information to generate physiological state characteristics and environmental state characteristics; performing sleep state analysis, time sequence component analysis and periodic quantitative analysis on the physiological state characteristics, evaluating the sleep baseline periodic variation intensity, baseline periodic variation trend and variation pattern rationality of the sleep state of the user, extracting the sleep periodic index, and generating a sleep periodic quantitative daily report;

the continuous tracking 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 periodicity, extracting the optimal sleep periodicity environment scheme and generating a sleep periodicity quantized report;

the environment auxiliary regulation and control module M400 is used for generating a sleep environment optimization regulation scheme according to an optimal sleep periodic environment scheme and combining current environment state information, and dynamically optimizing and regulating the sleep environment;

The data visualization module M500 is used for visualizing all process data, physiological state data, environmental state data, sleep periodic quantitative daily reports, sleep periodic quantitative reports, an optimal sleep periodic environment scheme and a sleep environment optimization adjustment scheme of the device;

the data management center module M600 is used for uniformly storing and operating and managing all process data of the device.

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

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

The invention also provides corresponding computer equipment, comprising a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the memory realizes the steps in the embodiment when the program is executed.

Although the embodiments of the present invention are described above, the embodiments are only used for facilitating understanding of the present invention, and are not intended to limit the present invention. Any person skilled in the art to which the present invention pertains may make any modifications, changes, equivalents, etc. in form and detail of the implementation without departing from the spirit and principles of the present invention disclosed herein, which are within the scope of the present invention. Accordingly, the scope of the invention should be determined from the following claims.

Claims

1. A method for sleep periodicity detection quantification and assisted intervention, comprising the steps of:

acquiring physiological state data and environmental state data of a user in a sleeping process, and performing signal data preprocessing and time frame processing to obtain physiological state information and environmental state information; the time frame processing is to conduct sliding segmentation of a preset framing duration window on signal data according to the sampling rate of the signal and with a preset framing step length;

performing time frame state characteristic analysis on the physiological state information and the environmental state information to generate physiological state characteristics and environmental state characteristics; the time frame state feature analysis comprises at least one of numerical feature analysis, envelope feature analysis, power spectrum feature analysis, entropy feature analysis, fractal feature analysis and complexity feature analysis;

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

The sleep periodicity index calculating method comprises the following steps:

1) Acquiring a sleep duration state characteristic curve;

5) Calculating the product of the sleep periodic intensity and the sleep periodic factor coefficient to generate the sleep periodic index;

the step 1) comprises the following steps: and carrying out sleep state analysis on 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.

2. The method of claim 1, wherein the physiological state data comprises at least one of brain electrical signal data, cardiac electrical signal data, respiratory signal data, blood oxygen signal data, and body temperature signal data; the physiological state information includes at least one of brain electrical state information, electrocardiographic state information, respiratory state information, blood oxygen state information, and body temperature state information.

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

4. The method of claim 1, wherein: the signal data preprocessing includes a/D conversion, resampling, de-artifacting, noise reduction, notch, bandpass filtering, de-invalidating, re-referencing, and smoothing.

5. The method of claim 1, wherein: the physiological state features include at least one of an electroencephalogram feature, an electrocardiographic feature, a respiratory signal feature, a blood oxygen signal feature, and a body temperature signal feature.

6. The method of claim 5, wherein: the environmental state characteristics comprise at least one of an environmental state index mean value characteristic sequence, an illumination signal characteristic, a spectrum signal characteristic, a barometric 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 average value characteristic sequence consists of state information averages of different information types in the environment state information, and comprises at least one of illuminance average value, spectrum fusion average value, air pressure average value, temperature average value, humidity average value, micro-particle average value, noise average value, oxygen concentration average value, carbon dioxide concentration average value and formaldehyde concentration average value.

7. The method according to any one of claims 1 to 6, wherein the steps of performing sleep state analysis, time sequence component analysis and periodic quantitative analysis on the physiological state characteristics, evaluating the sleep baseline periodic variation intensity, baseline periodic variation trend and variation pattern rationality of the sleep state of the user, extracting the sleep periodic index, and generating the sleep periodic quantitative daily report further specifically include:

8. The method of claim 7, wherein: the sleep state characteristic phases include a awake phase, a rapid eye movement sleep phase, a non-rapid eye movement light sleep phase, and a non-rapid eye movement deep sleep phase.

9. The method of claim 8, wherein the sleep state level is valued as follows:

Wherein->

Is a positive integer and->

；

Wherein->

Is a positive integer and->

；

Wherein->

Is a positive integer and->

；

Wherein->

Is a positive integer and->

。

10. The method of claim 7, wherein the sleep duration state characteristic is extracted by the following method:

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

11. The method of claim 10, wherein: the data smoothing method comprises at least one of moving average, mean value filtering, SG filtering, low-pass filtering and Kalman filtering.

12. The method of claim 7, wherein: the timing component analysis includes an additive timing component analysis and a multiplicative timing component analysis.

13. The method of claim 1, wherein the sleep cycle intensity is calculated by:

；

wherein,,

is sleep periodic intensity and->

，/>

For variance function>

；

wherein,,

is sleep periodic intensity and->

，/>

For variance function>

14. The method of claim 13 wherein the sleep periodicity factor coefficient is calculated as:

；

Wherein,,

is a sleep periodic factor coefficient and +>

，/>

15. The method of claim 1, wherein: the sleep periodicity quantization daily report comprises at least one of a sleep periodicity analysis summary, the sleep periodicity index, a sleep state level curve, a sleep duration state characteristic curve and an environmental state index average value characteristic sequence.

16. The method of claim 1, wherein: the steps are repeated, the sleep process of the user is continuously monitored and tracked and analyzed, the influence of the sleep environment on the sleep periodicity is evaluated, the optimal sleep periodicity environment scheme is extracted, the sleep environment is dynamically optimized and adjusted, and the step of generating the sleep periodicity quantized report further specifically comprises the following steps:

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

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

17. The method as recited in claim 16, wherein: the sleep periodicity quantitative report comprises at least one of a sleep periodicity analysis summary, a sleep periodicity adjustment scheme, the environmental status index mean characteristic sequence curve, the sleep periodicity index curve, the sleep periodicity environmental impact factor sequence and the optimal sleep periodicity environmental scheme.

18. The method as recited in claim 16, wherein: the sleep periodic environment influence factor sequence comprises an environment light source illumination correlation index, an environment light source spectrum correlation index, an environment air pressure correlation index, an environment temperature correlation index, an environment humidity correlation index, an environment micro-particle correlation index, an environment noise correlation index, an environment oxygen concentration correlation index, an environment carbon dioxide concentration correlation index and an environment formaldehyde concentration correlation index; the optimal sleep periodic environment scheme comprises at least one of an ambient light source illumination guiding parameter, an ambient light source spectrum guiding parameter, an ambient air pressure guiding parameter, an ambient temperature guiding parameter, an ambient humidity guiding parameter, an ambient micro-particle guiding parameter, an ambient noise guiding parameter, an ambient oxygen concentration guiding parameter, an ambient carbon dioxide concentration guiding parameter and an ambient formaldehyde concentration guiding parameter;

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

The sleep environment regulation and control equipment comprises at least one of environment light source illumination regulation and control equipment, environment light source spectrum regulation and control equipment, environment air pressure regulation and control equipment, environment temperature regulation and control equipment, environment humidity regulation and control equipment, environment micro-particle regulation and control equipment, environment noise regulation and control equipment, environment oxygen concentration regulation and control equipment, environment carbon dioxide concentration regulation and control equipment and environment formaldehyde concentration regulation and control equipment.

19. The method of claim 16, wherein the method of extracting the sleep periodic environmental impact factor sequence comprises:

20. The method of claim 16, wherein the method of extracting the optimal sleep periodic environment scheme comprises:

21. The method as recited in claim 20, wherein: the calculation mode of the environment state index fusion processing comprises at least one of mean value processing, normal weighting processing, increasing weighting processing and decreasing weighting processing.

22. A system for sleep periodicity detection quantification and assisted intervention, comprising the following modules:

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

the time frame processing is to conduct sliding segmentation of a preset framing duration window on signal data according to the sampling rate of the signal and with a preset framing step length; the time frame state feature analysis comprises at least one of numerical feature analysis, envelope feature analysis, power spectrum feature analysis, entropy feature analysis, fractal feature analysis and complexity feature analysis;

The sleep periodicity index calculating method comprises the following steps:

1) Acquiring a sleep duration state characteristic curve;

23. The system of claim 22, wherein the state acquisition processing module comprises the following functional units:

The physiological state monitoring unit is used for collecting the physiological state data of the sleeping process of the user; the physiological state data comprises at least one of brain electrical signal data, electrocardiosignal data, respiratory signal data, blood oxygen signal data and body temperature signal data;

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

a signal preprocessing unit for preprocessing the signal data for the physiological state data and the environmental state data; the signal data preprocessing comprises A/D conversion, resampling, artifact removal, noise reduction, notch, band-pass filtering, invalidation removal, re-referencing and smoothing;

24. The system of claim 22, wherein the timeframe feature analysis module comprises the following functional units:

a physiological characteristic integration unit, configured to integrate and generate the physiological status characteristic; the physiological state characteristics comprise at least one of brain electrical signal characteristics, electrocardiosignal characteristics, respiratory 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 characteristics; the environmental state characteristics comprise at least one of an environmental state index mean value characteristic sequence, an illumination signal characteristic, a spectrum signal characteristic, a barometric 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.

25. The system of claim 22, wherein the periodic quantitative analysis module comprises the following functional units:

the period quantitative analysis unit is used for carrying out time sequence component analysis and period quantitative analysis on the sleep duration state characteristic curve and extracting the sleep period index; the timing component analysis includes an additive timing component analysis and a multiplicative timing component analysis;

the quantized daily report generating unit is used for generating the sleep periodic quantized daily report; the sleep periodic quantification daily report comprises at least one of a sleep periodic analysis summary, the sleep periodic index, a sleep state level curve, the sleep duration state characteristic curve and an environmental state index average value characteristic sequence.

26. The system of claim 22, wherein the continuous trace analysis module comprises the following functional units:

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

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

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

The quantitative report generation unit is used for generating the sleep periodic quantitative report according to the environmental state index mean value characteristic sequence curve, the sleep periodic index curve, the sleep periodic environment influence factor sequence and the optimal sleep periodic environment scheme; the sleep periodicity quantitative report comprises at least one of a sleep periodicity analysis summary, a sleep periodicity adjustment scheme, the environmental status index mean characteristic sequence curve, the sleep periodicity index curve, the sleep periodicity environmental impact factor sequence and the optimal sleep periodicity environmental scheme.

27. The system of any one of claims 22-26, wherein the environmental assistance regulation module comprises the following functional units:

the environment scheme generating unit is used for generating the sleep environment optimization adjustment scheme according to the optimal sleep periodic environment scheme and combining 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 micro-particle 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 sleep environment regulation and control equipment according to the sleep environment optimization and adjustment scheme to dynamically optimize and control the sleep environment of the user; the sleep environment regulation and control equipment comprises at least one of environment light source illumination regulation and control equipment, environment light source spectrum regulation and control equipment, environment air pressure regulation and control equipment, environment temperature regulation and control equipment, environment humidity regulation and control equipment, environment micro-particle regulation and control equipment, environment noise regulation and control equipment, environment oxygen concentration regulation and control equipment, environment carbon dioxide concentration regulation and control equipment and environment formaldehyde concentration regulation and control equipment.

28. The device for periodically detecting, quantifying and assisting in intervention during sleeping is characterized by comprising the following modules:

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

The sleep periodicity index calculating method comprises the following steps:

1) Acquiring a sleep duration state characteristic curve;