CN116504357B - Sleep periodicity detection and adjustment method, system and device based on wavelet analysis - Google Patents
Sleep periodicity detection and adjustment method, system and device based on wavelet analysis Download PDFInfo
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Abstract
The invention provides a sleep periodicity detection and adjustment method based on wavelet analysis, which comprises the following steps: collecting and analyzing the sleeping physiological state of the user to obtain a sleeping state characteristic curve and a sleeping time phase curve; trending the sleep state characteristic curve, selecting a wavelet basis function, performing wavelet analysis, and calculating to obtain a sleep periodicity index and a sleep periodicity curve; dynamically predicting and analyzing a sleep time phase curve, a sleep state characteristic curve and a sleep periodic index curve and dynamically adjusting the sleep time phase curve, the sleep state characteristic curve and the sleep periodic index curve; extracting a phase periodical coupling index, a phase adjustment distribution characteristic and a period index adjustment coefficient according to a sleep phase curve, a sleep periodical index curve and a dynamic adjustment effect curve, continuously detecting, quantifying and dynamically adjusting, and establishing and dynamically updating a database; and dynamically optimizing parameters such as a wavelet analysis method and a dynamic regulation strategy according to the database. The invention can realize the efficient intervention and adjustment of the sleep cycle of the user.
Description
Technical Field
The invention relates to the field of sleep periodicity detection quantification and auxiliary regulation, in particular to a sleep periodicity and regulation method, system and device based on wavelet analysis.
Background
The healthy and high-quality human sleeping process has very good periodicity, namely, the periodic cycle of non-rapid eye movement sleep NREM and rapid eye movement sleep REM alternates, and each period lasts for 90-120 minutes. Sleep periodicity is a very important measure of human sleep health and sleep quality, but is subject to many disturbances and challenges due to a variety of factors such as mental stress, physiological conditions, and sleep environment.
The applicant proposed a prior solution chinese application CN2023101959912 which provides a method for quantifying sleep periodicity detection and assisting in interventions, 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; 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. According to the technical scheme, the sleep periodic index is provided as an innovative evaluation index for quantifying the sleep cycle period law, the sleep duration state time sequence periodic component is extracted from the sleep duration state characteristic curve through a time sequence decomposition method, the sleep periodic intensity is calculated, the sleep periodic index is obtained, and the problem of sleep periodic quantification can be primarily solved. There is room for lifting, mainly comprising: firstly, the time sequence decomposition method shows limitation in calculation efficiency, multi-scene evaluation and crowd fitness; secondly, the sleep duration state characteristic curve is obtained based on the step-type sleep time phase stage value smoothness, and only the sleep state of the user can be briefly described, but the sleep duration state characteristic curve cannot be finely described and quantified, so that the sleep periodicity evaluation is not fine and sensitive; secondly, how to realize efficient intervention regulation on the sleep periodicity of the user through sleep environment regulation more preferably; finally, how to realize the long-term and personalized sleep periodic index detection quantification and dynamic adjustment of the user and continuously improve the detection accuracy and the adjustment efficiency.
How to describe the sleep state characteristics and continuous changes of the user more fully and deeply; how to more accurately and rapidly complete sleep periodicity evaluation of different people; how to realize more accurate, efficient and multi-means periodic dynamic adjustment of the sleep of the user; how to construct an integrated cooperative mechanism of sleep periodic detection quantification and dynamic regulation, and improve the efficiency effects of user personalized detection quantification and intervention regulation, is a problem that the current domestic and foreign product technical scheme and practical application scene need to be further optimized or solved.
Disclosure of Invention
Aiming at the defects and improvement demands of the existing method, the invention aims to provide a sleep periodicity detection and adjustment method based on wavelet analysis, which is characterized in that a sleep state characteristic curve is obtained by extracting multi-characteristic analysis of fine granularity and continuous transformation of sleep physiological state signals, further trend processing and wavelet analysis are carried out to obtain a sleep period WT component signal, a sleep periodicity index is obtained by calculation, and a sleep periodicity index curve is generated; generating a sleep period dynamic regulation strategy through dynamic prediction analysis of the sleep state of the user, and carrying out dynamic regulation and regulation effect evaluation on the sleep process of the user; secondly, performing effectiveness and utility statistical analysis on the detection quantification and dynamic regulation process, and generating a periodic detection and regulation report of the sleep of the user; finally, a user sleep cycle characteristic database is established and dynamically updated, and a method and a strategy in the process of detecting quantification and dynamic adjustment are optimized through reverse feedback, so that the effects of individuation, detecting quantification and intervention adjustment efficiency of a user are continuously improved. The invention also provides a sleep periodicity detecting and adjusting system based on wavelet analysis, which is used for realizing the method. The invention also provides a sleep periodicity detecting and adjusting device based on wavelet analysis, which is used for realizing the system.
According to the purpose of the invention, the invention provides a sleep periodicity detection and adjustment method based on wavelet analysis, which comprises the following steps:
collecting and analyzing the sleeping physiological state of the user to obtain a sleeping state characteristic curve and a sleeping time phase curve;
Trending the sleep state characteristic curve, selecting a wavelet basis function, performing wavelet analysis, determining a periodic boundary frequency, extracting a periodic WT component signal, and calculating to obtain a sleep periodic index and a sleep periodic index curve;
Dynamically predicting and analyzing the sleep time phase curve, the sleep state characteristic curve and the sleep periodic index curve to generate a sleep period dynamic regulation strategy, and dynamically regulating and controlling the sleep process of a user and evaluating the regulation effect;
extracting a time phase periodic coupling index, a time phase adjustment distribution characteristic and a period index adjustment coefficient according to the sleep time phase curve, the sleep period index curve and the dynamic adjustment effect curve, and generating a user sleep period detection and adjustment report;
continuously detecting, quantifying and dynamically adjusting the sleep period process of the user, and establishing and dynamically updating a sleep period characteristic database of the user;
And dynamically optimizing wavelet analysis method parameters, the period boundary frequency, dynamic prediction analysis method parameters and the sleep period dynamic regulation strategy according to the sleep period characteristic database of the user.
More preferably, the specific steps of acquiring and analyzing the sleep physiological state of the user to obtain the sleep state characteristic curve and the sleep phase curve further comprise:
The method comprises the steps of collecting, monitoring and signal processing a sleep physiological state signal of a user to generate sleep physiological state time frame data;
Performing feature analysis and feature selection on all time frame data in the sleep physiological state time frame data to generate the sleep state feature curve;
And carrying out sleep phase analysis on all time frame data in the sleep physiological state time frame data to generate the sleep phase curve.
More preferably, the sleep physiological state signal includes at least any one of a brain central state signal and an autonomic nerve state signal; wherein the brain center state signal at least comprises any one of an electroencephalogram signal, a magnetoencephalography signal and an oxygen level dependent signal, and the autonomic nerve state signal at least comprises any one of a medium oxygen level dependent signal, an electrocardio signal, a pulse signal, a respiration signal, an oxygen blood signal, a body temperature signal and a skin electric signal.
More preferably, the signal processing at least comprises AD digital-to-analog conversion, resampling, re-referencing, noise reduction, artifact removal, power frequency notch, low-pass filtering, high-pass filtering, band-stop filtering, band-pass filtering, correction processing and framing processing; the correction processing specifically includes signal correction, signal prediction and smoothing processing on signal data segments containing artifacts or distortion in a target signal, and the framing processing refers to continuous sliding segmentation on the target signal according to a signal sampling rate and with a preset time window length and a preset time translation step length.
More preferably, the feature analysis includes at least any one of numerical analysis, envelope analysis, time-frequency analysis, entropy analysis, fractal analysis, and complexity analysis.
More preferably, the sleep state characteristic curve is a characteristic curve which accurately describes the continuous change of the sleep physiological state of the user in different sleep periods and different sleep phases, and is obtained by screening target characteristics with preset characteristic quantity from target characteristic sets obtained by the characteristic analysis and carrying out weighting calculation and combination; the sleep phase at least comprises a wake phase, a light sleep phase, a deep sleep phase and a rapid eye movement sleep phase.
More preferably, the method for generating the sleep phase curve specifically comprises the following steps:
1) Learning training and data modeling are carried out on the sleep physiological state time frame data of the scale sleep user sample and the sleep time phase stage data corresponding to the sleep physiological state time frame data through a deep learning algorithm, so that a sleep time phase stage recognition model is obtained;
2) Inputting the sleep physiological state time frame data of the current user into the sleep time phase stage identification model to obtain the corresponding sleep time phase stage and generating the sleep time phase curve according to time sequence.
More preferably, the step of performing trending processing on the sleep state characteristic curve, selecting a wavelet basis function and performing wavelet analysis, determining a periodic boundary frequency and extracting a periodic WT component signal, and calculating to obtain a sleep periodic index and a sleep periodic index curve further includes:
trending treatment is carried out on the sleep state characteristic curve to obtain a sleep state characteristic baseline curve;
Determining the cycle boundary frequency and the wavelet analysis method parameters according to the feature combination generation mode of the sleep state feature curve, and selecting the wavelet basis function;
performing wavelet decomposition and/or wavelet packet decomposition on the sleep state characteristic baseline curve to obtain a sleep state characteristic WT component signal set;
screening WT component signals meeting the periodic boundary frequency from the sleep state characteristic WT component signal set, and generating the periodic WT component signals by summation;
and calculating the sleep periodic index according to the sleep state characteristic baseline curve and the periodic WT component signal, and generating the sleep periodic index curve according to a time sequence.
More preferably, the trending process specifically removes linear trend components and very low frequency trend components in the target signal, and at least includes any one of a mean removing process, a low-pass filtering process, a trending analysis FDA, a multi-fractal trending analysis MFDFA, and an asymmetric removal trend fluctuation analysis ADFA.
More preferably, the method of wavelet analysis includes at least any one of wavelet decomposition and wavelet packet decomposition.
More preferably, the wavelet decomposition includes at least any one of continuous wavelet transform CWT, discrete wavelet transform DWT, empirical wavelet transform EWT, synchronous extrusion wavelet transform SWT, maximum overlap discrete wavelet transform MODWT, synchronous extraction wavelet transform WSET.
More preferably, the periodic boundary frequency includes at least any one of a band-pass cut-off frequency and a low-pass cut-off frequency.
More preferably, the method for calculating the sleep periodicity index specifically includes:
1) Acquiring the sleep state characteristic baseline curve and the periodic WT component signal;
2) Calculating a signal difference value between the sleep state characteristic baseline curve and the periodic WT component signal to obtain a sleep state characteristic residual curve;
3) Square calculation is carried out on the sleep state characteristic residual error curve and the periodic WT component signal respectively, so that a sleep state characteristic residual error square curve and a periodic WT component square signal are obtained;
4) According to the sleep time phase curve, the sleep state characteristic residual error square curve and the periodic WT component square signal, calculating to obtain a sleep periodic node coefficient and generating a sleep periodic node coefficient curve according to a time sequence;
5) And calculating the sleep periodicity index according to the sleep periodicity node coefficient curve, a preset method correction coefficient corresponding to a wavelet analysis method and a preset user individual correction coefficient related to the user biological state information.
More preferably, the calculation formula of the sleep periodic node coefficient is specifically:
；
Wherein, For the sleep periodicity node coefficient,/>/>, Respectively, in the sleep state characteristic residual square curveThe number of points and the/> in the periodic WT component squared signalDot number,/>Is the/>, in the sleep phase curvePhase correction coefficients corresponding to the phase values of the respective points.
More preferably, a calculation formula of the sleep cycle index specifically includes:
；
Wherein, For the sleep periodicity index,/>Respectively presetting a user individual correction coefficient and a preset method correction coefficient,/>For the/>, in the sleep periodic node coefficient curveDot number,/>And the data length of the sleep periodic node coefficient curve is the data length of the sleep periodic node coefficient curve.
More preferably, the sleep cycle index curve is specifically a curve generated by splicing the sleep cycle indexes according to a time sequence.
More preferably, the step of dynamically predicting and analyzing the sleep phase curve, the sleep state characteristic curve and the sleep cycle index curve to generate a sleep cycle dynamic regulation strategy, and the specific step of dynamically regulating and evaluating the regulation effect of the sleep process of the user further comprises:
trend prediction analysis is carried out on the sleep time phase curve to obtain a sleep time phase predicted value;
Trend prediction analysis is carried out on the sleep state characteristic curve to obtain a sleep state cycle characteristic prediction value;
trend prediction analysis is carried out on the sleep periodic index curve to obtain a sleep periodic index predicted value;
dynamically generating the sleep cycle dynamic regulation strategy according to a preset dynamic regulation cycle, the sleep time phase predicted value, the sleep state cycle characteristic predicted value, the sleep cycle index predicted value and a preset sleep cycle regulation knowledge base;
According to the sleep cycle dynamic regulation strategy, connecting and controlling sleep cycle regulation peripheral equipment to dynamically intervene and regulate the sleep process of a user;
and carrying out dynamic tracking evaluation on the effect of dynamic intervention adjustment, extracting a dynamic adjustment effect coefficient, generating a dynamic adjustment effect curve, and calculating to obtain an adjustment effect comprehensive index.
More preferably, the trend prediction analysis method at least comprises any one of AR, MR, ARMA, ARIMA, SARIMA, VAR and deep learning.
More preferably, the sleep cycle dynamic adjustment strategy at least comprises a sleep scene, a sleep time phase, an adjustment mode, an execution mode, an adjustment method, an adjustment intensity, an adjustment time point, a duration, a target adjustment value and a device control parameter; wherein the modulation means comprises at least vocal stimulation, ultrasound stimulation, light stimulation, electrical stimulation, magnetic stimulation, temperature stimulation, humidity stimulation, tactile stimulation, and/or the likeThe implementation mode at least comprises any mode of separation mode and contact mode.
More preferably, the sleep cycle adjustment peripheral device comprises at least a vocal stimulation device, an ultrasonic stimulation device, a light stimulation device, an electrical stimulation device, a magnetic stimulation device, a temperature stimulation device, a humidity stimulation device, a tactile stimulation device, and a tactile stimulation deviceAny of the concentration control devices, and is determined by the specific manner of adjustment.
More preferably, the calculation mode of the dynamic adjustment effect coefficient specifically includes:
；
Wherein, For the dynamic adjustment of the effect coefficient,/>For presetting user individual correction coefficient,/>Correction coefficient for trend prediction,/>The sleep periodic indexes before dynamic adjustment and after dynamic adjustment are respectively,To take absolute value operators.
More preferably, the calculation formula of the trend prediction correction coefficient specifically includes:
；
Wherein, Predicting a correction factor for said trend,/>The sleep periodicity index after dynamic adjustment and the sleep periodicity index predicted value before dynamic adjustment,/>, respectivelyAnd the sleep state period characteristic value in the sleep state characteristic curve after dynamic adjustment and the sleep state period characteristic predicted value before dynamic adjustment are respectively obtained.
More preferably, the dynamic adjustment effect coefficient is used for dynamic optimization of subsequent wavelet analysis method parameters, dynamic prediction analysis method parameters, method selection of trend prediction analysis and sleep period dynamic adjustment strategies.
More preferably, the comprehensive index of the regulating effect is specifically an average value or a root mean square of the dynamic regulating effect curve.
More preferably, the specific step of extracting the phase periodic coupling index, the phase adjustment distribution characteristic and the period index adjustment coefficient according to the sleep phase curve, the sleep periodic index curve and the dynamic adjustment effect curve, and generating the user sleep periodic detection and adjustment report further includes:
Calculating the correlation between the sleep time phase curve and the sleep periodic index curve to obtain the time phase periodic coupling index;
Calculating distribution characteristics of the sleep periodic index and the dynamic regulation effect coefficient under different sleep phases according to the sleep phase curve, the sleep periodic index curve and the dynamic regulation effect curve to obtain phase regulation distribution characteristics;
calculating the correlation between the sleep periodic index curve and the dynamic regulation effect curve to obtain the periodic index regulation coefficient;
and generating the user sleep periodic detection and adjustment report according to a preset report period.
More preferably, the correlation calculation method at least comprises any one of coherence analysis, pearson correlation analysis, jacquard similarity analysis, linear mutual information analysis, linear correlation analysis, euclidean distance analysis, manhattan distance analysis and chebyshev distance analysis.
More preferably, the distribution characteristics include at least any one of average, root mean square, maximum, minimum, variance, standard deviation, coefficient of variation, kurtosis, and skewness.
More preferably, the user sleep periodic detection and adjustment report at least comprises user biological state information, the sleep state characteristic curve, the sleep time phase curve, the sleep periodic index curve, the dynamic adjustment effect curve, the time phase periodic coupling index, the time phase adjustment distribution characteristic, the periodic index adjustment coefficient and a detection and adjustment summary.
More preferably, the specific steps of continuously detecting, quantifying and dynamically adjusting the sleep cycle process of the user and establishing and dynamically updating the sleep cycle characteristic database of the user further comprise:
Initializing, establishing and storing a sleep cycle characteristic database of the user;
And continuously detecting, quantifying and dynamically adjusting the sleep period process of the user, and dynamically updating the sleep period characteristic database of the user.
More preferably, the user sleep cycle characteristic database at least comprises user biological state information, the sleep state characteristic curve, the sleep time phase curve, the sleep cycle index curve, the dynamic regulation effect curve, the time phase periodic coupling index, the time phase regulation distribution characteristic, the cycle index regulation coefficient, a wavelet analysis method, a trend prediction analysis method and the sleep cycle dynamic regulation strategy.
More preferably, the specific steps of dynamically optimizing the wavelet analysis method parameter, the period boundary frequency, the dynamic prediction analysis method parameter and the sleep period dynamic adjustment strategy according to the sleep period characteristic database of the user further comprise:
Dynamically optimizing parameters of a wavelet analysis method and the periodic boundary frequency in the periodic detection and quantization process of the sleep of the user according to the characteristic database of the sleep period of the user;
And dynamically optimizing dynamic prediction analysis method parameters and the sleep cycle dynamic regulation strategy in the user sleep cycle dynamic regulation process according to the user sleep cycle characteristic database.
According to the purpose of the invention, the invention provides a sleep periodicity detecting and adjusting system based on wavelet analysis, which comprises the following modules:
the sleep state tracking module is used for acquiring and analyzing the sleep physiological state of the user to obtain a sleep state characteristic curve and a sleep time phase curve;
The wavelet index analysis module is used for carrying out trending treatment on the sleep state characteristic curve, selecting a wavelet basis function, carrying out wavelet analysis, determining a periodic boundary frequency, extracting a periodic WT component signal and calculating to obtain a sleep periodic index and a sleep periodic index curve;
The dynamic strategy regulation and control module is used for carrying out dynamic prediction analysis on the sleep time phase curve, the sleep state characteristic curve and the sleep periodic index curve to generate a sleep period dynamic regulation strategy and carrying out dynamic regulation and control on the sleep process of the user and regulation and control effect evaluation;
the detection, adjustment and analysis module is used for extracting a time phase periodic coupling index, a time phase adjustment distribution characteristic and a period index adjustment and control coefficient according to the sleep time phase curve, the sleep period index curve and the dynamic adjustment and control effect curve to generate a user sleep period detection and adjustment report;
the periodic data updating module is used for continuously detecting, quantifying and dynamically adjusting the sleep periodic process of the user, and establishing and dynamically updating a sleep periodic characteristic database of the user;
The detection adjustment optimization module is used for dynamically optimizing the wavelet analysis method parameters, the period boundary frequency, the dynamic prediction analysis method parameters and the sleep period dynamic adjustment strategy according to the user sleep period characteristic database;
and the data operation management module is used for carrying out visual management, unified storage and operation management on all process data of the system.
More preferably, the sleep state tracking module further comprises the following functional units:
The state acquisition processing unit is used for carrying out acquisition monitoring and signal processing on the sleep physiological state signals of the user and generating sleep physiological state time frame data;
the state curve extraction unit is used for carrying out feature analysis and feature selection on all time frame data in the sleep physiological state time frame data to generate the sleep state feature curve;
and the sleep phase analysis unit is used for carrying out sleep phase analysis on all time frame data in the sleep physiological state time frame data and generating the sleep phase curve.
More preferably, the wavelet index analysis module further comprises the following functional units:
the state trend processing unit is used for carrying out trend removal processing on the sleep state characteristic curve to obtain a sleep state characteristic baseline curve;
an analysis parameter selection unit, configured to determine the cycle boundary frequency and a method parameter of wavelet analysis according to a feature combination generation manner of the sleep state feature curve, and select the wavelet basis function;
The signal wavelet analysis unit is used for carrying out wavelet decomposition and/or wavelet packet decomposition on the sleep state characteristic baseline curve to obtain a sleep state characteristic WT component signal set;
a periodic component extraction unit, configured to screen WT component signals satisfying the periodic boundary frequency from the sleep state characteristic WT component signal set, and sum up to generate the periodic WT component signal;
and the period index calculation unit is used for calculating the sleep period index according to the sleep state characteristic baseline curve and the period WT component signal and generating the sleep period index curve according to a time sequence.
More preferably, the dynamic policy adjustment module further comprises the following functional units:
the sleep time phase prediction unit is used for carrying out trend prediction analysis on the sleep time phase curve to obtain a sleep time phase predicted value;
The period characteristic prediction unit is used for carrying out trend prediction analysis on the sleep state characteristic curve to obtain a sleep state period characteristic prediction value;
the period index prediction unit is used for carrying out trend prediction analysis on the sleep period index curve to obtain a sleep period index predicted value;
The regulation strategy generation unit is used for dynamically generating the sleep cycle dynamic regulation strategy according to a preset dynamic regulation cycle, the sleep phase predicted value, the sleep state cycle characteristic predicted value, the sleep cycle index predicted value and a preset sleep cycle regulation knowledge base;
The dynamic regulation execution unit is used for connecting and controlling the sleep cycle regulation peripheral equipment according to the sleep cycle dynamic regulation strategy and carrying out dynamic intervention regulation on the sleep process of the user;
the dynamic effect evaluation unit is used for carrying out dynamic tracking evaluation on the effect of dynamic intervention adjustment, extracting the dynamic adjustment effect coefficient, generating a dynamic adjustment effect curve and calculating to obtain an adjustment effect comprehensive index.
More preferably, the detection adjustment analysis module further comprises the following functional units:
The time phase coupling analysis unit is used for calculating the correlation between the sleep time phase curve and the sleep periodic index curve to obtain the time phase periodic coupling index;
the time phase distribution analysis unit is used for calculating the distribution characteristics of the sleep periodic index and the dynamic regulation effect coefficient under different sleep time phases according to the sleep time phase curve, the sleep periodic index curve and the dynamic regulation effect curve to obtain the time phase regulation distribution characteristics;
The period regulation analysis unit is used for calculating the correlation between the sleep period index curve and the dynamic regulation effect curve to obtain the period index regulation coefficient;
The user report generating unit is used for generating the user sleep periodic detection and adjustment report according to a preset report period;
and the user report output unit is used for uniformly managing the format output and the presentation form of the user sleep periodic detection and adjustment report.
More preferably, the periodic data updating module further comprises the following functional units:
The database initializing unit is used for initializing and establishing and storing the sleep cycle characteristic database of the user;
and the database dynamic updating unit is used for continuously detecting, quantifying and dynamically adjusting the sleep periodic process of the user and dynamically updating the sleep periodic characteristic database of the user.
More preferably, the detection adjustment optimization module further comprises the following functional units:
The detection and quantization optimization unit is used for dynamically optimizing wavelet analysis method parameters and the cycle boundary frequency in the user sleep cycle detection and quantization process according to the user sleep cycle characteristic database;
And the dynamic adjustment optimizing unit is used for dynamically optimizing the dynamic prediction analysis method parameters and the sleep period dynamic adjustment strategy in the user sleep period dynamic adjustment process according to the user sleep period characteristic database.
More preferably, the data operation management module further comprises the following functional units:
a user information management unit for registering input, editing, inquiry, output and deletion of user basic information;
the data visual management unit is used for visual display management of all data in the system;
the data storage management unit is used for uniformly storing and managing all data in the system;
and the data operation management unit is used for backing up, migrating and exporting all data in the system.
According to the purpose of the invention, the invention provides a sleep periodicity detecting and adjusting device based on wavelet analysis, which comprises the following modules:
The sleep state tracking module is used for acquiring and analyzing the sleep physiological state of the user to obtain a sleep state characteristic curve and a sleep time phase curve;
the wavelet index analysis module is used for carrying out trending treatment on the sleep state characteristic curve, selecting a wavelet basis function, carrying out wavelet analysis, determining a periodic boundary frequency, extracting a periodic WT component signal, and calculating to obtain a sleep periodic index and a sleep periodic index curve;
The dynamic strategy regulation and control module is used for carrying out dynamic prediction analysis on the sleep time phase curve, the sleep state characteristic curve and the sleep periodic index curve to generate a sleep period dynamic regulation strategy and carrying out dynamic regulation and control on the sleep process of the user and regulation and control effect evaluation;
the detection, adjustment and analysis module is used for extracting a time phase periodic coupling index, a time phase adjustment distribution characteristic and a period index adjustment and control coefficient according to the sleep time phase curve, the sleep period index curve and the dynamic adjustment and control effect curve to generate a user sleep period detection and adjustment report;
the periodic database module is used for continuously detecting, quantifying and dynamically adjusting the sleep periodic process of the user, and establishing and dynamically updating a sleep periodic characteristic database of the user;
the detection adjustment optimization module is used for dynamically optimizing the wavelet analysis method parameters, the period boundary frequency, the dynamic prediction analysis method parameters and the sleep period dynamic adjustment strategy according to the user sleep period characteristic database;
The data visualization module is used for carrying out unified visual display management on all process data and/or result data in the device;
And the data management center module is used for uniformly storing and managing data operation of all process data and/or result data in the device.
The application further optimizes the specific design of sleep periodic index quantification based on the prior research of the applicant, further optimizes the extraction mode of the sleep state characteristic curve, and further applies trending treatment, wavelet decomposition and/or wavelet packet decomposition to the extraction of sleep periodic information, thereby taking the continuous state characteristics of the complete sleep period into consideration, and being more comprehensive and wide in adaptability; the application further improves the calculation mode of the sleep periodic index, and improves the fine granularity and sensitivity of evaluation; the method further provides a calculation scheme and a feedback application framework of the dynamic adjustment effect coefficient, and a user-personalized user sleep period characteristic database establishment and update and feedback application framework, so that a powerful basis is provided for collaborative control of detection quantization and dynamic adjustment processes. The application can provide a more scientific and efficient implementation method for detecting, quantifying and dynamically adjusting the sleep periodicity and a landing scheme. In an actual application scene, the sleep periodicity detection and adjustment method, system and device based on wavelet analysis provided by the application can enable related sleep quantized or adjusted products and services, meet different user scene requirements and assist a user in sleeping.
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 application and are incorporated in and constitute a part of this specification, illustrate and do not limit the application.
FIG. 1 is a schematic diagram of steps in a sleep cycle detection and adjustment method based on wavelet analysis according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of the module composition of a sleep cycle detection and adjustment system based on wavelet analysis according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a module structure of a sleep cycle detecting and adjusting device based on wavelet analysis according to an embodiment of the present invention.
Detailed Description
In order to more clearly illustrate the objects and technical solutions of the present application, the present application will be further described below with reference to the accompanying drawings in the embodiments of the present application. It will be apparent that the embodiments described below are only some, but not all, embodiments of the application. Other embodiments, which are derived from the embodiments of the application by a person skilled in the art without creative efforts, shall fall within the protection scope of the application. It should be noted that, without conflict, the embodiments of the present application and features of the embodiments may be arbitrarily combined with each other.
The applicant found that in the normal case, the human sleep physiological state continuous change process is a non-stationary time sequence process, and the sleep state characteristic description time sequence curve is also a non-stationary signal. For information processing and information extraction of non-stationary signals, the applicant extracts a sleep state characteristic curve from the sleep state characteristic curve through trending, wavelet decomposition and/or wavelet packet decomposition, and further optimizes the detection quantization and dynamic adjustment process of sleep periodicity. Firstly, the linear trend component and the very low frequency trend component in the target signal can be effectively removed by the methods of removing the mean value, low-pass filtering, removing the trend, analyzing the FDA, multi-fractal removing the trend, analyzing the trend MFDFA, and analyzing the asymmetric eliminating trend fluctuation ADFA. Second, wavelet transformation is a mathematical method of time-scale analysis and multi-resolution analysis, which is a local transformation of space (time) and frequency, thus effectively extracting information from a signal. The wavelet transformation is simple and quick to calculate, and the functions or signals can be subjected to multi-scale refinement analysis through the operation functions of expansion, translation and the like, so that a plurality of difficult problems which cannot be solved by the Fourier transformation are solved. The wavelet transformation methods such as continuous wavelet transformation CWT, discrete wavelet transformation DWT, empirical wavelet transformation EWT, synchronous extrusion wavelet transformation SWT, maximum overlapping discrete wavelet transformation MODWT, synchronous extraction wavelet transformation WSET and the like can adapt to the signal analysis and signal decomposition requirements of different scenes, and are suitable for the extraction and analysis of sleep trend information. Further, the wavelet packet transformation is an expansion of wavelet transformation, the wavelet transformation only realizes the reconstruction of the low frequency band of the signal, and the wavelet packet transformation complements the reconstruction of the high frequency part. The wavelet packet transformation is to divide the frequency band part in multiple layers, and adaptively select a proper frequency band interval according to the characteristics of the analyzed signal, so as to improve the time-frequency resolution; the wavelet packet change can perform very good time-frequency localization analysis on the mixed signal containing low, medium and high frequency information, has no redundancy or omission, and is very widely applied to non-stationary signal analysis. Compared with wavelet analysis, wavelet packet analysis can divide a time-frequency plane more finely, and the resolution of a high-frequency part of a signal is better than that of wavelet analysis.
Referring to fig. 1, the sleep periodicity detecting and adjusting method based on wavelet analysis provided by the embodiment of the invention includes the following steps:
p100: and acquiring and analyzing the sleeping physiological state of the user to obtain a sleeping state characteristic curve and a sleeping time phase curve.
The first step, the sleep physiological state signals of the user are collected, monitored and processed to generate sleep physiological state time frame data.
In this embodiment, the sleep physiological state signal at least includes any one of a brain center state signal and an autonomic nerve state signal; wherein the brain center state signal at least comprises any one of an electroencephalogram signal, a magnetoencephalography signal and an oxygen level dependent signal, and the autonomic nerve state signal at least comprises any one of a medium oxygen level dependent signal, an electrocardio signal, a pulse signal, a respiratory signal, an oxygen signal, a body temperature signal and a skin electric signal.
In this embodiment, the signal processing at least includes AD digital-to-analog conversion, resampling, re-referencing, noise reduction, artifact removal, power frequency notch, low-pass filtering, high-pass filtering, band-stop filtering, band-pass filtering, correction processing, and framing processing; the correction processing specifically includes signal correction, signal prediction and smoothing processing on a signal data segment containing artifacts or distortion in a target signal, and the framing processing refers to continuous sliding segmentation on the target signal according to a signal sampling rate and with a preset time window length and a preset time translation step length.
In this embodiment, an electroencephalogram signal and a pulse signal for monitoring a sleeping process of a user are collected and used as sleeping physiological status signals to state a specific implementation process of the technical scheme. Firstly, acquiring and recording sleeping electroencephalogram of a user by an electroencephalograph, wherein the sampling rate is 1024Hz, the recording electrodes are F3, F4, C3 and C4, and the reference electrodes M1 and M2; the electroencephalogram signals are subjected to unified signal processing, including left and right cross re-referencing, artifact removal, wavelet noise reduction, 50Hz frequency doubling power frequency notch, 1.0-80Hz band-pass filtering and correction processing, by using M1 and M2, so that pure electroencephalogram signals are obtained. Collecting and extracting the pulse of the left index finger of the user by using a finger-clamping type pulse oximeter, wherein the sampling rate is 64Hz; and carrying out unified signal processing on the pulse signals, including noise reduction, artifact removal and correction processing, so as to obtain pure pulse signals. Secondly, extracting signal frequency bands of the pure brain electrical signals sequentially, wherein the signal frequency bands comprise delta rhythms (1-4 Hz), theta rhythms (4-8 Hz), alpha rhythms (8-12 Hz), beta rhythms (12-30 Hz) and gamma rhythms (30-80 Hz), and obtaining frequency band brain electrical signals; and further, continuously sliding and dividing the pure electroencephalogram signal, the frequency band electroencephalogram signal and the pure pulse signal by using a preset time window length 30s and a preset time shift step length 15s to obtain sleep physiological state time frame data.
And secondly, performing feature analysis and feature selection on all time frame data in the sleep physiological state time frame data to generate a sleep state feature curve.
In this embodiment, the sleep state characteristic curve is specifically a characteristic curve that accurately describes the continuous change of the sleep physiological states of the user in different sleep periods and different sleep phases, and is obtained by screening target characteristics of a preset feature quantity from a target characteristic set obtained by characteristic analysis and performing weighted calculation and combination; the sleeping period at least comprises a pre-sleep period, a sleep period and a post-sleep period, and the sleeping time phase at least comprises a wakefulness period, a light sleeping period, a deep sleeping period and a rapid eye movement sleeping period. The feature analysis includes at least any one of numerical analysis, envelope analysis, time-frequency analysis, entropy analysis, fractal analysis, and complexity analysis.
In the embodiment, time-frequency analysis (frequency band power, frequency band power duty ratio), entropy analysis (approximate entropy) and complexity analysis (LZC index: lempel-Ziv complexity index) are carried out on the electroencephalogram data in the sleep physiological state time frame data frame by frame; and carrying out numerical analysis (average value and variation coefficient) on pulse data in the sleep physiological state time frame data frame by frame. Through feature selection, the delta-theta (delta rhythm+theta rhythm) combined frequency band power duty ratio of the F3-M2 channel, approximate entropy normalized after taking negative and pulse mean normalized after taking negative are directly added to obtain a sleep state characteristic curve, so that the description granularity and time-varying property of the state characteristics of the sleep state, the cortex electrophysiology and the autonomic nerves of the user are better, and the continuous change of the sleep physiological state can be accurately and timely depicted.
Thirdly, performing sleep phase analysis on all time frame data in the sleep physiological state time frame data to generate a sleep phase curve.
In this embodiment, the method for generating the sleep phase curve specifically includes:
1) The method comprises the steps of performing learning training and data modeling on sleep physiological state time frame data of a scale sleep user sample and sleep time phase stage data corresponding to the sleep physiological state time frame data through a deep learning algorithm to obtain a sleep time phase stage recognition model;
2) Inputting the frame data of the current sleeping physiological state of the user into a sleeping time phase stage identification model to obtain the corresponding sleeping time phase stage and generating a sleeping time phase curve according to a time sequence.
P200: and carrying out trending treatment on the sleep state characteristic curve, selecting a wavelet basis function, carrying out wavelet analysis, determining the periodic boundary frequency, extracting periodic WT component signals, and calculating to obtain a sleep periodic index and a sleep periodic index curve.
And firstly, carrying out trend removal treatment on the sleep state characteristic curve to obtain a sleep state characteristic baseline curve.
In this embodiment, the trending process specifically removes linear trend components and very low frequency trend components in the target signal, and at least includes any one of a mean removing process, a low-pass filtering process, a trending analysis FDA, a multi-fractal trending analysis MFDFA, and an asymmetric removal trend fluctuation analysis ADFA.
In this embodiment, the mean-removing process is selected to perform trend-removing process on the sleep state characteristic curve.
And step two, determining the periodic boundary frequency and the wavelet analysis method parameters according to the characteristic combination generation mode of the sleep state characteristic curve, and selecting a wavelet basis function.
In this embodiment, the periodic boundary frequency includes at least any one of a band-pass cut-off frequency and a low-pass cut-off frequency.
In this embodiment, the sleep state characteristic curve is obtained by directly adding the delta-theta (delta rhythm+theta rhythm) combined frequency band power ratio, the approximate entropy normalized after taking the negative value, and the pulse mean normalized after taking the negative value, and the parameters of framing processing are the preset time window length 30s and the preset time shift step length 15s, so that the continuous wavelet transformation CWT and the 0.008Hz low-pass cutoff frequency (cycle boundary frequency) are selected, and db4 is selected as the wavelet basis function.
And thirdly, carrying out wavelet decomposition and/or wavelet packet decomposition on the sleep state characteristic baseline curve to obtain a sleep state characteristic WT component signal set.
In this embodiment, the method of wavelet analysis includes at least any one of wavelet decomposition and wavelet packet decomposition.
In this embodiment, the wavelet decomposition includes at least any one of continuous wavelet transform CWT, discrete wavelet transform DWT, empirical wavelet transform EWT, synchronous extrusion wavelet transform SWT, maximum overlap discrete wavelet transform MODWT, and synchronous extraction wavelet transform WSET.
In this embodiment, a continuous wavelet transform CWT is selected as a method of wavelet analysis. The continuous wavelet transform CWT has better frequency positioning capability at low frequencies and better time positioning capability at high frequencies, and more accurate estimation of the instantaneous frequency of the duration without concern about the size of the selection window. The method mainly comprises the following steps:
1) Selecting a wavelet basis function Fixing a scale factor, and associating it with the target signal/>Calculating wavelet coefficients (reflecting the similarity degree of the wavelet under the current scale and the corresponding signal segment) through a CWT calculation formula;
2) Shift the wavelet to right Units, get wavelet/>Repeating 1), repeating the step until the target signalEnding;
3) Changing scale factors, extending wavelet basis functions Obtain wavelet basis function/>Repeating steps 1) and 2).
4) Continuously changing scale factors and expanding wavelet basis functionsRepeating steps 1), 2) and 3) until analysis requirements are met.
In this embodiment, wavelet decomposition is performed on the sleep state characteristic baseline curve through continuous wavelet transformation CWT and wavelet basis functions db4 and db 6 layers, so as to obtain a sleep state characteristic WT component signal set. In an actual use scene, the continuous wavelet transform CWT and the wavelet packet transform are very practical, can meet most scene requirements, and are both good.
And fourthly, screening the WT component signals meeting the periodic boundary frequency from the sleep state characteristic WT component signal set, and generating the periodic WT component signals by summation.
In this embodiment, the sleep state characteristic WT component signal set is subjected to spectrum analysis by Welch power spectral density estimation, WT component signals satisfying the 0.008Hz low-pass cut-off frequency-cycle boundary frequency are identified, and the periodic WT component signals are summed to generate.
Fifthly, according to the sleep state characteristic baseline curve and the periodic WT component signal, calculating to obtain a sleep periodic index and generating a sleep periodic index curve according to a time sequence.
In this embodiment, the method for calculating the sleep periodicity index specifically includes:
1) Acquiring a sleep state characteristic baseline curve and a periodic WT component signal;
2) Calculating a signal difference value of the sleep state characteristic baseline curve and the periodic WT component signal to obtain a sleep state characteristic residual curve;
3) Square calculation is carried out on the sleep state characteristic residual error curve and the periodic WT component signal respectively, so that a sleep state characteristic residual error square curve and a periodic WT component square signal are obtained;
4) According to the sleep time phase curve, the sleep state characteristic residual error square curve and the periodic WT component square signal, calculating to obtain a sleep periodic node coefficient and generating a sleep periodic node coefficient curve according to a time sequence;
5) And calculating to obtain the sleep periodicity index according to the sleep periodicity node coefficient curve, the preset user personality correction coefficient corresponding to the preset method correction coefficient and related to the user biological state information by the wavelet analysis method.
In this embodiment, a calculation formula of the sleep periodic node coefficient specifically includes:
；
Wherein, For sleep periodicity node coefficients,/>/>, Respectively in sleep state characteristic residual square curvesPoint value and the first/> in the periodic WT component squared signalDot number,/>Is the/>, in the sleep phase curvePhase correction coefficients corresponding to the phase values of the respective points.
In this embodiment, a calculation formula of the sleep cycle index specifically includes:
；/>
Wherein, Is sleep periodicity index,/>Respectively presetting a user individual correction coefficient and a preset method correction coefficient,/>Is the/>, in the sleep periodic node coefficient curveDot number,/>Is the data length of the sleep periodic node coefficient curve.
In this embodiment, the sleep cycle index curve is specifically a curve generated by splicing sleep cycle indexes according to a time sequence order.
P300: and dynamically predicting and analyzing the sleep time phase curve, the sleep state characteristic curve and the sleep period index curve to generate a sleep period dynamic regulation strategy, and dynamically regulating and controlling the sleep process of the user and evaluating the regulation effect.
And firstly, carrying out trend prediction analysis on the sleep time phase curve to obtain a sleep time phase predicted value.
In this embodiment, the trend prediction analysis method at least includes any one of AR, MR, ARMA, ARIMA, SARIMA, VAR and deep learning.
In this embodiment, the SARIMA method is applied to obtain the sleep phase prediction value.
And secondly, carrying out trend prediction analysis on the sleep state characteristic curve to obtain a sleep state cycle characteristic predicted value.
In this embodiment, the SARIMA method is applied to obtain the sleep state cycle characteristic prediction value.
And thirdly, carrying out trend prediction analysis on the sleep periodic index curve to obtain a sleep periodic index predicted value.
In this embodiment, the SARIMA method is applied to obtain the sleep periodicity index prediction value.
And fourthly, dynamically generating a sleep cycle dynamic regulation strategy according to a preset dynamic regulation cycle, a sleep phase predicted value, a sleep state cycle characteristic predicted value, a sleep cycle index predicted value and a preset sleep cycle regulation knowledge base.
In this embodiment, the sleep cycle dynamic adjustment policy at least includes a sleep scene, a sleep phase, an adjustment mode, an execution mode, an adjustment method, an adjustment intensity, an adjustment time point, a duration, a target adjustment value, and a device control parameter; wherein the modulation means comprises at least vocal stimulation, ultrasound stimulation, optical stimulation, electrical stimulation, magnetic stimulation, temperature stimulation, humidity stimulation, tactile stimulation and/orThe concentration control mode at least comprises any mode of separation mode and contact mode. In an actual use scene, different baseline sleep cycle dynamic regulation strategies can be selected and formulated according to the individual situation of a user, the sleep environment or the facility equipment condition, and a regulation mode and an execution mode with little sleep interference and good experience to the user are selected.
And fifthly, connecting and controlling sleep cycle adjusting peripheral equipment according to a sleep cycle dynamic adjusting strategy, and carrying out dynamic intervention adjustment on the sleep process of the user.
In this embodiment, the sleep cycle adjustment peripheral device includes at least a vocal stimulation device, an ultrasonic stimulation device, a light stimulation device, an electrical stimulation device, a magnetic stimulation device, a temperature stimulation device, a humidity stimulation device, a tactile stimulation device, and a light stimulation deviceAny of the concentration control devices, and is determined by the specific manner of adjustment.
And step six, dynamically tracking and evaluating the effect of dynamic intervention adjustment, extracting a dynamic adjustment effect coefficient, generating a dynamic adjustment effect curve, and calculating to obtain an adjustment effect comprehensive index.
In this embodiment, a calculation manner of the dynamic adjustment effect coefficient specifically includes:
；
Wherein, For dynamically adjusting the effect coefficient,/>For presetting user individual correction coefficient,/>Correction coefficient for trend prediction,/>Sleep periodicity index before and after dynamic adjustment,/>, respectivelyTo take absolute value operators. /(I)
In this embodiment, a calculation formula of the trend prediction correction coefficient specifically includes:
；
Wherein, Correction coefficient for trend prediction,/>The sleep periodic index after dynamic regulation and the sleep periodic index predictive value before dynamic regulation are respectively/>The sleep state characteristic value in the sleep state characteristic curve after dynamic adjustment and the sleep state characteristic predicted value before dynamic adjustment are respectively.
In this embodiment, the dynamic adjustment effect coefficient will be used for dynamic optimization of the subsequent wavelet analysis method parameter, dynamic prediction analysis method parameter, method selection of trend prediction analysis, and sleep cycle dynamic adjustment strategy.
In this embodiment, the comprehensive index of the adjustment effect is specifically an average value or root mean square of the dynamic adjustment effect curve. In most practical use scenarios, an average value may be used.
P400: and extracting a time phase periodic coupling index, a time phase adjustment distribution characteristic and a period index adjustment coefficient according to the sleep time phase curve, the sleep period index curve and the dynamic adjustment effect curve, and generating a user sleep period detection and adjustment report.
And step one, calculating the correlation between the sleep time phase curve and the sleep periodic index curve to obtain the time phase periodic coupling index.
In this embodiment, the correlation calculation method at least includes any one of coherence analysis, pearson correlation analysis, jaccard similarity analysis, linear mutual information analysis, linear correlation analysis, euclidean distance analysis, manhattan distance analysis, and chebyshev distance analysis.
In this embodiment, pearson correlation analysis is chosen to obtain the phase periodic coupling index.
And secondly, calculating distribution characteristics of sleep periodic indexes and dynamic regulation effect coefficients under different sleep phases according to the sleep phase curve, the sleep periodic index curve and the dynamic regulation effect curve to obtain phase regulation distribution characteristics.
In this embodiment, the distribution characteristics include at least any one of average, root mean square, maximum, minimum, variance, standard deviation, coefficient of variation, kurtosis, and skewness.
In this embodiment, the phase adjustment distribution characteristics include sleep periodic indexes under different sleep phases, average values, maximum values, minimum values, standard deviations, variation coefficients, kurtosis and skewness of the dynamic adjustment effect coefficients.
And thirdly, calculating the correlation between the sleep periodic index curve and the dynamic regulation effect curve to obtain the periodic index regulation coefficient.
In this embodiment, pearson correlation analysis is selected to obtain the periodic index adjustment coefficients.
And fourthly, generating a user sleep periodic detection and adjustment report according to a preset report period.
In this embodiment, the user sleep periodic detection and adjustment report at least includes user biological status information, a sleep status characteristic curve, a sleep phase curve, a sleep periodic index curve, a dynamic adjustment effect curve, a phase periodic coupling index, a phase adjustment distribution characteristic, a periodic index adjustment coefficient, and a detection and adjustment summary. In the actual use process, different reporting periods, such as every hour, every 5 hours, every complete time, every day and the like, can be formulated according to the specific situations of users so as to meet the requirements of different sleep health management, sleep period deep analysis and the like.
P500: and continuously detecting, quantifying and dynamically adjusting the sleep period process of the user, and establishing and dynamically updating a sleep period characteristic database of the user.
First, a user sleep cycle characteristic database is built and stored in an initialized mode.
In this embodiment, the user sleep cycle characteristic database at least includes user biological state information, a sleep state characteristic curve, a sleep time phase curve, a sleep cycle index curve, a dynamic regulation effect curve, a time phase periodic coupling index, a time phase regulation distribution characteristic, a cycle index regulation coefficient, a wavelet analysis method, a trend prediction analysis method, and a sleep cycle dynamic regulation strategy.
And secondly, continuously detecting, quantifying and dynamically adjusting the sleep period process of the user, and dynamically updating the sleep period characteristic database of the user.
In this embodiment, key data generated in the processes of detection quantization and dynamic adjustment are updated to the user sleep cycle characteristic database in real time, so as to ensure the timeliness and effectiveness of the optimization of the subsequent detection quantization and dynamic adjustment method. In the actual use process, the data updating mechanism of the sleep cycle characteristic database of the user can be formulated in a more practical way.
P600: and dynamically optimizing wavelet analysis method parameters, the period boundary frequency, dynamic prediction analysis method parameters and the sleep period dynamic regulation strategy according to the sleep period characteristic database of the user.
The first step, dynamically optimizing parameters of a wavelet analysis method and cycle boundary frequency in the process of periodically detecting and quantifying the sleep of the user according to a user sleep cycle characteristic database.
In this embodiment, the method is applied to selection and optimization of the method for detecting and quantifying the current sleep period, such as selection of a wavelet basis function, selection of a specific wavelet decomposition or wavelet packet decomposition method, and adjustment of a period boundary frequency, according to the user sleep period feature database and the dynamic adjustment effect coefficient of the last dynamic adjustment period, so as to continuously improve the accuracy and rationality of the detection and quantification result.
And step two, dynamically optimizing dynamic prediction analysis method parameters and sleep cycle dynamic regulation strategies in the user sleep cycle dynamic regulation process according to the user sleep cycle characteristic database.
In this embodiment, the dynamic adjustment effect coefficient according to the user sleep cycle characteristic database and the last dynamic adjustment cycle is applied to the selection and optimization of the current sleep cycle dynamic adjustment method, such as the selection of a dynamic prediction analysis method and the detailed parameter combination optimization in the sleep cycle dynamic adjustment strategy, so as to achieve the continuous improvement of the dynamic adjustment effect and the user experience body feeling.
Referring now to fig. 2, a system for detecting and adjusting sleep periodicity based on wavelet analysis is provided, which is configured to perform the above-described method steps. The system comprises the following modules:
The sleep state tracking module S100 is used for acquiring and analyzing the sleep physiological state of the user to obtain a sleep state characteristic curve and a sleep time phase curve;
The wavelet index analysis module S200 is used for carrying out trending treatment on the sleep state characteristic curve, selecting a wavelet basis function, carrying out wavelet analysis, determining a periodic boundary frequency, extracting a periodic WT component signal, and calculating to obtain a sleep periodic index and a sleep periodic index curve;
the dynamic strategy regulation and control module S300 is used for dynamically predicting and analyzing a sleep time phase curve, a sleep state characteristic curve and a sleep periodicity index curve, generating a sleep period dynamic regulation strategy, and dynamically regulating and controlling the sleep process of a user and evaluating the regulation and control effect;
The detection, adjustment and analysis module S400 is used for extracting a time phase periodic coupling index, a time phase adjustment distribution characteristic and a period index adjustment and control coefficient according to a sleep time phase curve, a sleep period index curve and a dynamic adjustment and control effect curve to generate a user sleep period detection and adjustment report;
the periodic data updating module S500 is used for continuously detecting, quantifying and dynamically adjusting the sleep periodic process of the user, and establishing and dynamically updating a sleep periodic characteristic database of the user;
the detection adjustment optimization module S600 is used for dynamically optimizing the wavelet analysis method parameters, the period boundary frequency, the dynamic prediction analysis method parameters and the sleep period dynamic adjustment strategy according to the sleep period characteristic database of the user;
And the data operation management module S700 is used for carrying out visual management, unified storage and operation management on all process data of the system.
In this embodiment, the sleep state tracking module S100 further includes the following functional units:
The state acquisition processing unit is used for carrying out acquisition monitoring and signal processing on the sleep physiological state signals of the user and generating sleep physiological state time frame data;
The state curve extraction unit is used for carrying out feature analysis and feature selection on all time frame data in the sleep physiological state time frame data to generate a sleep state feature curve;
and the sleep phase analysis unit is used for carrying out sleep phase analysis on all time frame data in the sleep physiological state time frame data to generate a sleep phase curve.
In this embodiment, the wavelet index analysis module S200 further includes the following functional units:
the state trend processing unit is used for carrying out trend removal processing on the sleep state characteristic curve to obtain a sleep state characteristic baseline curve;
An analysis parameter selection unit, configured to determine a periodic boundary frequency and a method parameter of wavelet analysis according to a feature combination generation mode of a sleep state feature curve, and select a wavelet basis function;
the signal wavelet analysis unit is used for carrying out wavelet decomposition and/or wavelet packet decomposition on the sleep state characteristic baseline curve to obtain a sleep state characteristic WT component signal set;
A periodic component extraction unit, configured to screen WT component signals satisfying a periodic boundary frequency from a sleep state characteristic WT component signal set, and sum up to generate a periodic WT component signal;
The period index calculation unit is used for calculating a sleep period index according to the sleep state characteristic baseline curve and the period WT component signal and generating a sleep period index curve according to a time sequence.
In this embodiment, the dynamic policy adjustment module S300 further includes the following functional units:
The sleep time phase prediction unit is used for carrying out trend prediction analysis on the sleep time phase curve to obtain a sleep time phase predicted value;
The period characteristic prediction unit is used for carrying out trend prediction analysis on the sleep state characteristic curve to obtain a sleep state period characteristic prediction value;
the periodic index prediction unit is used for carrying out trend prediction analysis on the sleep periodic index curve to obtain a sleep periodic index predicted value;
The regulation strategy generation unit is used for dynamically generating a sleep cycle dynamic regulation strategy according to a preset dynamic regulation cycle, a sleep time phase predicted value, a sleep state cycle characteristic predicted value, a sleep cycle index predicted value and a preset sleep cycle regulation knowledge base;
The dynamic regulation execution unit is used for connecting and controlling the sleep cycle regulation peripheral equipment according to the sleep cycle dynamic regulation strategy and carrying out dynamic intervention regulation on the sleep process of the user;
the dynamic effect evaluation unit is used for carrying out dynamic tracking evaluation on the effect of dynamic intervention adjustment, extracting the dynamic adjustment effect coefficient, generating a dynamic adjustment effect curve and calculating to obtain an adjustment effect comprehensive index.
In this embodiment, the detection adjustment analysis module S400 further includes the following functional units:
the time phase coupling analysis unit is used for calculating the correlation between the sleep time phase curve and the sleep periodic index curve to obtain a time phase periodic coupling index;
The time phase distribution analysis unit is used for calculating the distribution characteristics of the sleep periodic index and the dynamic regulation effect coefficient under different sleep time phases according to the sleep time phase curve, the sleep periodic index curve and the dynamic regulation effect curve to obtain time phase regulation distribution characteristics;
The period regulation analysis unit is used for calculating the correlation between the sleep period index curve and the dynamic regulation effect curve to obtain a period index regulation coefficient;
the user report generating unit is used for generating a user sleep periodic detection and adjustment report according to a preset report period;
And the user report output unit is used for carrying out unified management on the format output and the presentation form of the user sleep periodic detection and regulation report.
In this embodiment, the periodic data updating module S500 further includes the following functional units:
The database initializing unit is used for initializing and establishing and storing a user sleep cycle characteristic database;
And the database dynamic updating unit is used for continuously detecting, quantifying and dynamically adjusting the sleep periodic process of the user and dynamically updating the sleep periodic characteristic database of the user.
In this embodiment, the detection adjustment optimization module S600 further includes the following functional units:
The detection and quantization optimization unit is used for dynamically optimizing parameters of a wavelet analysis method and cycle boundary frequency in the periodic detection and quantization process of the sleep of the user according to the sleep period characteristic database of the user;
the dynamic adjustment optimizing unit is used for dynamically optimizing the dynamic prediction analysis method parameters and the sleep period dynamic adjustment strategy in the user sleep period dynamic adjustment process according to the user sleep period characteristic database.
In this embodiment, the data operation management module S700 further includes the following functional units:
a user information management unit for registering input, editing, inquiry, output and deletion of user basic information;
The data visual management unit is used for visual display management of all data in the system;
The data storage management unit is used for uniformly storing and managing all data in the system;
and the data operation management unit is used for backing up, migrating and exporting all data in the system.
Referring to fig. 3, the sleep periodicity detecting and adjusting device based on wavelet analysis according to the embodiment of the present invention includes the following modules:
the sleep state tracking module M100 is used for acquiring and analyzing the sleep physiological state of the user to obtain a sleep state characteristic curve and a sleep time phase curve;
the wavelet index analysis module M200 is used for carrying out trending treatment on the sleep state characteristic curve, selecting a wavelet basis function, carrying out wavelet analysis, determining the periodic boundary frequency, extracting periodic WT component signals, and calculating to obtain a sleep periodic index and a sleep periodic index curve;
the dynamic strategy regulation and control module M300 is used for dynamically predicting and analyzing a sleep time phase curve, a sleep state characteristic curve and a sleep periodicity index curve, generating a sleep period dynamic regulation strategy, and dynamically regulating and controlling the sleep process of a user and evaluating the regulation and control effect;
The detection, adjustment and analysis module M400 is used for extracting a time phase periodic coupling index, a time phase adjustment distribution characteristic and a period index adjustment and control coefficient according to a sleep time phase curve, a sleep period index curve and a dynamic adjustment and control effect curve to generate a user sleep period detection and adjustment report;
The periodic database module M500 is used for continuously detecting, quantifying and dynamically adjusting the sleep periodic process of the user, and establishing and dynamically updating a sleep periodic characteristic database of the user;
the detection adjustment optimization module M600 is used for dynamically optimizing the wavelet analysis method parameters, the period boundary frequency, the dynamic prediction analysis method parameters and the sleep period dynamic adjustment strategy according to the sleep period characteristic database of the user;
the data visualization module M700 is used for carrying out unified visual display management on all process data and/or result data in the device;
The data management center module M800 is used for unified storage and data operation management of all process data and/or result data in the device.
The apparatus is configured to correspondingly perform the steps of the method 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 (37)
1. The sleep periodicity detection and adjustment method based on wavelet analysis is characterized by comprising the following steps:
collecting and analyzing the sleeping physiological state of the user to obtain a sleeping state characteristic curve and a sleeping time phase curve;
Trending the sleep state characteristic curve, selecting a wavelet basis function, performing wavelet analysis, determining a periodic boundary frequency, extracting a periodic WT component signal, and calculating to obtain a sleep periodic index and a sleep periodic index curve;
Dynamically predicting and analyzing the sleep time phase curve, the sleep state characteristic curve and the sleep periodic index curve to generate a sleep period dynamic regulation strategy, and dynamically regulating and controlling the sleep process of a user and evaluating the regulation effect;
extracting a time phase periodic coupling index, a time phase adjustment distribution characteristic and a period index adjustment coefficient according to the sleep time phase curve, the sleep period index curve and the dynamic adjustment effect curve, and generating a user sleep period detection and adjustment report;
continuously detecting, quantifying and dynamically adjusting the sleep period process of the user, and establishing and dynamically updating a sleep period characteristic database of the user;
dynamically optimizing wavelet analysis method parameters, the period boundary frequency, dynamic prediction analysis method parameters and the sleep period dynamic regulation strategy according to the sleep period characteristic database of the user;
The specific steps of performing trend removal processing on the sleep state characteristic curve, selecting a wavelet basis function, performing wavelet analysis, determining a periodic boundary frequency, extracting a periodic WT component signal, and calculating to obtain a sleep periodic index and a sleep periodic index curve include:
trending treatment is carried out on the sleep state characteristic curve to obtain a sleep state characteristic baseline curve;
Determining the cycle boundary frequency and the wavelet analysis method parameters according to the feature combination generation mode of the sleep state feature curve, and selecting the wavelet basis function;
Performing wavelet decomposition and/or wavelet packet decomposition on the sleep state characteristic baseline curve to obtain a sleep state characteristic WT component signal set;
screening WT component signals meeting the periodic boundary frequency from the sleep state characteristic WT component signal set, and generating the periodic WT component signals by summation;
the sleep periodicity index calculating method specifically comprises the following steps:
1) Acquiring the sleep state characteristic baseline curve and the periodic WT component signal;
2) Calculating a signal difference value between the sleep state characteristic baseline curve and the periodic WT component signal to obtain a sleep state characteristic residual curve;
3) Square calculation is carried out on the sleep state characteristic residual error curve and the periodic WT component signal respectively, so that a sleep state characteristic residual error square curve and a periodic WT component square signal are obtained;
4) According to the sleep time phase curve, the sleep state characteristic residual error square curve and the periodic WT component square signal, calculating to obtain a sleep periodic node coefficient and generating a sleep periodic node coefficient curve according to a time sequence;
5) And calculating the sleep periodicity index according to the sleep periodicity node coefficient curve, a preset method correction coefficient corresponding to a wavelet analysis method and a preset user individual correction coefficient related to the user biological state information.
2. The method of claim 1, wherein the step of collecting and analyzing the sleep physiological state of the user to obtain a sleep state characteristic curve and a sleep phase curve further comprises:
The method comprises the steps of collecting, monitoring and signal processing a sleep physiological state signal of a user to generate sleep physiological state time frame data;
Performing feature analysis and feature selection on all time frame data in the sleep physiological state time frame data to generate the sleep state feature curve;
And carrying out sleep phase analysis on all time frame data in the sleep physiological state time frame data to generate the sleep phase curve.
3. The method of claim 2, wherein: the sleep physiological state signal comprises at least any one of a brain center state signal and an autonomic nerve state signal; wherein the brain center state signal at least comprises any one of an electroencephalogram signal, a magnetoencephalography signal and an oxygen level dependent signal, and the autonomic nerve state signal at least comprises any one of an oxygen level dependent signal, an electrocardio signal, a pulse signal, a respiration signal, an oxygen blood signal, a body temperature signal and a skin electrical signal.
4. The method of claim 2, wherein: the signal processing at least comprises AD digital-to-analog conversion, resampling, re-referencing, noise reduction, artifact removal, power frequency notch, low-pass filtering, high-pass filtering, band-stop filtering, band-pass filtering, correction processing and framing processing; the correction processing specifically includes signal correction, signal prediction and smoothing processing on signal data segments containing artifacts or distortion in a target signal, and the framing processing refers to continuous sliding segmentation on the target signal according to a signal sampling rate and with a preset time window length and a preset time translation step length.
5. A method according to claim 2 or 3, wherein: the feature analysis includes at least any one of numerical analysis, envelope analysis, time-frequency analysis, entropy analysis, fractal analysis, and complexity analysis.
6. A method according to claim 2 or 3, wherein: the sleep state characteristic curve is a characteristic curve which accurately describes the continuous change of the sleep physiological states of the user in different sleep periods and different sleep phases, and is obtained by screening target characteristics with preset characteristic quantity from target characteristic sets obtained by characteristic analysis and carrying out weighting calculation and combination; the sleep phase at least comprises a wake phase, a light sleep phase, a deep sleep phase and a rapid eye movement sleep phase.
7. A method according to claim 2 or 3, wherein the method for generating the sleep phase curve specifically comprises:
1) Learning training and data modeling are carried out on the sleep physiological state time frame data of the preset scale sleep user sample and the sleep time phase stage data corresponding to the sleep physiological state time frame data through a deep learning algorithm, so that a sleep time phase stage recognition model is obtained;
2) Inputting the sleep physiological state time frame data of the current user into the sleep time phase stage identification model to obtain the corresponding sleep time phase stage and generating the sleep time phase curve according to time sequence.
8. The method according to claim 1 or 2, wherein the specific steps of performing trending processing on the sleep state characteristic curve, selecting a wavelet basis function and performing wavelet analysis, determining a period boundary frequency and extracting a period WT component signal, and calculating to obtain a sleep periodicity index and a sleep periodicity index curve further include:
and calculating the sleep periodic index according to the sleep state characteristic baseline curve and the periodic WT component signal, and generating the sleep periodic index curve according to a time sequence.
9. The method as recited in claim 8, wherein: the trending process specifically removes linear trend components and very low frequency trend components of the target signal, and at least comprises any one of mean value removing process, low-pass filtering process, trending analysis FDA, multi-fractal trending analysis MFDFA and asymmetric trend fluctuation eliminating analysis ADFA.
10. The method of claim 9, wherein: the wavelet decomposition at least comprises any one of continuous wavelet transform CWT, discrete wavelet transform DWT, empirical wavelet transform EWT, synchronous extrusion wavelet transform SWT, maximum overlap discrete wavelet transform MODWT and synchronous extraction wavelet transform WSET.
11. The method as recited in claim 8, wherein: the periodic boundary frequency includes at least any one of a band-pass cut-off frequency and a low-pass cut-off frequency.
12. The method of claim 1, wherein,
The calculation formula of the sleep periodic node coefficient is specifically as follows:
；
Wherein, For the sleep periodicity node coefficient,/>/>, Respectively, in the sleep state characteristic residual square curveThe number of points and the/> in the periodic WT component squared signalDot number,/>Is the/>, in the sleep phase curvePhase correction coefficients corresponding to the phase values of the respective points.
13. The method according to claim 1 or 12, wherein a calculation formula of the sleep periodicity index is:
；
Wherein, For the sleep periodicity index,/>Respectively presetting a user individual correction coefficient and a preset method correction coefficient,/>For the/>, in the sleep periodic node coefficient curveDot number,/>And the data length of the sleep periodic node coefficient curve is the data length of the sleep periodic node coefficient curve.
14. The method according to claim 1 or 2, wherein the specific steps of dynamically predicting and analyzing the sleep phase curve, the sleep state characteristic curve and the sleep cycle index curve to generate a sleep cycle dynamic adjustment strategy, and dynamically adjusting and evaluating the sleep process of the user further comprise:
trend prediction analysis is carried out on the sleep time phase curve to obtain a sleep time phase predicted value;
Trend prediction analysis is carried out on the sleep state characteristic curve to obtain a sleep state cycle characteristic prediction value;
trend prediction analysis is carried out on the sleep periodic index curve to obtain a sleep periodic index predicted value;
dynamically generating the sleep cycle dynamic regulation strategy according to a preset dynamic regulation cycle, the sleep time phase predicted value, the sleep state cycle characteristic predicted value, the sleep cycle index predicted value and a preset sleep cycle regulation knowledge base;
According to the sleep cycle dynamic regulation strategy, connecting and controlling sleep cycle regulation peripheral equipment to dynamically intervene and regulate the sleep process of a user;
and carrying out dynamic tracking evaluation on the effect of dynamic intervention adjustment, extracting a dynamic adjustment effect coefficient, generating a dynamic adjustment effect curve, and calculating to obtain an adjustment effect comprehensive index.
15. The method as recited in claim 14, wherein: the trend prediction analysis method at least comprises any one of AR, MA, ARMA, ARIMA, SARIMA, VAR and deep learning.
16. The method of claim 14, wherein the sleep cycle dynamic adjustment strategy comprises at least a sleep scenario, a sleep phase, an adjustment mode, an execution mode, an adjustment intensity, an adjustment time point, a duration, a target adjustment value, and a device control parameter; wherein the modulation means comprises at least vocal stimulation, ultrasound stimulation, light stimulation, electrical stimulation, magnetic stimulation, temperature stimulation, humidity stimulation, tactile stimulation, and/or the likeThe implementation mode at least comprises any mode of separation mode and contact mode.
17. The method of claim 14, wherein the sleep cycle adjustment peripheral device comprises at least a vocal stimulation device, an ultrasound stimulation device, a light stimulation device, an electrical stimulation device, a magnetic stimulation device, a temperature stimulation device, a humidity stimulation device, a tactile stimulation device, andAny of the concentration control devices, and is determined by the specific manner of adjustment.
18. The method according to claim 14, wherein one way of calculating the dynamic adjustment effect coefficient is:
；
Wherein, For the dynamic adjustment of the effect coefficient,/>For presetting user individual correction coefficient,/>Correction coefficient for trend prediction,/>The sleep cycle index before dynamic regulation and after dynamic regulation are respectively/>To take absolute value operators.
19. The method of claim 18, wherein the calculation formula of the trend prediction correction coefficient is specifically:
；
Wherein, Predicting a correction factor for said trend,/>The sleep periodicity index after dynamic adjustment and the sleep periodicity index predicted value before dynamic adjustment,/>, respectivelyAnd the sleep state period characteristic value in the sleep state characteristic curve after dynamic adjustment and the sleep state period characteristic predicted value before dynamic adjustment are respectively obtained.
20. The method of any of claims 15-19, wherein the dynamic adjustment effect coefficient is to be used for dynamic optimization of subsequent wavelet analysis method parameters, dynamic predictive analysis method parameters, method selection of the trend predictive analysis, the sleep cycle dynamic adjustment strategy.
21. The method according to any one of claims 15 to 19, wherein the regulatory effect composite index is in particular the mean value or root mean square of the dynamic regulatory effect curve.
22. A method according to claim 1 or 2, characterized in that: the specific steps of extracting the phase periodical coupling index, the phase adjustment distribution characteristic and the period index adjustment coefficient according to the sleep phase curve, the sleep periodical index curve and the dynamic adjustment effect curve, and generating the user sleep periodical detection and adjustment report further comprise:
Calculating the correlation between the sleep time phase curve and the sleep periodic index curve to obtain the time phase periodic coupling index;
Calculating distribution characteristics of the sleep periodic index and the dynamic regulation effect coefficient under different sleep phases according to the sleep phase curve, the sleep periodic index curve and the dynamic regulation effect curve to obtain phase regulation distribution characteristics;
calculating the correlation between the sleep periodic index curve and the dynamic regulation effect curve to obtain the periodic index regulation coefficient;
and generating the user sleep periodic detection and adjustment report according to a preset report period.
23. The method as recited in claim 22, wherein: the correlation calculation method at least comprises any one of coherence analysis, pearson correlation analysis, jacquard similarity analysis, linear mutual information analysis, linear correlation analysis, euclidean distance analysis, manhattan distance analysis and Chebyshev distance analysis.
24. The method as recited in claim 22, wherein: the distribution characteristics at least comprise any one of average value, root mean square, maximum value, minimum value, variance, standard deviation, variation coefficient, kurtosis and skewness.
25. The method as recited in claim 22, wherein: the user sleep periodic detection and adjustment report at least comprises user biological state information, a sleep state characteristic curve, a sleep time phase curve, a sleep periodic index curve, a dynamic regulation effect curve, a time phase periodic coupling index, a time phase adjustment distribution characteristic, a period index regulation coefficient and a detection and adjustment summary.
26. A method according to claim 1 or 2, characterized in that: the specific steps of continuously detecting, quantifying and dynamically adjusting the sleep cycle process of the user and establishing and dynamically updating the sleep cycle characteristic database of the user further comprise the following steps:
Initializing, establishing and storing a sleep cycle characteristic database of the user;
And continuously detecting, quantifying and dynamically adjusting the sleep period process of the user, and dynamically updating the sleep period characteristic database of the user.
27. The method of claim 26, wherein: the user sleep cycle characteristic database at least comprises user biological state information, a sleep state characteristic curve, a sleep time phase curve, a sleep cycle index curve, a dynamic regulation effect curve, a time phase periodic coupling index, a time phase regulation distribution characteristic, a cycle index regulation coefficient, a wavelet analysis method, a trend prediction analysis method and a sleep cycle dynamic regulation strategy.
28. A method according to claim 1 or 2, characterized in that: the specific steps of dynamically optimizing the wavelet analysis method parameter, the period boundary frequency, the dynamic prediction analysis method parameter and the sleep period dynamic regulation strategy according to the user sleep period characteristic database further comprise:
Dynamically optimizing parameters of a wavelet analysis method and the periodic boundary frequency in the periodic detection and quantization process of the sleep of the user according to the characteristic database of the sleep period of the user;
And dynamically optimizing dynamic prediction analysis method parameters and the sleep cycle dynamic regulation strategy in the user sleep cycle dynamic regulation process according to the user sleep cycle characteristic database.
29. A sleep periodicity detection and adjustment system based on wavelet analysis, comprising the following modules:
the sleep state tracking module is used for acquiring and analyzing the sleep physiological state of the user to obtain a sleep state characteristic curve and a sleep time phase curve;
The wavelet index analysis module is used for carrying out trending treatment on the sleep state characteristic curve, selecting a wavelet basis function, carrying out wavelet analysis, determining a periodic boundary frequency, extracting a periodic WT component signal and calculating to obtain a sleep periodic index and a sleep periodic index curve;
The dynamic strategy regulation and control module is used for carrying out dynamic prediction analysis on the sleep time phase curve, the sleep state characteristic curve and the sleep periodic index curve to generate a sleep period dynamic regulation strategy and carrying out dynamic regulation and control on the sleep process of the user and regulation and control effect evaluation;
the detection, adjustment and analysis module is used for extracting a time phase periodic coupling index, a time phase adjustment distribution characteristic and a period index adjustment and control coefficient according to the sleep time phase curve, the sleep period index curve and the dynamic adjustment and control effect curve to generate a user sleep period detection and adjustment report;
the periodic data updating module is used for continuously detecting, quantifying and dynamically adjusting the sleep periodic process of the user, and establishing and dynamically updating a sleep periodic characteristic database of the user;
The detection adjustment optimization module is used for dynamically optimizing the wavelet analysis method parameters, the period boundary frequency, the dynamic prediction analysis method parameters and the sleep period dynamic adjustment strategy according to the user sleep period characteristic database;
the data operation management module is used for carrying out visual management, unified storage and operation management on all process data of the system;
The specific steps of performing trend removal processing on the sleep state characteristic curve, selecting a wavelet basis function, performing wavelet analysis, determining a periodic boundary frequency, extracting a periodic WT component signal, and calculating to obtain a sleep periodic index and a sleep periodic index curve include:
trending treatment is carried out on the sleep state characteristic curve to obtain a sleep state characteristic baseline curve;
Determining the cycle boundary frequency and the wavelet analysis method parameters according to the feature combination generation mode of the sleep state feature curve, and selecting the wavelet basis function;
Performing wavelet decomposition and/or wavelet packet decomposition on the sleep state characteristic baseline curve to obtain a sleep state characteristic WT component signal set;
screening WT component signals meeting the periodic boundary frequency from the sleep state characteristic WT component signal set, and generating the periodic WT component signals by summation;
the sleep periodicity index calculating method specifically comprises the following steps:
1) Acquiring the sleep state characteristic baseline curve and the periodic WT component signal;
2) Calculating a signal difference value between the sleep state characteristic baseline curve and the periodic WT component signal to obtain a sleep state characteristic residual curve;
3) Square calculation is carried out on the sleep state characteristic residual error curve and the periodic WT component signal respectively, so that a sleep state characteristic residual error square curve and a periodic WT component square signal are obtained;
4) According to the sleep time phase curve, the sleep state characteristic residual error square curve and the periodic WT component square signal, calculating to obtain a sleep periodic node coefficient and generating a sleep periodic node coefficient curve according to a time sequence;
5) And calculating the sleep periodicity index according to the sleep periodicity node coefficient curve, a preset method correction coefficient corresponding to a wavelet analysis method and a preset user individual correction coefficient related to the user biological state information.
30. The system of claim 29, wherein the sleep state tracking module further comprises the following functional units:
The state acquisition processing unit is used for carrying out acquisition monitoring and signal processing on the sleep physiological state signals of the user and generating sleep physiological state time frame data;
the state curve extraction unit is used for carrying out feature analysis and feature selection on all time frame data in the sleep physiological state time frame data to generate the sleep state feature curve;
and the sleep phase analysis unit is used for carrying out sleep phase analysis on all time frame data in the sleep physiological state time frame data and generating the sleep phase curve.
31. The system of claim 29, wherein the wavelet index analysis module further comprises the following functional units:
the state trend processing unit is used for carrying out trend removal processing on the sleep state characteristic curve to obtain a sleep state characteristic baseline curve;
an analysis parameter selection unit, configured to determine the cycle boundary frequency and a method parameter of wavelet analysis according to a feature combination generation manner of the sleep state feature curve, and select the wavelet basis function;
The signal wavelet analysis unit is used for carrying out wavelet decomposition and/or wavelet packet decomposition on the sleep state characteristic baseline curve to obtain a sleep state characteristic WT component signal set;
a periodic component extraction unit, configured to screen WT component signals satisfying the periodic boundary frequency from the sleep state characteristic WT component signal set, and sum up to generate the periodic WT component signal;
and the period index calculation unit is used for calculating the sleep period index according to the sleep state characteristic baseline curve and the period WT component signal and generating the sleep period index curve according to a time sequence.
32. The system of claim 29, wherein the dynamic policy enforcement module further comprises the following functional units:
the sleep time phase prediction unit is used for carrying out trend prediction analysis on the sleep time phase curve to obtain a sleep time phase predicted value;
The period characteristic prediction unit is used for carrying out trend prediction analysis on the sleep state characteristic curve to obtain a sleep state period characteristic prediction value;
the period index prediction unit is used for carrying out trend prediction analysis on the sleep period index curve to obtain a sleep period index predicted value;
The regulation strategy generation unit is used for dynamically generating the sleep cycle dynamic regulation strategy according to a preset dynamic regulation cycle, the sleep phase predicted value, the sleep state cycle characteristic predicted value, the sleep cycle index predicted value and a preset sleep cycle regulation knowledge base;
The dynamic regulation execution unit is used for connecting and controlling the sleep cycle regulation peripheral equipment according to the sleep cycle dynamic regulation strategy and carrying out dynamic intervention regulation on the sleep process of the user;
the dynamic effect evaluation unit is used for carrying out dynamic tracking evaluation on the effect of dynamic intervention adjustment, extracting the dynamic adjustment effect coefficient, generating a dynamic adjustment effect curve and calculating to obtain an adjustment effect comprehensive index.
33. The system of any of claims 29-32, wherein the detection adjustment analysis module further comprises the following functional units:
The time phase coupling analysis unit is used for calculating the correlation between the sleep time phase curve and the sleep periodic index curve to obtain the time phase periodic coupling index;
the time phase distribution analysis unit is used for calculating the distribution characteristics of the sleep periodic index and the dynamic regulation effect coefficient under different sleep time phases according to the sleep time phase curve, the sleep periodic index curve and the dynamic regulation effect curve to obtain the time phase regulation distribution characteristics;
The period regulation analysis unit is used for calculating the correlation between the sleep period index curve and the dynamic regulation effect curve to obtain the period index regulation coefficient;
The user report generating unit is used for generating the user sleep periodic detection and adjustment report according to a preset report period;
and the user report output unit is used for uniformly managing the format output and the presentation form of the user sleep periodic detection and adjustment report.
34. The system of claim 33, wherein the periodic data update module further comprises the following functional units:
The database initializing unit is used for initializing and establishing and storing the sleep cycle characteristic database of the user;
and the database dynamic updating unit is used for continuously detecting, quantifying and dynamically adjusting the sleep periodic process of the user and dynamically updating the sleep periodic characteristic database of the user.
35. The system of claim 34, wherein the detection adjustment optimization module further comprises the following functional units:
The detection and quantization optimization unit is used for dynamically optimizing wavelet analysis method parameters and the cycle boundary frequency in the user sleep cycle detection and quantization process according to the user sleep cycle characteristic database;
And the dynamic adjustment optimizing unit is used for dynamically optimizing the dynamic prediction analysis method parameters and the sleep period dynamic adjustment strategy in the user sleep period dynamic adjustment process according to the user sleep period characteristic database.
36. The system of claim 29, wherein the data operation management module further comprises the following functional units:
a user information management unit for registering input, editing, inquiry, output and deletion of user basic information;
the data visual management unit is used for visual display management of all data in the system;
the data storage management unit is used for uniformly storing and managing all data in the system;
and the data operation management unit is used for backing up, migrating and exporting all data in the system.
37. The sleep periodicity detecting and adjusting device based on wavelet analysis is characterized by comprising the following modules:
The sleep state tracking module is used for acquiring and analyzing the sleep physiological state of the user to obtain a sleep state characteristic curve and a sleep time phase curve;
the wavelet index analysis module is used for carrying out trending treatment on the sleep state characteristic curve, selecting a wavelet basis function, carrying out wavelet analysis, determining a periodic boundary frequency, extracting a periodic WT component signal, and calculating to obtain a sleep periodic index and a sleep periodic index curve;
The dynamic strategy regulation and control module is used for carrying out dynamic prediction analysis on the sleep time phase curve, the sleep state characteristic curve and the sleep periodic index curve to generate a sleep period dynamic regulation strategy and carrying out dynamic regulation and control on the sleep process of the user and regulation and control effect evaluation;
the detection, adjustment and analysis module is used for extracting a time phase periodic coupling index, a time phase adjustment distribution characteristic and a period index adjustment and control coefficient according to the sleep time phase curve, the sleep period index curve and the dynamic adjustment and control effect curve to generate a user sleep period detection and adjustment report;
the periodic database module is used for continuously detecting, quantifying and dynamically adjusting the sleep periodic process of the user, and establishing and dynamically updating a sleep periodic characteristic database of the user;
the detection adjustment optimization module is used for dynamically optimizing the wavelet analysis method parameters, the period boundary frequency, the dynamic prediction analysis method parameters and the sleep period dynamic adjustment strategy according to the user sleep period characteristic database;
The data visualization module is used for carrying out unified visual display management on all process data and/or result data in the device;
The data management center module is used for uniformly storing and managing data operation of all process data and/or result data in the device;
The specific steps of performing trend removal processing on the sleep state characteristic curve, selecting a wavelet basis function, performing wavelet analysis, determining a periodic boundary frequency, extracting a periodic WT component signal, and calculating to obtain a sleep periodic index and a sleep periodic index curve include:
trending treatment is carried out on the sleep state characteristic curve to obtain a sleep state characteristic baseline curve;
Determining the cycle boundary frequency and the wavelet analysis method parameters according to the feature combination generation mode of the sleep state feature curve, and selecting the wavelet basis function;
Performing wavelet decomposition and/or wavelet packet decomposition on the sleep state characteristic baseline curve to obtain a sleep state characteristic WT component signal set;
screening WT component signals meeting the periodic boundary frequency from the sleep state characteristic WT component signal set, and generating the periodic WT component signals by summation;
the sleep periodicity index calculating method specifically comprises the following steps:
1) Acquiring the sleep state characteristic baseline curve and the periodic WT component signal;
2) Calculating a signal difference value between the sleep state characteristic baseline curve and the periodic WT component signal to obtain a sleep state characteristic residual curve;
3) Square calculation is carried out on the sleep state characteristic residual error curve and the periodic WT component signal respectively, so that a sleep state characteristic residual error square curve and a periodic WT component square signal are obtained;
4) According to the sleep time phase curve, the sleep state characteristic residual error square curve and the periodic WT component square signal, calculating to obtain a sleep periodic node coefficient and generating a sleep periodic node coefficient curve according to a time sequence;
5) And calculating the sleep periodicity index according to the sleep periodicity node coefficient curve, a preset method correction coefficient corresponding to a wavelet analysis method and a preset user individual correction coefficient related to the user biological state information.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
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