CN116509338B - Sleep periodicity detection and adjustment method, system and device based on modal analysis - Google Patents

Sleep periodicity detection and adjustment method, system and device based on modal analysis Download PDF

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CN116509338B
CN116509338B CN202310779721.6A CN202310779721A CN116509338B CN 116509338 B CN116509338 B CN 116509338B CN 202310779721 A CN202310779721 A CN 202310779721A CN 116509338 B CN116509338 B CN 116509338B
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何将
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Anhui Xingchen Zhiyue Technology Co ltd
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Abstract

The invention provides a sleep periodicity detection and adjustment method based on modal 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; performing trending treatment and modal decomposition, determining a periodic boundary frequency, extracting a periodic IMF component signal, and calculating to obtain a sleep periodic index and a sleep periodic index curve; carrying out dynamic predictive analysis on the curve to generate a sleep cycle dynamic regulation strategy and regulate; according to the curve, extracting a time phase periodic coupling index, a time phase adjustment distribution characteristic and a period index adjustment coefficient, and establishing and updating a user sleep period characteristic database; and generating a user sleep periodic detection and adjustment report according to the user database dynamic optimization mode decomposition method parameters, the periodic boundary frequency, the dynamic prediction analysis method parameters and the sleep period dynamic adjustment strategy. The invention can realize the efficient intervention and adjustment of the sleep cycle of the user.

Description

Sleep periodicity detection and adjustment method, system and device based on modal analysis
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 modal 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 modal analysis, which is characterized in that a sleep state characteristic curve with strong time-varying characteristics is extracted from a sleep physiological state, trend removal processing and modal decomposition are further carried out, a sleep period IMF component signal is extracted, a sleep periodicity index and a sleep periodicity index curve are obtained through calculation, further dynamic prediction analysis of the sleep state is completed, a sleep period dynamic adjustment strategy is generated, dynamic adjustment and regulation effect evaluation are carried out on a sleep process of a user, finally, statistical analysis is carried out on the whole detection quantization and dynamic adjustment process, a user sleep period characteristic database is established and updated, and the detection quantization and dynamic adjustment process method and strategy are reversely optimized, so that the user personalized, detection quantization and intervention adjustment efficiency effect is continuously improved. The invention also provides a sleep periodicity detection and adjustment system based on modal analysis, which is used for realizing the method. The invention also provides a sleep periodicity detecting and adjusting device based on modal 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 modal 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;
Performing trending treatment and modal decomposition on the sleep state characteristic curve, determining a periodic boundary frequency, extracting a periodic IMF 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 establishing and updating a user sleep period characteristic database;
And dynamically optimizing modal decomposition method parameters, the period boundary frequency, dynamic prediction analysis method parameters and the sleep period dynamic regulation strategy according to the user sleep period characteristic database to generate a user sleep period detection and regulation report.
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 steps of performing trending treatment and modal decomposition on the sleep state characteristic curve, determining a periodic boundary frequency, extracting a periodic IMF component signal, and calculating to obtain a sleep periodic index and a sleep periodic index curve further 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 method parameters of modal decomposition according to the characteristic combination generation mode of the sleep state characteristic curve;
performing empirical mode decomposition and/or variational mode decomposition on the sleep state characteristic baseline curve to obtain a sleep state characteristic IMF component signal set;
Performing spectrum analysis on the sleep state characteristic IMF component signal set, identifying IMF component signals meeting the periodic boundary frequency, and adding to generate the periodic IMF component signals;
And calculating the sleep periodic index according to the sleep state characteristic baseline curve and the periodic IMF 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 modal decomposition includes at least any one of empirical modal decomposition and variational modal decomposition.
More preferably, the empirical mode decomposition method at least comprises any one of EMD, EEMD, CEEMD, CEEMDAN, ICEEMDAN, ESMD; the ESMD is specifically a pole symmetric mode decomposition method, which uses the EMD idea to change the external envelope interpolation into the internal pole symmetric interpolation, and optimizes the last residual mode by using the least square idea to make the last residual mode become an adaptive global average line of the whole data so as to determine the optimal screening times.
More preferably, the method for decomposing the variation mode at least comprises any one of VMD and modified VMD; the improved VMD specifically performs an organic combination of a signal processing algorithm and the VMD to adapt to signal analysis and signal decomposition requirements of different scenes.
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 IMF component signal;
2) Calculating a signal difference value between the sleep state characteristic baseline curve and the periodic IMF component signal to obtain a sleep state characteristic residual curve;
3) Respectively carrying out absolute value calculation on the sleep state characteristic residual error curve and the periodic IMF component signal to obtain a sleep state characteristic residual error absolute value curve and a periodic IMF component absolute value signal;
4) According to the sleep time phase curve, the sleep state characteristic residual error absolute value curve and the periodic IMF component absolute value 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 periodic index according to the sleep periodic node coefficient curve, a preset method correction coefficient corresponding to a modal decomposition 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 the/>, in the sleep state characteristic residual absolute value curveThe number of points and the absolute value of the periodic IMF component 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,/>The coefficients are modified for the preset user personality,For the phase correction coefficient of the current sleep phase,/>The sleep cycle index before dynamic regulation and after dynamic regulation are respectively/>To take absolute value operators.
More preferably, the dynamic adjustment effect coefficient is used for dynamic optimization of a subsequent modal decomposition method parameter, a dynamic prediction analysis method parameter, a method selection of the trend prediction analysis and the sleep cycle dynamic adjustment strategy.
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 period index curve and the dynamic adjustment effect curve, and establishing and updating the user sleep period characteristic database further comprises:
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 initializing, establishing and continuously and dynamically updating the sleep cycle characteristic database of the user.
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 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 modal decomposition method, a trend prediction analysis method and the sleep cycle dynamic regulation strategy.
More preferably, the specific steps of generating the user sleep cycle detection and adjustment report according to the user sleep cycle characteristic database, the dynamic optimization mode decomposition method parameter, the cycle boundary frequency, the dynamic prediction analysis method parameter and the sleep cycle dynamic adjustment strategy further include:
According to the user sleep cycle characteristic database, dynamically optimizing modal decomposition method parameters, dynamic prediction analysis method parameters and the sleep cycle dynamic regulation strategy, and improving quality and efficiency of user sleep cycle detection quantification and dynamic regulation;
And generating the user sleep periodic detection and adjustment report according to a preset report period.
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.
According to the purpose of the invention, the invention provides a sleep periodicity detecting and adjusting system based on modal analysis, which comprises the following modules:
The sleep state analysis 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 modal index calculation module is used for carrying out trending treatment and modal decomposition on the sleep state characteristic curve, determining the periodic boundary frequency, extracting periodic IMF component signals, 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 regulation data analysis module is used for extracting a time phase periodic coupling index, a time phase regulation distribution characteristic and a period index regulation coefficient according to the sleep time phase curve, the sleep period index curve and the dynamic regulation effect curve, and establishing and updating a user sleep period characteristic database;
The detection adjustment optimization module is used for generating a user sleep periodicity detection and adjustment report according to the user sleep period characteristic database, the dynamic optimization mode decomposition method parameters, the period boundary frequency, the dynamic prediction analysis method parameters and the sleep period dynamic adjustment strategy;
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 analysis module further comprises the following functional units:
The signal 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 sleep state analysis 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;
the time phase stage identification unit is used for carrying out sleep time phase analysis on all time frame data in the sleep physiological state time frame data and generating the sleep time phase curve.
More preferably, the modal index calculation module further includes the following functional units:
the curve 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;
The analysis parameter selection unit is used for determining the cycle boundary frequency and the modal decomposition method parameters according to the characteristic combination generation mode of the sleep state characteristic curve;
the signal modal decomposition unit is used for carrying out empirical modal decomposition and/or variation modal decomposition on the sleep state characteristic baseline curve to obtain a sleep state characteristic IMF component signal set;
The periodic signal extraction unit is used for carrying out frequency spectrum analysis on the sleep state characteristic IMF component signal set, identifying IMF component signals meeting the periodic boundary frequency, and generating the periodic IMF component signals by adding;
And the period index extraction unit is used for calculating the sleep period index according to the sleep state characteristic baseline curve and the period IMF 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 adjustment data 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;
And the feature database management unit is used for initializing and establishing and continuously and dynamically updating the sleep cycle feature database of the user.
More preferably, the detection adjustment optimization module further comprises the following functional units:
the detection adjustment optimization unit is used for dynamically optimizing the modal decomposition method parameters, the dynamic prediction analysis method parameters and the sleep period dynamic adjustment strategy according to the user sleep period characteristic database, so that the quality and the efficiency of the user sleep period periodic detection quantification and dynamic adjustment are improved;
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 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 modal analysis, which comprises the following modules:
the sleep state analysis module is used for collecting and analyzing the sleep physiological state of the user to obtain a sleep state characteristic curve and a sleep time phase curve;
the modal index calculation module is used for carrying out trending treatment and modal decomposition on the sleep state characteristic curve, determining the periodic boundary frequency, extracting periodic IMF component signals, 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 regulation data analysis module is used for extracting a time phase periodic coupling index, a time phase regulation distribution characteristic and a period index regulation coefficient according to the sleep time phase curve, the sleep period index curve and the dynamic regulation effect curve, and establishing and updating a user sleep period characteristic database;
the detection adjustment optimization module is used for generating a user sleep periodicity detection and adjustment report according to the user sleep period characteristic database, the dynamic optimization mode decomposition method parameters, the period boundary frequency, the dynamic prediction analysis method parameters and the sleep period dynamic adjustment strategy;
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, further applies the methods of trending treatment, empirical mode decomposition, variational mode decomposition and the like to the extraction of sleep periodic information, considers the continuous state characteristics of the complete sleep period, and has more comprehensive and wide 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 modal analysis can enable related sleep quantification or adjustment 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.
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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 showing steps of a sleep cycle detecting and adjusting method based on modal analysis according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a module composition of a sleep periodic detection and adjustment system based on modal 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 modal analysis according to an embodiment of the 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, empirical mode decomposition and variational mode decomposition, and further optimizes the detection quantization and dynamic adjustment process of sleep periodicity. The linear trend component and the very low frequency trend component in the target signal can be effectively removed by the methods of mean removal processing, low-pass filtering processing, trending analysis FDA, multi-fractal trending analysis MFDFA, asymmetric trend fluctuation elimination analysis ADFA and the like in trending processing. The empirical mode decomposition is a time-frequency domain signal processing mode, and the signal decomposition is carried out according to the time scale characteristics of the data without presetting any basis function; the method has EMD, EEMD, CEEMD, CEEMDAN, ICEEMDAN and other optimizing, evolving or improving methods, and can meet and adapt to the needs of different scenes; the method has obvious advantages in processing non-stationary and nonlinear data, and can obtain higher signal-to-noise ratio when the method is used for a signal sequence in a sleeping process. The variational modal decomposition VMD is a self-adaptive and completely non-recursive modal variational and signal processing method, and the core idea is to construct and solve variational problems, and determine the central frequency and bandwidth of each decomposed component by using an iterative search variational model optimal solution; the modal decomposition number of the target signal sequence is determined according to the actual situation, the optimal center frequency and the limited bandwidth of each modal can be adaptively matched in the subsequent searching and solving process, effective separation of inherent modal components (IMFs), frequency domain division of signals and further effective decomposition components of given signals can be achieved, and finally the optimal solution of the variation problem is obtained.
Referring to fig. 1, the sleep periodicity detecting and adjusting method based on modal 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 for monitoring a sleeping process of a user is collected and used as a sleeping physiological state signal to state a specific implementation process of the technical scheme. The electroencephalogram machine is used for collecting and recording sleep electroencephalogram of a user, the sampling rate is 1024Hz, the recording electrodes are F3, F4, C3, C4, O1 and O2, and the reference electrodes M1 and M2. Meanwhile, 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, 0.5-95Hz band-pass filtering and signal correction processing by using M1 or M2, so that pure electroencephalogram signals are obtained; secondly, sequentially extracting signal frequency bands of pure brain electrical signals, including delta rhythm (0.5-4 Hz), theta rhythm (4-8 Hz), alpha rhythm (8-12 Hz), beta rhythm (12-30 Hz), gamma 1 rhythm (30-50 Hz) and gamma 2 rhythm (50-95 Hz), so as to obtain frequency band brain electrical signals; and further, continuously sliding and dividing the pure brain electrical signal and the frequency band brain electrical 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 feature analysis includes at least any one of numerical analysis, envelope analysis, time-frequency analysis, entropy analysis, fractal analysis, and complexity analysis.
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.
In this embodiment, frame data in a sleep physiological state is subjected to time-frequency analysis (band power, band power duty ratio), entropy analysis (sample entropy) and complexity analysis (lyapunov index) on a frame-by-frame basis. Through feature selection, the delta-theta (delta rhythm+theta rhythm) combined frequency band power duty ratio of the F3-M2 channel, the sample entropy normalized after taking the negative and the Lyapunov exponent normalized after taking the negative are directly added to obtain a sleep state feature curve, so that the description granularity and time-varying property of the state features of the sleep state and the cortex electrophysiology 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.
In an actual use scene, the accuracy of the sleep phase stage recognition model is higher and higher through data accumulation of user samples and deep learning of the stage model.
P200: and carrying out trending treatment and modal decomposition on the sleep state characteristic curve, determining the periodic boundary frequency, extracting periodic IMF 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 value removing process is selected as the trending processing method.
And secondly, determining cycle boundary frequency and mode decomposition method parameters according to a feature combination generation mode of the sleep state feature curve.
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 generated by combining the delta-theta (delta rhythm+theta rhythm) with the frequency band power ratio, taking the sample entropy normalized again after the negative, taking the lyapunov exponent normalized again after the negative, and the parameters of framing processing are the preset time window length 30s and the preset time shift step length 15s, so that the standard Variation Mode Decomposition (VMD) and the 0.008Hz low pass cut-off frequency (cycle boundary frequency) are selected.
Thirdly, performing empirical mode decomposition and/or variational mode decomposition on the sleep state characteristic baseline curve to obtain a sleep state characteristic IMF component signal set.
In this embodiment, the modal decomposition includes at least any one of empirical modal decomposition and variational modal decomposition. The empirical mode decomposition method at least comprises any one of EMD, EEMD, CEEMD, CEEMDAN, ICEEMDAN, ESMD; the ESMD is specifically a pole symmetric mode decomposition method, which uses the EMD idea to change the external envelope interpolation into the internal pole symmetric interpolation, and optimizes the last residual mode by using the least square idea to make the last residual mode become an adaptive global average line of the whole data so as to determine the optimal screening times. The method for decomposing the variation modes at least comprises any one of VMD and modified VMD; the improved VMD specifically performs an organic combination of a signal processing algorithm and the VMD to adapt to signal analysis and signal decomposition requirements of different scenes.
In this embodiment, standard Variation Modal Decomposition (VMD) is selected to perform variation modal decomposition on the sleep state characteristic baseline curve, so as to obtain the sleep state characteristic IMF component signal set. Compared with EMD empirical mode decomposition, VMD variation modal decomposition has better anti-noise capability, overcomes the problems of end effect, modal component/frequency aliasing and the like of the empirical mode decomposition, can reduce the time sequence non-stationarity with high complexity and strong nonlinearity, decomposes the time sequence data into a series of Intrinsic Mode Functions (IMFs) with limited bandwidth, can adaptively update the optimal center frequency and bandwidth of each IMF, and is suitable for a non-stationarity sequence. The method mainly comprises the following steps:
1) And performing low-pass filtering on the original signal for a plurality of times to obtain a plurality of frequency band signals.
2) And carrying out variation estimation on each frequency band signal to obtain the local vibration mode of the frequency band signal.
3) And adding the local vibration modes corresponding to all the frequency band signals to obtain VMD decomposition of the original signals.
In this embodiment, 10 IMF component signals are extracted by the VMD to generate an IMF component signal set.
And fourthly, performing frequency spectrum analysis on the sleep state characteristic IMF component signal set, identifying IMF component signals meeting the periodic boundary frequency, and adding to generate periodic IMF component signals.
In this embodiment, the sleep state feature IMF component signal set is subjected to spectrum analysis by Welch power spectral density estimation, IMF component signals satisfying the 0.008Hz low-pass cut-off frequency-cycle boundary frequency are identified, and the cycle IMF component signals are generated by summation.
Fifthly, according to the sleep state characteristic baseline curve and the periodic IMF component signals, 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 IMF component signal;
2) Calculating a signal difference value of the sleep state characteristic baseline curve and the periodic IMF component signal to obtain a sleep state characteristic residual curve;
3) Respectively carrying out absolute value calculation on the sleep state characteristic residual error curve and the periodic IMF component signal to obtain the sleep state characteristic residual error absolute value curve and the periodic IMF component absolute value signal;
4) According to the sleep time phase curve, the sleep state characteristic residual error absolute value curve and the periodic IMF component absolute value 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 periodic index according to the sleep periodic node coefficient curve, the preset method correction coefficient corresponding to the modal decomposition method and the preset user individual correction coefficient related to the user biological state information.
In this embodiment, a calculation formula of the sleep periodic node coefficient specifically includes:
；
Wherein, For sleep periodicity node coefficients,/>The first/>, respectively, of the sleep state characteristic residual absolute value curvesThe/>, in the individual point values and periodic IMF component absolute value signalsDot 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 ARMA 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 ARMA 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 ARMA 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. /(I)
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,/>For the phase correction coefficient of the current sleep phase,/>Sleep periodicity index before and after dynamic adjustment,/>, respectivelyTo take absolute value operators.
In this embodiment, the dynamic adjustment effect coefficient will be used for dynamic optimization of the subsequent modal decomposition 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 establishing and updating a sleep period characteristic database of the user.
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, and variation coefficients of 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, initializing, establishing and continuously and dynamically updating a sleep cycle characteristic database of the user.
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 modal decomposition method, a trend prediction analysis method, and a sleep cycle dynamic regulation strategy.
In this embodiment, the user sleep cycle process is continuously detected, quantified and dynamically adjusted, and the user sleep cycle characteristic database is dynamically updated. Key data generated in the detection quantization and dynamic adjustment processes are updated into a user sleep cycle characteristic database in real time, so that timeliness and effectiveness of optimization of a subsequent detection quantization and dynamic adjustment method are guaranteed. 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.
P500: and dynamically optimizing modal decomposition method parameters, the period boundary frequency, dynamic prediction analysis method parameters and the sleep period dynamic regulation strategy according to the user sleep period characteristic database to generate a user sleep period detection and regulation report.
According to the sleep cycle characteristic database of the user, the mode decomposition method parameters, the dynamic prediction analysis method parameters and the sleep cycle dynamic regulation strategy are dynamically optimized, and the quality and the efficiency of the sleep cycle detection quantification and the dynamic regulation of the user are improved.
In the embodiment, the user sleep cycle characteristic database is updated in time, so that the mode analysis method parameters in the detection and quantization process are reversely optimized in real time, and the accuracy of detection and quantization is adjusted; the dynamic prediction analysis method parameters and the sleep period dynamic regulation strategy in the dynamic regulation process are reversely optimized in real time, and the effectiveness of dynamic regulation is regulated; finally, the quality and efficiency of the whole detection and adjustment process are realized.
And secondly, 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.
Referring to fig. 2, a sleep periodicity detection and adjustment system based on modal analysis is provided according to an embodiment of the present invention, where the system is configured to perform the above-described method steps. The system comprises the following modules:
The sleep state analysis 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 modal index calculation module S200 is used for carrying out trending treatment and modal decomposition on the sleep state characteristic curve, determining the periodic boundary frequency, extracting periodic IMF component signals, 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 regulation data analysis module S400 is used for extracting a time phase periodic coupling index, a time phase regulation distribution characteristic and a period index regulation coefficient according to a sleep time phase curve, a sleep period index curve and a dynamic regulation effect curve, and establishing and updating a sleep period characteristic database of a user;
The detection adjustment optimization module S500 is used for dynamically optimizing the modal decomposition 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 to generate a user sleep period periodic detection and adjustment report;
And the data operation management module S600 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 analysis module S100 further includes the following functional units:
The signal 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 sleep state analysis 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;
the time phase stage identification unit is used for carrying out sleep time phase analysis on all time frame data in the sleep physiological state time frame data to generate a sleep time phase curve.
In this embodiment, the modal index calculation module S200 further includes the following functional units:
The curve 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;
The analysis parameter selection unit is used for determining the cycle boundary frequency and the method parameters of modal decomposition according to the characteristic combination generation mode of the sleep state characteristic curve;
The signal modal decomposition unit is used for carrying out empirical modal decomposition and/or variation modal decomposition on the sleep state characteristic baseline curve to obtain a sleep state characteristic IMF component signal set;
the periodic signal extraction unit is used for carrying out frequency spectrum analysis on the sleep state characteristic IMF component signal set, identifying IMF component signals meeting the periodic boundary frequency, and generating periodic IMF component signals by adding;
The period index extraction unit is used for calculating the sleep period index according to the sleep state characteristic baseline curve and the period IMF component signal and generating a sleep period index curve according to the 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 adjustment data 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;
and the feature database management unit is used for initializing, establishing and continuously and dynamically updating the sleep cycle feature database of the user.
In this embodiment, the detection adjustment optimization module S500 further includes the following functional units:
The detection adjustment optimization unit is used for dynamically optimizing the modal decomposition method parameters, the dynamic prediction analysis method parameters and the sleep period dynamic adjustment strategy according to the sleep period characteristic database of the user, so that the quality and the efficiency of the periodic detection quantification and the dynamic adjustment of the sleep of the user are improved;
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 data operation management module S600 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 modal analysis according to the embodiment of the present invention includes the following modules:
The sleep state analysis module M100 is used for collecting and analyzing the sleep physiological state of the user to obtain a sleep state characteristic curve and a sleep time phase curve;
The modal index calculation module M200 is used for carrying out trending treatment and modal decomposition on the sleep state characteristic curve, determining the periodic boundary frequency, extracting periodic IMF 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 regulation data analysis module M400 is used for extracting a time phase periodic coupling index, a time phase regulation distribution characteristic and a period index regulation coefficient according to a sleep time phase curve, a sleep period index curve and a dynamic regulation effect curve, and establishing and updating a sleep period characteristic database of a user;
The detection adjustment optimization module M500 is used for dynamically optimizing the modal decomposition 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 to generate a sleep period detection and adjustment report of the user;
the data visualization module M600 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 M700 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 (33)

1. The sleep periodicity detection and adjustment method based on modal 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;
Performing trending treatment and modal decomposition on the sleep state characteristic curve, determining a periodic boundary frequency, extracting a periodic IMF 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 establishing and updating a user sleep period characteristic database;
according to the user sleep cycle characteristic database, dynamically optimizing modal decomposition method parameters, the cycle boundary frequency, dynamic prediction analysis method parameters and the sleep cycle dynamic regulation strategy, and generating a user sleep cycle detection and regulation report;
the sleep periodicity index calculating method specifically comprises the following steps:
1) Acquiring a sleep state characteristic baseline curve and the periodic IMF component signal;
2) Calculating a signal difference value between the sleep state characteristic baseline curve and the periodic IMF component signal to obtain a sleep state characteristic residual curve;
3) Respectively carrying out absolute value calculation on the sleep state characteristic residual error curve and the periodic IMF component signal to obtain a sleep state characteristic residual error absolute value curve and a periodic IMF component absolute value signal;
4) According to the sleep time phase curve, the sleep state characteristic residual error absolute value curve and the periodic IMF component absolute value signal, calculating to obtain a sleep periodic node coefficient and generating a sleep periodic node coefficient curve according to a time sequence;
5) Calculating to obtain the sleep periodicity index according to the sleep periodicity node coefficient curve, a preset method correction coefficient corresponding to a modal decomposition method and a preset user personality correction coefficient related to the user biological state information;
the calculation formula of the sleep periodic node coefficient is specifically as follows:
Wherein csi is the sleep periodic node coefficient, SRL i、SSLi is the i-th point value in the sleep state characteristic residual absolute value curve and the i-th point value in the periodic IMF component absolute value signal, and K istage is the phase correction coefficient corresponding to the i-th point phase value in the sleep phase curve;
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 establishing and updating the user sleep period characteristic database further comprise the following steps:
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;
Initializing, establishing and continuously and dynamically updating the sleep cycle characteristic database of the user;
a calculation formula of the sleep periodicity index specifically comprises the following steps:
Wherein SSI is the sleep periodicity index, K user、Kmethod is a preset user personality correction coefficient and a preset method correction coefficient, CSIL i is an i-th point value in the sleep periodicity node coefficient curve, and N is a data length of the sleep periodicity node coefficient curve.
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 analog-to-digital 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 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 and modal decomposition on the sleep state characteristic curve, determining a periodic boundary frequency and extracting periodic IMF component signals, and calculating to obtain a sleep periodic index and a sleep periodic index curve further 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 method parameters of modal decomposition according to the characteristic combination generation mode of the sleep state characteristic curve;
performing empirical mode decomposition and/or variational mode decomposition on the sleep state characteristic baseline curve to obtain a sleep state characteristic IMF component signal set;
Performing spectrum analysis on the sleep state characteristic IMF component signal set, identifying IMF component signals meeting the periodic boundary frequency, and adding to generate the periodic IMF component signals;
And calculating the sleep periodic index according to the sleep state characteristic baseline curve and the periodic IMF 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 modal decomposition includes at least any one of empirical modal decomposition and variational modal decomposition.
11. The method of claim 9, wherein: the empirical mode decomposition method at least comprises any one of EMD, EEMD, CEEMD, CEEMDAN, ICEEMDAN, ESMD; the ESMD is specifically a pole symmetric mode decomposition method, which uses the EMD idea to change the external envelope interpolation into the internal pole symmetric interpolation, and optimizes the last residual mode by using the least square idea to make the last residual mode become an adaptive global average line of the whole data so as to determine the optimal screening times.
12. The method as recited in claim 8, wherein: the method for decomposing the variation modes at least comprises any one of VMD and modified VMD; the improved VMD specifically performs an organic combination of a signal processing algorithm and the VMD to adapt to signal analysis and signal decomposition requirements of different scenes.
13. The method of claim 8, wherein the periodic boundary frequency comprises at least any one of a bandpass cutoff frequency, a lowpass cutoff frequency.
14. The method according to claim 1, wherein the sleep cycle index curve is a curve generated by stitching the sleep cycle indexes in a time sequence order.
15. 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 regulation, extracting a dynamic regulation effect coefficient, generating a dynamic regulation effect curve, and calculating to obtain a comprehensive index of the regulation effect.
16. The method of claim 15, wherein the method of trend predictive analysis comprises at least any one of AR, MA, ARMA, ARIMA, SARIMA, VAR, deep learning.
17. The method of claim 15, wherein the sleep cycle dynamic adjustment strategy comprises at least 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; the regulation mode at least comprises any mode of vocal music stimulation, ultrasonic stimulation, optical stimulation, electric stimulation, magnetic stimulation, temperature stimulation, humidity stimulation, touch stimulation and CO 2 concentration regulation, and the execution mode at least comprises any mode of separation mode and contact mode.
18. The method of claim 15, wherein the sleep cycle adjustment peripheral device comprises at least any one of 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, and a CO 2 concentration regulation device, and is determined by a specific adjustment mode.
19. The method according to claim 15, wherein one way of calculating the dynamic adjustment effect coefficient is:
Wherein, OPI is the dynamic adjustment effect coefficient, K user is a preset user personality correction coefficient, K now_stage is a phase correction coefficient corresponding to the current sleep phase, SSI pre、SSIaft is the sleep periodic index before and after dynamic adjustment, respectively, and it is an absolute value operator.
20. The method of any one of claims 16-19, wherein: the dynamic adjustment effect coefficient is used for dynamic optimization of a subsequent modal decomposition method parameter, a dynamic prediction analysis method parameter, a trend prediction analysis method selection and the sleep period dynamic adjustment strategy.
21. The method of any one of claims 16-19, wherein: the comprehensive index of the regulating effect is specifically an average value or root mean square of the dynamic regulating effect curve.
22. The method of claim 1, 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.
23. The method of claim 1, 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.
24. The method of claim 23, 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 modal decomposition method, a trend prediction analysis method and a sleep cycle dynamic regulation strategy.
25. A method according to claim 1 or 2, characterized in that: the specific steps of generating the user sleep periodicity detection and adjustment report according to the user sleep periodicity characteristic database, the dynamic optimization mode decomposition method parameter, the period boundary frequency, the dynamic prediction analysis method parameter and the sleep periodicity dynamic adjustment strategy further comprise:
According to the user sleep cycle characteristic database, dynamically optimizing modal decomposition method parameters, dynamic prediction analysis method parameters and the sleep cycle dynamic regulation strategy, and improving quality and efficiency of user sleep cycle detection quantification and dynamic regulation;
And generating the user sleep periodic detection and adjustment report according to a preset report period.
26. The method as recited in claim 25, 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.
27. The sleep periodicity detecting and adjusting system based on the modal analysis is characterized by comprising the following modules: the sleep state analysis 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 modal index calculation module is used for carrying out trending treatment and modal decomposition on the sleep state characteristic curve, determining the periodic boundary frequency, extracting periodic IMF component signals, 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 regulation data analysis module is used for extracting a time phase periodic coupling index, a time phase regulation distribution characteristic and a period index regulation coefficient according to the sleep time phase curve, the sleep period index curve and the dynamic regulation effect curve, and establishing and updating a user sleep period characteristic database;
The detection adjustment optimization module is used for generating a user sleep periodicity detection and adjustment report according to the user sleep period characteristic database, the dynamic optimization mode decomposition method parameters, the period boundary frequency, the dynamic prediction analysis method parameters and the sleep period dynamic adjustment strategy;
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 sleep periodicity index calculating method specifically comprises the following steps:
1) Acquiring a sleep state characteristic baseline curve and the periodic IMF component signal;
2) Calculating a signal difference value between the sleep state characteristic baseline curve and the periodic IMF component signal to obtain a sleep state characteristic residual curve;
3) Respectively carrying out absolute value calculation on the sleep state characteristic residual error curve and the periodic IMF component signal to obtain a sleep state characteristic residual error absolute value curve and a periodic IMF component absolute value signal;
4) According to the sleep time phase curve, the sleep state characteristic residual error absolute value curve and the periodic IMF component absolute value signal, calculating to obtain a sleep periodic node coefficient and generating a sleep periodic node coefficient curve according to a time sequence;
5) Calculating to obtain the sleep periodicity index according to the sleep periodicity node coefficient curve, a preset method correction coefficient corresponding to a modal decomposition method and a preset user personality correction coefficient related to the user biological state information;
the calculation formula of the sleep periodic node coefficient is specifically as follows:
Wherein csi is the sleep periodic node coefficient, SRL i、SSLi is the i-th point value in the sleep state characteristic residual absolute value curve and the i-th point value in the periodic IMF component absolute value signal, and K istage is the phase correction coefficient corresponding to the i-th point phase value in the sleep phase curve;
the adjustment data 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 feature database management unit is used for initializing, establishing and continuously and dynamically updating the sleep cycle feature database of the user; a calculation formula of the sleep periodicity index specifically comprises the following steps:
Wherein SSI is the sleep periodicity index, K user、Kmethod is a preset user personality correction coefficient and a preset method correction coefficient, CSIL i is an i-th point value in the sleep periodicity node coefficient curve, and N is a data length of the sleep periodicity node coefficient curve.
28. The system of claim 27, wherein the sleep state analysis module further comprises the following functional units:
The signal 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 sleep state analysis 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;
the time phase stage identification unit is used for carrying out sleep time phase analysis on all time frame data in the sleep physiological state time frame data and generating the sleep time phase curve.
29. The system of claim 27, wherein the modal index calculation module further comprises the following functional units:
the curve 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;
The analysis parameter selection unit is used for determining the cycle boundary frequency and the modal decomposition method parameters according to the characteristic combination generation mode of the sleep state characteristic curve;
The signal modal decomposition unit is used for carrying out empirical modal decomposition and/or variation modal decomposition on the sleep state characteristic baseline curve to obtain a sleep state characteristic IMF component signal set;
the periodic signal extraction unit is used for carrying out frequency spectrum analysis on the sleep state characteristic IMF component signal set, identifying IMF component signals meeting the periodic boundary frequency, and generating the periodic IMF component signals by adding; and the period index extraction unit is used for calculating the sleep period index according to the sleep state characteristic baseline curve and the period IMF component signal and generating the sleep period index curve according to a time sequence.
30. The system of claim 27 or 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.
31. The system of claim 27 or 29, wherein the detection adjustment optimization module further comprises the following functional units:
the detection adjustment optimization unit is used for dynamically optimizing the modal decomposition method parameters, the dynamic prediction analysis method parameters and the sleep period dynamic adjustment strategy according to the user sleep period characteristic database, so that the quality and the efficiency of the user sleep period periodic detection quantification and dynamic adjustment are improved;
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.
32. The system of claim 27, 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.
33. The sleep periodicity detecting and adjusting device based on modal analysis is characterized by comprising the following modules: the sleep state analysis module is used for collecting and analyzing the sleep physiological state of the user to obtain a sleep state characteristic curve and a sleep time phase curve;
the modal index calculation module is used for carrying out trending treatment and modal decomposition on the sleep state characteristic curve, determining the periodic boundary frequency, extracting periodic IMF component signals, 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 regulation data analysis module is used for extracting a time phase periodic coupling index, a time phase regulation distribution characteristic and a period index regulation coefficient according to the sleep time phase curve, the sleep period index curve and the dynamic regulation effect curve, and establishing and updating a user sleep period characteristic database;
the detection adjustment optimization module is used for generating a user sleep periodicity detection and adjustment report according to the user sleep period characteristic database, the dynamic optimization mode decomposition method parameters, the period boundary frequency, the dynamic prediction analysis method parameters and the sleep period dynamic adjustment strategy;
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 sleep periodicity index calculating method specifically comprises the following steps:
1) Acquiring a sleep state characteristic baseline curve and the periodic IMF component signal;
2) Calculating a signal difference value between the sleep state characteristic baseline curve and the periodic IMF component signal to obtain a sleep state characteristic residual curve;
3) Respectively carrying out absolute value calculation on the sleep state characteristic residual error curve and the periodic IMF component signal to obtain a sleep state characteristic residual error absolute value curve and a periodic IMF component absolute value signal;
4) According to the sleep time phase curve, the sleep state characteristic residual error absolute value curve and the periodic IMF component absolute value signal, calculating to obtain a sleep periodic node coefficient and generating a sleep periodic node coefficient curve according to a time sequence;
5) Calculating to obtain the sleep periodicity index according to the sleep periodicity node coefficient curve, a preset method correction coefficient corresponding to a modal decomposition method and a preset user personality correction coefficient related to the user biological state information;
the calculation formula of the sleep periodic node coefficient is specifically as follows:
Wherein csi is the sleep periodic node coefficient, SRL i、SSLi is the i-th point value in the sleep state characteristic residual absolute value curve and the i-th point value in the periodic IMF component absolute value signal, and K istage is the phase correction coefficient corresponding to the i-th point phase value in the sleep phase curve;
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 establishing and updating the user sleep period characteristic database further comprise the following steps:
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;
Initializing, establishing and continuously and dynamically updating the sleep cycle characteristic database of the user;
a calculation formula of the sleep periodicity index specifically comprises the following steps:
Wherein SSI is the sleep periodicity index, K user、Kmethod is a preset user personality correction coefficient and a preset method correction coefficient, CSIL i is an i-th point value in the sleep periodicity node coefficient curve, and N is a data length of the sleep periodicity node coefficient curve.
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