CN107961429A - Householder method of sleeping and system, sleeping aid - Google Patents
Householder method of sleeping and system, sleeping aid Download PDFInfo
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Abstract
The present invention relates to one kind sleep householder method and system, sleeping aid, belongs to sleep ancillary technique field；The described method includes：Brain wave is extracted from the EEG signals of sleep auxiliary object, and calculates the energy feature information of the brain wave；The electrocardiosignal of sleep auxiliary object is obtained, identifies the R ripples in electrocardiosignal, calculates the characteristic information and heart rate variability of R ripples；The energy feature information, the characteristic information of R ripples and heart rate variability are input in sleepiness depth detection model trained in advance and are identified, obtain sleepiness depth levels；Sleep auxiliary content is selected according to the sleepiness depth levels, the sleep auxiliary content is played out to the sleep auxiliary object.The technical solution solves the problems, such as that existing electronic instrument can not bring stable sleep auxiliaring effect, improves the science of sleep auxiliary information, enhances the sleep auxiliaring effect of electronic instrument.
Description
Technical field
The present invention relates to sleep ancillary technique field, is aided in more particularly to one kind sleep householder method and system, sleep
Device.
Background technology
With the development of society, the accelerating rhythm of life, the increase of operating pressure, the shortage or other reasons of amount of exercise are drawn
Agitation, the body and mind uneasiness risen, causes insomniac more and more.Noise pollution it is growing day by day, the incidence of insomnia is presented
The trend risen, has seriously affected the physical and mental health of people, work efficiency is declined with quality of life.Thus, treatment insomnia becomes
For particularly urgent thing.
At present, treating the method for insomnia has many kinds, medicinal treatment, psychotherapy, dietetic treatment and self-control therapy
Serial side effect can be also brought to human body while curing the disease Deng, medicine, and it is bad that long-term use easily leads to Liver and kidney function, spirit
Confusion etc., and dependence is produced to medicine.Psychotherapy etc. can only also play the role of auxiliary treatment.
With the fast development of electronic technology, medicine is combined with electronic technology, occurs the electricity of assisting sleep on the market
Sub- instrument.These electronic instruments promote sleep purpose by playing sleep auxiliary information to user, to reach.
But the present inventor has found that at least there are the following problems among actual use：Since the sleep of broadcasting is auxiliary
Supplementary information lacks science, and for different user, these electronic instruments can not often bring stable sleep auxiliaring effect.
The content of the invention
Based on this, it is necessary to the problem of can not bringing stable sleep auxiliaring effect for existing electronic instrument, there is provided one
Kind sleep householder method and system.
One kind sleep householder method, including：
Brain wave is extracted from the EEG signals of sleep auxiliary object, and calculates the energy feature information of the brain wave；
The electrocardiosignal of sleep auxiliary object is obtained, identifies the R ripples in electrocardiosignal, calculates the characteristic information and the heart of R ripples
Rate variability；
The energy feature information, the characteristic information of R ripples and heart rate variability are input to sleepiness depth trained in advance
It is identified in detection model, obtains sleepiness depth levels；
Sleep auxiliary content is selected according to the sleepiness depth levels, the sleep auxiliary content is aided in the sleep
Object plays out.
A kind of sleeping-assisting system, including：
Extraction module, for extracting brain wave from the EEG signals of sleep auxiliary object, and calculates the brain wave
Energy feature information；
Computing module, for obtaining the electrocardiosignal of sleep auxiliary object, identifies the R ripples in electrocardiosignal, calculates R ripples
Characteristic information and heart rate variability；
Identification module, for the energy feature information, the characteristic information of R ripples and heart rate variability to be input to advance instruction
It is identified in experienced sleepiness depth detection model, obtains sleepiness depth levels；
Playing module, for selecting sleep auxiliary content according to the sleepiness depth levels, by the sleep auxiliary content
Played out to the sleep auxiliary object.
Above-mentioned sleep householder method and system, are believed by extracting and calculating the energy feature of brain wave of sleep auxiliary object
Breath；With reference to the characteristic information and heart rate variability of the R ripples of the electrocardiosignal of sleep auxiliary object；It is input to sleepiness trained in advance
Sleepiness depth levels are identified in depth detection model；Sleep auxiliary content is selected to play out according to sleepiness depth levels.Profit
With the variation characteristic of the faint electricity physiological signal of sleep auxiliary object, the reference index of sleepiness depth levels is devised, is passed through
The reference index imports different sleep auxiliary contents, improves the science of played sleep auxiliary information, enhances electricity
The sleep auxiliaring effect of sub- instrument.
In addition, it there is a need to the problem of can not bringing stable sleep auxiliaring effect for existing electronic instrument, there is provided one
Kind sleeping aid, computer equipment and computer-readable storage medium.
A kind of sleeping aid, including：Terminal and electrode, the electrode are used for the biological electricity for gathering sleep auxiliary object
Signal, and it is transmitted to terminal；
The step of terminal is configured as performing the sleep householder method.
Above-mentioned sleeping aid, terminal are improved in sleep auxiliary by the playback method of above-mentioned sleep auxiliary content
The science of appearance, enhances sleep auxiliaring effect.
A kind of computer equipment, including memory, processor and be stored on the memory and can be in the processing
The computer program run on device, the processor realize the sleep householder method when performing the computer program.
Above computer equipment, by the computer program run on the processor, improves sleep auxiliary content
Science, enhances sleep auxiliaring effect.
A kind of computer-readable storage medium, is stored thereon with computer program, it is characterised in that the program is executed by processor
Sleep householder method described in Shi Shixian.
Above computer storage medium, by its storage computer program, improve sleep auxiliary content science,
Enhance sleep auxiliaring effect.
Brief description of the drawings
Fig. 1 is the sleep aided process flow sheet figure of one embodiment；
Fig. 2 is brain cutting piece schematic diagram；
Fig. 3 is electrocardiosignal schematic diagram；
Fig. 4 is the sleeping-assisting system structure diagram of one embodiment；
Fig. 5 is the sleeping aid structure diagram of one embodiment.
Embodiment
The sleep householder method of the present invention and the embodiment of system are illustrated below in conjunction with the accompanying drawings.
In the corresponding terminal of scheme of the embodiment of the present invention, terminal here can be smart mobile phone, tablet computer, PDA
(Personal Digital Assistant, personal digital assistant), PC etc..The terminal, which possesses, plays video and audio letter
The functions such as breath, can arrange in pairs or groups the equipment such as earphone, carry out content broadcasting.
Refering to what is shown in Fig. 1, Fig. 1 is the sleep aided process flow sheet figure of one embodiment, including：
S10, brain wave is extracted from the EEG signals of sleep auxiliary object, and calculates the energy feature letter of the brain wave
Breath；
In this step, sleep auxiliary object (user) can be the user for carrying out sleep auxiliary, in sleep supporting process
In, EEG signals are extracted by related device, and the brain wave in EEG signals is extracted, and its energy is calculated with this brain wave
Measure feature information.As embodiment, the energy feature information can include energy value and its energy profile density.
Specifically, Delta (δ), Theta (θ), Alpha (α), the Beta of the brain electricity of sleep auxiliary object can be extracted
(β), Gamma (γ) ripple, and calculate the energy feature information of Delta, Theta, Alpha, Beta, Gamma ripple.
In one embodiment, Delta, Theta of brain electricity of the extraction sleep auxiliary object of the S10, Alpha,
Beta, Gamma ripple, and the energy and its energy profile density of Delta, Theta, Alpha, Beta, Gamma ripple is calculated
Step, can include as follows：
S101, pre-processes the EEG signals to obtain brain cutting piece；
As shown in Fig. 2, Fig. 2 is brain cutting piece schematic diagram；First original EEG signals can be carried out with average, suppression baseline
Drift, remove myoelectricity/eye electricity artefact etc., removes higher than the pretreatment such as 100Hz brain electric informations, carries out slip to EEG signals and cut
Piece.
S102, Delta, Theta, Alpha, Beta, Gamma ripple are extracted from the brain cutting piece；
The brain cutting piece that can be obtained for pretreatment, the method being combined using wavelet transformation and independent component analysis,
Extract the wherein Delta (0.5~3Hz) of EEG signals, Theta (3~7Hz), Alpha (8~13Hz), Beta (14~
17Hz), Gamma (34~Hz) ripple.
For the step S102 Delta, Theta, Alpha, Beta, Gamma ripple are extracted from the brain cutting piece
Step, can include as follows：
(1) centralization and whitening processing are carried out to the EEG signals in brain cutting piece；
(2) EEG signals are carried out with the wavelet coefficient under each resolution ratio of wavelet transformation acquisition, wavelet coefficient is carried out
Compromise threshold process, and carry out inverse wavelet transform and obtain multiple eeg signals；
Specifically, carrying out wavelet transformation to EEG signals, the wavelet coefficient under each resolution ratio is obtained, wavelet coefficient is carried out
Compromise threshold process, carries out inverse wavelet transform and obtains multiple eeg signals.
(3) successively to each eeg signal carry out independent component analysis, extract eeg signal Delta, Theta,
Alpha, Beta, Gamma ripple.
Specifically, one initialization (can randomly select) vector w with unit norm of selection.According to formula w ← E { zg
(wTz)}-E{g'(wTZ) } w updates w；Wherein, wTZ is projections of the z on w.
Then w is standardized：W ← w/ │ │ w │ │, for multiple isolated components, each time from sight after one isolated component of extraction
The step of subtracting the isolated component in survey signal, repeat to update w, untill the important all extractions completion of institute.Wherein, E is number
Hope in term, g can be arbitrary non-quadratic function；The inverse of g' representative functions g；The z-transform of zg representative functions g；
The method that the isolated component is subtracted from observation signal can be as shown in formula (1)：
Assuming that P component is have estimated, when adjacent w twice is varied less or is not changed, it is believed that y=s,
Iterative process terminates；Wherein, wjRepresent j-th of w vector, wp+1Represent+1 w vector of pth, T represents transposition computing；S and y is tool
There is the gaussian variable of identical average and covariance matrix, represent that s represents source signal here, y is represented after independent analysis
Signal；
The method being combined using independent component analysis and wavelet transformation, successively extraction obtain the isolated component of brain electricity
Delta, Theta, Alpha, Beta, Gamma ripple.
S103, extracts the range value of Delta, Theta, Alpha, Beta, Gamma ripple respectively；
Specifically, by extracting obtained Delta, Theta, Alpha, Beta, Gamma ripple, numerical value represents amplitude A, T=
1/fs, therefore A (t) can be expressed as；Delta, Theta, Alpha, Beta, Gamma ripple are respectively A1 (t)~A5 (t).
S104, according to the range value of Delta, Theta, Alpha, Beta, Gamma ripple and frequency values calculate Delta,
The energy and its energy profile density of Theta, Alpha, Beta, Gamma ripple；
Specifically, for calculating energy P, can as shown in formula 2, wherein limit of integration arrives T/2 for-T/2.So
The energy of Delta, Theta, Alpha, Beta, Gamma ripple is respectively P1~P5, at this time energy P1~P5 just represent Delta,
Theta, Alpha, Beta, Gamma ripple signal；For calculating energy profile density S, as shown in formula 3, wherein, the π f=of ω=2
2 π/T, f are frequency, T=1/f；The energy profile density of so Delta, Theta, Alpha, Beta, Gamma ripple is respectively S1
~S5；
Wherein, S (ω) for angular frequency it is corresponding can force density, the π f=2 π/T of ω=2 are angular frequency, and f is frequency, T=
1/f is time constant, and A represents amplitude.
S20, obtains the electrocardiosignal of sleep auxiliary object, identifies the R ripples in electrocardiosignal, calculates the characteristic information of R ripples
And heart rate variability；
As embodiment, the characteristic information of the R ripples can include phase and R wave amplitudes between RR；Refering to what is shown in Fig. 3, Fig. 3 is
Electrocardiosignal schematic diagram, the electrocardiosignal that actual acquisition obtains include various noises, and waveform is coarse, rough, causes in QRS wave
Useful information be difficult to be extracted.
It is therefore possible to use lowpass digital filter (Butterworth filter) carries out low-pass filtering, high-frequency noise is filtered out
(general more than 300Hz), so as to obtain the QRS wave in electrocardiosignal.
In one embodiment, the R ripples in the identification electrocardiosignal of the step S20, calculate the characteristic information and the heart of R ripples
The step of rate variability, can include as follows：
S201, carries out empirical mode decomposition to electrocardiosignal and obtains intrinsic mode functions, identified according to the intrinsic mode functions
R ripples in electrocardiosignal；
Specifically, electrocardiosignal is decomposed using empirical modal.Assuming that the electrocardiosignal after filtered is x (t), it is right
It carries out the empirical mode decomposition (EMD) of three scales, obtains the intrinsic mode functions of three characterization signal characteristic time scales (referred to as
IMF), it is respectively IMF1, IMF2, IMF3 and residual R, wherein IMF1, IMF2 and IMF3 are for the identification of R ripples, then electrocardiosignal
It can be expressed as shown in formula (4).
X=IMF1+IMF2+IMF3 (4)
S202, the threshold value of electrocardiosignal is determined using differential threshold searching method；
Specifically, the threshold value D of electrocardiosignal can be tried to achieve using difference threshold algorithm；Differential threshold basic principle such as formula
(5) shown in, it is assumed that initial threshold D0=λ * Deriv (0<λ<1), λ=0.6 in the present embodiment.
Deriv=0.125 × [2 × x (i-3)+x (i-2)-x (i-1) -2 × x (i)] (5)
Wherein, x is sampled point, i=4,5,6 ....
S203, is scanned for using the threshold value on the electrocardiosignal, using the maximum of electrocardiosignal as first
The position of a R ripples；
The position T of first R ripple is determined using threshold search method1。First, which is searched for, on original signal x (t) compares D0Big
Initial position T as first R ripple10, the maximum of calculating original signal between 12 sampled points before and after the initial position of R ripples
Value, the position T as R ripples1, R-wave amplitude RA。
S204, searches for the position of each R ripples one by one on the electrocardiosignal, and calculates the phase between the RR of two neighboring R ripples；
Specifically, search for the position of each R ripples one by one, threshold search is carried out according to formula (6), usual μ=0.4, according to
The general minimum interval that electrocardiosignal is beaten every time is 400ms, will not within 400ms after the position at a R peak is searched
There is the position of secondary R wave, often search the initial position of a R ripple, then original is calculated between its front and rear 12 sampled point
The maximum of beginning signal x (t), the position T as R ripplesn。
D=μ D+ μ RA (6)
Thus, it is possible to the spacing RR of two neighboring R ripples is calculated, i.e. the phase between RR, as shown in formula (7)：
RR=Tn-Tn-1 (7)
S205, the heart rate variability of the electrocardiosignal is determined according to the phase between the RR；Specifically, heart rate variability HRV
Usually represented with phase difference root mean square between adjacent R R, as shown in formula (8)：
Above-mentioned technical proposal, it is proposed that a kind of improved differential threshold method, identify electrocardiosignal in R ripples, and according to
Obtained R ripples are extracted, easy to calculate phase, R wave amplitudes and heart rate variability between RR.
The energy feature information, the characteristic information of R ripples and heart rate variability, are input to sleepiness trained in advance by S30
It is identified in depth detection model, obtains sleepiness depth levels；
In this step, using sleepiness depth detection model trained in advance to the characteristic information of energy feature information, R ripples and
Heart rate variability is identified, so as to obtain the current sleepiness depth of sleep auxiliary object.Here it is input to sleepiness depth
Detection model be synchronization energy feature information, the characteristic information and heart rate variability of R ripples, that is, meet that the time is equal
Characteristic.
Sleepiness is human body from a kind of clear-headed state for being transitioned into sleep, it is presented with the absent minded, reaction time and becomes
Long and exercise not harmony etc., in sleep procedure is entered, EEG signals, electrocardiosignal, electro-ocular signal, electromyography signal, skin
The faint electricity physiological signal such as resistance and breathing can change.Therefore, at this by extracting in EEG signals and electrocardiosignal
Parameter, carries out sleepiness identification.
, can be by features described above (P1~P5, S1~S5, R such as previous embodimentA, RR, HRV) be input to and trained
Sleepiness depth detection model in, obtain comprehensive sleepiness scoring., can be with for the sleepiness depth detection model trained in advance
It is the detection model for being directed to sleepiness scoring and training in advance, as embodiment, the sleepiness depth detection model was trained
Journey can include as follows：
SVM regression models are established according to the brain electrical feature parameter of input and ecg characteristics parameter first, then described in extraction
The characteristic information of brain electrical feature parameter and ecg characteristics parameter is as training sample；The training sample is finally input to SVM
(Support Vector Machine, support vector machines) regression model and the score value progress SVM training for combining input, obtain
The sleepiness depth detection model.
In implementation process, it can be established according to brain electrical feature parameter, the ecg characteristics parameter of input by training sample
SVM regression models, and test sample output testing result is acted on, realize that sleepiness identifies；Training principle can be as follows：
For given sample to { (xi, yi), xi∈RN, yi={ 0,1,2 ..., 100 } }, xiFor training sample, x is to wait to sentence
Certainly sample, chooses RBF kernel functions, as shown in formula (9).
K(x,xi)=exp (- γ * | | x-xi||2) (9)
Wherein, γ is the width of RBF kernel functions, is adjustable parameter important in SVM；Then, obtained feature will be extracted
As the input sample X of training SVM models, using the sleepiness that the score value (can be expert analysis mode) of input obtains as standard,
That is the output Y of SVM regression models；(X, Y) collectively constitutes the training sample pair of SVM regression models, carries out SVM and trains to obtain sleepiness
Depth detection model, will extract obtained feature as the input sample X input models of training sleepiness depth detection model, carries out
Sleepiness identifies that identification obtains sleepiness depth levels；Under normal circumstances, sleepiness depth levels can using value as 0~100 integer.
S40, sleep auxiliary content is selected according to the sleepiness depth levels, by the sleep auxiliary content to the sleep
Auxiliary object plays out；
In this step, according to sleepiness depth levels, the sleep auxiliary content of most suitable user can be accurately chosen, to sleeping
Dormancy auxiliary object plays out.
As embodiment, the S40's selects sleep auxiliary content according to the sleepiness depth levels, and the sleep is auxiliary
Help content to it is described sleep auxiliary object play out the step of, can include as follows：
S401, establish the sleep auxiliary content switching table of user, and the sleep auxiliary content for recording the user is broadcast
Switching law is put, including plays content, sleepiness score value and volume change rule.
Sleep auxiliary content switching table process for establishing user, can include as follows：
Establish sleep auxiliary content storehouse beyond the clouds first, sleep auxiliary content includes music, voice guidance or hypnosis guiding
Word.
Then the sleep auxiliary content plays out test user, obtains the sleepiness depth levels of test user,
And the sleep auxiliary content is marked using the sleepiness depth levels；
General switching law table is finally formed according to the sleep auxiliary content of mark, including the title for auxiliary content of sleeping,
Initial stage sleepiness depth levels, latter stage sleepiness depth levels and content duration；Established further according to the general switching law table each
The sleep auxiliary content switching table of user.
S402, corresponding sleep auxiliary content is selected according to the sleepiness depth levels from sleep auxiliary content switching table
Played out to the user.
S403, after playback ends, reads the sleepiness depth levels of user, and slept to described according to the sleepiness depth levels
Dormancy auxiliary content switching table is updated.
Such as the scheme of above-described embodiment, among application process, it is auxiliary can first to establish the sleep based on sleepiness depth levels
Help the general switching law of content；Then the personalized switching of the sleep auxiliary content based on sleepiness depth levels of each user is established
Rule.
(1) first, the sleep auxiliary content storehouse in high in the clouds, including the content such as music, voice guidance, hypnosis guiding are established；So
Afterwards, tested by a collection of test user using the sleep auxiliary content in the sleep auxiliary content storehouse, and it is auxiliary to all sleeps
Help content to carry out regular marks, and test is made choice to all labeled sleep auxiliary contents and its switching law, obtain
To sleep auxiliary content switching law storehouse.And a sleep auxiliary content switching general purpose table is ultimately formed, it can record institute on table
There is sleep auxiliary content relevant information, used for the first time suitable for new user；For example, it is bent to include sleep auxiliary content
Mesh, sleepiness state at initial stage, latter stage sleepiness state, content duration etc., wherein sleepiness state can use sleepiness depth levels to mark.
(2) target of sleep auxiliary is to improve the sleepiness depth levels of user, therefore, for each user used
Speech, can be directed to it and set independent sleep auxiliary content personalization switching law.The step of implementation, can be as follows：
Step1:User logs in sleep auxiliary content switching law storehouse；
Step2:Sleep in analysis sleep auxiliary content switching law storehouse with the presence or absence of the user name containing the user is auxiliary
Help content switching table；If so, then jumping to Step4, Step3 is otherwise jumped to；
Step3：A new sleep auxiliary content switching table is established for the user, it is general based on sleep auxiliary content switching
Table updates the table, and comprising user name, sleep auxiliary content switching law switches the Universal gauge of general purpose table using sleep auxiliary content
Then, during the follow-up use of the user, constantly update as the switching law of user itself.
Step4：Based on the current sleepiness depth levels Gi of user (i=1,2 ..., 100), the sleep with reference to user is auxiliary
Content switching table is helped, the highest sleep auxiliary content song of sleepiness depth levels in table, knot are found in sleep auxiliary content storehouse
Volume change rule is closed, user is played to suitable volume.
Step5：Terminate in broadcasting, the current sleepiness depth levels of record user, and update the switching rule of the song again
Then, it is the switching law of user individual.
Step6：Repeat Step4~Step5, until the sleepiness of user maintain certain sleepiness depth levels (such as 80~
100) a period of time, time threshold can voluntarily be changed by user, default settings T.
Above-mentioned technical proposal, selection, mark and the broadcasting of sleep auxiliary content, Neng Goujing are carried out according to sleepiness depth levels
The sleep auxiliary content of most suitable user is chosen accurately, and earphone of arranging in pairs or groups, plays to user, help user to loosen body and mind, alleviate
Anxiety-depression, cultivates one's taste, improves individual character weakness, eliminate mental behavior disorder, keep psychology and Body health.And realize real
When the sleep auxiliary fed back so that user is subsequently using among process, can keeping obtaining most suitable sleep auxiliary
Content.
Refering to what is shown in Fig. 4, Fig. 4 is the sleeping-assisting system structure diagram of one embodiment, including：
Extraction module 10, for extracting brain wave from the EEG signals of sleep auxiliary object, and calculates the brain wave
Energy feature information；
Computing module 20, for obtaining the electrocardiosignal of sleep auxiliary object, identifies the R ripples in electrocardiosignal, calculates R ripples
Characteristic information and heart rate variability；
Identification module 30, for the energy feature information, the characteristic information of R ripples and heart rate variability to be input in advance
It is identified in trained sleepiness depth detection model, obtains sleepiness depth levels；
Playing module 40, for selecting sleep auxiliary content according to the sleepiness depth levels, by the sleep auxiliary
Hold to the sleep auxiliary object and play out.
The sleeping-assisting system of the present invention and the sleep householder method of the present invention correspond, in above-mentioned sleep householder method
Embodiment illustrate technical characteristic and its advantage suitable for the embodiment of sleeping-assisting system, hereby give notice that.
The embodiment of the present invention provides a kind of sleeping aid, computer equipment and computer-readable storage medium.
A kind of sleeping aid, including：Terminal and electrode, the electrode are used for the biological electricity for gathering sleep auxiliary object
Signal, and it is transmitted to terminal；The terminal is configured as performing the step of sleep householder method of above-mentioned any embodiment.
Refering to what is shown in Fig. 5, Fig. 5 is the sleeping aid structure diagram of one embodiment；Shown in figure using intelligence
The scheme of energy tablet, connects an electrode, Intelligent flat can install a program, the journey in the form of APP by Intelligent flat
Sequence performs the step of sleep householder method of above-mentioned any embodiment；It is scientific stronger so as to fulfill being played to sleep auxiliary object
Sleep auxiliary information, there is preferable sleep auxiliaring effect.
As embodiment, which can also can also be led to by being realized in the terminals such as smart mobile phone/PDA
PC is crossed to realize.
In one embodiment, it can both utilize single device to realize, terminal and clothes can also be utilized as shown in Figure 5
Business device is combined to realize, that is, the terminal of each user is connected to the server in high in the clouds, stores the sleep of user on the server
Auxiliary content switching table, after user begins to use, sleeps according to the relevant information of user from sleep auxiliary content switching table selection
Dormancy auxiliary content, after terminal plays, the operation such as is updated to the broadcasting switching law for auxiliary content switching table of sleeping.
For this reason, the process presented below handled on the server sleep auxiliary contents data, including it is as follows：
First, the sleep auxiliary content switching table of user is generated, wherein, sleep auxiliary content switching table is used to record described
The sleep auxiliary content and its broadcasting switching law of user, the sleep auxiliary content are stored in content library.
Establish the content library of sleep auxiliary content；Test user is carried out using the sleep auxiliary content in the content library
Play, obtain the sleepiness depth levels of test user, and using the sleepiness depth levels to the sleep auxiliary content into rower
Note；According to the sleep auxiliary content formation rule storehouse of mark, and obtain a general switching law table；According to the general switching
The sleep auxiliary content switching table that rule list establishes each user is stored into the rule base.
Secondly, the current sleepiness depth levels of the user are obtained, it is auxiliary from sleeping according to the current sleepiness depth levels
Help in content switching table and select corresponding sleep auxiliary content to send to the client of the user and play out；
Method for the current sleepiness depth levels for obtaining user, specifically, the sleep auxiliary content from the user
The highest sleep auxiliary content of sleepiness depth levels is selected in switching table；Corresponding sleep auxiliary content is obtained from content library to send
Client to user plays out；Wherein, the client detects user's electricity physiological signal and is input to sleeping for training in advance
It is identified in meaning depth detection model, obtains sleepiness depth levels；Receive the sleepiness depth of the client feedback of the user
Grade.
Further, it is logical from what is prestored when the sleep auxiliary content switching table of the user is not present in the rule base
With the selection highest sleep auxiliary content of sleepiness grade in switching law table；The sleep auxiliary content switching table of the user is established,
And the switching law of sleep auxiliary content switching table is set according to the general switching law table.
The method that sleep auxiliary content is played out for client, specifically, current sleepiness depth based on user etc.
Level, the highest sleep auxiliary content of sleepiness depth levels is selected from sleep auxiliary content switching table, regular with reference to volume change,
Send to the client of the user；The client is played out according to the volume change rule with corresponding volume.
Then, after playback ends, the sleepiness depth levels of the user feedback are received, and according to the sleepiness depth levels
The broadcasting switching law of the sleep auxiliary content switching table is updated.
Specifically, after the client terminal playing sleep auxiliary content of user, the client feedback for receiving the user is slept
Meaning depth levels；If the sleepiness depth levels of the feedback are not up to the sleepiness depth threshold scope set, sleep and aid in the section
The broadcasting switching law of content is updated, and forms new sleep auxiliary content switching table.
Based on example as described above, a kind of computer equipment is also provided in one embodiment, the computer equipment bag
The computer program that includes memory, processor and storage on a memory and can run on a processor, wherein, processor performs
Realized during described program such as any one sleep householder method in the various embodiments described above.
One of ordinary skill in the art will appreciate that realize all or part of flow in above-described embodiment method, being can be with
Relevant hardware is instructed to complete by computer program, it is non-volatile computer-readable that the program can be stored in one
Take in storage medium, in the embodiment of the present invention, which can be stored in the storage medium of computer system, and be calculated by this
At least one processor in machine system performs, to realize the flow for including the embodiment such as above-mentioned each sleep householder method.Its
In, the storage medium can be magnetic disc, CD, read-only memory (Read-Only Memory, ROM) or random storage
Memory body (Random Access Memory, RAM) etc..
Accordingly, a kind of storage medium is also provided in one embodiment, is stored thereon with computer program, wherein, the journey
Realized when sequence is executed by processor such as any one sleep householder method in the various embodiments described above.
Each technical characteristic of embodiment described above can be combined arbitrarily, to make description succinct, not to above-mentioned reality
Apply all possible combination of each technical characteristic in example to be all described, as long as however, the combination of these technical characteristics is not deposited
In contradiction, the scope that this specification is recorded all is considered to be.
Embodiment described above only expresses the several embodiments of the present invention, its description is more specific and detailed, but simultaneously
Cannot therefore it be construed as limiting the scope of the patent.It should be pointed out that come for those of ordinary skill in the art
Say, without departing from the inventive concept of the premise, various modifications and improvements can be made, these belong to the protection of the present invention
Scope.Therefore, the protection domain of patent of the present invention should be determined by the appended claims.
Claims (13)
1. one kind sleep householder method, it is characterised in that including：
Brain wave is extracted from the EEG signals of sleep auxiliary object, and calculates the energy feature information of the brain wave；
The electrocardiosignal of sleep auxiliary object is obtained, identifies the R ripples in electrocardiosignal, the characteristic information and heart rate for calculating R ripples become
The opposite sex；
The energy feature information, the characteristic information of R ripples and heart rate variability are input to sleepiness depth detection trained in advance
It is identified in model, obtains sleepiness depth levels；
Sleep auxiliary content is selected according to the sleepiness depth levels, by the sleep auxiliary content to the sleep auxiliary object
Play out.
2. sleep householder method according to claim 1, it is characterised in that the energy feature information include energy value and
Its energy profile density；The characteristic information of the R ripples includes phase and R wave amplitudes between RR.
3. sleep householder method according to claim 1, it is characterised in that the brain electricity of the extraction sleep auxiliary object
Delta, Theta, Alpha, Beta, Gamma ripple, and the energy of Delta, Theta, Alpha, Beta, Gamma ripple is calculated
And its step of energy profile density, includes：
The EEG signals are pre-processed to obtain brain cutting piece；
Delta, Theta, Alpha, Beta, Gamma ripple are extracted from the brain cutting piece；
The range value of Delta, Theta, Alpha, Beta, Gamma ripple is extracted respectively；
According to the range value of Delta, Theta, Alpha, Beta, Gamma ripple and frequency values calculate Delta, Theta, Alpha,
The energy and its energy profile density of Beta, Gamma ripple.
4. sleep householder method according to claim 3, it is characterised in that it is described from the brain cutting piece extraction Delta,
The step of Theta, Alpha, Beta, Gamma ripple, includes：
Centralization and whitening processing are carried out to the EEG signals in brain cutting piece；
The EEG signals are carried out with the wavelet coefficient under each resolution ratio of wavelet transformation acquisition, compromise threshold value is carried out to wavelet coefficient
Processing, and carry out inverse wavelet transform and obtain multiple eeg signals；
Independent component analysis is carried out to each eeg signal successively, extract Delta, Theta of eeg signal, Alpha,
Beta, Gamma ripple.
5. sleep householder method according to claim 2, it is characterised in that the EEG signals from sleep auxiliary object
Middle extraction brain wave, and the step of calculating the energy feature information of the brain wave include：
Extract the range value of Delta, Theta, Alpha, Beta, Gamma ripple；
According to the range value of Delta, Theta, Alpha, Beta, Gamma ripple calculate respectively Delta, Theta, Alpha,
The energy value of Beta, Gamma ripple；
Energy profile density is calculated according to the energy value of Delta, Theta, Alpha, Beta, Gamma ripple.
6. sleep householder method according to claim 1, it is characterised in that the R ripples in the identification electrocardiosignal, calculate
The step of characteristic information and heart rate variability of R ripples, includes：
Empirical mode decomposition is carried out to electrocardiosignal and obtains intrinsic mode functions, is identified according to the intrinsic mode functions in electrocardiosignal
R ripples；
The threshold value of electrocardiosignal is determined using differential threshold searching method；
Scanned for using the threshold value on the electrocardiosignal, the position using the maximum of electrocardiosignal as first R ripple
Put；
Search for the position of each R ripples one by one on the electrocardiosignal, and calculate the phase between the RR of two neighboring R ripples；
The heart rate variability of the electrocardiosignal is determined according to the phase between the RR.
7. sleep householder method according to claim 1, it is characterised in that further include：
SVM regression models are established according to the brain electrical feature parameter of input and ecg characteristics parameter；
The characteristic information of the brain electrical feature parameter and ecg characteristics parameter is extracted as training sample；
The score value that the training sample is input to SVM regression models and combines input is subjected to SVM training, obtains the sleepiness
Depth detection model.
8. sleep householder method according to claim 1, it is characterised in that select to sleep according to the sleepiness depth levels
Auxiliary content, by it is described sleep auxiliary content to it is described sleep auxiliary object play out the step of include：
The sleep auxiliary content switching table of user is established, switches rule for recording the broadcasting of sleep auxiliary content of the user
Then；
Corresponding sleep auxiliary content is selected to the use from sleep auxiliary content switching table according to the sleepiness depth levels
Family plays out；
After playback ends, the sleepiness depth levels of user are read, and according to the sleepiness depth levels in the sleep auxiliary
Hold switching table to be updated.
9. sleep householder method according to claim 8, it is characterised in that the sleep auxiliary content for establishing user is cut
The step of changing table includes：
Establish storage sleep auxiliary content sleep auxiliary content storehouse, it is described sleep auxiliary content include music, voice guidance or
Hypnosis introducer；
Sleep auxiliary content in the sleep auxiliary content storehouse plays out test user, obtains the sleepiness of test user
Depth levels, and the sleep auxiliary content is marked using the sleepiness depth levels；
General switching law table is formed according to the sleep auxiliary content of mark, includes title, the sleepiness at initial stage of sleep auxiliary content
Depth levels, latter stage sleepiness depth levels and content duration；
The sleep auxiliary content switching table of each user is established according to the general switching law table.
A kind of 10. sleeping-assisting system, it is characterised in that including：
Extraction module, for extracting brain wave from the EEG signals of sleep auxiliary object, and calculates the energy of the brain wave
Characteristic information；
Computing module, for obtaining the electrocardiosignal of sleep auxiliary object, identifies the R ripples in electrocardiosignal, calculates the feature of R ripples
Information and heart rate variability；
Identification module, for the energy feature information, the characteristic information of R ripples and heart rate variability to be input to training in advance
It is identified in sleepiness depth detection model, obtains sleepiness depth levels；
Playing module, for selecting sleep auxiliary content according to the sleepiness depth levels, by the sleep auxiliary content to institute
Sleep auxiliary object is stated to play out.
A kind of 11. sleeping aid, it is characterised in that including：Terminal and electrode, the electrode are used to gather sleep auxiliary pair
The bioelectrical signals of elephant, and it is transmitted to terminal；
The terminal is configured as the step of perform claim requires any one of 1 to the 9 sleep householder method.
12. a kind of computer equipment, including memory, processor and it is stored on the memory and can be in the processor
The computer program of upper operation, it is characterised in that the processor realized when performing the computer program as claim 1 to
Sleep householder method described in 9 any one.
13. a kind of computer-readable storage medium, is stored thereon with computer program, it is characterised in that the program is executed by processor
Sleep householder methods of the Shi Shixian as described in claim 1 to 9 any one.
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