CN112435741B - Sleep-aiding system using sleep latency database - Google Patents
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Abstract
The invention relates to a sleep-aid system using a sleep-in latency database. The prior sleep-assisting equipment can not adjust the sleep-assisting stimulation according to the sleep state of a user. The sleep-aiding device comprises a sleep-aiding latency database, a data collector, a processor and sleep-aiding equipment, wherein the sleep-aiding data packet matched with the individual user is obtained through calculation of the sleep-aiding latency database, the reference brain wave frequency and the change trend of the individual user are obtained through calculation of the processor, the sleep-aiding equipment is further controlled, the sleep-aiding effect is improved through improving the matching of sleep-aiding stimulation and the sleep state of the individual user, and the use experience is improved.
Description
Technical Field
The invention relates to the field of sleep, in particular to a sleep-aiding system using a sleep-in latency database.
Background
The current common sleep-aiding method in the market generally adjusts the light to a sleep mode when a user tries to fall asleep, pulls a curtain or starts playing some kind of sleep-aiding music, but the method can be executed only according to a preset program (for example, the light and the music are turned off when the preset time is reached), and the method cannot be changed correspondingly according to the sleep state of the user; the sleep-aid intervention program may still be executing while the user is asleep, or may have ended while the user is still awake, affecting the use experience.
Disclosure of Invention
In order to solve the defects of the prior art, the invention provides a sleep-aiding system using a sleep-aiding latency database, the sleep-aiding latency database matched with each target group is established, and a sleep-aiding data packet in the sleep-aiding latency database is used for providing a basis for the operation of sleep-aiding equipment, so that sleep-aiding stimulation generated by the sleep-aiding equipment can be changed, and the sleep-aiding effect and the use experience are effectively improved.
The invention is realized by the following modes: a method for establishing a sleep-in latency database includes acquiring basic information of each detection sample in a target group, obtaining stage average time length corresponding to each stage by carrying out stage division on brain wave frequency, calculating brain wave frequency change parameters corresponding to each stage of the target group one by utilizing the stage average time length to form sleep-aid data packets corresponding to the target group, acquiring the target groups with differentiated feature information one by one respectively to form corresponding sleep-aid data packets, and recording the sleep-in latency database with multiple groups of sleep-aid data packets. Based on the principle that all detection samples in the same target group have similar brain wave frequency change parameters, all the detection samples are divided and classified, and the brain wave frequency change parameters of the target group are obtained, so that sleep-assisting data packets corresponding to all the target groups are provided for users.
When the target population is divided, the characteristic information includes age, sex, economic level, work type, stress level, sleep work and rest, living habits, exercise habits, sleep latency and the like, and the target population is obtained by statistically dividing the characteristic information related to sleep. Specifically, the feature information is divided within a suitable range, and detection samples having the same feature information are classified into the same target group.
Preferably, when the sleep latency database is established, the sleep-aid data packet is formed by the following steps:
firstly, carrying out target group classification according to the characteristic information alignment of each detection sample, so that a single target group contains a plurality of detection samples with the same characteristic information. The accuracy of the target group corresponding to the sleep-assisting data packet is improved by increasing the number of the detection samples, and then sleep-assisting stimulation matched with a user is provided for the sleep-assisting equipment.
And secondly, performing sleep monitoring on each detection sample by using a data acquisition unit, acquiring a single-frame data packet which corresponds to each detection sample and contains brain wave frequency data by taking 30 seconds as unit duration, and completely storing the single-frame data packet. The sleep-in latent period and the light sleep stage of the detection sample are effectively judged and distinguished by recording and storing the sleep process of the steamer of the detection sample, so that a calculation basis is provided for forming a sleep-assisting data packet; the duration of the single-frame data packet is set to be 30 seconds, the monitoring precision can be improved by reducing the duration of the single-frame data packet, the control precision of the sleep-assisting equipment is improved, the number of the single-frame data packets can be effectively controlled, the performance requirement on the computing equipment is reduced by reducing the operation workload, and the cost is reduced.
Thirdly, calculating the single-frame average brain wave frequency EEG of the detection sample in the corresponding time interval according to the single-frame data packetN-JAnd defining a sleep-in latency period and a light sleep stage corresponding to the detection sample, wherein the sleep-in latency period comprises a preparation stage, a first stage, a second stage, a third stage and a fourth stage which are sequentially converted, wherein N is the number of the detection sample in the same target group, and J is the number of a single-frame data packet arranged along the time sequence. Dividing the sleep-in latency period and the light sleep stage corresponding to the detected sample into six stages including a preparation stage, a first stage, a second stage, a third stage, a fourth stage and the light sleep stage according to the brain wave frequency of the detected sample, and providing a switching reference basis for implementing differential sleep-assisting stimulation for the sleep-assisting equipment. The sleep-assisting stimulation is set to six levels, so that the sleep-assisting stimulation can be matched with the real-time situation of a user, the requirement on the performance of sleep-assisting equipment is reduced by controlling the number of the levels, and the practicability is improved.
Fourthly, carrying out stage duration statistics on the preparation stage, the first stage, the second stage, the third stage, the fourth stage and the light sleep stage of each detection sample to sequentially obtain the stage duration T of each detection sampleN-perWherein per is 1-6, and the preparation stage, the first stage, the second stage, the third stage, the fourth stage and the light sleep stage are numbered in sequence. And counting the duration of each detection sample in each stage, and providing parameter support for the subsequent calculation of the average duration corresponding to each stage in the target group.
Fifthly, accumulating and averaging the same stage time length of each detection sample in the same target group to obtain the average time length of each stage corresponding to the target group as Tper. The corresponding stages of the detection samples in the target group are accumulated and averaged, so that the corresponding stage continuance of the target group is obtainedThe duration provides reference basis for the duration of the differential sleep-assisting stimulation implemented by the sleep-assisting equipment.
Sixthly, each detection sample carries out accumulation and average to the single frame data packet corresponding to each stage of the detection sample, thereby obtaining the stage brain wave frequency EEG of each stage of the detection sampleN-p. Because the divided brain wave frequency ranges of all stages are unified standards, but the brain wave frequency changes of all target groups at all stages have differences, data are provided for calculating the stage average brain wave frequency of all stages in the same target group by counting the stage brain wave frequency of all detection samples at all stages.
Seventhly, accumulating and averaging the brain wave frequency of the same stage of each detection sample in the same target group so as to obtain EEG (electroencephalogram) of the average brain wave frequency of each stage corresponding to the target groupper. The difference between different target groups is distinguished by the average brain wave frequency of each stage, and parameters corresponding to the target groups are provided for a user when the sleep-assisting device is used.
The eighth step, the average time length of each stage is TperConverting into P cumulative minutes, and calculating to obtain EEG of phase cumulative brain wave frequency corresponding to each cumulative minuteper-P. The accumulative minute is the stage accumulative brainwave frequency with one minute of time formed by splicing the data of two adjacent single-frame data packets, and the influence of individual bias data on the accuracy is eliminated by calculating the stage accumulative brainwave frequency. The bias data is data which has a larger difference value compared with normal data and is generated when equipment detection errors or detection samples are in unexpected conditions such as turning over.
The ninth step, accumulating the EEG of brain wave frequency for the adjacent stages in each stageper-PAnd EEGper-(P-1)Calculating to obtain M brain wave frequency change parameters A of the target population at corresponding stagesper. The method comprises the steps of obtaining a plurality of brain wave frequency change parameters which are arranged in time sequence and correspond to the stages by comparing accumulated brain wave frequencies of adjacent stages, and providing basis for subsequently calculating brain wave change curves of users. The brain wave frequency variation parameters are recorded in the sleep-aid data packet corresponding to the target crowd.
Preferably, in the fourth stepWhen the EEG corresponding to two continuous single-frame data packets has a single-frame average brain wave frequencyN-JWhen the frequency of the detected sample is less than 50HZ, judging that the detected sample enters a preparation stage, and starting to count the stage duration T of the preparation stageN-1When the EEG corresponding to two continuous single-frame data packets has a single-frame average brain wave frequencyN-JWhen the detected sample is less than 4HZ, judging that the detected sample is separated from the light sleep stage, and finishing the stage duration T of the light sleep stageN-6And (4) counting. The initial threshold is 50Hz, whether the detection sample enters the preparation stage or not can be conveniently judged by setting the initial threshold, and the stage duration of the preparation stage and the subsequent stages is recorded by taking the initial threshold as a starting point. The ending threshold value is 4HZ, the fact that the light sleep stage is ended by the detection sample is judged by setting the ending threshold value, the stage duration of the light sleep stage is obtained by taking the ending threshold value as an end point, and a calculation basis is provided for calculating the average duration of each stage.
Preferably, the EEG is calculated by averaging the brain wave frequencies in a single frameN-JWhen the temperature is 31-50HZ, the detection sample is in a preparation stage; EEG (electroencephalogram) capable of averaging brain wave frequency in single frameN-JAt 26-30HZ, the detection sample is in a first stage; EEG (electroencephalogram) capable of averaging brain wave frequency in single frameN-JWhen the temperature is between 20 and 25HZ, the detection sample is in a second stage; when single frame averaging brain wave frequency EEGN-JAt 14-19HZ, the detection sample is in the third stage; when single frame averaging brain wave frequency EEGN-JWhen the sample is at 8-13HZ, the detection sample is at a fourth stage; EEG (electroencephalogram) capable of averaging brain wave frequency in single frameN-JAnd when the temperature is between 4 and 7HZ, the detection sample is in a light sleep standby stage. The sleep process of the test samples was staged using the sleep staging interpretation criteria of the american society of sleep medicine. Specifically, the corresponding brain wave frequencies of each stage are divided, so that the stage duration of each stage of the detection sample is convenient to count, and not only are the stages effectively divided, but also a basis can be provided for calculating parameters related to each stage.
Preferably, in the second step, the sleep monitoring time of a single detection sample is not less than 7 hours, and complete sleep data is obtained by monitoring the detection sample for more than 7 hours, so that staging is accurate and effective.
Preferably, in the fifth step,
the average duration of each stage is obtained by calculation according to the formula, wherein per is a fixed value and is used for representing a specific stage of the detection sample, and the parameter T is obtained by accumulating and averaging the duration of the stages of the same stage of each detection sample in the same target groupper。
Preferably, in the seventh step,
the average brain wave frequency of each stage is obtained by calculation according to the formula, wherein per is a constant value and is used for representing a specific stage of the detection sample, and the average brain wave frequency of each detection sample in the same stage in the same target group is accumulated and averaged to obtain the parameter EEGper。
Preferably, in the eighth step, P ═ Tper2-1, since the cumulative minutes include the time represented by two adjacent single-frame packets, the number of cumulative minutes P can be calculated by the above formula. T isperThe average duration of each stage of the detection sample in the same target group, and the parameter P is the accumulated number of minutes corresponding to the target group.
Preferably, the duration of the cumulative minutes is 1 minute, and the corresponding phases of the cumulative electroencephalogram frequency EEGper-PFormed by two adjacent single frame packet computations.
Preferably, in the ninth step, a ═ EEGper--EEGper-(P-1)]/EEGper-P. The parameter A is used for representing a variation parameter between the accumulated brain wave frequencies of the corresponding stages of two adjacent accumulated minutes, wherein P is M +1, the parameter P is a variation value, and the accumulated brain wave frequencies of the stages arranged along the time sequence are identified through variation.
Preferably, the duration T is determined when the stage of the sample is examined in the same target populationN-perGreater than phase average duration of TperTaking the average time length of the corresponding stage of the detection sample from front to back and the stage as TperEqual-duration single-frame data packet calculation corresponding phase electroencephalogram frequency EEGN-per(ii) a Or, atIn the same target group, the period of time T when detecting the sampleN-perLess than the average duration of a phase of TperThen, the single frame data packet at the end of the corresponding stage of the detection sample is used until the time length reaches the stage average time length TperThereby calculating the corresponding stage brain wave frequency EEGN-per. Because the stage duration of each detection sample is different from the stage average duration of the target group, the excessive single-frame data packets and the deficient single-frame data packets are corrected according to the principle, so that each detection sample can provide the number of the single-frame data packets corresponding to the stage average duration, and further the subsequent calculation is facilitated.
A sleep-aid system using a sleep-in latency database building method comprises: the data acquisition unit is used for acquiring basic information corresponding to the user individuals through the portable equipment, continuously transmitting real-time single-frame data packets to the processor, continuously acquiring single-frame data packets with the same duration for the user individuals by using the data acquisition unit, and providing basis for the processor in stages; the processor acquires the characteristic information of the individual user, acquires a sleep assisting data packet matched with the individual user by comparing the characteristic information with the characteristic information in the sleep-aid latency database, controls the sleep-aid equipment to operate by sending an operation instruction, acquires the sleep-aid data packet corresponding to the individual user by comparing the characteristic information, calculates the duration of the individual user at each stage according to the data in the sleep-aid data packet, and further controls the sleep-aid equipment to operate; the sleep-aiding device receives the operation instruction from the processor and generates corresponding sleep-aiding stimulus with differentiation, and the sleep-aiding effect is improved by implementing the differentiated sleep-aiding stimulus to the user individual, so that the use experience is improved.
When the system is operated, sleep assistance is realized through the following steps:
the method comprises the steps that firstly, a processor judges the time point when a user individual enters a preparation stage according to a real-time single-frame data packet, acquires and obtains the initial brain wave frequency formed by the first real-time single-frame data packet of the user individual, and applies corresponding sleep-assisting stimulation to the user individual through sleep-assisting equipment;
secondly, based on the initial brain wave frequency,utilizing the corresponding brain wave frequency variation parameter A in the sleep-aid data packetper-MObtaining a reference brain wave frequency at a next node, calculating the stage of the user individual by the processor through the reference brain wave frequency, and controlling the sleep-assisting equipment to maintain the corresponding sleep-assisting stimulation before reaching the next node, wherein the time length between adjacent nodes is 1 cumulative minute;
thirdly, the processor calculates the stage of the user individual by referring to the brain wave frequency and provides a conversion basis for the sleep-aiding equipment to generate differentiated sleep-aiding stimuli so that the user individual can sequentially obtain corresponding sleep-aiding stimuli in a preparation stage, a first stage, a second stage, a third stage, a fourth stage and a shallow sleep stage respectively;
fourthly, when the frequency of the reference brain wave is less than 4HZ, the sleep-assisting equipment stops running.
The sleep-assisting data packets prestored in the sleep-aid latency database are used for providing sleep-assisting stimulation for the individual user in the corresponding stage duration, the influence on sleep experience caused by the complicated structure of the data acquisition unit by the system is effectively avoided, and other matched sleep-assisting data packets can be obtained by classifying the individual user in advance, so that the sleep-assisting device can provide the sleep-assisting stimulation matched with the sleep habit of the individual user, and the use experience is improved.
Preferably, in the second step, the system is provided with a correction module, the processor corrects the time length between adjacent nodes through the correction module and records the obtained correction time length, and when in subsequent use, the original time length of the adjacent nodes is replaced by the correction time length and repeated correction is carried out again. Because the sleep-assisting data packets corresponding to the target group are different from the individual user, the screened sleep-assisting data packets are corrected by setting the correction module, so that the system can implement matched sleep-assisting stimulation on the individual user, specifically, the duration of each stage of the individual user is corrected, so that the sleep-assisting equipment can implement corresponding sleep-assisting stimulation in the stage corresponding to the individual user, and the matching between the sleep-assisting stimulation and the corresponding stage is improved.
Preferably, the correction module performs the correction operation by:
firstly, a data collector obtains real-time single-frame data packets after a user individual enters a preparation stage, and the EEG (electroencephalogram) of the phase accumulated brain wave frequency is obtained by calculating adjacent real-time single-frame data packetsper-POne-by-one corresponding real-time accumulated brain wave frequency EEGP；
Thereafter, by accumulating the brainwave frequency EEG for a phaseper-PAnd accumulating brainwave frequency EEG in real timePCarrying out comparison calculation to obtain a correction parameter delta T, correcting the time length between adjacent nodes and obtaining the correction time length so as to control the sleep-assisting equipment to maintain the corresponding sleep-assisting stimulation before reaching the next node;
and finally, after the sleep-assisting stimulus with the duration correction time is applied to the user individual, the processor repeatedly applies the steps and obtains the correction time corresponding to the time between the subsequent adjacent nodes so as to enable the sleep-assisting stimulus to be matched with the real-time stage of the user individual.
The parameter delta T represents a correction value on the stage accumulated brain wave frequency, so that the corrected stage accumulated brain wave frequency approaches to the real-time accumulated brain wave frequency obtained by monitoring, and further the matching between the sleep-assisting stimulus and the corresponding stage is improved, and the sleep-assisting effect is improved.
Preferably, the EEG is accumulated at the time of the phaseper-PEEG with less than real-time cumulative brainwave frequencyPAnd the difference between the two is greater than 1.96, the correction parameter DeltaT is equal to (EEG)P-EEGper-P)*AperThe time length between adjacent nodes is extended by delta T to obtain the correction time length, and the stage accumulated brain wave frequency is extended in the corresponding accumulated minutes;
current stage accumulated brain wave frequency EEGper-PEEG with greater than real-time cumulative brain wave frequencyPAnd the difference between the two is greater than 1.96, the correction parameter delta T is equal to (EEG)per-P-EEGP)*Aper-MThe time length between adjacent nodes is corrected by shortening delta T, and the stage accumulated brain wave frequency is shortened in corresponding accumulated minutes;
current stage accumulated brain wave frequency EEGperAnd accumulating brainwave frequency EEG in real timePDelta is less than 1.96And T is 0, the correction time length is 1 minute, and the stage accumulated brainwave frequency is not corrected within the corresponding accumulated minutes.
The triggering threshold value is set for the difference value between the stage accumulated brain wave frequency and the real-time accumulated brain wave frequency, so that the correction module can be started when the difference value between the stage accumulated brain wave frequency and the real-time accumulated brain wave frequency exceeds the triggering threshold value, the difference value between the stage accumulated brain wave frequency after correction and the real-time accumulated brain wave frequency is ensured to be smaller than the triggering threshold value, and then the individual user is ensured to obtain corresponding sleep-assisting stimulation in each stage.
Preferably, the sleep-assisting device is at least one, and at least six combinations with different sleep-assisting stimuli can be provided for the user individual, so that the user individual can obtain the different sleep-assisting stimuli when in the preparation stage, the first stage, the second stage, the third stage, the fourth stage and the light sleep stage respectively. The type and the stimulation degree of the sleep-assisting stimulation can be set according to needs, so that individual personalized requirements of users are met, and the sleep-assisting effect of the users is improved.
The invention has the following outstanding beneficial effects: based on that each detection sample in the same target group has similar brain wave frequency variation parameter principle, divide all detection samples and classify and obtain this target group's brain wave frequency variation parameter, for the user provides the help dormancy data package that corresponds with each target group, provide basic reference when using for user individuality, the correspondence between help dormancy stimulus and user individuality sleep habit has been effectively promoted, still need not to carry out deep comprehensive data acquisition to user individuality, effectively eliminate the wearing travelling comfort problem that brings because of data collection station structure is complicated, in addition, set up the correction module and carry out the adaptability to help dormancy data package, make help dormancy stimulus can effectively match with user individuality's sleep habit, promote and help dormancy effect.
Detailed Description
The essential features of the invention will be further explained with reference to the description and the embodiments.
The invention relates to a sleep-aiding system using a sleep-in latency database, which comprises a method for establishing the sleep-in latency database and a sleep-aiding system using the sleep-in latency database.
When a sleep-in latent period database is established, a large number of detection samples are classified according to corresponding characteristic information of the detection samples, so that each target group is provided with a large number of detection samples, and the accuracy of the sleep-assisting data packet is improved by increasing the number of the detection samples. Specifically, the brain wave frequency is divided into stages by acquiring basic information of each detection sample in a target group to obtain stage average time length corresponding to each stage, brain wave frequency change parameters corresponding to each stage of the target group one by one are further calculated by using the stage average time length to form sleep-aiding data packets corresponding to the target group, and the target groups with differentiated characteristic information are respectively acquired one by one to form corresponding sleep-aiding data packets, so that a sleep-in latency database with multiple groups of sleep-aiding data packets is formed through recording. And storing the sleep-aiding data packets corresponding to the target groups in a sleep-onset latency database so as to facilitate the screening of the user individuals according to the characteristic information of the user individuals to obtain the matched sleep-aiding data packets.
When a sleep latency database is established, a sleep-aid data packet is formed through the following steps:
firstly, carrying out target group classification according to the characteristic information alignment of each detection sample, so that a single target group contains a plurality of detection samples with the same characteristic information.
And secondly, performing sleep monitoring on each detection sample by using a data acquisition unit, acquiring a single-frame data packet which corresponds to each detection sample and contains brain wave frequency data by taking 30 seconds as unit duration, and completely storing the single-frame data packet.
Specifically, the sleep monitoring duration for a single test sample is not less than 7 hours to ensure that the sleep process encompasses the stage.
Thirdly, calculating the single-frame average brain wave frequency EEG of the detection sample in the corresponding time interval according to the single-frame data packetN-JDefining a sleep-in latent period and a light sleep stage corresponding to the detection sample, wherein the sleep-in latent period comprises a preparation stage, a first preparation stage and a second preparation stage which are sequentially convertedThe method comprises a stage, a second stage, a third stage and a fourth stage, wherein N is the number of detection samples in the same target group, and J is the number of single-frame data packets arranged along the time sequence.
Fourthly, carrying out stage duration statistics on the preparation stage, the first stage, the second stage, the third stage, the fourth stage and the light sleep stage of each detection sample to sequentially obtain the stage duration T of each detection sampleN-perWherein per is 1-6, and the preparation stage, the first stage, the second stage, the third stage, the fourth stage and the light sleep stage are numbered in sequence.
Specifically, when two continuous single-frame data packets correspond to a single-frame average brain wave frequency EEGN-JWhen the frequency of the detected sample is less than 50HZ, judging that the detected sample enters a preparation stage, and starting to count the stage duration T of the preparation stageN-1When the EEG corresponding to two continuous single-frame data packets has a single-frame average brain wave frequencyN-JWhen the detected sample is less than 4HZ, judging that the detected sample is separated from the light sleep stage, and ending the stage duration T of the light sleep stageN-6Counting;
specifically, the sleep process of the test sample is staged by the following principle:
EEG (electroencephalogram) capable of averaging brain wave frequency in single frameN-JWhen the temperature is 31-50HZ, the detection sample is in a preparation stage;
when single frame averaging brain wave frequency EEGN-JAt 26-30HZ, the detection sample is in a first stage;
EEG (electroencephalogram) capable of averaging brain wave frequency in single frameN-JWhen the temperature is between 20 and 25HZ, the detection sample is in a second stage;
EEG (electroencephalogram) capable of averaging brain wave frequency in single frameN-JAt 14-19HZ, the detection sample is in the third stage;
EEG (electroencephalogram) capable of averaging brain wave frequency in single frameN-JWhen the sample is at 8-13HZ, the detection sample is at a fourth stage;
EEG (electroencephalogram) capable of averaging brain wave frequency in single frameN-JAnd when the temperature is between 4 and 7HZ, the detection sample is in a light sleep standby stage.
The method comprises the steps that a single-frame average brain wave frequency fluctuates due to self reasons or external reasons in the sleeping process of a detection sample, and when the single-frame average brain wave frequency is reduced to a frequency range corresponding to the next stage and then is increased to a frequency range corresponding to the current stage, the detection sample is determined to start timing of the next stage. For example, when the average brain wave frequency of the single frame of the detection sample is decreased from 35HZ to 29ZH and then increased to 32HZ, the phase duration of the first phase starts to be recorded when the average brain wave frequency of the single frame of the detection sample is lower than 30HZ until the average brain wave frequency of the single frame of the detection sample is lower than 25HZ, and the phase duration of the second phase starts to be recorded.
Specifically, the frequency range of each stage can be adjusted as required to ensure that the individual of the user can obtain proper sleep-aiding stimulation in the range of the brain wave frequency of each stage, thereby effectively improving the sleep-aiding effect.
Fifthly, accumulating and averaging the same stage time length of each detection sample in the same target group to obtain the average time length of each stage corresponding to the target group as Tper。
In particular, the amount of the solvent to be used,
and accumulating and averaging the stage time lengths of the same stage of each detection sample to obtain the average time length of each stage of the target group. In the formula, N is the number of detection samples, per is a fixed value, and is used for representing a corresponding stage, so that the original data and the conclusion data are used for representing the same stage in the calculation.
Sixthly, each detection sample carries out accumulation and average to the single frame data packet corresponding to each stage of the detection sample, thereby obtaining the stage brain wave frequency EEG of each stage of the detection sampleN-per。
Seventhly, accumulating and averaging the brainwave frequencies of the same stage of each detection sample in the same target group so as to obtain EEG (electroencephalogram) of the average brainwave frequencies of all stages corresponding to the target groupper。
In particular, the amount of the solvent to be used,
by cumulatively averaging the brainwave frequencies of the same stage of each detection sampleAnd obtaining the average brain wave frequency of each stage of the target population. In the formula, N is the number of detection samples, per is a fixed value, and is used for representing a corresponding stage, so that the original data and the conclusion data are used for representing the same stage in the calculation.
The eighth step, the average time length of each stage is TperConverting into P cumulative minutes, and calculating to obtain EEG of phase cumulative brain wave frequency corresponding to each cumulative minuteper-P。
Specifically, the duration of the cumulative minute is 1 minute, and the corresponding stage is the cumulative electroencephalogram frequency EEGper-PThe phase average time length is divided into unit sections with half-minute time length, the adjacent unit sections are spliced to form an accumulated minute, and the unit section in the middle of the phase average time length can form two adjacent accumulated minutes with the unit sections adjacent to the front and the rear. P ═ Tper2-1, calculating and obtaining the accumulated number of minutes corresponding to the average duration of the stage through the formula, and performing superposition analysis on the data in the adjacent single-frame data packets, so that the biased data can be effectively eliminated, the accurate change trend can be obtained, and the data in the sleep-aiding data packets are ensured to be matched with the real-time condition of the individual user.
The ninth step, accumulating the EEG of brain wave frequency for the adjacent stages in each stageper-PAnd EEGper-(P-1)Calculating to obtain M brain wave frequency change parameters A of the target population at corresponding stagesper-M。
Specifically, a ═ EEGper-P-EEGper-(P-1)]/EEGper-PIn the formula, P is a variable and is more than 1, and is used for representing each accumulated minute arranged along the time sequence, per is a fixed value and is used for representing the corresponding stage, and the original data and the conclusion data for calculation are ensured to represent the same stage.
In real-time operation, in the same target population, the duration T is determined when the phase of the sample is detectedN-perGreater than phase average duration of TperTaking the average time length of the corresponding stage of the detection sample from front to back and the stage as TperEqual-duration single-frame data packet calculation corresponding phase electroencephalogram frequency EEGN-per(ii) a In thatIn the same target group, the period of time T when detecting the sampleN-perLess than the average duration of a phase of TperThen, the single frame data packet at the end of the corresponding stage of the detection sample is used until the time length reaches the stage average time length TperTo calculate the corresponding stage brain wave frequency EEGN-per. Because each stage of each detection sample is different from the corresponding stage duration, the corresponding stage of each detection sample in the target group is ensured to have the stage duration matched with the stage average duration in the above way, and the subsequent calculation and statistics are facilitated.
The sleep-assisting system comprises a data collector, a processor and sleep-assisting equipment. The data acquisition unit acquires basic information of a corresponding user individual through the portable equipment and continuously transmits a real-time single-frame data packet to the processor; the processor acquires the characteristic information of the user individual, acquires a sleep-aiding data packet matched with the user individual by comparing the characteristic information with the characteristic information in the sleep-onset latency database, and controls the sleep-aiding equipment to operate by sending an operation instruction; and the sleep-aiding device receives the running instruction from the processor and generates corresponding sleep-aiding stimuli with differentiation.
When the system is operated, sleep assistance is realized through the following steps:
the method comprises the steps that firstly, a processor judges the time point when a user individual enters a preparation stage according to a real-time single-frame data packet, acquires and obtains the initial brain wave frequency formed by the first real-time single-frame data packet of the user individual, and applies corresponding sleep-assisting stimulation to the user individual through sleep-assisting equipment.
Specifically, when the average brain wave frequency of a single frame corresponding to a single frame data packet is less than 50HZ, the individual surface user enters a preparation stage, and the average brain wave frequency of the single frame obtained by the first real-time single frame data packet is set as the initial brain wave frequency, so that the personalized difference between the individual user and a target group is effectively eliminated, and initial data is provided for subsequent prejudgment calculation.
Secondly, based on the initial brain wave frequency, the corresponding brain wave frequency variation parameter A in the sleep-aid data packet is utilizedper-To obtain a reference brain wave frequency at the next nodeThe processor calculates the stage of the user individual by referring to the brain wave frequency, and controls the sleep-assisting equipment to maintain the corresponding sleep-assisting stimulation before reaching the next node, wherein the time length between adjacent nodes is 1 cumulative minute.
Specifically, the brain wave frequency change of the individual user is calculated through the initial brain wave frequency and the brain wave frequency change parameters in the sleep-aiding data packet, the switching time of the individual user in each adjacent stage is calculated and defined according to the brain wave frequency change parameters, and a basis is provided for controlling the sleep-aiding equipment to switch among different sleep-aiding stimuli.
Thirdly, the processor calculates the stage of the user individual by referring to the brain wave frequency and provides a conversion basis for the sleep-aiding equipment to generate differentiated sleep-aiding stimuli so that the user individual can sequentially obtain corresponding sleep-aiding stimuli in a preparation stage, a first stage, a second stage, a third stage, a fourth stage and a shallow sleep stage respectively;
fourthly, when the frequency of the reference brain wave is less than 4HZ, the sleep-assisting equipment stops running.
When the user individual uses the sleep-aiding system, the processor classifies a target group according to the characteristic information of the user individual, finds a sleep-aiding data packet with characteristic information identical to that of the user individual in the sleep-aiding latency database, and calculates and pre-judges the brain wave frequency variation trend of the user individual by using the brain wave frequency variation parameters in the sleep-aiding data packet, so that the sleep-aiding stimulus generated by the sleep-aiding equipment can be changed along with the real-time stage of the user individual, the condition that the user individual feels uncomfortable due to the fact that the sleep-aiding stimulus received by the user individual is not consistent with the sleep stage of the user individual is effectively prevented, and the use experience is ensured.
In this embodiment, since there is a difference between the real-time brainwave frequency of the user individual and the reference brainwave frequency obtained by the sleep-aid data packet calculation and the variation trend thereof, a correction module is set in the system for this purpose. The processor corrects the time length between the adjacent nodes through the correction module and records the obtained correction time length, and when the processor is used subsequently, the original time length of the adjacent nodes is replaced by the correction time length and repeated correction is carried out again.
Specifically, the correction module performs the correction operation by:
firstly, a data collector obtains real-time single-frame data packets after a user individual enters a preparation stage, and the EEG (electroencephalogram) of the phase accumulated brain wave frequency is obtained by calculating adjacent real-time single-frame data packetsper-POne-by-one corresponding real-time accumulated electroencephalogram frequency EEGP；
Thereafter, by accumulating the brainwave frequency EEG for a phaseper-PAnd accumulating brainwave frequency EEG in real timePCarrying out comparison calculation to obtain a correction parameter delta T, correcting the time length between adjacent nodes and obtaining the correction time length so as to control the sleep-assisting equipment to maintain the corresponding sleep-assisting stimulation before reaching the next node;
specifically, the correction module can independently correct the time section corresponding to each brainwave frequency change parameter, so that the subsequent deduction calculation can be performed only after the real-time brainwave frequency of the user individual changes to the reference brainwave frequency. Because the brain wave frequency change parameter is formed by comparing the accumulated brain wave frequencies corresponding to the adjacent accumulated minutes, the duration time corresponding to the brain wave frequency change parameter is 1 minute, the correction parameter delta T is used for correcting the corresponding 1 minute and obtaining the correction time, and the processor can control the sleep-assisting equipment to obtain the reference brain wave frequency and the change trend thereof required by the subsequent 1 minute comparison by using the next reference brain wave frequency change parameter after implementing the sleep-assisting stimulation of the duration correction time on the user individual.
And finally, after the sleep-assisting stimulus with the duration correction time is applied to the user individual, the processor repeatedly applies the steps and obtains the correction time corresponding to the time between the subsequent adjacent nodes so as to enable the sleep-assisting stimulus to be matched with the real-time stage of the user individual.
In this embodiment, the correction module operates after obtaining the real-time accumulated brainwave frequency by:
current stage accumulated brain wave frequency EEGper-EEG with less than real time cumulative brainwave frequencyPAnd the difference between the two is greater than 1.96, the correction parameter delta T is equal to (EEG)P-EEGper-)*Aper-MThe time length between adjacent nodes is extended by delta T to obtain the correction time length;
current stage accumulated brain wave frequency EEGper-PEEG with greater than real-time cumulative brain wave frequencyPAnd the difference between the two is greater than 1.96, the correction parameter delta T is equal to (EEG)per-P-EEGP)*Aper-MThe time length between adjacent nodes is corrected by shortening delta T;
current phase accumulated brainwave frequency EEGper-PAnd accumulating brainwave frequency EEG in real timePWhen the difference between the values is less than 1.96, Δ T is 0, and the correction period is 1 minute.
In this embodiment, the sleep-assisting device is at least one type, and can provide at least six combinations with different sleep-assisting stimuli for the user individual, so that the user individual can obtain different sleep-assisting stimuli when in the preparation stage, the first stage, the second stage, the third stage, the fourth stage and the light sleep stage respectively.
The first way is that the sleep-aiding device is one type, and six kinds of sleep-aiding stimuli with different strengths can be formed so as to correspond to each stage;
and in the second mode, at least two types of sleep-assisting devices are adopted, and the sleep-assisting devices can form six types of differentiated sleep-assisting stimuli in a single use mode or a matched use mode so as to correspond to each stage.
Claims (9)
1. A sleep-aid system using a sleep latency database, comprising:
the data acquisition unit acquires basic information of a corresponding user individual through the portable equipment and continuously transmits a real-time single-frame data packet to the processor;
the processor acquires the characteristic information of the user individual, acquires a sleep-aiding data packet matched with the user individual by comparing the characteristic information with the characteristic information in the sleep-onset latency database, and controls the sleep-aiding equipment to operate by sending an operation instruction;
the sleep-aid data packet generation system comprises a sleep-aid data base, a sleep-aid data packet generation module and a sleep-aid data packet generation module, wherein the sleep-aid data packet generation module is used for generating a sleep-aid data packet according to the frequency of a brain wave, acquiring basic information of each detection sample in a target group, acquiring average stage duration corresponding to each stage by dividing the brain wave frequency, calculating brain wave frequency variation parameters corresponding to each stage of the target group one by utilizing the average stage duration to form sleep-aid data packets corresponding to the target group, acquiring the target group with differentiated characteristic information one by one respectively to form corresponding sleep-aid data packets, and recording the sleep-aid data packets to form the sleep-aid data base with a plurality of groups of sleep-aid data packets;
the sleep-aiding device receives the running instruction from the processor and generates corresponding sleep-aiding stimulation with differentiation;
when the system is operated, sleep assistance is realized through the following steps:
firstly, a processor judges the time point when a user individual enters a preparation stage according to a real-time single-frame data packet, acquires and obtains an initial brain wave frequency formed by a first real-time single-frame data packet of the user individual, and applies corresponding sleep-assisting stimulation to the user individual through sleep-assisting equipment;
secondly, based on the initial brain wave frequency, the corresponding brain wave frequency variation parameter A in the sleep-aid data packet is utilizedper-MObtaining a reference brain wave frequency at a next node, calculating the stage of the user individual by the processor through the reference brain wave frequency, and controlling the sleep-assisting equipment to maintain the corresponding sleep-assisting stimulation before reaching the next node, wherein the time length between adjacent nodes is 1 cumulative minute, Aper-MDividing and classifying all detection samples in the same target group to obtain brain wave frequency change parameters of the target group;
thirdly, the processor calculates the stage of the user individual by referring to the brain wave frequency and provides a conversion basis for the sleep-aiding equipment to generate differentiated sleep-aiding stimuli so that the user individual can sequentially obtain corresponding sleep-aiding stimuli in a preparation stage, a first stage, a second stage, a third stage, a fourth stage and a shallow sleep stage respectively;
fourthly, when the frequency of the reference brain wave is less than 4HZ, the sleep-assisting equipment stops running.
2. A sleep aid system using sleep latency database according to claim 1,
when a sleep latency database is established, a sleep-aid data packet is formed through the following steps:
step one, performing target group classification according to characteristic information alignment of all detection samples so that a single target group contains a plurality of detection samples with the same characteristic information;
secondly, performing sleep monitoring on each detection sample by using a data acquisition unit, acquiring a single-frame data packet which corresponds to each detection sample and contains brain wave frequency data by taking 30 seconds as unit duration, and completely storing the single-frame data packet;
thirdly, calculating the single-frame average brain wave frequency EEG of the detection sample in the corresponding time interval according to the single-frame data packetN-JDefining a sleep-in latent period and a light sleep stage corresponding to the detection sample, wherein the sleep-in latent period comprises a preparation stage, a first stage, a second stage, a third stage and a fourth stage which are sequentially converted, wherein N is a detection sample number in the same target group, and J is a single-frame data packet number arranged along the time sequence;
fourthly, carrying out stage duration statistics on the preparation stage, the first stage, the second stage, the third stage, the fourth stage and the light sleep stage of each detection sample to sequentially obtain the stage duration T of each detection sampleN-perWherein per is 1-6, and the preparation stage, the first stage, the second stage, the third stage, the fourth stage and the light sleep stage are numbered in sequence;
fifthly, accumulating and averaging the same stage time length of each detection sample in the same target group to obtain the average time length of each stage corresponding to the target group as Tper；
Sixthly, each detection sample carries out accumulation and average to the single frame data packet corresponding to each stage of the detection sample, thereby obtaining the stage brain wave frequency EEG of each stage of the detection sampleN-per；
Seventhly, accumulating and averaging the brain wave frequency of the same stage of each detection sample in the same target group so as to obtain EEG (electroencephalogram) of the average brain wave frequency of each stage corresponding to the target groupper；
The eighth step, the average time length of each stage is TperConverted into P cumulative minutes, anCalculating to obtain EEG corresponding to each accumulated minuteper-P；
The ninth step, accumulating the EEG of brain wave frequency for the adjacent stages in each stageper-PAnd EEGper-(P-1)Calculating to obtain M brain wave frequency change parameters A of the target population at corresponding stagesper-M。
3. The sleep-aid system using the sleep-onset latency database as claimed in claim 2, wherein in the fourth step, when the EEG of the average brain wave frequency of the single frame corresponding to two consecutive single frame data packets is detectedN-JWhen the frequency of the detected sample is less than 50HZ, judging that the detected sample enters a preparation stage, and starting to count the stage duration T of the preparation stageN-1When the EEG corresponding to two continuous single-frame data packets has a single-frame average brain wave frequencyN-JWhen the detected sample is less than 4HZ, judging that the detected sample is separated from the light sleep stage, and ending the stage duration T of the light sleep stageN-6Counting;
alternatively, when the single frame averages the brain wave frequency EEGN-JWhen the temperature is 31-50HZ, the detection sample is in a preparation stage; alternatively, when the single frame averages the brain wave frequency EEGN-JAt 26-30HZ, the detection sample is in a first stage;
alternatively, when the single frame averages the brain wave frequency EEGN-JWhen the temperature is between 20 and 25HZ, the detection sample is in a second stage;
alternatively, when the single frame averages the brain wave frequency EEGN-JAt 14-19HZ, the detection sample is in the third stage;
alternatively, when the single frame averages the brain wave frequency EEGN-JWhen the sample is at 8-13HZ, the detection sample is at a fourth stage;
alternatively, when the single frame averages the brain wave frequency EEGN-JAnd when the temperature is between 4 and 7HZ, the detection sample is in a light sleep standby stage.
4. A sleep aid system using a sleep latency database according to claim 2, wherein in the second step, the sleep monitoring time for a single test sample is not less than 7 hours;
or, in the fifthIn the step (a), the step (b),
alternatively, in the seventh step,
alternatively, in the eighth step, P ═ Tper*2-1；
Or the duration of the accumulated minutes is 1 minute, and the corresponding stage accumulates the electroencephalogram frequency EEGper-PIs calculated by two adjacent single-frame data packets;
or, in the ninth step, Aper-M＝[EEGper-P-EEGper-(P-1)]/EEGper-P。
5. A sleep aid system using sleep latency database as claimed in claim 2, wherein the period duration T when detecting the sample in the same target populationN-perGreater than phase average duration of TperTaking the average time length of the corresponding stage of the detection sample from front to back and the stage as TperEqual-duration single-frame data packet calculation corresponding phase electroencephalogram frequency EEGN-per(ii) a Or, in the same target group, the period T when the sample is detectedN-perLess than the average duration of a phase of TperThen, the single-frame data packet at the end of the corresponding stage of the detection sample is used until the average time length of the stage reaches TperTo calculate the corresponding stage brain wave frequency EEGN-per。
6. A sleep aid system using sleep latency database as claimed in claim 1, wherein in the second step, the system is provided with a modification module, the processor modifies the time length between adjacent nodes through the modification module and records the obtained modified time length, and in subsequent use, the modified time length is used to replace the original time length of adjacent nodes and repeat the modification again.
7. A sleep aid system using sleep latency database according to claim 6, wherein the revising module performs a revising operation by specifically:
firstly, a data collector obtains real-time single-frame data packets after a user individual enters a preparation stage, and the EEG (electroencephalogram) of the phase accumulated brain wave frequency is obtained by calculating adjacent real-time single-frame data packetsper-POne-by-one corresponding real-time accumulated electroencephalogram frequency EEGP；
Thereafter, by accumulating the brainwave frequency EEG for a phaseper-PAnd accumulating brainwave frequency EEG in real timePCarrying out comparison calculation to obtain a correction parameter delta T, correcting the time length between adjacent nodes and obtaining the correction time length so as to control the sleep-aiding equipment to maintain the corresponding sleep-aiding stimulation before reaching the next node;
and finally, after the sleep-assisting stimulation with the continuous correction duration is applied to the user individual, the processor repeatedly implements the steps and obtains the correction duration corresponding to the duration between the subsequent adjacent nodes so as to enable the sleep-assisting stimulation to be matched with the real-time stage of the user individual.
8. The sleep-aid system using the sleep-onset latency database as claimed in claim 7, wherein the EEG is a cumulative brain wave frequency during the periodper-PEEG with less than real-time cumulative brainwave frequencyPAnd the difference between the two is greater than 1.96, the correction parameter Δ T is (EEG)P-EEGper-P)*Aper-MThe time length between adjacent nodes is prolonged by delta T to obtain the correction time length;
current stage accumulated brain wave frequency EEGper-PEEG with greater than real-time cumulative brain wave frequencyPAnd when the difference between the two is greater than 1.96, the correction parameter delta T is equal to (EEG)per-P-EEGP)*Aper-MThe time length between adjacent nodes is corrected by shortening delta T;
current stage accumulated brain wave frequency EEGper-PAnd accumulating brainwave frequency EEG in real timePWhen the difference between the values is less than 1.96, Δ T is 0, and the correction period is 1 minute.
9. A sleep aid system using sleep latency database as claimed in claim 1, wherein the sleep aid device is at least one type, and provides at least six combinations of different sleep aid stimuli to the user to obtain different sleep aid stimuli when the user is in the preparation stage, the first stage, the second stage, the third stage, the fourth stage and the light sleep stage.
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