CN115212422B - Sleep regulation and control system and method based on shaking stimulation - Google Patents
Sleep regulation and control system and method based on shaking stimulation Download PDFInfo
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
- A61M21/00—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis
- A61M21/02—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis for inducing sleep or relaxation, e.g. by direct nerve stimulation, hypnosis, analgesia
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
- A61M21/00—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
- A61M21/00—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis
- A61M2021/0005—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis by the use of a particular sense, or stimulus
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
- A61M2230/00—Measuring parameters of the user
- A61M2230/005—Parameter used as control input for the apparatus
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
- A61M2230/00—Measuring parameters of the user
- A61M2230/08—Other bio-electrical signals
- A61M2230/10—Electroencephalographic signals
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Abstract
The invention relates to the technical field of human brain electrical signal processing and brain regulation, discloses a sleep regulation system and method based on shaking stimulation, and aims to solve the problem of poor stimulation effect of the existing stimulation sleep-entering method, and the scheme mainly comprises the following steps: the electroencephalogram signal acquisition module is used for acquiring electroencephalogram signals of a user, preprocessing the electroencephalogram signals and then sending the preprocessed electroencephalogram signals to the data calculation processing module; the data calculation processing module is used for receiving the preprocessed electroencephalogram signals, determining the sleep state of a user according to the preprocessed electroencephalogram signals, determining shaking stimulation parameters according to the sleep state, and sending the shaking stimulation parameters to the motor control module; and the motor control module is used for receiving the shaking stimulation parameters and driving the shaking table to shake according to the shaking stimulation parameters. The invention improves the efficiency of shaking stimulation, optimizes the effect of stimulating sleep, and is particularly suitable for insomnia users.
Description
Technical Field
The invention relates to the technical field of human body electroencephalogram signal processing and brain regulation, in particular to a system and a method for sleep regulation and control based on shaking stimulation.
Background
The modern society has rapid pace and great pressure, and the sleeping problems puzzle everywhere, so that the people generally have insufficient sleep and difficult sleep. Meanwhile, people with poor sleeping quality have a plurality of health hidden troubles. Moreover, poor sleep quality can seriously affect daily economic production activities and cause a great deal of economic loss, and the adoption of drug treatment can often bring toxic and side effects such as drug dependence enhancement, endocrine disturbance and the like. It is very important to explore a non-invasive, effective and safe physical sleep regulation and control means.
During sleep, the brain's higher executive functional activity diminishes in response to external stimuli, however, sensory processing of the central nervous system does not stop during sleep. Studies have shown that shaking promotes brain activity in slow wave sleep. Shaking with fixed frequency and amplitude can shorten sleep latency, enhance night deep sleep, and reduce wakefulness. The study shows that in the physiological mechanism, the shaking stimulation is sensed by the vestibular system and acts on the vestibular nucleus. Vestibular check has direct influence on brainstem structures related to sleep regulation, such as pituitary nucleus, ventricles, solitary tract nucleus and the like, the vestibular system has extensive neuronal projection on thalamus and hypothalamus, shaking stimulation is a potential non-invasive physical sleep regulation means, a sleep homeostasis loop can be indirectly regulated, safety is high, and great development potential is achieved.
Due to the structural specificity of the individual brain, the difference of sleeping habits and the uncertainty of the sleeping process, the shaking stimulation generated by using fixed frequency and amplitude at present is used, the achieved sleeping aid effect is greatly reduced, and the effectiveness cannot be evaluated. The shaking bed sleep promoting technology aiming at adult sleep disorder is not completely perfect, and the common disclosed technology is to shake and stimulate at fixed frequency and amplitude, so that the stimulation effect is poor.
Disclosure of Invention
The invention aims to solve the problem of poor stimulation effect of the existing method for promoting sleep by physical stimulation, and provides a system and a method for regulating and controlling sleep based on shaking stimulation.
The technical scheme adopted by the invention for solving the technical problems is as follows:
in one aspect, a sway stimulus-based sleep regulation system is provided, comprising:
the electroencephalogram signal acquisition module is used for acquiring electroencephalogram signals of a user, preprocessing the electroencephalogram signals and then sending the preprocessed electroencephalogram signals to the data calculation processing module;
the data calculation processing module is used for receiving the preprocessed electroencephalogram signals, determining the sleep state of a user according to the preprocessed electroencephalogram signals, determining shaking stimulation parameters according to the sleep state, and sending the shaking stimulation parameters to the motor control module;
and the motor control module is used for receiving the shaking stimulation parameters and driving the shaking table to shake according to the shaking stimulation parameters.
Further, the data calculation processing module is specifically configured to:
segmenting the electroencephalogram signal through a sliding time window to obtain electroencephalogram signals of a plurality of segments;
respectively representing the corresponding state of the electroencephalogram signals of each segment through a state vector to obtain a multivariable electroencephalogram signal, and mapping the multivariable electroencephalogram signal to a multidimensional state space;
reconstructing a state space track according to the multi-dimensional state space, representing the adjacency relation among system states in the reconstructed state space track through a recursive graph, and re-interpreting each recursive graph into an adjacency matrix to construct a recursive network;
global topological features are extracted from the recursive network, and a sleep state of the user is determined based on the extracted global topological features.
Further, the preprocessing the electroencephalogram signal specifically includes:
and amplifying, filtering and performing analog-to-digital conversion on the electroencephalogram signals in sequence.
Further, in the multi-dimensional state space, the ith state of the system is represented by a column vector x i Represents, i.e.:
x i ＝(v 1+(i-1)τ ,v 2+(i-1)τ ,…,v m+(i-1)τ )，i∈1,2,…,T；
wherein m represents an embedding dimension parameter, tau represents a time delay parameter, T represents a segment of an electroencephalogram signal, and v represents an observed value corresponding to each moment;
in the recursion diagram, the recursion mode of the multivariable brain electrical signals is represented by the following formula:
R i,j ＝Θ(ε-||x i -x j ||)，x i ,x j ∈R m ，i,j∈1,2,…T；
where Θ () represents a Herveseidel function, ε represents a threshold value of the adjacency relation, | | | represents a function of the distance between any two points on the quantization state space trajectory, R m A vector space representing m dimensions;
the adjacency matrix is represented by the following formula:
A i,j (ε)＝R i,j (ε)-I(T)；
wherein R is i,j The term "(ε) denotes a recursive graph whose adjacency threshold is ε, and I (T) denotes a T-dimensional identity matrix.
Further, extracting global topological features from the recursive network, and determining the sleep state of the user based on the extracted global topological features specifically includes:
decomposing eigenvalues of the Laplacian matrix of the recursive network to obtain eigenvalues and eigenvectors;
connecting the obtained characteristic values by using an original time sequence, and smoothing by adopting a five-point moving average filter to obtain a characteristic value corresponding to a new time sequence;
and determining the upper control limit and the lower control limit according to the type of the characteristic value, and determining the sleep state of the user according to the size of the characteristic value corresponding to the new time series and the upper control limit or the lower control limit.
Further, the laplacian matrix of the recursive network is represented by the following formula:
L＝D-A i,j (ε)；
wherein D represents a diagonal matrix, D = D i,j And d represents the order of the node.
Further, the determining the upper control limit and the lower control limit according to the type of the characteristic value specifically includes:
determining an estimated average value and an estimated standard deviation corresponding to the characteristic value according to the type of the characteristic value, and determining a control upper limit and a control lower limit according to the estimated average value and the estimated standard deviation;
the upper control limit is defined as follows:
the lower control limits are defined as follows:
wherein the content of the first and second substances,
representing an estimated mean value corresponding to a characteristic value>
And k represents the control parameter.
Further, the determining the sleep state of the user according to the feature value corresponding to the new time series and the size of the upper control limit or the lower control limit specifically includes:
and obtaining characteristic values corresponding to the latest k time nodes, if the characteristic values corresponding to the time nodes exceeding the preset number are all smaller than the lower control limit, judging that the user is in a deep sleep state, if the characteristic values corresponding to the time nodes exceeding the preset number are all larger than the upper control limit, judging that the user is in an awakening state, and otherwise, judging that the user is in a light sleep state.
Further, the sway stimulation parameters include at least a sway frequency and a sway amplitude.
In another aspect, a method for sleep regulation based on shaking stimuli is provided, comprising the steps of:
collecting an electroencephalogram signal of a user, and preprocessing the electroencephalogram signal;
determining the sleep state of a user according to the preprocessed electroencephalogram signals, and determining shaking stimulation parameters according to the sleep state;
and driving the shaking table to shake according to the shaking stimulation parameters.
The invention has the beneficial effects that: the system and the method for sleep regulation and control based on shaking stimulation can determine the sleep state of the user according to the specific characteristics of the detected electroencephalogram signals, and the sleep state judgment method is accurate. And different shaking stimulation parameters are selected according to the sleeping state of the user, so that the shaking stimulation efficiency is improved, and the effect of stimulating and promoting slow wave sleep is optimized.
Drawings
FIG. 1 is a schematic diagram of a system for sleep regulation based on shaking stimuli according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart illustrating a process of determining a sleep state of a user according to an electroencephalogram signal according to an embodiment of the present invention;
fig. 3 is a schematic flowchart of a process of determining a sleep state of a user according to a global topology feature according to an embodiment of the present invention;
fig. 4 is a flowchart illustrating a sleep regulation method based on shaking stimuli according to an embodiment of the present invention.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
The invention aims to obtain a better stimulation sleep-in effect, and provides a sleep regulation system and a sleep regulation method based on shaking stimulation, wherein the main technical scheme comprises the following steps: the electroencephalogram signal acquisition module acquires electroencephalogram signals of a user, preprocesses the electroencephalogram signals and sends the preprocessed electroencephalogram signals to the data calculation processing module; the data calculation processing module receives the preprocessed electroencephalogram signals, determines the sleep state of a user according to the preprocessed electroencephalogram signals, determines shaking stimulation parameters according to the sleep state, and sends the shaking stimulation parameters to the motor control module; and the motor control module receives the shaking stimulation parameters and drives the shaking table to shake according to the shaking stimulation parameters.
It can be understood that the electroencephalogram signal is the overall reflection of the electrophysiological activity of the cranial nerve tissues on the surface of the cerebral cortex, and the change activity data of the electrical signal can form an electroencephalogram when the brain is active. Electroencephalography reflects the summation of a large number of nerve cell activities. The electroencephalogram signal generated by a single nerve cell is very weak, so that different amplitudes of multiple frequencies can be generated on the cerebral cortex. These oscillatory features of brain electrical activity describe different brain states. When a user is in different sleep states, the characteristics corresponding to the electroencephalogram signals of the user can also be changed differently. Based on the method, the sleep state of the user is determined according to the electroencephalogram signals by collecting the electroencephalogram signals when the user sleeps, and the shaking stimulation parameters are adjusted according to the sleep state of the user, so that the shaking table shakes at the corresponding frequency and amplitude, and a better stimulation sleep-in effect is achieved.
Examples
The sleep regulation system based on shaking stimulus according to the embodiment of the present invention, as shown in fig. 1, includes: the device comprises a power module, an electroencephalogram signal acquisition module, a data calculation processing module, a communication module, a motor control module, a motor and a shaking table. The power module is respectively connected with the electroencephalogram signal module, the data calculation processing module and the communication module, the electroencephalogram signal acquisition module is connected with the data calculation processing module, the data calculation processing module is connected with the motor control module through the communication module, and the motor control module is connected with a motor of the shaking table.
In this embodiment, the power module, the electroencephalogram signal acquisition module, the data calculation processing module and the communication module may be integrated in the head-mounted device. When a user wears the head-wearing equipment during sleeping, the sleep control of the user can be realized.
The power module can comprise a lithium battery and a USB charging module, the lithium battery supplies power to the electroencephalogram signal acquisition module, the data processing calculation module and the communication module in the head-mounted equipment, and the USB charging module is used for charging the lithium battery.
In this embodiment, the communication module may be, but is not limited to, a bluetooth communication module, a WiFi communication module, a 4G/5G communication module, and the like. It should be noted that, the communication module is used to facilitate data transmission, and when the data calculation processing module is connected to the motor control module by wire, the communication module is not needed.
In practical application, the electroencephalogram signal acquisition module acquires electroencephalogram signals of a user, preprocesses the electroencephalogram signals and sends the preprocessed electroencephalogram signals to the data calculation processing module. After receiving the preprocessed electroencephalogram signals, the data computing and processing module determines the sleep state of a user according to the preprocessed electroencephalogram signals, determines shaking stimulation parameters according to the sleep state, and sends the shaking stimulation parameters to the motor control module through the communication module. And after receiving the shaking stimulation parameters, the motor control module controls the motor to operate according to the shaking stimulation parameters, and the motor operates to drive the shaking table to shake.
Specifically, the electroencephalogram signal acquisition module comprises a signal electrode, an electroencephalogram amplifier, a filter and an analog-to-digital converter. The signal electrode is tightly attached to the skin of a user, weak electroencephalogram signals of human cortex are collected, and are filtered by the multi-stage amplification circuit of the electroencephalogram amplifier and the filter to obtain preprocessed electroencephalogram analog signals. Then, through an analog-to-digital converter, electroencephalogram digital signals are obtained at a sampling rate of 500Hz and are transmitted to a data processing and calculating module.
In this embodiment, after receiving the preprocessed electroencephalogram signal, the data processing module determines the sleep state of the user according to the preprocessed electroencephalogram signal, as shown in fig. 2, which specifically includes the following steps:
step 1, segmenting the electroencephalogram signal through a sliding time window to obtain electroencephalogram signals of a plurality of segments;
in this embodiment, the preprocessed electroencephalogram signal line is segmented by a sliding time window of 1 s.
Step 2, respectively representing the state corresponding to the electroencephalogram signal of each segment through a state vector to obtain a multivariable electroencephalogram signal, and mapping the multivariable electroencephalogram signal to a multidimensional state space;
specifically, according to the tachnis theorem, the bottom layer dynamic structure of the multivariate electroencephalogram signal is reconstructed by adopting a time-delay embedding method. The Tarkens theorem (Takens theorem) is a basic proposition to calculate the size of the embedded facies spatial dimension. The method is a theoretical basis of a reconstructed phase space technology, and an important problem of chaos application is to reconstruct an N-dimensional phase space which can accommodate the chaos motion from a time sequence of a single variable.
In the multidimensional state space, the ith state of the system is represented by a column vector x i Represents, i.e.:
x i ＝(v 1+(i-1)τ ,v 2+(i-1)τ ,…,v m+(i-1)τ )，i∈1,2,…,T；
where m denotes an embedding dimension parameter, τ denotes a time delay parameter, T denotes a segment (time state) of the electroencephalogram signal, and v denotes an observed value corresponding to each time.
Step 3, reconstructing a state space track according to the multi-dimensional state space, representing the adjacency relation among system states in the reconstructed state space track through a recursive graph, and re-interpreting each recursive graph into an adjacency matrix to construct a recursive network;
in the recursion diagram, the recursion mode of the multivariable electroencephalogram signal is represented by the following formula:
R i,j ＝Θ(ε-||x i -x j ||)，x i ,x j ∈R m ，i,j∈1,2,…T；
wherein Θ () represents a hervesaide function, epsilon represents a threshold value of an adjacency relation, and | | represents a function of a distance between any two points on a quantized state space trajectory, the present embodiment selects a euclidean distance to represent a distance between system states, and R represents a distance between system states m A vector space representing m dimensions;
the recursion diagram is a multivariable generalized model which is extended from a univariate time series and is used for representing an n-dimensional system and accordingly representing the change mode of the multivariable brain electrical signals. The present embodiment does not consider time delay embedding, i.e., m =1, τ =1. The ith state of the system is represented by the column vector x i Represents, the column vector x i Consisting of observations of n channels.
In this embodiment, the adjacency matrix A of the recursive network i,j (ε) is represented by the following equation:
A i,j (ε)＝R i,j (ε)-I(T)；
wherein R is i,j The term "(ε) denotes a recursive graph whose adjacency threshold is ε, and I (T) denotes a T-dimensional identity matrix.
Step 4, extracting global topological features from the recursive network, and determining the sleep state of the user based on the extracted global topological features, as shown in fig. 3, specifically including:
step 41, performing eigenvalue decomposition on the Laplacian matrix of the recursive network to obtain an eigenvalue and an eigenvector;
in this embodiment, the laplacian matrix of the recursive network is represented by the following formula:
L＝D-A i,j (ε)；
wherein D represents a diagonal matrix, D = D i,j And d represents the order of the node.
The embodiment determines the sleep state of the user by screening a specific feature value from the features obtained by feature value decomposition, for example, the second smallest feature value λ 2 。
Step 42, connecting the obtained characteristic values by using an original time sequence, and smoothing by adopting a five-point moving average filter to obtain a characteristic value corresponding to a new time sequence;
specifically, for each type of feature, the extracted feature values are concatenated in time series to represent the original time series EEG signal, and a five-point moving average filter is employed to smooth the new time series.
And 43, determining a control upper limit and a control lower limit according to the type of the characteristic value, and determining the sleep state of the user according to the size of the characteristic value corresponding to the new time series and the control upper limit or the control lower limit.
In this embodiment, determining the upper control limit and the lower control limit according to the type of the feature value specifically includes:
determining an estimated average value and an estimated standard deviation corresponding to the characteristic value according to the type of the characteristic value, and determining a control upper limit and a control lower limit according to the estimated average value and the estimated standard deviation;
the upper control limit is defined as follows:
the lower control limit is defined as follows:
wherein, the first and the second end of the pipe are connected with each other,
represents the corresponding estimated mean value, which represents the characteristic value>
And k represents the estimated standard deviation corresponding to the characteristic value, and k represents the control parameter.
After the control upper limit is determined, the control lower limit is determined, the characteristic values corresponding to the nearest k time nodes are obtained, if the characteristic values corresponding to the time nodes exceeding the preset number are all smaller than the control lower limit, the user is judged to be in a deep sleep state, if the characteristic values corresponding to the time nodes exceeding the preset number are all larger than the control upper limit, the user is judged to be in an awakening state, and if the characteristic values are not larger than the control upper limit, the user is judged to be in a light sleep state.
In this embodiment, k =3 because
Limits, which are a common criterion in statistical control charts, may ensure that 99.73% of the total observed values are within the control limits. When k =3, the preset number may be 2, that is, feature values corresponding to the latest 3 time nodes are obtained, if the feature values corresponding to more than 2 time nodes are all smaller than the lower control limit, it is determined that the user is in a deep sleep state, if the feature values corresponding to more than 2 time nodes are all larger than the upper control limit, it is determined that the user is in an awake state, and otherwise, it is determined that the user is in a light sleep state.
In the present embodiment, three kinds of shaking stimulation parameters corresponding to the three kinds of sleep states are set in advance. Specifically, in the awake state, the shaking frequency is set to f 1 Amplitude of A 1 To accelerate sleep. In a light sleep state, setting the shaking frequency as f 2 Amplitude of A 2 To deepen sleep. In the deep sleep state, the shaking frequency is set to f 3 Amplitude of A 3 And maintaining sleep homeostasis.
After receiving the shaking stimulation parameters, the motor control module makes corresponding changes to the shaking frequency and the maximum amplitude, and the changes comprise:
the motor adopts an alternating current servo motor, and when the frequency parameter is changed, the target frequency can be realized by changing the input current intensity through changing the rotating speed.
Three sets of transmission structures are arranged in the motor, and when the amplitude parameter is changed, three gears can be selected through the motor control module to select different transmission structures, so that the target amplitude is realized.
In this embodiment, the corresponding shaking stimulation parameters are selected by recognizing three sleep states, the motor drives the shaking table to move, and corresponding shaking stimulation is applied to the user in the bed.
When using this embodiment, the user only needs to wear the head-mounted device, turns on the power switch of the device and the cradle, and lies still on the bed. The head-mounted equipment can acquire real-time electroencephalogram signals of a user, judge the wakefulness state, the light sleep state and the deep sleep state according to the electroencephalogram signals, and transmit stimulation parameters corresponding to different sleep states to the motor control module. The motor control module changes frequency and amplitude change correspondingly through changing the current intensity and the transmission structure of the motor, and acts on the shaking table through the motor, so that different shaking stimuli are applied to a user, and the effect of promoting the stimulation to fall asleep is achieved.
Based on the above technical solution, this embodiment further provides a sleep regulation method based on shaking stimulation, as shown in fig. 4, including the following steps:
collecting electroencephalogram signals of a user, and preprocessing the electroencephalogram signals;
determining the sleep state of a user according to the preprocessed electroencephalogram signals, and determining shaking stimulation parameters according to the sleep state;
and driving the shaking table to shake according to the shaking stimulation parameters.
It can be understood that, since the sleep regulation method based on shaking stimulus according to the embodiment of the present invention is implemented based on the sleep regulation system based on shaking stimulus according to the embodiment, the method disclosed in the embodiment is relatively simple in description since it corresponds to the system disclosed in the embodiment, and the relevant points can be referred to the partial description of the system.
Claims (7)
1. A sway stimulus based sleep regulation system comprising:
the electroencephalogram signal acquisition module is used for acquiring electroencephalogram signals of a user, preprocessing the electroencephalogram signals and then sending the preprocessed electroencephalogram signals to the data calculation processing module;
the data calculation processing module is used for receiving the preprocessed electroencephalogram signals, determining the sleep state of a user according to the preprocessed electroencephalogram signals, determining shaking stimulation parameters according to the sleep state, and sending the shaking stimulation parameters to the motor control module;
the data calculation processing module is specifically configured to:
segmenting the electroencephalogram signal through a sliding time window to obtain electroencephalogram signals of a plurality of segments;
respectively representing the corresponding state of the electroencephalogram signals of each segment through a state vector to obtain a multivariable electroencephalogram signal, and mapping the multivariable electroencephalogram signal to a multidimensional state space;
reconstructing a state space track according to the multi-dimensional state space, representing the adjacency relation among system states in the reconstructed state space track through a recursive graph, and re-interpreting each recursive graph into an adjacency matrix to construct a recursive network;
extracting global topological features from the recursive network, and determining the sleep state of the user based on the extracted global topological features;
the extracting global topological features from the recursive network and determining the sleep state of the user based on the extracted global topological features specifically include:
decomposing eigenvalues of the Laplacian matrix of the recursive network to obtain eigenvalues and eigenvectors;
connecting the obtained characteristic values by using an original time sequence, and smoothing by adopting a five-point moving average filter to obtain a characteristic value corresponding to a new time sequence;
determining a control upper limit and a control lower limit according to the type of the characteristic value, and determining the sleep state of the user according to the size of the characteristic value corresponding to the new time sequence and the control upper limit or the control lower limit;
the determining of the upper control limit and the lower control limit according to the type of the characteristic value specifically includes:
determining an estimated average value and an estimated standard deviation corresponding to the characteristic value according to the type of the characteristic value, and determining a control upper limit and a control lower limit according to the estimated average value and the estimated standard deviation;
the upper control limit is defined as follows:
the lower control limit is defined as follows:
wherein, the first and the second end of the pipe are connected with each other,
representing an estimated mean value corresponding to a characteristic value>
Representing the estimation standard deviation corresponding to the characteristic value, and k represents a control parameter;
and the motor control module is used for receiving the shaking stimulation parameters and driving the shaking table to shake according to the shaking stimulation parameters.
2. The system of claim 1, wherein the pre-processing of the brain electrical signals comprises:
and amplifying, filtering and performing analog-to-digital conversion on the electroencephalogram signals in sequence.
3. The sway stimulus-based sleep regulation system of claim 1 wherein an ith state of the system in the multi-dimensional state space is represented by a column vector x i Represents, i.e.:
x i ＝(v 1+(i-1)τ ,v 2+(i-1)τ ,…,v m+(i-1)τ )，i∈1,2,…,T；
wherein m represents an embedding dimension parameter, tau represents a time delay parameter, T represents a segment of an electroencephalogram signal, and v represents an observed value corresponding to each moment;
in the recursion diagram, the recursion mode of the multi-variable brain volume electrical signal is represented by the following formula:
R i,j ＝Θ(ε-||x i -x j ||)，x i ,x j ∈R m ，i,j∈1,2,…,T；
wherein Θ () represents a Hervesaide function, ε represents a threshold value of the adjacency relation, | | | | represents a function of the distance between any two points on the quantization state space trajectory, R m A vector space representing m dimensions;
the adjacency matrix is represented by the following formula:
A i,j (ε)＝R i,j (ε)-I(T)；
wherein R is i,j The term "(ε) denotes a recursive graph whose adjacency threshold is ε, and I (T) denotes a T-dimensional identity matrix.
4. The sway stimulus based sleep regulation system of claim 1, wherein the Laplace matrix of the recursive network is represented by the following equation:
L＝D-A i,j (ε)；
wherein D represents a diagonal matrix, D = D i,j And d represents the order of the node.
5. The system as claimed in claim 1, wherein the determining the sleep state of the user according to the new time series of the corresponding characteristic values and the size of the upper control limit or the lower control limit specifically comprises:
and obtaining characteristic values corresponding to the latest k time nodes, if the characteristic values corresponding to the time nodes exceeding the preset number are all smaller than the lower control limit, judging that the user is in a deep sleep state, if the characteristic values corresponding to the time nodes exceeding the preset number are all larger than the upper control limit, judging that the user is in an awakening state, and otherwise, judging that the user is in a light sleep state.
6. The sway stimulus-based sleep regulation system of any one of claims 1-5, wherein the sway stimulus parameters include at least a sway frequency and a sway amplitude.
7. A method of sleep regulation based on shaking stimuli, comprising the steps of:
collecting electroencephalogram signals of a user, and preprocessing the electroencephalogram signals;
determining the sleep state of a user according to the preprocessed electroencephalogram signals, and determining shaking stimulation parameters according to the sleep state;
determining the sleep state of the user according to the preprocessed electroencephalogram signals, which specifically comprises the following steps:
segmenting the electroencephalogram signal through a sliding time window to obtain electroencephalogram signals of a plurality of segments;
respectively representing the state corresponding to the electroencephalogram signal of each segment through a state vector to obtain a multivariable electroencephalogram signal, and mapping the multivariable electroencephalogram signal to a multidimensional state space;
reconstructing a state space track according to the multi-dimensional state space, representing the adjacency relation among system states in the reconstructed state space track through a recursive graph, and re-interpreting each recursive graph into an adjacency matrix to construct a recursive network;
extracting global topological features from the recursive network, and determining the sleep state of the user based on the extracted global topological features;
the extracting global topological features from the recursive network and determining the sleep state of the user based on the extracted global topological features specifically include:
decomposing eigenvalues of the Laplacian matrix of the recursive network to obtain eigenvalues and eigenvectors;
connecting the obtained characteristic values by using an original time sequence, and smoothing by adopting a five-point moving average filter to obtain a characteristic value corresponding to a new time sequence;
determining a control upper limit and a control lower limit according to the type of the characteristic value, and determining the sleep state of the user according to the size of the characteristic value corresponding to the new time sequence and the control upper limit or the control lower limit;
the determining of the upper control limit and the lower control limit according to the type of the characteristic value specifically includes:
determining an estimated average value and an estimated standard deviation corresponding to the characteristic value according to the type of the characteristic value, and determining a control upper limit and a control lower limit according to the estimated average value and the estimated standard deviation;
the upper control limit is defined as follows:
the lower control limits are defined as follows:
wherein the content of the first and second substances,
represents the corresponding estimated mean value, which represents the characteristic value>
Representing an estimated standard deviation corresponding to the characteristic value, and k represents a control parameter;
and driving the shaking table to shake according to the shaking stimulation parameters.
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