CN106388779B - The mask method and system of sleep state sample data type - Google Patents

The mask method and system of sleep state sample data type Download PDF

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CN106388779B
CN106388779B CN201610843519.5A CN201610843519A CN106388779B CN 106388779 B CN106388779 B CN 106388779B CN 201610843519 A CN201610843519 A CN 201610843519A CN 106388779 B CN106388779 B CN 106388779B
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sample data
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atom
sleep state
sleep
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CN106388779A (en
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赵巍
胡静
韩志
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Guangzhou Shiyuan Electronics Thecnology Co Ltd
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    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/24Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316Modalities, i.e. specific diagnostic methods
    • A61B5/369Electroencephalography [EEG]
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/24Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316Modalities, i.e. specific diagnostic methods
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/48Other medical applications
    • A61B5/4806Sleep evaluation
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/48Other medical applications
    • A61B5/4806Sleep evaluation
    • A61B5/4809Sleep detection, i.e. determining whether a subject is asleep or not
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/48Other medical applications
    • A61B5/4806Sleep evaluation
    • A61B5/4815Sleep quality
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Measuring for diagnostic purposes; Identification of persons
    • A61B5/72Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235Details of waveform analysis
    • A61B5/7264Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems

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Abstract

The present invention relates to the mask methods and system of a kind of sleep state sample data type, the described method includes: sample data of the acquisition user in sleep state analysis, cluster center made of the feature vector and feature vector aggregation of the sample data of a variety of sleep state types is constructed, objective function is established according to feature vector and its cluster center;Wherein, the sample data of objective function characterization minimum same type is at a distance from dictionary atom, and the distance between different types of atom of maximization;It selects several feature vectors as the initial value of atom from the sample data of a variety of sleep state types respectively, each sample data is distributed to the atom and solves the objective function, obtains classifying dictionary;Sample data is inputted into classifying dictionary, compares the type and distance of the atom nearest with sample data, if distance is less than preset threshold value, the type of the sample data is labeled as consistent with the type of the atom.The present invention can accurately mark the type of sample data.

Description

The mask method and system of sleep state sample data type
Technical field
The present invention relates to assisting sleep technical fields, more particularly to a kind of mark side of sleep state sample data type Method and system.
Background technique
In sleep, human body has carried out the process self loosened and restored, therefore good sleep is to maintain health A primary condition;But due to operating pressure is big, daily life system is irregular etc., result in the sleep matter of part population It measures not good enough, shows as that insomnia, midnight wakes up with a start.
There are some equipment that people is helped to fall asleep on the market at present, has improved sleep quality.Such as it specific is slept a certain By manual interventions such as sound, optical signals under dormancy state, avoid waking user etc. under the state of sleeping soundly.For setting for assisting sleep For standby, in order to be really achieved the purpose for improving user's sleep quality, it is extremely important for correctly detecting the sleep state of user 's.
At present clinically mainly using polysomnogram identify sleep state, mainly using EEG signals come to sleep into Row analysis, identifies the sleep state of measured by training sleep state model, such as judges which of sleep user be in Stage, but since the specificity of EEG signals is stronger, and intensity is very weak easy by external interference.Point of prior art training There are errors for detection of the class device to many users, it is difficult to be labeled to the type of sleep state sample data.
Summary of the invention
Based on this, it is necessary in view of the above-mentioned problems, providing a kind of mask method of sleep state sample data type and being System effectively improves the accuracy of sleep state classification device identification.
A kind of mask method of sleep state sample data type, comprising:
The EEG signals that acquisition user generates in sleep state analysis, obtain sample data;
Cluster center made of the feature vector and feature vector aggregation of the sample data of a variety of sleep state types is constructed, Objective function is established according to described eigenvector and its cluster center;Wherein, the objective function characterization minimizes same type Sample data is at a distance from dictionary atom, and the distance between different types of atom of maximization;
Select several feature vectors as the initial value of atom from the sample data of a variety of sleep state types respectively, Each sample data is distributed to the atom and solves the objective function, obtains classifying dictionary;
Sample data is inputted into classifying dictionary, compares the type and distance of the atom nearest with sample data, if apart from small In preset threshold value, then the type of the sample data is labeled as consistent with the type of the atom.
A kind of labeling system of sleep state sample data type, comprising:
Sample collection module, the EEG signals generated in sleep state analysis for acquiring user, obtains sample data;
Dictionary constructs module, the feature vector and feature vector of the sample data for constructing a variety of sleep state types Objective function is established according to described eigenvector and its cluster center in cluster center made of aggregation;Wherein, the objective function characterization The distance between the sample data of same type is minimized at a distance from dictionary atom, and maximizes different types of atom;
Dictionary training module, for selecting several feature vectors from the sample data of a variety of sleep state types respectively As the initial value of atom, each sample data is distributed to the atom and solves the objective function, obtains classifying dictionary;
Sample labeling module compares the class of the atom nearest with sample data for sample data to be inputted classifying dictionary The type of the sample data is labeled as consistent with the type of the atom by type and distance if distance is less than preset threshold value.
The mask method and system of above-mentioned sleep state sample data type, based on the clustering algorithm with classification capacity come Training dictionary, atom respectively corresponds a kind of sleep state in dictionary, using the number of atom as the parameter of algorithm, by most when training The sample of smallization same type is at a distance from dictionary atom, while the mode for the distance between maximizing different types of atom, To train corresponding atom for every kind of sleep state, then utilizes and correspond to different types of atom and distance to sample Type is judged, so as to accurately mark the type of sample data, so that being subsequently used for oneself of sleep state classification device More accurate sleep state classification device can be trained in learning process, promote the accuracy of sleep state detection.
Detailed description of the invention
Fig. 1 is the flow chart of the mask method of the sleep state sample data type of one embodiment;
Fig. 2 is the labeling system structural schematic diagram of the sleep state sample data type of one embodiment.
Specific embodiment
The mask method of sleep state sample data type of the invention and the embodiment of system are illustrated with reference to the accompanying drawing.
Refering to what is shown in Fig. 1, Fig. 1 is the flow chart of the mask method of the sleep state sample data type of one embodiment, Include:
Step S101, the EEG signals that acquisition user generates in sleep state analysis, obtains sample data;
In this step, when carrying out assisting sleep analysis to user, related transducer equipment is worn by user, detects user EEG signals, when acquiring EEG signals, can with 30s be a frame be acquired.
The carrying out sleep state identification as needed of the task, determines feature data types, extracts therewith from EEG signals Corresponding sample data;For example, to identify 1~N kind sleep state, the sample data for carrying out this N kind state recognition is extracted.
Step S102, the feature vector and feature vector for constructing the sample data of a variety of sleep state types are assembled Cluster center, objective function is established according to described eigenvector and its cluster center;Wherein, the objective function characterization minimizes phase The sample data of same type is at a distance from dictionary atom, and the distance between different types of atom of maximization;
In this step, on the basis of KMeans (K mean value) and KNN (K is closest) algorithm, designing has classification capacity Clustering algorithm carrys out training dictionary, and atom respectively corresponds a kind of sleep state (such as waking state, sleep state etc.), atom in dictionary Number be algorithm parameter.
When input sample is sufficiently small at a distance from atom (when similarity is sufficiently large), it may be considered that the type of sample and original The type of son is consistent;Establish objective function, training when by minimize same type sample and dictionary atom away from From, while the mode for the distance between maximizing different types of atom, all to be trained accordingly for every kind of particular state Atom.
If it is more classification problems, the sample equipped with a shared t seed type,Be characterized to Amount,For cluster center made of feature vector aggregation, the common version of objective function can be expressed as Following form:
In formula, the sample data equipped with t kind sleep state type,For feature vector,For cluster center made of feature vector aggregation.
By taking waking state and dormant two classes problem as an example, ifFor waking state type (wake) Feature vector,Cluster center made of feature vector aggregation for waking state type,For The feature vector of sleep state type (sleep),Cluster made of feature vector aggregation for sleep state type Center, objective function can be expressed as form:
The objective function are as follows:
In formula,For regain consciousness type feature vector,For the spy for type of regaining consciousness Cluster center made of vector aggregation is levied,For the feature vector of sleep pattern,To sleep Cluster center made of the feature vector aggregation of dormancy type, wake indicate awake type, and sleep indicates sleep pattern.
Step S103 selects several feature vectors as atom from the sample data of a variety of sleep state types respectively Initial value, each sample data is distributed to the atom and solves the objective function, obtains classifying dictionary;
In this step, it is based on the objective function, training classifying dictionary, in training, this programme is in classical KMeans It is improved on the basis of algorithm, by taking waking state and dormant two classification problem as an example, training process be can be such that
(1) when initializing, if being set at random from the sample data of awake type and the sample data of sleep pattern respectively Dry feature vector is as atom;Each sample data is distributed to away from nearest atom;
(2) atom is updated, if it (is waking state that all sample datas for belonging to this atom are consistent with the type of atom Type is sleep state type), then the mean value for belonging to all sample datas of the atom is calculated, and in this, as new Atom;
The sample data inconsistent with atomic type if it exists then calculates separately the sample data and sleep class of awake type The mean value of the sample data of type, calculating process may include following formula:
In formula, c'wakeFor the mean value of the sample data for type of regaining consciousness, c'sleepFor the mean value of the sample data of sleep pattern;
According to the quantity of the sample data (negative sample) inconsistent with atomic type and its position of position correction atom, By the location updating of atom in the farther position apart from negative sample data, calculating process may include following formula:
In formula, c is the position of atom after amendment, and g is discriminant function, and w is weighted value;
Further, the calculation formula of the weighted value w can be such that
In formula, wwakeFor the weighted value for type of regaining consciousness, wsleepFor the weighted value of sleep pattern.
As another embodiment, the calculation formula of the weighted value w also be can be such that
(3) if belong to this atom all sample datas and atom type it is inconsistent, change the class of the atom Type, and the mean value for belonging to all sample datas of the atom is calculated, and using the mean value as new atom;
(4) it repeats step (2) and (3) is iterated, the difference of the atom before and after iteration is less than setting range (foot It is enough small), or be assigned without sample data and store current classifying dictionary to the new atomic time and exit training.
Sample data is inputted classifying dictionary by step S104, compares the type and distance of the atom nearest with sample data, If distance is less than preset threshold value, the type of the sample data is labeled as consistent with the type of the atom.
In this step, classifying dictionary is tested using sample data, by comparing the atom nearest with sample data Type and distance carry out the types of judgement sample data, if distance is less than threshold value, then it is assumed that the type of sample data and the atom Type it is consistent, export the judgement of "true", the type of the sample data is labeled as it is consistent with the type of the atom, it is on the contrary then refuse Judgement absolutely.
The type that sample data is marked using above scheme.It can be used for training classifier, for example, svm is selected (to support Vector machine) classifier, neural metwork training disaggregated model.
Refering to what is shown in Fig. 2, Fig. 2 is the labeling system structural representation of the sleep state sample data type of one embodiment Figure, comprising:
Sample collection module 101, the EEG signals generated in sleep state analysis for acquiring user, obtains sample number According to;
Dictionary constructs module 102, the feature vector and feature of the sample data for constructing a variety of sleep state types Cluster center made of vector aggregation, establishes objective function according to described eigenvector and its cluster center;Wherein, the objective function Characterization minimizes the sample data of same type with dictionary atom at a distance from, and between the different types of atom of maximization away from From;
Dictionary training module 103, for selecting several features from the sample data of a variety of sleep state types respectively Each sample data is distributed to the atom and solves the objective function, classified by initial value of the vector as atom Dictionary;
Sample labeling module 104 compares the atom nearest with sample data for sample data to be inputted classifying dictionary The type of the sample data is labeled as consistent with the type of the atom by type and distance if distance is less than preset threshold value.
The labeling system of sleep state sample data type of the invention and sleep state sample data type of the invention Mask method correspond, above-mentioned sleep state sample data type mask method embodiment illustrate technical characteristic And its advantages are suitable for the embodiment of the labeling system of sleep state sample data type, hereby give notice that.
Each technical characteristic of embodiment described above can be combined arbitrarily, for simplicity of description, not to above-mentioned reality It applies all possible combination of each technical characteristic in example to be all described, as long as however, the combination of these technical characteristics is not deposited In contradiction, all should be considered as described in this specification.
The embodiments described above only express several embodiments of the present invention, and the description thereof is more specific and detailed, but simultaneously It cannot therefore be construed as limiting the scope of the patent.It should be pointed out that coming for those of ordinary skill in the art It says, without departing from the inventive concept of the premise, various modifications and improvements can be made, these belong to protection of the invention Range.Therefore, the scope of protection of the patent of the invention shall be subject to the appended claims.

Claims (10)

1. a kind of mask method of sleep state sample data type characterized by comprising
The EEG signals that acquisition user generates in sleep state analysis, obtain sample data;
Cluster center made of the feature vector and feature vector aggregation of the sample data of a variety of sleep state types is constructed, according to Objective function is established at described eigenvector and its cluster center;Wherein, the objective function is used to minimize the sample of same type Data are at a distance from dictionary atom and the distance between maximize different types of atom;
Select several feature vectors as the initial value of atom from the sample data of a variety of sleep state types respectively, it will be each A sample data distributes to the atom and solves the objective function, obtains classifying dictionary;
Sample data is inputted into classifying dictionary, compares the type and distance of the atom nearest with sample data, if distance is less than in advance If threshold value, then the type of the sample data is labeled as consistent with the type of the atom.
2. the mask method of sleep state sample data type according to claim 1, which is characterized in that the target letter Number are as follows:
In formula, the sample data equipped with t kind sleep state type,For feature vector,For cluster made of feature vector aggregation Center, wherein i=1 ..., np, j=1 ... kp, p=1 ..., t.
3. the mask method of sleep state sample data type according to claim 2, which is characterized in that the sleep shape State type includes awake type and sleep pattern;
The objective function are as follows:
In formula,For the feature vector for type of regaining consciousness, i=1 ..., nwake,For regain consciousness type feature vector aggregation and At cluster center, j=1 ..., kwake,For the feature vector of sleep pattern, i=1 ..., nsleep,For sleep pattern Feature vector aggregation made of cluster center, j=1 ..., ksleep, the awake type of wake expression, sleep expression sleep pattern.
4. the mask method of sleep state sample data type according to claim 2, which is characterized in that it is described respectively from Select several feature vectors as the initial value of atom in the sample data of a variety of sleep state types, by each sample data It distributes to the atom and the step of solving the objective function, obtain classifying dictionary and includes:
(1) several feature vectors work is set at random from the sample data of awake type and the sample data of sleep pattern respectively For atom;Each sample data is distributed to away from nearest atom;
(2) if all sample datas for belonging to this atom are consistent with the type of atom, all samples for belonging to the atom are calculated The mean value of notebook data, and in this, as new atom;
The sample data inconsistent with atomic type if it exists then calculates separately the sample data and sleep pattern of awake type The mean value of sample data;And according to the quantity of the sample data inconsistent with atomic type and its position of position correction atom, By the location updating of atom in the farther position apart from negative sample data;
(3) if belong to this atom all sample datas and atom type it is inconsistent, change the type of the atom, and The mean value for belonging to all sample datas of the atom is calculated, and using the mean value as new atom;
(4) it repeats step (2) and (3) is iterated, the difference of the atom before and after iteration is less than setting range, or does not have There is sample data to be assigned to store current classifying dictionary to the new atomic time and exit training.
5. the mask method of sleep state sample data type according to claim 4, which is characterized in that the step (2) falling into a trap, to settle the sample data of awake type and the mean value process of sample data of sleep pattern include following formula:
In formula, c'wakeFor the mean value of the sample data for type of regaining consciousness, c'sleepFor the mean value of the sample data of sleep pattern.
6. the mask method of sleep state sample data type according to claim 4, which is characterized in that the step (2) position of amendment atom includes following formula in:
In formula, c is the position of atom after amendment, and g is discriminant function, and w is weighted value.
7. the mask method of sleep state sample data type according to claim 6, which is characterized in that the weighted value The calculation formula of w is as follows:
In formula, wwakeFor the weighted value for type of regaining consciousness, wsleepFor the weighted value of sleep pattern.
8. the mask method of sleep state sample data type according to claim 6, which is characterized in that the weighted value The calculation formula of w is as follows:
9. the mask method of sleep state sample data type according to claim 3, which is characterized in that described by the sample The type of notebook data is labeled as the step consistent with the type of the atom
According to the atomic type of classifying dictionary, the sample data of awake type and the sample data of sleep pattern are identified, then Sample data is marked into corresponding data type.
10. a kind of labeling system of sleep state sample data type characterized by comprising
Sample collection module, the EEG signals generated in sleep state analysis for acquiring user, obtains sample data;
Dictionary constructs module, for constructing the feature vector and feature vector aggregation of the sample data of a variety of sleep state types Made of cluster center, objective function is established according to described eigenvector and its cluster center;Wherein, the objective function is for minimum The distance between change the sample data of same type at a distance from dictionary atom and maximize different types of atom;
Dictionary training module, for selected from the sample data of a variety of sleep state types respectively several feature vectors as Each sample data is distributed to the atom and solves the objective function, obtains classifying dictionary by the initial value of atom;
Sample labeling module, for sample data to be inputted classifying dictionary, compare the type of the atom nearest with sample data with The type of the sample data is labeled as consistent with the type of the atom by distance if distance is less than preset threshold value.
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