CN113191203A - Multi-dimensional data storage method and device for electroencephalogram data - Google Patents
Multi-dimensional data storage method and device for electroencephalogram data Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 29
- 238000013500 data storage Methods 0.000 title claims abstract description 28
- 238000007405 data analysis Methods 0.000 claims abstract description 22
- 210000004556 brain Anatomy 0.000 claims description 31
- 238000002372 labelling Methods 0.000 claims description 20
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Abstract
The invention discloses a multi-dimensional data storage method and device for electroencephalogram data. Wherein, the method comprises the following steps: acquiring target electroencephalogram data; analyzing the target electroencephalogram data through a data analysis module; and storing the analyzed target electroencephalogram data through a multi-dimensional storage module. The method can analyze and store the electroencephalogram data more intuitively, and has the beneficial effect that a user can extract the needed electroencephalogram data more conveniently.
Description
Technical Field
The invention relates to the field of data storage, in particular to a brain wave data-oriented multi-dimensional data storage method and device.
Background
The current method for storing and processing electroencephalogram data is to store original data, extract the data when determining what data is needed, and analyze, process and screen the data. The electroencephalogram data storage system/software is relatively simple and closed, the intelligent level is low, and more data can be extracted only by a professional through relatively complex operation; when the data volume is large, the data is easy to forget; in addition, analyzing data that was not processed at the time of storage also complicates later analysis; finally, the current electroencephalogram data formats are not unified, format unification processing is not performed on the electroencephalogram data before storage, great inconvenience is brought to existing integrated storage of the electroencephalogram data, and the utilization rate of electroencephalogram resources for users is low.
Disclosure of Invention
The invention aims to provide a brain wave data-oriented multi-dimensional data storage method and system, so that brain wave data analysis can be performed more intuitively, and a user can extract needed brain wave data more conveniently.
In order to solve the technical problem, the invention provides a multi-dimensional data storage method for electroencephalogram data, which comprises the following steps:
acquiring target electroencephalogram data;
analyzing the target electroencephalogram data through a data analysis module;
and storing the analyzed target electroencephalogram data through a multi-dimensional storage module.
Further, the step of analyzing the target electroencephalogram data through a data analysis module comprises the following steps:
carrying out format conversion on the target electroencephalogram data;
determining a plurality of dimensional data included in the target electroencephalogram data after format conversion;
and carrying out classification and labeling on the multiple dimensional data included in the target electroencephalogram data to obtain classification and labeling of the multiple dimensional data included in the target electroencephalogram data.
Further, the step of classifying and labeling the multiple dimension data included in the target electroencephalogram data comprises the following steps:
determining dimension labels corresponding to a plurality of dimension data included in the target electroencephalogram data;
matching the dimension labels respectively corresponding to a plurality of dimension data included in the target electroencephalogram data with the labels respectively corresponding to a plurality of preset cubic models;
and determining classification labels to which the multiple dimensional data included in the target electroencephalogram data belong according to the matching result.
Further, the step of determining the dimension labels corresponding to the plurality of dimension data included in the target electroencephalogram data includes:
determining dimension parameters respectively corresponding to a plurality of dimension data included in the target electroencephalogram data based on a preset machine learning algorithm model;
if the dimension parameters respectively corresponding to any dimension data included in the target electroencephalogram data fall into the dimension parameter range of any determined dimension label, the determined dimension label is used as the dimension label of any dimension data included in the target electroencephalogram data.
Further, the step of classifying and labeling the multiple dimension data included in the target electroencephalogram data further includes:
if the dimension of any dimension data included in the target electroencephalogram data is a disease type or a task state, obtaining classification labels of the artificial labels aiming at any dimension data based on a preset interface.
Further, the step of storing the analyzed target electroencephalogram data through a multi-dimensional storage module comprises the following steps:
and storing the plurality of dimensional data included by the target electroencephalogram data to positions corresponding to the cubic models conforming to the label coordinate systems according to the classification labels to which the plurality of dimensional data included by the target electroencephalogram data belong, wherein different label coordinate systems correspond to different cubic models.
Further, according to the classification labels to which the plurality of dimensional data included in the target electroencephalogram data belong, the step of storing the plurality of dimensional data included in the target electroencephalogram data to the positions corresponding to the cubic model conforming to the label coordinate system includes:
identifying whether the dimension label of any dimension data included in the target electroencephalogram data is similar to or completely consistent with any existing dimension label or not through a dimension label identification formula;
if so, classifying any dimension data included in the target brain electrical data into the data of the similar or completely consistent dimension label, and storing the data.
The invention also provides a multi-dimensional data storage device for electroencephalogram data, which comprises:
the data acquisition module is used for acquiring target electroencephalogram data;
the data analysis module is used for analyzing the target electroencephalogram data through the data analysis module;
and the data storage module is used for storing the analyzed target electroencephalogram data through the multi-dimensional storage module.
According to the electroencephalogram data-oriented multi-dimensional data storage method and system, electroencephalogram data of different formats are uniformly converted and analyzed, and data are stored in a multi-dimensional form, so that electroencephalogram data analysis is performed more intuitively, and a user can extract needed electroencephalogram data more conveniently.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
FIG. 1 is a flow chart of a multi-dimensional data storage method for electroencephalogram data according to the present invention;
FIG. 2 is a flowchart illustrating an application of an embodiment of a method for storing multi-dimensional data for electroencephalogram data according to the present invention;
FIG. 3 is a schematic diagram of a multi-dimensional data analysis module for electroencephalogram data according to the present invention;
fig. 4 is a schematic diagram of a multi-dimensional data storage module for electroencephalogram data provided by the present invention.
Fig. 5 is a block diagram structural diagram of a multi-dimensional data storage device for electroencephalogram data provided by the present invention.
Detailed Description
The core of the invention is to provide a brain wave data-oriented multi-dimensional data storage method and system to detect the dream of a user, identify the content of the dream and guide and adjust the user's dream.
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, a flow chart of a method for storing multi-dimensional data oriented to electroencephalogram data according to the present invention is shown. The method comprises the following steps: step S101, step S102, and step S103.
Step S101: acquiring target electroencephalogram data;
step S102: analyzing the target electroencephalogram data through a data analysis module;
step S103: and storing the analyzed target electroencephalogram data through a multi-dimensional storage module.
In particular, before applying the method provided herein, reference may be made to fig. 2. The registration part needs to be filled with personal information by a user and is used for logging in a brain wave multi-dimensional storage system; in addition, it is also necessary to perform operations such as importing data into the collected target data with the user's personal authority.
Referring to fig. 3, fig. 3 is a schematic diagram of a multidimensional data analysis module for electroencephalogram data according to the present invention. The data analysis of the embodiment can include format conversion of the target electroencephalogram data, and uniform conversion of electroencephalogram data in different formats into the same file format. For example, the data collected by the electroencephalogram collecting equipment at present and on the market are.fif,. edf,. mat, etc., and the formats of the electroencephalogram data are uniformly converted into the.edf files through the storage system of the invention;
the analysis of the data further comprises the classification and labeling of all the dimension labels on the target electroencephalogram data. In an embodiment, the electroencephalogram data dimension can be divided into 6 dimensions of time, channels, brain areas, disease types, task states, consciousness states and the like. Dimension labeling is performed on the data, such as labeling the data of a first channel as 0, labeling the data of a second channel as 1, labeling the data of a third channel as 2 … …, and so on, and labeling the data of other dimensions. And constructing a complete brain electrical data cubic model and a corresponding coordinate system according to the label levels of the 6 dimensions. In the classification and labeling process, the standard head model electrode coordinates of an international electroencephalogram 10-20 system and a 10-10 system are preferably used for automatically identifying and classifying two dimensions of a data channel and a brain area, and machine learning algorithm models such as a support vector machine and a random forest are preferably used for automatically identifying the consciousness state dimension of the electroencephalogram data.
It should be noted that, for the disease type and task state dimension, it is preferable to use manual labeling, and combine manual and intelligent labeling to maximize the efficiency and accuracy of data dimension labeling classification.
Referring to fig. 4, fig. 4 is a schematic diagram of a multi-dimensional data storage module for electroencephalogram data according to the present invention. And the storage module identifies and judges the dimension label level of each input electroencephalogram data according to the classification and marking result of the data on each dimension, and finally stores the data to a specific cubic model position conforming to a label coordinate system.
In this embodiment, the calculation formula of the dimension label identification classification is as follows:
If the dimension label of the electroencephalogram data is identified to be similar to or completely consistent with the existing dimension label through the calculation formula, the electroencephalogram data is identified and classified as the data of the dimension; the invention can identify all dimension labels of each piece of input electroencephalogram data one by one and match the labels of the existing cubic model one by one, thereby realizing the multi-dimension storage of the data. And storing according to the digital label on each piece of data during storage, and storing the source data according to the time dimension. The intersection of each dimension is a corresponding one of the multi-dimensional cubic memory blocks, and thus, in the multi-dimensional data storage system, the multi-dimensional data storage system is composed of a plurality of these cubic memory blocks.
When a user extracts data, the extraction target data set is specified according to the target dimension level characteristics, for example, the user wants to extract frontal lobe (brain area dimension) electroencephalogram data characteristics of a schizophrenic patient (disease type dimension) in a resting state (task type dimension), and at the moment, corresponding electroencephalogram data can be accurately extracted by simultaneously setting the levels of the dimensions.
The invention also provides a block diagram structure schematic diagram of the brain wave data-oriented multi-dimensional data storage device. As shown in fig. 5, the apparatus includes: a data acquisition module 501, a data analysis module 502 and a data storage module.
A data obtaining module 501, configured to obtain target electroencephalogram data;
the data analysis module 502 is used for analyzing the target electroencephalogram data through the data analysis module;
and the data storage module 503 is configured to store the analyzed target electroencephalogram data through the multidimensional storage module.
The brain wave data storage device oriented to the brain wave data provided by the invention uniformly converts and analyzes the brain wave data with different formats and stores the data in a multi-dimensional form, so that the brain wave data analysis is more intuitively carried out, and a user can more conveniently extract the required brain wave data.
Further, the data analysis module includes:
the data format conversion submodule is used for carrying out format conversion on the target electroencephalogram data;
the dimension data determining submodule is used for determining a plurality of dimension data included in the target electroencephalogram data after format conversion;
the first labeling determination submodule is used for performing classification labeling on a plurality of dimensional data included in the target electroencephalogram data so as to obtain classification labels to which the plurality of dimensional data included in the target electroencephalogram data belong.
Further, the first annotation determination sub-module includes:
the label determining unit is used for determining dimension labels corresponding to a plurality of dimension data included in the target electroencephalogram data;
the label matching unit is used for matching the dimension labels respectively corresponding to the plurality of dimension data included in the target electroencephalogram data with the labels respectively corresponding to the plurality of preset cubic models;
and the label determining unit is used for determining classification labels of the multiple pieces of dimensional data included in the target electroencephalogram data according to the matching result.
Further, the tag determination unit includes:
the parameter determining subunit is used for determining dimension parameters corresponding to a plurality of dimension data included in the target electroencephalogram data based on a preset machine learning algorithm model;
and the label determining subunit is configured to, if the dimension parameter respectively corresponding to any one of the dimension data included in the target electroencephalogram data falls within the range of the dimension parameter of any one of the determined dimension labels, use the any one of the determined dimension labels as the dimension label of any one of the dimension data included in the target electroencephalogram data.
Further, the data analysis module further comprises:
and the second label determining submodule is used for acquiring classification labels of the artificial labels aiming at any dimension data based on a preset interface if the dimension to which any dimension data included in the target electroencephalogram data belongs is a disease type or a task state.
Further, the data storage module includes:
and the multi-dimension storage sub-module is used for storing the multi-dimension data included in the target electroencephalogram data to the corresponding positions of the cubic models conforming to the label coordinate system according to the classification labels of the multi-dimension data included in the target electroencephalogram data, wherein different label coordinate systems correspond to different cubic models.
Further, the multi-dimensional storage submodule includes:
the identification unit is used for identifying whether the dimension label of any dimension data included in the target electroencephalogram data is similar to or completely consistent with any existing dimension label or not through a dimension label identification formula;
and the storage unit is used for classifying any dimension data included in the target electroencephalogram data into the data of the similar or completely consistent dimension label and storing the data if the dimension data is the similar or completely consistent dimension label.
Therefore, the brain wave data in different formats are uniformly converted and analyzed, and the data are stored in a multi-dimensional form, so that the brain wave data analysis is more intuitively realized, and a user can more conveniently and rapidly extract the required brain wave data.
For the introduction of the brain wave data-oriented multi-dimensional data storage system provided by the present invention, reference is made to the foregoing embodiment of the brain wave data-oriented multi-dimensional data storage method, and the embodiments of the present invention are not described herein again.
The embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same or similar parts among the embodiments are referred to each other.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The method and the system for storing the multi-dimensional data facing the electroencephalogram data provided by the invention are described in detail above. The principles and embodiments of the present invention are explained herein using specific examples, which are presented only to assist in understanding the method and its core concepts. It should be noted that, for those skilled in the art, it is possible to make various improvements and modifications to the present invention without departing from the principle of the present invention, and those improvements and modifications also fall within the scope of the claims of the present invention.
Claims (8)
1. A brain wave data-oriented multidimensional data storage method is characterized by comprising the following steps:
acquiring target electroencephalogram data;
analyzing the target electroencephalogram data through a data analysis module;
and storing the analyzed target electroencephalogram data through a multi-dimensional storage module.
2. The method of claim 1, wherein said analyzing said target brain electrical data by a data analysis module comprises:
carrying out format conversion on the target electroencephalogram data;
determining a plurality of dimensional data included in the target electroencephalogram data after format conversion;
and carrying out classification and labeling on the multiple dimensional data included in the target electroencephalogram data to obtain classification and labeling of the multiple dimensional data included in the target electroencephalogram data.
3. The method of claim 2, wherein the step of performing classification labeling on the plurality of dimensional data included in the target brain electrical data comprises:
determining dimension labels corresponding to a plurality of dimension data included in the target electroencephalogram data;
matching the dimension labels respectively corresponding to a plurality of dimension data included in the target electroencephalogram data with the labels respectively corresponding to a plurality of preset cubic models;
and determining classification labels to which the multiple pieces of dimensional data included in the target electroencephalogram data belong according to the matching result.
4. The method of claim 3, wherein the step of determining the dimension labels corresponding to the plurality of dimension data respectively included in the target brain electrical data comprises:
determining dimension parameters respectively corresponding to a plurality of dimension data included in the target electroencephalogram data based on a preset machine learning algorithm model;
if the dimension parameters respectively corresponding to any dimension data included in the target electroencephalogram data fall into the dimension parameter range of any determined dimension label, the determined dimension label is used as the dimension label of any dimension data included in the target electroencephalogram data.
5. The method of claim 2, wherein the step of performing classification labeling on the plurality of dimensional data included in the target brain electrical data further comprises:
if the dimension of any dimension data included in the target electroencephalogram data is a disease type or a task state, obtaining classification labels of the artificial labels aiming at any dimension data based on a preset interface.
6. The method of claim 2, wherein the step of storing the analyzed target brain electrical data via a multi-dimensional storage module comprises:
and storing the plurality of dimensional data included by the target electroencephalogram data to positions corresponding to the cubic models conforming to the label coordinate system according to the classification labels to which the plurality of dimensional data included by the target electroencephalogram data belong, wherein different label coordinate systems correspond to different cubic models.
7. The method of claim 6, wherein the step of storing the plurality of dimensional data included in the target brain electrical data to a position corresponding to a cubic model conforming to a label coordinate system according to the classification label to which the plurality of dimensional data included in the target brain electrical data belong comprises:
identifying whether the dimension label of any dimension data included in the target electroencephalogram data is similar to or completely consistent with any existing dimension label or not through a dimension label identification formula;
if so, classifying any dimension data included in the target electroencephalogram data into the data of the similar or completely consistent dimension label, and storing.
8. A multi-dimensional data storage device for brain wave data, comprising:
the data acquisition module is used for acquiring target electroencephalogram data;
the data analysis module is used for analyzing the target electroencephalogram data through the data analysis module;
and the data storage module is used for storing the analyzed target electroencephalogram data through the multi-dimensional storage module.
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| US20200387489A1 (en) * | 2018-02-22 | 2020-12-10 | Beijing Didi Infinity Technology And Development Co., Ltd. | Systems and methods for data storage and querying |
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| CN107967572A (en) * | 2017-12-15 | 2018-04-27 | 华中师范大学 | A kind of intelligent server based on education big data |
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