CN115153589A - Method, system, electronic device and storage medium for recording electroencephalogram signal data - Google Patents
Method, system, electronic device and storage medium for recording electroencephalogram signal data Download PDFInfo
- Publication number
- CN115153589A CN115153589A CN202210878795.0A CN202210878795A CN115153589A CN 115153589 A CN115153589 A CN 115153589A CN 202210878795 A CN202210878795 A CN 202210878795A CN 115153589 A CN115153589 A CN 115153589A
- Authority
- CN
- China
- Prior art keywords
- electroencephalogram
- signal
- recorded
- electroencephalogram signal
- value
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/369—Electroencephalography [EEG]
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/369—Electroencephalography [EEG]
- A61B5/372—Analysis of electroencephalograms
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
- A61B5/7267—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
Landscapes
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Surgery (AREA)
- Public Health (AREA)
- Pathology (AREA)
- Artificial Intelligence (AREA)
- Biomedical Technology (AREA)
- Heart & Thoracic Surgery (AREA)
- Medical Informatics (AREA)
- Molecular Biology (AREA)
- Psychiatry (AREA)
- Animal Behavior & Ethology (AREA)
- General Health & Medical Sciences (AREA)
- Biophysics (AREA)
- Veterinary Medicine (AREA)
- Psychology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Physiology (AREA)
- Signal Processing (AREA)
- Evolutionary Computation (AREA)
- Fuzzy Systems (AREA)
- Mathematical Physics (AREA)
- Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)
Abstract
The application provides a method, a system, an electronic device and a storage medium for recording electroencephalogram signal data, comprising: acquiring an electroencephalogram signal to be recorded in target electroencephalogram data, wherein the electroencephalogram signal to be recorded is an electroencephalogram signal with nerve impulses; counting the value of the electroencephalogram signal to be recorded, and acquiring the electroencephalogram signal characteristics of the target electroencephalogram data; according to the electroencephalogram signal characteristics, carrying out classification marking on the electroencephalogram signals to be recorded, wherein the classification comprises the following steps: normal electroencephalogram signals and abnormal electroencephalogram signals; and carrying out classified recording on the normal electroencephalogram signals and/or the abnormal electroencephalogram signals. The electroencephalogram signals are divided into normal electroencephalogram signals and abnormal electroencephalogram signals according to the electroencephalogram graphs, corresponding index addresses are recorded for different types of electroencephalogram signals, and retrieval time of the electroencephalogram signals is greatly shortened.
Description
Technical Field
The application belongs to the field of electroencephalogram, and particularly relates to a method and a system for recording electroencephalogram signal data, an electronic device and a storage medium.
Background
At present, the electroencephalogram signals are mainly recorded in a multi-lead signal mode, the number of signal channels and the recording time of the multi-lead signal are limited, original data are recorded mostly, the amount of recorded data is small, the data size of data analysis is small, data required by data traversing mode is searched mostly when the data are searched, and the data are not searched in an indexing mode.
The searching time of searching the electroencephalogram signal in the traversal mode is greatly influenced by the data volume, the searching time is short when the data volume is small, the searching time can be accepted, and if the data volume is increased, the time for searching the required data is increased, and a large amount of resources are occupied.
Disclosure of Invention
The embodiment of the invention mainly aims to provide a method, a system, electronic equipment and a storage medium for recording electroencephalogram signal data, so that the electroencephalogram signals are divided into normal electroencephalogram signals and abnormal electroencephalogram signals according to electroencephalogram graphs, corresponding index addresses are recorded for different types of electroencephalogram signals respectively, and the retrieval time of the electroencephalogram signals is greatly shortened.
In a first aspect, a method of electroencephalographic signal data recording is provided, the method comprising:
acquiring an electroencephalogram signal to be recorded in target electroencephalogram data, wherein the electroencephalogram signal to be recorded is an electroencephalogram signal with nerve impulses;
counting the value of the electroencephalogram signal to be recorded, and acquiring the electroencephalogram signal characteristics of the target electroencephalogram data;
according to the electroencephalogram signal characteristics, carrying out classification marking on the electroencephalogram signals to be recorded, wherein the classification comprises the following steps: normal electroencephalogram signals and abnormal electroencephalogram signals;
and carrying out classified recording on the normal electroencephalogram signals and/or the abnormal electroencephalogram signals.
In a possible implementation manner, the classifying and recording the normal electroencephalogram signal and/or the abnormal electroencephalogram signal includes:
if the electroencephalogram signal to be recorded is a normal electroencephalogram signal, recording the starting time of the normal electroencephalogram signal, the frequency of a wave crest/wave trough, the duration and an index address; and the number of the first and second groups,
and if the electroencephalogram signal to be recorded is an abnormal electroencephalogram signal, recording the starting time of the abnormal electroencephalogram signal, the frequency of a wave crest or a wave trough, the duration and the index address.
In another possible implementation manner, the counting the value of the electroencephalogram signal to be recorded, and obtaining the characteristics of the electroencephalogram signal of the target electroencephalogram data includes:
counting the number of wave crests and/or wave troughs in the electroencephalogram signal to be recorded within a set time period, and acquiring the numerical value of each wave crest and/or wave trough;
and acquiring an average peak value and/or an average trough value according to the number and the value.
In another possible implementation manner, the classifying and marking the electroencephalogram signal to be recorded according to the characteristics of the electroencephalogram signal includes:
acquiring a peak value difference and/or a trough value difference between a peak value and/or a trough value of the electroencephalogram signal to be recorded and the average peak value and/or the average trough value;
if the absolute value of the wave peak value difference and/or the wave trough value difference is located outside the threshold range, the electroencephalogram signal to be recorded is an abnormal electroencephalogram signal; and if the absolute value of the wave peak value difference and/or the wave trough value difference is within a threshold range, the electroencephalogram signal to be recorded is a normal electroencephalogram signal.
In a second aspect, a system for electroencephalographic signal data recording is provided, the system comprising:
the electroencephalogram signal to be recorded acquisition module is used for acquiring an electroencephalogram signal to be recorded in target electroencephalogram data, wherein the electroencephalogram signal to be recorded is an electroencephalogram signal with nerve impulses;
the electroencephalogram signal characteristic acquisition module is used for counting the value of the electroencephalogram signal to be recorded and acquiring the electroencephalogram signal characteristic of the target electroencephalogram data;
the classification marking module is used for classifying and recording the normal electroencephalogram signals and/or the abnormal electroencephalogram signals, and the classification comprises the following steps: normal electroencephalogram signals and abnormal electroencephalogram signals;
and the index address recording module is used for respectively recording corresponding index addresses for the normal brain electrical signals and/or the abnormal brain electrical signals.
In a possible implementation manner, the classifying and recording the normal electroencephalogram signal and/or the abnormal electroencephalogram signal includes:
if the electroencephalogram signal to be recorded is a normal electroencephalogram signal, recording the starting time of the normal electroencephalogram signal, the frequency of a wave crest/wave trough, the duration and an index address; and the number of the first and second groups,
and if the electroencephalogram signal to be recorded is an abnormal electroencephalogram signal, recording the starting time, the frequency of the wave crest or the wave trough, the duration and the index address of the abnormal electroencephalogram signal.
In another possible implementation manner, the electroencephalogram signal feature obtaining module includes:
the quantity and value acquisition unit is used for counting the quantity of wave crests and/or wave troughs in the electroencephalogram signal to be recorded and acquiring the value of each wave crest and/or wave trough;
and the average value obtaining unit is used for obtaining an average peak value and/or an average trough value according to the number and the value.
In another possible implementation, the classification tagging module includes:
the digital difference acquisition unit is used for acquiring a peak digital difference and/or a trough digital difference between a peak digital value and/or a trough digital value of the electroencephalogram signal to be recorded and the average peak digital value and/or the average trough digital value;
the classification unit is used for determining the electroencephalogram signal to be recorded as an abnormal electroencephalogram signal if the absolute value of the wave peak value difference and/or the wave trough value difference is larger than a preset value difference threshold value; and if the absolute value of the wave peak value difference and/or the wave trough value difference is smaller than a preset value difference threshold value, the electroencephalogram signal to be recorded is a normal electroencephalogram signal.
In a third aspect, an electronic device is provided, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method for electroencephalogram data recording as provided in the first aspect when executing the program.
In a fourth aspect, a non-transitory computer readable storage medium is provided, having stored thereon a computer program which, when executed by a processor, implements the method of electroencephalographic signal data recording as provided in the first aspect.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings used in the description of the embodiments of the present application will be briefly described below.
FIG. 1 is a flow chart of a method for electroencephalogram data recording provided by one embodiment of the present invention;
FIG. 2 is a flow chart of a method of electroencephalographic data recording provided in accordance with yet another embodiment of the present invention;
FIG. 3 is a flow chart of a method of electroencephalographic data recording provided in accordance with another embodiment of the present invention;
FIG. 4 is a block diagram of a system for electroencephalogram data recording provided by one embodiment of the present invention;
FIG. 5 is a block diagram of a system for electroencephalogram data recording according to yet another embodiment of the present invention;
FIG. 6 is a block diagram of a system for electroencephalogram data recording according to another embodiment of the present invention;
fig. 7 is a schematic physical structure diagram of an electronic device according to the present invention.
Detailed description of the invention
Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar modules or modules having the same or similar functionality throughout. The embodiments described below with reference to the drawings are exemplary only for the purpose of explaining the present application and are not to be construed as limiting the present invention.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, modules, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, modules, components, and/or groups thereof. It will be understood that when a module is referred to as being "connected" or "coupled" to another module, it can be directly connected or coupled to the other module or intervening modules may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or wirelessly coupled. As used herein, the term "and/or" includes all or any module and all combinations of one or more of the associated listed items.
To make the objectives, technical solutions and advantages of the present application more clear, the following detailed description of the implementations of the present application will be made with reference to the accompanying drawings.
The technical solutions of the present application and the technical solutions of the present application, for example, and solving the above technical problems, will be described in detail with specific examples below. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments. Embodiments of the present application will be described below with reference to the accompanying drawings.
Fig. 1 is a flowchart of a method for recording electroencephalogram data, according to an embodiment of the present invention, the method includes:
102, counting the value of the electroencephalogram signal to be recorded, and acquiring the electroencephalogram signal characteristics of the target electroencephalogram data;
103, carrying out classification marking on the electroencephalogram signals to be recorded according to the characteristics of the electroencephalogram signals, wherein the classification comprises the following steps: normal electroencephalogram signals and abnormal electroencephalogram signals;
and 104, performing classified recording on the normal electroencephalogram signals and/or the abnormal electroencephalogram signals.
In the embodiment of the invention, one section of complete electroencephalogram data comprises a plurality of sections of electroencephalogram signals with short-time nerve impulse waveforms. When the recording is started, the electroencephalogram signal with the nerve impulse waveform can be extracted from a section of complete electroencephalogram data through visual vision, and the electroencephalogram signal is marked as the electroencephalogram signal to be recorded. The electroencephalogram signal to be recorded is a nerve impulse signal in a wave crest-wave trough form, and the electroencephalogram signal characteristics of the target electroencephalogram data can be obtained by counting the numerical values of each wave crest and each wave trough. Comparing the electroencephalogram signal to be recorded with the characteristics of the electroencephalogram signal, classifying the electroencephalogram signal to be recorded into a normal electroencephalogram signal and an abnormal electroencephalogram signal according to the comparison result, and simultaneously respectively recording corresponding index addresses for the normal electroencephalogram signal and the abnormal electroencephalogram signal to finish the classification recording of the electroencephalogram signal.
Wherein the electroencephalogram signal characteristics include: action potential thresholds, waveform envelope feature points, number of peaks, number of valleys, duration, and/or power spectral density.
Wherein, the classifying and recording the normal brain electrical signals and/or abnormal brain electrical signals comprises:
if the electroencephalogram signal to be recorded is a normal electroencephalogram signal, recording the starting time of the normal electroencephalogram signal, the frequency of a wave crest/wave trough, the duration and an index address; and the number of the first and second groups,
and if the electroencephalogram signal to be recorded is an abnormal electroencephalogram signal, recording the starting time, the frequency of the wave crest or the wave trough, the duration and the index address of the abnormal electroencephalogram signal.
Specifically, the classifying and recording the normal electroencephalogram signal and/or the abnormal electroencephalogram signal comprises: and creating corresponding recording information according to the normal electroencephalogram signals and/or the abnormal electroencephalogram signals to establish an index database, wherein each piece of recording information comprises an index segment, a data segment and a signal type.
The index segment comprises index addresses of the electroencephalogram signals, the index addresses are storage positions of the electroencephalogram signals in an index library, the data segment comprises action potential thresholds, waveform envelope characteristic points, wave crest quantity, wave trough quantity, duration and/or power spectral density of the electroencephalogram signals, and the signal types comprise normal electroencephalogram signals, abnormal electroencephalogram signals and undefined electroencephalogram signals. The ambiguous electroencephalogram signal refers to a signal which cannot be determined to be an abnormal electroencephalogram signal or a normal electroencephalogram signal at present.
In a preferred embodiment, the data segment further includes a brain tissue location corresponding to the brain electrical signal.
According to the method and the device, the electroencephalograms to be recorded in the target electroencephalogram data are obtained, the numerical values of the electroencephalograms to be recorded are counted, the electroencephalogram characteristics of the target electroencephalogram data are obtained, the electroencephalograms to be recorded are classified and marked as normal electroencephalograms and/or abnormal electroencephalograms according to the electroencephalogram characteristics, and corresponding index addresses are respectively recorded for the normal electroencephalograms and/or the abnormal electroencephalograms. Therefore, the electroencephalogram signals in the electroencephalogram data are classified for recording and counting, and the time for retrieving the electroencephalogram signals is reduced.
Fig. 2 is a flowchart of a method for recording electroencephalogram data according to another embodiment of the present invention, where the counting the value of the electroencephalogram signal to be recorded and obtaining the electroencephalogram signal characteristics of the target electroencephalogram data includes:
and 202, acquiring an average peak value and/or an average trough value according to the quantity and the value. In the embodiment of the invention, the electroencephalogram signal characteristics mainly comprise an average peak value and/or an average trough value, and the average peak value and/or the average trough value can be used as a judgment basis for a normal electroencephalogram signal and/or an abnormal electroencephalogram signal in a subsequent process.
As shown in fig. 3, a flowchart of a method for recording electroencephalogram data according to another embodiment of the present invention is provided, where the classifying and marking the electroencephalogram signal to be recorded according to the characteristics of the electroencephalogram signal includes:
301, acquiring a peak value difference and/or a trough value difference between a peak value and/or a trough value of an electroencephalogram signal to be recorded and the average peak value and/or the average trough value;
In the embodiment of the invention, the type of the electroencephalogram signal is judged mainly according to the numerical difference between the peak value and the average peak value of the electroencephalogram signal to be recorded and the numerical difference between the trough value and the average trough value. Because the wave crest and/or the wave trough of the electroencephalogram signal to be recorded can be larger than the average wave crest and/or the average wave trough, and can also be smaller than the average wave crest and/or the average wave trough, after the wave crest value difference and/or the wave trough value difference are obtained, the absolute value of the wave crest value difference and/or the wave trough value difference is taken, the absolute value is compared with a preset value difference threshold value, if the absolute value is larger than the value difference threshold value, the electroencephalogram signal to be recorded is a normal electroencephalogram signal, and if the absolute value is smaller than the value difference threshold value, the electroencephalogram signal to be recorded is an abnormal electroencephalogram signal.
Fig. 4 is a block diagram of a system for recording electroencephalogram data, according to an embodiment of the present invention, the system includes:
the electroencephalogram signal to be recorded acquisition module 401 is used for acquiring an electroencephalogram signal to be recorded in the target electroencephalogram data, wherein the electroencephalogram signal to be recorded is an electroencephalogram signal with nerve impulses;
an electroencephalogram signal feature acquisition module 402, configured to count a value of the electroencephalogram signal to be recorded, and acquire an electroencephalogram signal feature of the target electroencephalogram data;
a classification and labeling module 403, configured to perform classification and recording on the normal electroencephalogram signal and/or the abnormal electroencephalogram signal, where the classification includes: normal electroencephalogram signals and abnormal electroencephalogram signals;
and an index address recording module 404, configured to record corresponding index addresses for the normal electroencephalogram signals and/or the abnormal electroencephalogram signals, respectively.
In the embodiment of the invention, one section of complete electroencephalogram data comprises a plurality of sections of electroencephalogram signals with short-time nerve impulse waveforms. When recording is started, the electroencephalogram signal with the nerve impulse waveform can be extracted from a section of complete electroencephalogram data through visual vision, and the electroencephalogram signal is marked as the electroencephalogram signal to be recorded. The electroencephalogram signal to be recorded is a nerve impulse signal in a wave crest-wave trough form, and the electroencephalogram signal characteristics of the target electroencephalogram data can be obtained by counting the numerical values of each wave crest and each wave trough. Comparing the electroencephalogram signal to be recorded with the characteristics of the electroencephalogram signal, classifying the electroencephalogram signal to be recorded into a normal electroencephalogram signal and an abnormal electroencephalogram signal according to the comparison result, and simultaneously respectively recording corresponding index addresses for the normal electroencephalogram signal and the abnormal electroencephalogram signal to finish the classification recording of the electroencephalogram signals.
Wherein, the classifying and recording the normal brain electrical signals and/or abnormal brain electrical signals comprises:
if the electroencephalogram signal to be recorded is a normal electroencephalogram signal, recording the starting time of the normal electroencephalogram signal, the frequency of a wave crest/wave trough, the duration and an index address; and the number of the first and second groups,
and if the electroencephalogram signal to be recorded is an abnormal electroencephalogram signal, recording the starting time, the frequency of the wave crest or the wave trough, the duration and the index address of the abnormal electroencephalogram signal.
According to the method and the device, the electroencephalogram signals to be recorded in the target electroencephalogram data are obtained, the numerical values of the electroencephalogram signals to be recorded are counted, the electroencephalogram signal characteristics of the target electroencephalogram data are obtained, the electroencephalogram signals to be recorded are classified and marked as normal electroencephalograms and/or abnormal electroencephalograms according to the electroencephalogram signal characteristics, and corresponding index addresses are respectively recorded for the normal electroencephalograms and/or the abnormal electroencephalograms. Therefore, the electroencephalogram signals in the electroencephalogram data are classified for recording and counting, and the time for retrieving the electroencephalogram signals is reduced.
As shown in fig. 5, which is a structural diagram of a system for electroencephalogram data recording according to another embodiment of the present invention, the electroencephalogram signal feature acquisition module 402 includes:
a number and value obtaining unit 501, configured to count the number of peaks and/or troughs in the electroencephalogram signal to be recorded, and obtain a value of each peak and/or trough;
an average obtaining unit 502, configured to obtain an average peak value and/or an average trough value according to the number and the value.
In the embodiment of the invention, the electroencephalogram signal characteristics mainly comprise an average peak value and/or an average trough value, and the average peak value and/or the average trough value can be used as a judgment basis for a normal electroencephalogram signal and/or an abnormal electroencephalogram signal in a subsequent process.
As shown in fig. 6, which is a block diagram of a system for electroencephalogram data recording according to another embodiment of the present invention, the classification marking module 403 includes:
a value difference obtaining unit 601, configured to obtain a peak value difference and/or a trough value difference between a peak value and/or a trough value of the electroencephalogram signal to be recorded and the average peak value and/or the average trough value;
a classification unit 602, configured to determine that the electroencephalogram signal to be recorded is an abnormal electroencephalogram signal if an absolute value of the peak value difference and/or the trough value difference is greater than a preset value difference threshold; and if the absolute value of the wave peak value difference and/or the wave trough value difference is smaller than a preset value difference threshold value, the electroencephalogram signal to be recorded is a normal electroencephalogram signal.
In the embodiment of the invention, the type of the electroencephalogram signal is judged mainly according to the numerical difference between the peak value and the average peak value of the electroencephalogram signal to be recorded and the numerical difference between the trough value and the average trough value. Because the wave crest and/or the wave trough of the electroencephalogram signal to be recorded can be larger than the average wave crest and/or the average wave trough, and can also be smaller than the average wave crest and/or the average wave trough, after the wave crest value difference and/or the wave trough value difference are obtained, the absolute value of the wave crest value difference and/or the wave trough value difference is taken, the absolute value is compared with a preset value difference threshold value, if the absolute value is larger than the value difference threshold value, the electroencephalogram signal to be recorded is a normal electroencephalogram signal, and if the absolute value is smaller than the value difference threshold value, the electroencephalogram signal to be recorded is an abnormal electroencephalogram signal.
Fig. 7 illustrates a physical structure diagram of an electronic device, which may include, as shown in fig. 7: a processor (processor) 701, a communication Interface (Communications Interface) 702, a memory (memory) 703 and a communication bus 704, wherein the processor, the communication Interface and the memory complete communication with each other through the communication bus. The processor may invoke logic instructions in the memory to perform a method of electroencephalographic signal data recording, the method comprising: acquiring an electroencephalogram signal to be recorded in target electroencephalogram data, wherein the electroencephalogram signal to be recorded is an electroencephalogram signal with nerve impulses; counting the value of the electroencephalogram signal to be recorded, and acquiring the electroencephalogram signal characteristics of the target electroencephalogram data; according to the electroencephalogram signal characteristics, carrying out classification marking on the electroencephalogram signals to be recorded, wherein the classification comprises the following steps: normal electroencephalogram signals and abnormal electroencephalogram signals; and carrying out classified recording on the normal brain electrical signals and/or the abnormal brain electrical signals.
In addition, the logic instructions in the memory may be implemented in the form of software functional units and stored in a computer readable storage medium when the logic instructions are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In another aspect, an embodiment of the present invention further provides a computer program product, where the computer program product includes a computer program stored on a non-transitory computer-readable storage medium, the computer program includes program instructions, and when the program instructions are executed by a computer, the computer can execute the method for electroencephalogram signal data recording provided by the above-mentioned method embodiments, the method includes: acquiring an electroencephalogram signal to be recorded in target electroencephalogram data, wherein the electroencephalogram signal to be recorded is an electroencephalogram signal with nerve impulses; counting the value of the electroencephalogram signal to be recorded, and acquiring the electroencephalogram signal characteristics of the target electroencephalogram data; according to the electroencephalogram signal characteristics, carrying out classification marking on the electroencephalogram signals to be recorded, wherein the classification comprises the following steps: normal electroencephalogram signals and abnormal electroencephalogram signals; and carrying out classified recording on the normal electroencephalogram signals and/or the abnormal electroencephalogram signals.
In still another aspect, an embodiment of the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program is implemented by a processor to execute the method for electroencephalogram data recording provided by the above embodiments, and the method includes: acquiring an electroencephalogram signal to be recorded in target electroencephalogram data, wherein the electroencephalogram signal to be recorded is an electroencephalogram signal with nerve impulses; counting the value of the electroencephalogram signal to be recorded, and acquiring the electroencephalogram signal characteristics of the target electroencephalogram data; according to the electroencephalogram signal characteristics, carrying out classification marking on the electroencephalogram signals to be recorded, wherein the classification comprises the following steps: normal electroencephalogram signals and abnormal electroencephalogram signals; and carrying out classified recording on the normal brain electrical signals and/or the abnormal brain electrical signals.
It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and may be performed in other orders unless otherwise indicated herein. Moreover, at least a portion of the steps in the flow chart of the figure may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed alternately or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
The foregoing is only a partial implementation of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (10)
1. A method of electroencephalographic signal data recording, the method comprising:
acquiring an electroencephalogram signal to be recorded in target electroencephalogram data, wherein the electroencephalogram signal to be recorded is an electroencephalogram signal with nerve impulses;
acquiring electroencephalogram signal characteristics of the target electroencephalogram data;
according to the electroencephalogram signal characteristics, carrying out classification marking on the electroencephalogram signals to be recorded, wherein the classification comprises the following steps: normal electroencephalogram signals and abnormal electroencephalogram signals;
and carrying out classified recording on the normal electroencephalogram signals and/or the abnormal electroencephalogram signals.
2. The method of claim 1, wherein said categorically recording the normal brain electrical signal and/or abnormal brain electrical signal comprises:
and creating corresponding recording information according to the normal electroencephalogram signals and/or the abnormal electroencephalogram signals, wherein each piece of recording information comprises an index segment, a data segment and a signal type.
3. The method of claim 2, wherein the index segment comprises an index address of the brain electrical signal, the data segment comprises an action potential threshold, a waveform envelope feature point, a number of peaks, a number of valleys, a duration, and/or a power spectral density of the brain electrical signal, and the signal types comprise a normal brain electrical signal, an abnormal brain electrical signal, and an ambiguous brain electrical signal.
4. The method of claim 1, wherein the brain electrical signal features comprise: action potential thresholds, waveform envelope feature points, number of peaks, number of valleys, duration, and/or power spectral density.
5. The method of claim 1, wherein said obtaining brain electrical signal characteristics of said target brain electrical data comprises:
counting the number of wave crests and/or wave troughs in the electroencephalogram signal to be recorded within a set time period, and acquiring the numerical value of each wave crest and/or wave trough;
and acquiring an average peak value and/or an average trough value according to the number and the value.
6. The method of claim 5, wherein said categorically labeling the brain electrical signal to be recorded according to the brain electrical signal features comprises:
acquiring a peak value difference and/or a trough value difference between a peak value and/or a trough value of the electroencephalogram signal to be recorded and the average peak value and/or the average trough value;
if the absolute value of the wave peak value difference and/or the wave trough value difference is located outside the threshold range, the electroencephalogram signal to be recorded is an abnormal electroencephalogram signal; and if the absolute value of the wave peak value difference and/or the wave trough value difference is within a threshold range, the electroencephalogram signal to be recorded is a normal electroencephalogram signal.
7. A system for electroencephalographic signal data recording, said system comprising:
the electroencephalogram signal to be recorded acquiring module is used for acquiring an electroencephalogram signal to be recorded in the target electroencephalogram data, wherein the electroencephalogram signal to be recorded is an electroencephalogram signal with nerve impulses;
the electroencephalogram signal characteristic acquisition module is used for counting the value of the electroencephalogram signal to be recorded and acquiring the electroencephalogram signal characteristic of the target electroencephalogram data;
the classification marking module is used for performing classification recording on the normal electroencephalogram signals and/or the abnormal electroencephalogram signals, and the classification comprises the following steps: normal electroencephalogram signals and abnormal electroencephalogram signals;
and the index address recording module is used for respectively recording corresponding index addresses for the normal electroencephalogram signals and/or the abnormal electroencephalogram signals.
8. The system of claim 7, wherein the brain electrical signal feature acquisition module comprises:
the quantity and value acquisition unit is used for counting the quantity of wave crests and/or wave troughs in the electroencephalogram signal to be recorded and acquiring the value of each wave crest and/or wave trough;
and the average value obtaining unit is used for obtaining an average peak value and/or an average trough value according to the number and the value.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor, when executing the program, implements a method of electroencephalographic signal data recording according to any one of claims 1 to 6.
10. A non-transitory computer-readable storage medium on which a computer program is stored, the computer program, when being executed by a processor, implementing the method of electroencephalographic signal data recording according to any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210878795.0A CN115153589A (en) | 2022-07-25 | 2022-07-25 | Method, system, electronic device and storage medium for recording electroencephalogram signal data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210878795.0A CN115153589A (en) | 2022-07-25 | 2022-07-25 | Method, system, electronic device and storage medium for recording electroencephalogram signal data |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115153589A true CN115153589A (en) | 2022-10-11 |
Family
ID=83496196
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210878795.0A Pending CN115153589A (en) | 2022-07-25 | 2022-07-25 | Method, system, electronic device and storage medium for recording electroencephalogram signal data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115153589A (en) |
-
2022
- 2022-07-25 CN CN202210878795.0A patent/CN115153589A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107180056B (en) | Method and device for matching segments in video | |
| US20140297606A1 (en) | Method and device for processing a time sequence based on dimensionality reduction | |
| KR101841985B1 (en) | Method and Apparatus for Extracting Audio Fingerprint | |
| CN110147469A (en) | A kind of data processing method, equipment and storage medium | |
| CN113992340B (en) | User abnormal behavior identification method, device, equipment and storage medium | |
| CN111104398A (en) | Detection method and elimination method for approximate repeated record of intelligent ship | |
| CN112199935A (en) | Data comparison method and device, electronic equipment and computer readable storage medium | |
| CN103810300A (en) | Method and device for inquiring non-index coverage data | |
| CN107147947A (en) | Key frame recognition methods and device | |
| CN114742124A (en) | Abnormal data processing method, system and device | |
| CN115153589A (en) | Method, system, electronic device and storage medium for recording electroencephalogram signal data | |
| CN101826070A (en) | Key point-based data sequence linear fitting method | |
| CN107332681A (en) | A kind of failure dimensional analysis method and the network equipment | |
| CN113705625A (en) | Method and device for identifying abnormal life guarantee application families and electronic equipment | |
| CN116725486A (en) | Dynamic electrocardiosignal atrial fibrillation detection method and device based on double phases | |
| CN114550288B (en) | Action recognition method and device based on event data | |
| CN112232290B (en) | Data clustering method, server, system and computer readable storage medium | |
| CN116008735A (en) | Partial discharge signal extraction method and system based on density | |
| CN112393799B (en) | Far-field voice equipment detection method and device and television terminal | |
| CN113946717A (en) | Sub-map index feature obtaining method, device, equipment and storage medium | |
| CN112272157A (en) | Host IP address conversion method and device, computer equipment and storage medium | |
| CN118535102B (en) | Data processing method of lightweight intelligent storage system of unmanned platform | |
| CN112540960A (en) | Data storage management method and system | |
| CN112217896A (en) | JSON message conversion method and related device | |
| CN118656519B (en) | Method and device for retrieving video |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |