CN116570297A - Epileptic brain wave signal identification method - Google Patents
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Abstract
The application provides an epileptic brain wave signal identification method, which relates to the technical field of signal identification and processing and comprises the following steps: collecting brain waves of a patient in a resting state, and periodically calculating characteristic values of the brain waves in the resting state; calculating a period characteristic value of the brain wave in the resting state based on the characteristic value of the brain wave in the resting state and the time period in the resting state; collecting brain waves of the state of the patient after the electric stimulation, and periodically calculating characteristic values of the brain waves after the electric stimulation; calculating a cycle characteristic value of the brain wave after the electric stimulation based on the characteristic value of the brain wave after the electric stimulation and the time cycle after the electric stimulation; calculating and counting the ratio of the periodic characteristic value of the electroencephalogram after the electric stimulation to the periodic characteristic value of the electroencephalogram in a resting state in real time, and comparing the result with a preset threshold value based on the statistical result to output the ending moment of the electroencephalogram attack; the application can simply and conveniently obtain the epileptic seizure time after the electrotherapy output of the electric shock to be used as a doctor for measuring the treatment effect.
Description
Technical Field
The application relates to the technical field of signal identification and processing, in particular to an epileptic brain wave signal identification method.
Background
Spontaneous resting electroencephalogram signals (EEG) are low-frequency weak signals, and the typical signals are only about 10-50u V, and the highest frequency is 100uV, and the frequency is 0.5-42Hz. Whereas the electroencephalogram characteristic of epileptic seizure is characterized by normal background activity or non-specific abnormality, and abnormal waves at intervals between seizures can be seen in spike, spike slow wave, spike-slow wave, multi-spike wave and the like in two hemispheres; epileptic brain waves are characterized by an alpha rhythm of eight to twelve hertz, and a small number of beta rhythms of fourteen to twenty-five hertz can be found on the frontotemporal lobe, with slow waves less than eight hertz being very rare. Typical absence episodes are predominantly manifested as a spike-slow complex, a spike followed by a slow wave. The tonic clonic attacks can see a multi-spike complex, i.e. more than two high amplitude biphasic spikes exhibit rhythmic appearance. The electroencephalogram tonic period in the attack period starts with the release of rhythmic spike waves of 10-20 Hz, the amplitude is gradually high and the frequency is gradually slow; diffuse slow wave activity is seen after the seizure is finished, and background activity is gradually restored. Electroencephalogram of generalized coarse myoclonus onset is manifested as a high amplitude multi-spike slow wave burst, or suddenly appears as a widely low voltage. The special electroencephalogram response can be generated during epileptic seizure, and the electroencephalogram has obvious and effective effects on diagnosis of epileptic, and is an index for rapid and reliable diagnosis of epileptic. Definitive diagnosis of epileptic syndrome facilitates selection of appropriate antiepileptic drugs, thereby promoting patient recovery.
The characteristics of the brain wave of the manually induced epilepsy are more obvious, the brain wave is in a relatively 'resting' state under the anesthesia and muscle relaxation state before the electric shock treatment, the waveform characteristics are similar to a straight line, the energy value and the power value contained in the waveform are in a lower state, the waveform amplitude is often below 200uV, and meanwhile, abnormal waveforms such as spike, sharp wave, spike-slow wave, sharp-slow wave, multi-spike wave and the like can not occur. After the electric shock treatment, the brain wave rapidly shows the characteristics of epileptic brain wave, at the moment, the brain wave shows striking the brain like a storm, the whole process lasts for about 60-120 seconds, and then the process is rapidly ended. Seizure time is one of the important indicators for measuring the effect of this manual induced seizures, and according to the handbook of electric shock, fourth edition of Charles h. Kellner, 2019, the doctor needs to intervene for more than 120 seconds, beyond which time various side effects can increase.
The clinical electroencephalogram identification and analysis mainly depends on visual detection and manual labeling of medical workers, and is also a gold standard for detecting epilepsy based on EEG at present. However, the seizure time and duration of the epilepsy have uncertainty, the work of reading the data of the seizure period from massive electroencephalogram data so as to analyze the illness state is complex, and the work is greatly dependent on subjective judgment of an inspector, in the process of treating the electric shock, the work center of gravity of a doctor is above electrotherapy, and the complex electroencephalogram reading work clearly brings extra work burden to the doctor, and although experienced clinicians can identify whether the patient is in the seizure stage or not through clinical manifestations of the patient, the actual clinical manifestations of a quite large part of the patients are not substantially related to the electroencephalogram seizure stage, and the clinical manifestations are not obvious, so that misguidance is easily caused to the doctor.
Based on the above, the application provides an epileptic brain wave signal identification method for solving the above problems.
Disclosure of Invention
The application aims to provide an epileptic brain wave signal identification method which can simply and conveniently acquire epileptic seizure time after electric shock electrotherapy output to be used as a doctor to measure treatment effect.
The technical scheme of the application is as follows:
in a first aspect, the present application provides a method for identifying epileptic brain wave signals, comprising the steps of:
s1, acquiring brain waves of a patient in a resting state, and periodically calculating characteristic values of the brain waves in the resting state;
s2, calculating a periodic characteristic value of the brain wave in the resting state based on the characteristic value of the brain wave in the resting state and the time period in the resting state;
s3, collecting brain waves of the patient in an electric stimulation state, and periodically calculating characteristic values of the brain waves after electric stimulation;
s4, calculating a cycle characteristic value of the brain wave after the electric stimulation based on the characteristic value of the brain wave after the electric stimulation and the time cycle after the electric stimulation;
and S5, calculating and counting the ratio of the periodic characteristic value of the electroencephalogram after the electric stimulation to the periodic characteristic value of the electroencephalogram in a resting state in real time, and comparing the result with a preset threshold value based on the statistical result to output the ending moment of the electroencephalogram.
Further, the calculation cycle of the characteristic value of the brain wave in the rest state and the characteristic value of the brain wave after the electrical stimulation is 1 second.
Further, in step S2, the calculation formula of the periodic eigenvalue of the electroencephalogram in the rest state includes:
wherein ,periodic characteristic value of brain wave in resting state, +.>Time domain feature representing brain wave in resting state, < ->Representing the frequency domain characteristics of brain waves in resting state, < >>Time-frequency domain characteristics of brain waves in a resting state are represented, < ->The time period of brain waves in a resting state is represented.
Further, in step S4, the calculation formula of the periodic characteristic value of the electroencephalogram after the electrical stimulation includes:
wherein ,characteristic value of brain wave after electric stimulation, < +.>Time domain features representing brain waves after electrical stimulation, < + >>Frequency domain characteristics of brain wave after electric stimulation are represented, < + >>Represents the time-frequency domain characteristics of brain waves after electric stimulation,indicating the time period of brain waves after electrical stimulation.
Further, the method also comprises the step of carrying out noise reduction treatment on the time-frequency domain characteristics of the brain waves in a resting state and after the electric stimulation, and specifically comprises the following steps:
dividing and processing the time-frequency domain characteristics of the brain waves in a resting state and after electric stimulation to obtain a plurality of initial time-frequency domain sub-bands;
respectively carrying out two-dimensional convolution on each initial time-frequency domain sub-band to obtain each initial time-frequency domain sub-band after convolution processing;
the processed time-frequency domain features are generated based on each of the initial time-frequency domain subbands after the convolution processing.
Further, in step S5, the process of comparing the statistical result with a preset threshold value to output the brain wave episode termination time includes:
recording the duration time that the ratio of the cycle characteristic value of the electroencephalogram after the electric stimulation to the cycle characteristic value of the electroencephalogram in the resting state is smaller than a preset threshold value based on the statistical result, and acquiring the electroencephalogram time when the duration time exceeds 5 seconds to obtain the brain wave attack termination time.
Compared with the prior art, the application has at least the following advantages or beneficial effects:
according to the epileptic brain wave signal identification method, whether the epileptic seizure is caused or not is judged by periodically calculating the time domain characteristics, the frequency domain characteristics, the time-frequency domain characteristics and the nonlinear characteristics of the brain waves in a resting state and after electric stimulation and comparing with the preset threshold value, so that the epileptic seizure time can be simply and conveniently obtained after electric shock electrotherapy output to be used as a doctor for measuring the treatment effect, visual inspection is facilitated for the doctor, and the workload of the doctor is reduced.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a step diagram of an epileptic brain wave signal recognition method according to the present application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments of the present application. The components of the embodiments of the present application generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the application, as presented in the figures, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures.
It should be noted that, in this document, the term "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
In the description of the present application, it should also be noted that, unless explicitly specified and limited otherwise, the terms "disposed", "connected" and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected or integrally connected; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present application will be understood in specific cases by those of ordinary skill in the art.
Some embodiments of the present application are described in detail below with reference to the accompanying drawings. The various embodiments and features of the embodiments described below may be combined with one another without conflict.
Examples
Referring to fig. 1, fig. 1 is a step diagram of an epileptic brain wave signal recognition method according to an embodiment of the present application.
The application provides an epileptic brain wave signal identification method, which comprises the following steps:
s1, acquiring brain waves of a patient in a resting state, and periodically calculating characteristic values of the brain waves in the resting state;
s2, calculating a periodic characteristic value of the brain wave in the resting state based on the characteristic value of the brain wave in the resting state and the time period in the resting state;
s3, collecting brain waves of the patient in an electric stimulation state, and periodically calculating characteristic values of the brain waves after electric stimulation;
s4, calculating a cycle characteristic value of the brain wave after the electric stimulation based on the characteristic value of the brain wave after the electric stimulation and the time cycle after the electric stimulation;
and S5, calculating and counting the ratio of the periodic characteristic value of the electroencephalogram after the electric stimulation to the periodic characteristic value of the electroencephalogram in a resting state in real time, and comparing the result with a preset threshold value based on the statistical result to output the ending moment of the electroencephalogram.
The characteristic values of the brain waves in the resting state comprise time domain characteristics, frequency domain characteristics, time-frequency domain characteristics and nonlinear characteristics of the brain waves in the resting state; the characteristic values of the brain wave after the electric stimulation comprise time domain characteristics, frequency domain characteristics, time-frequency domain characteristics and nonlinear characteristics of the brain wave after the electric stimulation.
As a preferred embodiment, the calculation cycle of the characteristic value of the brain wave in the resting state and the characteristic value of the brain wave after the electrical stimulation are each 1 second.
In a preferred embodiment, in step S2, the calculation formula of the periodic characteristic value of the electroencephalogram in the resting state includes:
wherein ,periodic characteristic value of brain wave in resting state, +.>Time domain feature representing brain wave in resting state, < ->Representing the frequency domain characteristics of brain waves in resting state, < >>Time-frequency domain characteristics of brain waves in a resting state are represented, < ->The time period of brain waves in a resting state is represented.
The time domain characteristics of the brain wave in the resting state can be obtained by summing the amplitude, the average value, the peak-to-peak value, the effective value, the inter-sealing slope, the extremum times and the zero crossing rate of the brain wave in the resting state, the frequency domain characteristics of the brain wave in the resting state can be obtained by summing the power spectrum average value, the peak frequency and the main frequency peak bandwidth of the brain wave in the resting state, and the time-frequency domain characteristics of the brain wave in the resting state can be obtained by short-time Fourier transform calculation.
In a preferred embodiment, in step S4, the calculation formula of the periodic characteristic value of the electroencephalogram after the electrical stimulation includes:
wherein ,characteristic value of brain wave after electric stimulation, < +.>Time domain features representing brain waves after electrical stimulation, < + >>Frequency domain characteristics of brain wave after electric stimulation are represented, < + >>Represents the time-frequency domain characteristics of brain waves after electric stimulation,indicating the time period of brain waves after electrical stimulation.
The time domain characteristics of the brain wave after the electric stimulation can be obtained by summing the amplitude, the average value, the peak-to-peak value, the effective value, the inter-sealing slope, the extremum times and the zero crossing rate of the brain wave after the electric stimulation, the frequency domain characteristics of the brain wave after the electric stimulation can be obtained by summing the power spectrum average value, the peak frequency and the main frequency peak bandwidth of the brain wave after the electric stimulation, and the time-frequency domain characteristics of the brain wave after the electric stimulation can be obtained by short-time Fourier transform calculation.
As a preferred embodiment, the method further includes performing noise reduction processing on time-frequency domain characteristics of brain waves in a resting state and after electric stimulation, and specifically includes:
dividing and processing the time-frequency domain characteristics of the brain waves in a resting state and after electric stimulation to obtain a plurality of initial time-frequency domain sub-bands;
respectively carrying out two-dimensional convolution on each initial time-frequency domain sub-band to obtain each initial time-frequency domain sub-band after convolution processing;
the processed time-frequency domain features are generated based on each of the initial time-frequency domain subbands after the convolution processing.
As a preferred embodiment, in step S5, the process of comparing the statistical result with a preset threshold value to output the brain wave episode termination time includes:
recording the duration time that the ratio of the cycle characteristic value of the electroencephalogram after the electric stimulation to the cycle characteristic value of the electroencephalogram in the resting state is smaller than a preset threshold value based on the statistical result, and acquiring the electroencephalogram time when the duration time exceeds 5 seconds to obtain the brain wave attack termination time.
It will be appreciated that the configuration shown in the figures is illustrative only and that an epileptic brain wave signal recognition method may also include more or fewer components than shown in the figures, or have a different configuration than shown in the figures. The components shown in the figures may be implemented in hardware, software, or a combination thereof.
In the embodiments provided in the present application, it should be understood that the disclosed method may be implemented in other manners as well. The above-described embodiments are merely illustrative, for example, of the flowchart or block diagrams in the figures, which illustrate the architecture, functionality, and operation of possible implementations of methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present application may be integrated together to form a single part, or each module may exist alone, or two or more modules may be integrated to form a single part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In summary, according to the epileptic brain wave signal identification method provided by the embodiment of the application, whether the epileptic is seized is judged by periodically calculating the time domain characteristics, the frequency domain characteristics, the time-frequency domain characteristics and the nonlinear characteristics of the brain wave under the resting state and after the electric stimulation and comparing with the preset threshold value, so that the epileptic seizure time can be obtained, the epileptic seizure time after the electric shock electrotherapy output can be simply and conveniently obtained to be used as the effect of measuring the treatment by a doctor, the visual inspection by the doctor is facilitated, and the workload of the doctor is reduced.
The above description is only of the preferred embodiments of the present application and is not intended to limit the present application, but various modifications and variations can be made to the present application by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.
It will be evident to those skilled in the art that the application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (6)
1. The epileptic brain wave signal identification method is characterized by comprising the following steps of:
s1, acquiring brain waves of a patient in a resting state, and periodically calculating characteristic values of the brain waves in the resting state;
s2, calculating a periodic characteristic value of the brain wave in the resting state based on the characteristic value of the brain wave in the resting state and the time period in the resting state;
s3, collecting brain waves of the patient in an electric stimulation state, and periodically calculating characteristic values of the brain waves after electric stimulation;
s4, calculating a cycle characteristic value of the brain wave after the electric stimulation based on the characteristic value of the brain wave after the electric stimulation and the time cycle after the electric stimulation;
and S5, calculating and counting the ratio of the periodic characteristic value of the electroencephalogram after the electric stimulation to the periodic characteristic value of the electroencephalogram in a resting state in real time, and comparing the result with a preset threshold value based on the statistical result to output the ending moment of the electroencephalogram.
2. The epileptic brain wave signal recognition method according to claim 1, wherein the calculation cycle of the characteristic value of the brain wave in the rest state and the calculation cycle of the characteristic value of the brain wave after the electrical stimulation are both 1 second.
3. The method for recognizing epileptic brain wave signals according to claim 1, wherein in the step S2, the calculation formula of the periodic eigenvalue of the brain wave in the resting state includes:
wherein ,periodic characteristic value of brain wave in resting state, +.>Time domain feature representing brain wave in resting state, < ->Representing the frequency domain characteristics of brain waves in resting state, < >>Time-frequency domain characteristics of brain waves in a resting state are represented, < ->The time period of brain waves in a resting state is represented.
4. The method for recognizing epileptic brain wave signals according to claim 1, wherein in the step S4, the calculation formula of the cycle characteristic value of the brain wave after the electrical stimulation includes:
wherein ,characteristic value of brain wave after electric stimulation, < +.>Time domain features representing brain waves after electrical stimulation, < + >>Frequency domain characteristics of brain wave after electric stimulation are represented, < + >>Time-frequency domain characteristics of brain wave after electric stimulation are represented, < + >>Indicating the time period of brain waves after electrical stimulation.
5. The epileptic brain wave signal recognition method according to claim 3 or 4, further comprising performing noise reduction processing on time-frequency domain features of brain waves in a resting state and after electric stimulation, and specifically comprising:
dividing and processing the time-frequency domain characteristics of the brain waves in a resting state and after electric stimulation to obtain a plurality of initial time-frequency domain sub-bands;
respectively carrying out two-dimensional convolution on each initial time-frequency domain sub-band to obtain each initial time-frequency domain sub-band after convolution processing;
the processed time-frequency domain features are generated based on each of the initial time-frequency domain subbands after the convolution processing.
6. The method for recognizing epileptic brain wave signals according to claim 1, wherein in the step S5, the process of comparing the statistical result with a preset threshold value to output the brain wave seizure termination time includes:
recording the duration time that the ratio of the cycle characteristic value of the electroencephalogram after the electric stimulation to the cycle characteristic value of the electroencephalogram in the resting state is smaller than a preset threshold value based on the statistical result, and acquiring the electroencephalogram time when the duration time exceeds 5 seconds to obtain the brain wave attack termination time.
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| Application Number | Priority Date | Filing Date | Title |
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| CN202310857922.3A CN116570297B (en) | 2023-07-13 | 2023-07-13 | Epileptic brain wave signal identification method |
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| CN202310857922.3A CN116570297B (en) | 2023-07-13 | 2023-07-13 | Epileptic brain wave signal identification method |
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| CN116570297A true CN116570297A (en) | 2023-08-11 |
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