CN112472108B - Neuron discharge spike signal picking method and device and computer equipment - Google Patents
Neuron discharge spike signal picking method and device and computer equipment Download PDFInfo
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Abstract
The application relates to a neuron firing spike picking method, a neuron firing spike picking device, computer equipment and a storage medium. The method comprises the following steps: preprocessing a neuron discharge signal to be picked to obtain a continuous filtering discharge signal; performing energy calculation on the continuous filtering discharge signal to obtain a continuous energy signal; carrying out peak value statistics on the continuous energy signals to determine energy peak values; establishing a cumulative distribution function of chi-square distribution according to the continuous energy signals for analysis, and determining a picking threshold; screening the energy peak value through a preliminary peak value extraction model established by the picking threshold value to obtain a preliminary peak value; extracting a signal section in the continuous filtering discharge signal according to the preliminary peak value to obtain a discharge signal section; and analyzing the firing signal section according to the picking threshold value to determine a neuron firing spike signal. The method enables the robustness of the neuron discharge spike signal to be stronger, and improves the accuracy of the picking result.
Description
Technical Field
The present application relates to the field of digital signal processing technologies, and in particular, to a method and an apparatus for picking neuron firing spikes, a computer device, and a storage medium.
Background
During the neural signal acquisition, a high sampling rate mode is used for recording continuous signals around the neurons, and the neuron discharge Spike signal (Spike) is a short discharge process lasting for about 1 ms. In order to extract the signal of the short time period, a method is often used to observe the signal, set a threshold value, and select the signal exceeding the threshold value as the firing process of a neuron.
This approach requires a person to observe the signal threshold, increasing the workload, and since the threshold is artificially specified, increasing the instability of signal extraction; and the method can only sort out the nerve discharge spikes with larger amplitude, and some spikes with smaller amplitude are ignored in the method, so that the statistical error of the nerve signals is caused.
Therefore, the accuracy of the neuron firing spikes picked by the current neural firing spike picking method is low.
Disclosure of Invention
In view of the above, there is a need to provide a neuron spiking method, a neuron spiking device, a computer device and a storage medium, which can improve the accuracy of picking neuron spiking.
A method of neuron firing spike picking, the method comprising:
preprocessing a neuron discharge signal to be picked to obtain a continuous filtering discharge signal;
performing energy calculation on the continuous filtering discharge signal to obtain a continuous energy signal;
carrying out peak value statistics on the continuous energy signals to determine energy peak values;
establishing a cumulative distribution function of chi-square distribution according to the continuous energy signals for analysis, and determining a picking threshold;
screening the energy peak value through a preliminary peak value extraction model established by the picking threshold value to obtain a preliminary peak value;
extracting a signal section in the continuous filtering discharge signal according to the preliminary peak value to obtain a discharge signal section;
and analyzing the firing signal section according to the picking threshold value to determine a neuron firing spike signal.
In one embodiment, the energy calculation is formulated as:
wherein the content of the first and second substances,
in order to be a continuous energy signal,
is a continuous filtering discharge signal.
In one embodiment, the step of determining a picking threshold by analyzing a cumulative distribution function that establishes a chi-squared distribution based on the continuous energy signal comprises:
establishing a cumulative distribution function of chi-square distribution according to the continuous energy signals;
performing second-order difference on the cumulative distribution function, and determining an outlier critical value of the cumulative distribution function;
and taking the outlier critical value as a picking threshold value.
In one embodiment, the cumulative distribution function is:
wherein the content of the first and second substances,
the function of the cumulative distribution is represented,
which represents a continuous energy signal, is,
represents the signal length of the continuous energy signal,
an outlier threshold representing the cumulative distribution function,
a natural base number is represented by a number,
is the gamma distribution.
In one embodiment, the step of screening the energy peaks by the preliminary peak extraction model constructed by the picking threshold to obtain preliminary peaks includes:
carrying out maximum extraction on the energy peak value to obtain a maximum value point;
and comparing the maximum point with the picking threshold value through a preliminary peak value extraction model established by the picking threshold value to determine a preliminary peak value.
In one embodiment, the step of extracting the signal segment in the continuous filtered discharge signal according to the preliminary peak value to obtain the discharge signal segment includes:
determining 1 energy peak value before and after the initial peak value is adjacent to the initial peak value according to the time point corresponding to the initial peak value;
determining time points between the front and rear 1 energy peak values adjacent to the preliminary peak value as time nodes of a discharge section;
and determining a discharge signal section of the discharge section on the continuous filtering discharge signal according to the time node of the discharge section.
In one embodiment, the step of analyzing the firing signal segment according to the picking threshold to determine the neuron firing spike comprises:
determining the amplitude of the discharge signal section according to the maximum peak value and the minimum peak value in the discharge signal section;
and comparing the amplitude of the firing signal section with 2 times of the picking threshold value, and determining the firing signal section with the amplitude larger than 2 times of the picking threshold value as a neuron firing spike signal.
A neuron firing spike picking device, the device comprising:
the preprocessing module is used for preprocessing the discharge signals of the neurons to be picked to obtain continuous filtering discharge signals;
the energy calculation module is used for performing energy calculation on the continuous filtering discharge signal to obtain a continuous energy signal;
the peak value counting module is used for carrying out peak value counting on the continuous energy signals and determining an energy peak value;
the threshold value determining module is used for establishing a cumulative distribution function of chi-square distribution according to the continuous energy signals for analysis and determining a picking threshold value;
the screening module is used for screening the energy peak value through a primary peak value extraction model constructed by the picking threshold value to obtain a primary peak value;
the signal segment extraction module is used for extracting the signal segments in the continuous filtering discharge signals according to the preliminary peak value to obtain discharge signal segments;
and the spike signal determining module is used for analyzing the firing signal section according to the picking threshold value and determining the neuron firing spike signal.
A computer device comprising a memory storing a computer program and a processor implementing the steps of the method when executing the computer program.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method.
According to the neuron discharge spike signal picking method, the neuron discharge spike signal picking device, the computer equipment and the storage medium, the discharge signal of the neuron to be picked is preprocessed to obtain the continuous filtering discharge signal, the energy calculation is carried out on the continuous filtering discharge signal to obtain the continuous energy signal, and the difference between the spike signal and the non-discharge process signal in the discharge process of the neuron in the filtered continuous filtering discharge signal is increased; further determining an energy peak value by performing peak value statistics on the continuous energy signal; establishing a chi-square distribution cumulative distribution function according to the continuous energy signals for analysis, determining a picking threshold value, enabling the picking of the neuron discharge signals to be more accurate, and screening the energy peak value through a primary peak value extraction model established by the picking threshold value to obtain a primary peak value; extracting a signal section in the continuous filtering discharge signal according to the preliminary peak value to obtain a discharge signal section; and analyzing the discharge signal section according to the picking threshold value to determine the neuron discharge spike signal, so that the robustness of the method for picking the neuron discharge spike signal is stronger, and the accuracy rate of picking the neuron discharge spike signal is improved.
Drawings
FIG. 1 is a schematic flow chart illustrating a method for neuron spiking in one embodiment;
FIG. 2 is a schematic diagram of a filtered continuous discharge signal;
FIG. 3 is a schematic diagram of a function inflection point of a cumulative distribution function;
FIG. 4 is a schematic diagram of neuron firing spike picking;
FIG. 5 is a block diagram of a neuron firing spike picking device in accordance with one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
In one embodiment, as shown in fig. 1, there is provided a method for picking neuron firing spikes, comprising the following steps:
step S220, preprocessing the neuron firing signals to be picked to obtain continuous filtering firing signals.
The neuron firing signals to be picked are continuous original neuron firing signals which are continuously collected by neuron firing signal collecting equipment within a period of time. The preprocessing comprises up-sampling and filtering processing, the frequency band range of the neuron firing spike signal is 300 Hz-6000 Hz, so that the original neuron firing signal needs to be up-sampled to more than 30Ksps, and the picked neuron firing signal with the duration of 1ms can have enough points; and filtering the up-sampled discharge signal to filter out the low-frequency local field potential in the discharge signal. The continuous filtered discharge signal is a filtered continuous discharge signal, such as the schematic diagram of the filtered continuous discharge signal shown in fig. 2.
In one embodiment, the step of preprocessing the firing signals of the neurons to be picked to obtain the continuous filtering firing signals comprises: performing up-sampling processing on the discharge signals of the neurons to be picked to obtain continuous up-sampling discharge signals; and carrying out filtering processing on the continuous up-sampling discharge signal to obtain a continuous filtering discharge signal.
In one embodiment, the step of performing filtering processing on the up-sampled discharge signal to obtain a filtered discharge signal includes:
according to a wavelet packet decomposition reconstruction formula, performing 5-layer wavelet transform decomposition on the upsampled discharge signal by using a db10 wavelet basis to obtain a decomposed signal; denoising the decomposed signal by adopting a soft threshold method to obtain a denoised signal; and reconstructing the denoised signal according to a wavelet packet decomposition reconstruction formula to obtain a filtering discharge signal.
The wavelet packet decomposition reconstruction formula is as follows:
wherein the content of the first and second substances,
for the wavelet packet decomposition reconstruction function,jthe number of layers is reconstructed for the decomposition of the top wavelet packet,kthe number of layers is decomposed and reconstructed for the bottom layer wavelet packet,Sfor the wavelet decomposition of the maximum number of layers,Zis an integer which is the number of the whole,qfor the signal degrees of freedom of the decomposed and reconstructed discharge signal,
in order to reconstruct the coefficients,tis a decomposed and reconstructed discharge signal and is,
wavelet packets determined for the basis functions.
In one embodiment, the basis function used is the db10 wavelet basis.jAndkis determined according to the number of layers to be decomposed or reconstructed, in this embodiment
. Determining and establishing a scale function and a wavelet function according to the basis function, and determining a wavelet packet according to the scale function and the wavelet function
The following expression:
wherein the content of the first and second substances,
determining the wavelet packet for db10 base function according to the parity of signal freedom degree
Defined as a scale function, with odd degrees of freedom
Is defined as a function of the wavelet,
is a filter function that is a function of the scale,
a filter function that is a wavelet function.
Step S240, performing energy calculation on the continuous filtering discharge signal to obtain a continuous energy signal.
The energy calculation of the continuous filtering discharge signal is to calculate the energy of the signal at each time point in the continuous filtering discharge signal, and the formula of the energy calculation is as follows:
wherein the content of the first and second substances,
in order to be a continuous energy signal,
in order to continuously filter the discharge signal, the discharge signal is,
representing the point in time of the signal.
The method enlarges the difference degree of signal fluctuation, can detect the peak value and the valley value of the continuous filtering discharge signal at the same time, reduces the algorithm complexity and improves the calculation efficiency.
Step S260, peak value statistics is performed on the continuous energy signal, and an energy peak value is determined.
Wherein, the peak value statistics is to find out the peak value point in the continuous energy signal. The energy peak comprises the values of the energy signal corresponding to all peak points in the continuous energy signal.
Step S280, establishing a cumulative distribution function of chi-square distribution according to the continuous energy signals for analysis, and determining a picking threshold value.
The continuous filtering discharge signal accords with the normal distribution characteristic, and the continuous energy signal is the square term relation of the continuous filtering discharge signal, so that the data of the continuous energy signal accords with the chi-square distribution characteristic, the minimum value and the maximum value of the peak value are used as the definition domain of the independent variable, the energy peak value is used as the independent variable, the accumulation probability of the peak value is used as the dependent variable, and the accumulation distribution function of the chi-square distribution is established. After peak data is extracted from the continuous filtering discharge signal, the distribution of the peak value also presents normal distribution characteristics, so that a function related to normal distribution is adopted to determine the picking threshold value.
In one embodiment, the step of determining the picking threshold by establishing a cumulative distribution function of the chi-squared distribution from the continuous energy signal for analysis comprises:
establishing a cumulative distribution function of chi-square distribution according to the continuous energy signals; performing second-order difference on the cumulative distribution function, and determining an outlier critical value of the cumulative distribution function; and taking the cluster critical value as a picking threshold value.
Wherein the cumulative distribution function is:
wherein the content of the first and second substances,
the function of the cumulative distribution is represented,
which represents a continuous energy signal, is,
the point in time of the representation of the signal,
an outlier threshold representing the cumulative distribution function,
a natural base number is represented by a number,
is the gamma distribution.
In the filtered high-frequency signal, the spike signal in the neuron discharging process is statistically low-frequency appearing signal relative to the non-discharging process, and the neuron discharging spike signal has a high amplitude, so the picking threshold is an outlier critical value in the cumulative distribution function of chi-square distribution, the cumulative distribution function of chi-square distribution can be subjected to second order difference, a function inflection point as shown in fig. 3 is found, the value of the energy signal corresponding to the function inflection point is the outlier critical value, and the outlier critical value is recorded as
Time point of
As a function of the inflection point, i.e.: in actual operation
Make a second order difference if
In which, according to the actual data, the data is in an exponential distribution mode, so that the time point is adopted
The method for calculating the logarithm of the energy signal reduces the discrete degree of data and then searches for an inflection point. The current time point is
，
The energy signal logarithm value at the current time point is used as an independent variable, the cumulative distribution function value corresponding to the current time point is used as a second-order difference result of a dependent variable,
the energy signal logarithm value of the next time point of the current time point is used as an independent variable, and the cumulative distribution function value corresponding to the current time point is used as a second-order difference result of the dependent variable.
When the cumulative contribution function reaches the function inflection point (which can be adjusted according to the actual situation during detection), the energy signal at the function inflection point is considered to be the outlier threshold of the continuous energy signal, and the firing signal corresponding to the energy signal whose value is greater than the outlier threshold may belong to the neuron firing spike signal.
And step S300, screening the energy peak value through a primary peak value extraction model established by the picking threshold value to obtain a primary peak value.
In one embodiment, the step of screening the energy peaks through a preliminary peak extraction model constructed by picking threshold values to obtain preliminary peaks includes:
carrying out maximum extraction on the energy peak value to obtain a maximum point; and comparing the maximum point with the picking threshold value through a primary peak value extraction model established by the picking threshold value to determine a primary peak value.
The maximum value point is a time point corresponding to a peak (i.e., a maximum value) extracted from a waveform formed by energy peaks, and for a discrete signal, if the discrete signal is a discrete signal
Is as follows
At a time point, if adopted
The energy peak value of each time point is differentiated from the energy peak values of two adjacent time points, the difference of the energy peak value of the previous time point is larger than zero, and the difference of the energy peak value of the next time point is smaller than 0, then
At the maximum point, i.e. the first
Each time point is a time point corresponding to a maximum value in the energy peak value, and the formula is expressed as follows:
wherein the content of the first and second substances,
is as follows
As a result of the difference of the energy peaks at the time points,
is as follows
The difference of the energy peaks at the time points.
All maximum points are recorded as peak values
Wherein
，
Is a natural number which is a natural number,
is as follows
Energy peaks at each time point.
Wherein the content of the first and second substances,
the data is an empty set,
in order to be the preliminary peak value,
to pick the threshold, when the peak value
Greater than the picking threshold
If so, the peak point is retained, otherwise, the peak point is discarded.
And step S320, extracting the signal section in the continuous filtering discharge signal according to the preliminary peak value to obtain a discharge signal section.
In one embodiment, the step of extracting the signal segment in the continuous filtered discharge signal according to the preliminary peak value to obtain the discharge signal segment includes:
determining 1 energy peak value before and after the initial peak value is adjacent to the initial peak value according to the time point corresponding to the initial peak value; determining time points between the front and rear 1 energy peak values adjacent to the primary peak value as time nodes of the discharge section; and determining a discharge signal section of the discharge section on the continuous filtering discharge signal according to the time node of the discharge section.
Determining front and rear 1 energy peak values adjacent to the preliminary peak value according to the corresponding time point of the preliminary peak value; recording and preliminary peak values
The time point between the adjacent front and back 1 energy peak values is determined as the time node of the discharge section and is recorded as
(ii) a Determining the discharge signal section of the discharge section on the continuous filtering discharge signal according to the time node of the discharge section, and recording the discharge signal section as the discharge signal section
Wherein, in the step (A),
in order to discharge the signal segment, the signal segment is,
the time point of the first 1 energy peak of the preliminary peak,
the time point of the last 1 energy peak value of the primary peak value, the number of the discharge sections is determined according to the number of the primary peak values, such as: there are 2 preliminary peaks, and then there are 2 discharge segments. Each time point of the continuous energy signal corresponds to each time point of the continuous filtering discharge signal, each energy peak value corresponds to one time point, each preliminary peak value corresponds to one time point, therefore, each front energy peak value and each rear energy peak value correspond to two time points, the energy signal between the two time points is used as a discharge section, the starting time point and the ending time point of the discharge section are time nodes of the discharge section, and the discharge signal between the starting time point and the ending time point on the continuous filtering discharge signal is used as a discharge signal section.
Step S340, analyzing the firing signal segment according to the picking threshold to determine the neuron firing spike signal.
In one embodiment, the step of determining a neuron firing spike by analyzing the firing signal segment according to a picking threshold comprises:
determining the amplitude of the discharge signal section according to the maximum peak value and the minimum peak value in the discharge signal section; and comparing the amplitude of the discharge signal section with 2 times of the picking threshold value, and determining the discharge signal section with the amplitude larger than 2 times of the picking threshold value as the neuron discharge spike signal.
Determining the amplitude of the discharge signal section according to the maximum peak value and the minimum peak value in the discharge signal section, and recording the amplitude as the maximum peak value and the minimum peak value
(ii) a And solving the difference value between the maximum peak value and the minimum peak value in the discharge signal section, wherein the difference value is the amplitude value of the discharge signal section, namely:
，
is the maximum peak in the discharge signal sectionThe value of the one or more of,
comparing the amplitude of the firing signal section with 2 times of the picking threshold value for the minimum peak value in the firing signal section, and determining the firing signal section with the amplitude larger than 2 times of the picking threshold value as the neuron firing spike signal, wherein the neuron firing spike signal formula is as follows:
wherein the content of the first and second substances,
a neuron fires a spike. As shown in fig. 4, the amplitude of the firing signal segment is greater than 2 times of the picking threshold, and is retained as the neuron firing spike signal, and the amplitude of the firing signal segment is less than or equal to 2 times of the picking threshold, and does not belong to the neuron firing spike signal, the firing signal segment is discarded, and the finally picked neuron firing spike signal is the firing signal segment with the amplitude greater than 2 times of the picking threshold.
According to the neuron discharge spike signal picking method, the discharge signal of the neuron to be picked is preprocessed to obtain the continuous filtering discharge signal, the energy calculation is carried out on the continuous filtering discharge signal to obtain the continuous energy signal, and the difference between the discharge process and the non-discharge process of the neuron in the filtered continuous filtering discharge signal is increased; further performing peak value statistics on the continuous energy signals to determine energy peak values; establishing an accumulative distribution function of chi-square distribution according to the continuous energy signals for analysis, determining a picking threshold value, enabling the picking of the neuron discharge signals to be more accurate, and screening the energy peak value through a primary peak value extraction model established by the picking threshold value to obtain a primary peak value; extracting a signal section in the continuous filtering discharge signal according to the initial peak value to obtain a discharge signal section; the discharge signal section is analyzed according to the picking threshold value, so that the robustness of the method for picking the neuron discharge spike signal is higher, and the accuracy rate of the picking neuron discharge spike signal is higher.
It should be understood that, although the steps in the flowchart of fig. 1 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 described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a portion of the steps in fig. 1 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
In one embodiment, as shown in fig. 5, there is provided a neuron firing spike picking device comprising: a preprocessing module 310, an energy calculation module 320, a peak statistics module 330, a threshold determination module 340, a screening module 350, a signal segment extraction module 360, and a spike determination module 370.
The preprocessing module 310 is configured to preprocess the firing signals of the neurons to be picked to obtain continuous filtering firing signals;
the energy calculation module 320 is configured to perform energy calculation on the continuous filtering discharge signal to obtain a continuous energy signal;
a peak value statistic module 330, configured to perform peak value statistics on the continuous energy signal to determine an energy peak value;
a threshold determination module 340, configured to establish a cumulative distribution function of chi-square distribution according to the continuous energy signals for analysis, and determine a picking threshold;
the screening module 350 is configured to screen the energy peak through a preliminary peak extraction model established by the picking threshold to obtain a preliminary peak;
the signal segment extraction module 360 is configured to extract a signal segment in the continuous filtering discharge signal according to the preliminary peak value to obtain a discharge signal segment;
and the spike signal determining module 370 is configured to analyze the firing signal segments according to the picking threshold to determine a neuron firing spike signal.
In one embodiment, the formula for the energy calculation in the energy calculation module 320 is:
wherein the content of the first and second substances,
in order to be a continuous energy signal,
is a continuous filtering discharge signal.
In one embodiment, the threshold determination module 340 is further configured to: establishing a cumulative distribution function of chi-square distribution according to the continuous energy signals; performing second-order difference on the cumulative distribution function, and determining an outlier critical value of the cumulative distribution function; and taking the cluster critical value as a picking threshold value.
In one embodiment, the cumulative distribution function in the threshold determination module 340 is:
wherein the content of the first and second substances,
the function of the cumulative distribution is represented,
which represents a continuous energy signal, is,
the point in time of the representation of the signal,y 0an outlier threshold representing the cumulative distribution function,
a natural base number is represented by a number,
is the gamma distribution.
In one embodiment, the screening module 350 is further configured to: carrying out maximum extraction on the energy peak value to obtain a maximum point; and comparing the maximum point with the picking threshold value through a primary peak value extraction model established by the picking threshold value to determine a primary peak value.
In one embodiment, the signal segment extraction module 360 is further configured to: determining 1 energy peak value before and after the initial peak value is adjacent to the initial peak value according to the time point corresponding to the initial peak value; determining time points between the front and rear 1 energy peak values adjacent to the primary peak value as time nodes of the discharge section; and determining a discharge signal section of the discharge section on the continuous filtering discharge signal according to the time node of the discharge section.
In one embodiment, spike determination module 370 is further configured to: determining the amplitude of the discharge signal section according to the maximum peak value and the minimum peak value in the discharge signal section; and comparing the amplitude of the discharge signal section with 2 times of the picking threshold value, and determining the discharge signal section with the amplitude larger than 2 times of the picking threshold value as the neuron discharge spike signal.
For specific definition of the neuron firing spike picking device, see the above definition of the neuron firing spike picking method, which is not described herein again. The modules in the above neuron firing spike picking apparatus may be implemented in whole or in part by software, hardware, or a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of the above-mentioned neuron firing spike picking method when executing the computer program.
In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, which when executed by a processor implements the steps of the above-described method for neuron firing spike picking.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware related to instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (9)
1. A method of neuron firing spike picking, the method comprising:
preprocessing a neuron discharge signal to be picked to obtain a continuous filtering discharge signal;
performing energy calculation on the continuous filtering discharge signal to obtain a continuous energy signal;
carrying out peak value statistics on the continuous energy signals to determine energy peak values;
establishing a cumulative distribution function of chi-square distribution according to the continuous energy signals for analysis, and determining a picking threshold;
screening the energy peak value through a preliminary peak value extraction model established by the picking threshold value to obtain a preliminary peak value;
extracting a signal section in the continuous filtering discharge signal according to the preliminary peak value to obtain a discharge signal section;
analyzing the firing signal section according to the picking threshold value to determine a neuron firing spike signal;
the step of analyzing the firing signal segment according to the picking threshold to determine a neuron firing spike signal comprises:
determining the amplitude of the discharge signal section according to the maximum peak value and the minimum peak value in the discharge signal section;
and comparing the amplitude of the firing signal section with 2 times of the picking threshold value, and determining the firing signal section with the amplitude larger than 2 times of the picking threshold value as a neuron firing spike signal.
2. The method of claim 1, wherein the energy calculation is formulated as:
yE(n)＝|x(n)|2
wherein, yE(n) is a continuous energy signal, and x (n) is a continuous filtering discharge signal.
3. The method of claim 1, wherein said step of establishing a cumulative distribution function of chi-squared distributions from said continuous energy signals for analysis to determine a picking threshold comprises:
establishing a cumulative distribution function of chi-square distribution according to the continuous energy signals;
performing second-order difference on the cumulative distribution function, and determining an outlier critical value of the cumulative distribution function;
and taking the outlier critical value as a picking threshold value.
4. The method of claim 3, wherein the cumulative distribution function is:
where chi2cdf (x, n) represents the cumulative distribution function, x represents the continuous energy signal, n represents the time point of the signal, y0Representing the outlier threshold of the cumulative distribution function, e representing the natural base, and Γ being the gamma distribution.
5. The method according to claim 1, wherein the preliminary peak extraction model constructed by the picking threshold screens the energy peaks to obtain preliminary peaks, and the preliminary peak extraction model comprises:
carrying out maximum extraction on the energy peak value to obtain a maximum value point;
and comparing the maximum point with the picking threshold value through a preliminary peak value extraction model established by the picking threshold value to determine a preliminary peak value.
6. The method according to claim 1, wherein the step of extracting the signal segments in the continuously filtered discharge signal according to the preliminary peak values to obtain the discharge signal segments comprises:
determining 1 energy peak value before and after the initial peak value is adjacent to the initial peak value according to the time point corresponding to the initial peak value;
determining time points between the front and rear 1 energy peak values adjacent to the preliminary peak value as time nodes of a discharge section;
and determining a discharge signal section of the discharge section on the continuous filtering discharge signal according to the time node of the discharge section.
7. A neuron firing spike picking device, the device comprising:
the preprocessing module is used for preprocessing the discharge signals of the neurons to be picked to obtain continuous filtering discharge signals;
the energy calculation module is used for performing energy calculation on the continuous filtering discharge signal to obtain a continuous energy signal;
the peak value counting module is used for carrying out peak value counting on the continuous energy signals and determining an energy peak value;
the threshold value determining module is used for establishing a cumulative distribution function of chi-square distribution according to the continuous energy signals for analysis and determining a picking threshold value;
the screening module is used for screening the energy peak value through a primary peak value extraction model constructed by the picking threshold value to obtain a primary peak value;
the signal segment extraction module is used for extracting the signal segments in the continuous filtering discharge signals according to the preliminary peak value to obtain discharge signal segments;
the spike signal determining module is used for analyzing the firing signal section according to the picking threshold value and determining a neuron firing spike signal;
the spike determination module is further to: determining the amplitude of the discharge signal section according to the maximum peak value and the minimum peak value in the discharge signal section; and comparing the amplitude of the firing signal section with 2 times of the picking threshold value, and determining the firing signal section with the amplitude larger than 2 times of the picking threshold value as a neuron firing spike signal.
8. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 6 when executing the computer program.
9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 6.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN109700463A (en) * | 2019-03-12 | 2019-05-03 | 四川大学 | A kind of magneticencephalogram epileptic spike recognition methods and system |
| CN110945597A (en) * | 2017-07-25 | 2020-03-31 | 赛尔医疗股份有限公司 | Method and system for predicting epileptic seizures |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN110945597A (en) * | 2017-07-25 | 2020-03-31 | 赛尔医疗股份有限公司 | Method and system for predicting epileptic seizures |
| CN109700463A (en) * | 2019-03-12 | 2019-05-03 | 四川大学 | A kind of magneticencephalogram epileptic spike recognition methods and system |
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