Method for judging noise interference degree of electroencephalogram signal and related device

Abstract

The invention discloses a method for judging the noise interference degree of electroencephalogram signals and a related device.The method comprises the steps that a first target curve and a second target curve corresponding to target electroencephalogram signals are obtained respectively, the first target curve and the second target curve are a plurality of unilateral frequency spectrum curves corresponding to electroencephalogram signals collected by different electroencephalogram signal sampling channels, respectively calculating a maximum value in the first target curve and a maximum value in the second target curve, and correspondingly obtaining a first maximum value and a second maximum value, according to the first maximum value and the second maximum value, the noise interference degree of the target electroencephalogram signals is judged. According to the method, the characteristic parameters of the electroencephalogram signals are calculated, the noise interference degree in the electroencephalogram signal collecting process is judged in an auxiliary mode through the single-side frequency spectrum curve, the parameters for judging noise interference are increased, the result is more credible, the measured frequency spectrum characteristics of the electroencephalogram signals can be displayed more visually, and the accuracy of the electroencephalogram signals is improved. Therefore, a doctor is assisted to better judge the brain state of the patient.

Description

Technical Field

[0001] The present invention relates to the technical field of electroencephalogram (EEG) signal processing, and in particular to a method for judging the degree of noise interference of an EEG signal and a related device. Background Art

[0002] Bispectral Index (BIS) is a commonly used clinical index to monitor the depth of anesthesia of patients. The use of BIS can effectively reduce the use of anesthetics during surgery and the incidence of patient awakening. However, BIS is also easily affected by various interferences, such as changes in electrooculogram signals caused by eye movement, changes in electromyography signals caused by muscle activity, and the patient's mental state. The EEG signal acquisition equipment itself contains many signal preprocessing methods to reduce noise interference, and uses the Signal Quality Index (SQI) to represent the current EEG signal (EEG signal) quality, but using only one index as the basis for noise judgment is relatively simple. Summary of the invention

[0003] The main purpose of the present invention is to provide a method and a related device for judging the degree of noise interference of an EEG signal, which can solve the problem that the basis for noise judgment in the prior art is relatively single. The related device at least includes a monitor and a storage medium.

[0004] To achieve the above object, the present invention provides a method for determining the degree of noise interference of an EEG signal in a first aspect, the method comprising:

[0005] Respectively acquiring a first target curve and a second target curve corresponding to a target EEG signal, wherein the target EEG signal includes a first EEG signal and a second EEG signal, the first EEG signal is an EEG signal collected by the first sampling channel within a preset time period, and the second EEG signal is an EEG signal collected by the second sampling channel within the preset time period; the first sampling channel and the second sampling channel are two different channels for sampling EEG signals; the first target curve includes a plurality of first unilateral spectrum curves corresponding to the first EEG signal in the target EEG signal, and the second target curve includes a plurality of second unilateral spectrum curves corresponding to the second EEG signal in the target EEG signal;

[0006] The maximum value in the first target curve and the maximum value in the second target curve are calculated respectively to obtain a first maximum value and a second maximum value correspondingly, and the degree to which the target EEG signal is interfered by noise is determined according to the first maximum value and the second maximum value.

[0007] In combination with the first aspect, in a possible implementation, the method further includes: respectively acquiring a first voltage value and a second voltage value collected within the same preset time period as the target EEG signal; wherein the first voltage value includes voltage values ​​corresponding to n data points in the first sampling channel, and the second voltage value includes voltage values ​​corresponding to n data points in the second sampling channel; calculating the field power of the n data points respectively according to the first voltage value to obtain a first target field power, and calculating the field power of the n data points respectively according to the second voltage value to obtain a second target field power.

[0008] In combination with the first aspect, in a possible implementation method, the above-mentioned judging the degree to which the target EEG signal is interfered with by noise based on the first maximum value and the second maximum value includes: obtaining a preset number of maximum values ​​of the first target field power to obtain a first maximum value, and calculating the average value of the first maximum value to obtain a first target value, obtaining a preset number of maximum values ​​of the second target field power to obtain a second maximum value, and calculating the average value of the second maximum value to obtain a second target value; judging the degree to which the target EEG signal is interfered with by noise based on the first maximum value, the second maximum value, the first target value and the second target value.

[0009] In combination with the first aspect, in a possible implementation method, the above-mentioned judging the degree to which the target EEG signal is interfered with by noise based on the first maximum value, the second maximum value, the first target value and the second target value includes: if the first target value is less than the first threshold value and the first maximum value is less than the second threshold value, then determining that the degree to which the target EEG signal is interfered with by noise is the first level; wherein the first threshold value is the product of the second target value and the first preset value, the second threshold value is the product of the second maximum value and the second preset value, both the first preset value and the second preset value are less than 1, and the first level is that the credibility of the preset parameters calculated according to the target EEG signal is strong; if the first target value is less than the first threshold value and the first maximum value is greater than the second threshold value, then determining that the degree to which the target EEG signal is interfered with by noise is the second level; the second level is that the credibility of the preset parameters calculated according to the target EEG signal is medium; if the first target value is greater than the first threshold value and the first maximum value is greater than the second threshold value, then determining that the degree to which the target EEG signal is interfered with by noise is the third level; wherein the third level is that the credibility of the preset parameters calculated according to the target EEG signal is low.

[0010] In combination with the first aspect, in a possible implementation, before respectively obtaining the first target curve and the second target curve corresponding to the target EEG signal, the method includes: at each preset time interval, respectively obtaining the EEG signals collected by the first sampling channel and the second sampling channel within a preset time period, and correspondingly obtaining the first EEG signal and the second EEG signal; performing Fourier calculations on the first EEG data corresponding to the first EEG signal and the second EEG data corresponding to the second EEG signal, and obtaining multiple first unilateral frequency spectrum curves corresponding to the first EEG signal, and multiple second unilateral frequency spectrum curves corresponding to the second EEG signal.

[0011] In combination with the first aspect, in a possible implementation manner, the calculation formulas for the first target field power and the second target field power are as follows:

[0012]

[0013] Among them, i ranges from 1 to n, x i represents the voltage value corresponding to the i-th data point, Represents the mean voltage value of n data points, FP i Represents the field power corresponding to the i-th data point.

[0014] In combination with the first aspect, in a possible implementation method, the above-mentioned obtaining of the maximum values ​​of a preset number of the first target field power to obtain the first maximum value includes: obtaining all the maximum values ​​of the first target field power, sorting all the maximum values ​​of the first target field power according to size, and obtaining a set of first sequences; if sorted from small to large, then the maximum values ​​of the preset number after the first sequence are determined as the first maximum value; if sorted from large to small, then the maximum values ​​of the preset number before the first sequence are determined as the first maximum value.

[0015] In combination with the first aspect, in a possible implementation method, the above-mentioned obtaining of the maximum value of a preset number of the second target field power to obtain the second maximum value includes: obtaining all the maximum values ​​of the second target field power, sorting all the maximum values ​​of the second target field power according to size, and obtaining a set of second sequences; if sorted from small to large, then the maximum value of the preset number after the second sequence is determined as the second maximum value; if sorted from large to small, then the maximum value of the preset number before the second sequence is determined as the second maximum value.

[0016] To achieve the above-mentioned object, the second aspect of the present invention provides a monitor, which includes an electroencephalogram signal acquisition device, a processor and an output device;

[0017] The EEG signal acquisition device is used to acquire EEG signals;

[0018] The processor is used to respectively obtain a first target curve and a second target curve corresponding to a target EEG signal, wherein the target EEG signal includes a first EEG signal and a second EEG signal, the first EEG signal is an EEG signal collected by the first sampling channel within a preset time period, and the second EEG signal is an EEG signal collected by the second sampling channel within the preset time period; the first sampling channel and the second sampling channel are two different channels for sampling EEG signals; the first target curve includes a plurality of first unilateral spectrum curves corresponding to the first EEG signal in the target EEG signal, and the second target curve includes a plurality of second unilateral spectrum curves corresponding to the second EEG signal in the target EEG signal; the maximum value in the first target curve and the maximum value in the second target curve are respectively calculated to obtain a first maximum value and a second maximum value, and the degree to which the target EEG signal is interfered by noise is judged according to the first maximum value and the second maximum value;

[0019] The output device is used to output a signal representing the degree to which the target EEG signal is interfered by noise.

[0020] To achieve the above objectives, the third aspect of the present invention provides a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the steps in any of the above methods.

[0021] To achieve the above objectives, the fourth aspect of the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps in any of the above methods.

[0022] The embodiments of the present invention have the following beneficial effects:

[0023] The present invention provides a method for judging the degree of noise interference of an electroencephalogram signal, and respectively obtains a first target curve and a second target curve corresponding to a target electroencephalogram signal, wherein the target electroencephalogram signal includes a first electroencephalogram signal and a second electroencephalogram signal, the first electroencephalogram signal is an electroencephalogram signal collected by the first sampling channel within a preset time period, and the second electroencephalogram signal is an electroencephalogram signal collected by the second sampling channel within the preset time period, the first target curve includes a plurality of first unilateral spectrum curves corresponding to the first electroencephalogram signal in the target electroencephalogram signal, and the second target curve includes a plurality of second unilateral spectrum curves corresponding to the second electroencephalogram signal in the target electroencephalogram signal, and respectively calculates the maximum value in the first target curve and the maximum value in the second target curve, and correspondingly obtains the first maximum value and the second maximum value, and judges the degree of noise interference of the target electroencephalogram signal according to the first maximum value and the second maximum value. The present invention calculates characteristic parameters of the collected electroencephalogram signal (Electroencephalogram, EEG), and uses a unilateral spectrum curve to assist in judging the degree of noise interference in the process of collecting electroencephalogram signals. On the one hand, the parameters for judging noise interference are increased to make the result more credible, and on the other hand, the spectrum characteristics of the measured EEG signal can be more intuitively displayed to assist doctors in better judging the patient's brain state. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0025] in:

[0026] Figure 1 Schematic diagram of a flow chart of a method for determining the degree of noise interference of an EEG signal in an embodiment of the present invention;

[0027] Figure 2 This is a structural block diagram of a monitor in an embodiment of the present invention;

[0028] Figure 3 4 is a structural block diagram of a computer device in an embodiment of the present invention. DETAILED DESCRIPTION

[0029] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0030] The embodiment of the present invention provides a method for judging the degree of noise interference of an EEG signal. The method is applicable to any scenario for judging the degree of noise interference of an EEG signal. Figure 1 , Figure 1 A flowchart of a method for determining the degree of noise interference of an EEG signal provided by an embodiment of the present invention is shown in FIG. Figure 1 As shown, the specific steps of this method are as follows:

[0031] Step S101, respectively obtaining a first target curve and a second target curve corresponding to a target EEG signal.

[0032] Among them, the target EEG signal includes a first EEG signal and a second EEG signal, the first EEG signal is the EEG signal collected by the first sampling channel within a preset time period, the second EEG signal is the EEG signal collected by the second sampling channel within a preset time period, the first sampling channel and the second sampling channel are two different channels for sampling EEG signals, the first target curve includes multiple first unilateral spectrum curves corresponding to the first EEG signal in the target EEG signal, and the second target curve includes multiple second unilateral spectrum curves corresponding to the second EEG signal in the target EEG signal.

[0033] Step S102, respectively calculating the maximum value in the first target curve and the maximum value in the second target curve to obtain a first maximum value and a second maximum value respectively, and judging the degree to which the target EEG signal is interfered by noise according to the first maximum value and the second maximum value.

[0034] In this embodiment, the EEG signals of the subject are sampled in real time through two preset EEG signal sampling channels. For the convenience of description, the two preset EEG signal sampling channels are respectively recorded as the first sampling channel and the second sampling channel in this embodiment. The first sampling channel and the second sampling channel collect EEG signals in real time, as shown in steps S201-S202:

[0035] Step S201 : acquiring the EEG signals collected by the first sampling channel and the second sampling channel in a preset time period at intervals of a preset time, respectively, to obtain the first EEG signal and the second EEG signal accordingly.

[0036] Step S202: Perform Fourier calculation on the first EEG data corresponding to the first EEG signal and the second EEG data corresponding to the second EEG signal to obtain a plurality of first unilateral spectrum curves corresponding to the first EEG signal and a plurality of second unilateral spectrum curves corresponding to the second EEG signal.

[0037] In this embodiment, the unilateral spectrum curve corresponding to the EEG signal collected within a preset time period is updated with a preset time length as a step length. Specifically, during the first calculation, the EEG data of a preset fixed time length is saved first. For example, if the fixed time length is 10s, the EEG data within 10s is saved first, and the EEG signals collected by the first sampling channel and the second sampling channel within 10s are respectively recorded as the first EEG signal and the second EEG signal. The EEG data obtained according to the first EEG signal is recorded as the first EEG data, and the EEG data obtained according to the second EEG signal is recorded as the second EEG data. Fourier calculation is performed on the first EEG data to obtain a first unilateral spectrum curve, and Fourier calculation is performed on the second EEG data to obtain a second unilateral spectrum curve.

[0038] After that, the preset time length is used as the update step length to continuously update the data of the past fixed time length. For example, the preset time length is 2s and the fixed time length is 10s. Every 2s, the unilateral spectrum curve corresponding to the EEG signal collected within the most recent 10s is updated. For example, after calculating the 5 first unilateral spectrum curves and 5 second unilateral spectrum curves corresponding to 0-10s, 2s have passed and the EEG signals at 10-12s are collected. Then, the first unilateral spectrum curve and the second unilateral spectrum curve corresponding to the first EEG signal and the second EEG signal within 10-12s are calculated respectively. By analogy, the first unilateral spectrum curve and the second unilateral spectrum curve are continuously updated. That is to say, the time dimension of the unilateral spectrum curve is 2s, that is, 2s produces a unilateral spectrum curve. If the fixed time length is 1min, the target curve (CSA curve) will be composed of 30 unilateral spectrum curves. The preset time length and the fixed time length can be adjusted according to the actual situation.

[0039] In this embodiment, the degree of noise interference of the EEG signal collected within the preset time period can be judged in real time. Specifically, the first target curve (first CSA curve) and the second target curve (second CSA curve) corresponding to the target EEG signal are respectively obtained. The preset time period at this time can be the time period corresponding to the latest fixed time length among the fixed time lengths corresponding to the collected EEG signal. For example, the preset time length is 2s, the fixed time length is 10s, and the currently collected EEG signal includes the EEG signal within 30s. Then the preset time period is 20-30s, and the target EEG signal includes the first EEG signal collected by the first sampling channel within 20-30s and the second EEG signal collected by the second sampling channel within 20-30s. The first target curve includes a first unilateral spectrum curve corresponding to the first EEG signal collected by the first sampling channel within 20-30s, and the second target curve includes a second unilateral spectrum curve corresponding to the second EEG signal collected by the second sampling channel within 20-30s.

[0040] The target curve can be understood as a compressed spectrum array (CSA), which is obtained by arranging multiple single-sided spectrum curves in time sequence.

[0041] In addition, the embodiment of the present invention further performs step S301-step S302:

[0042] Step S301, respectively acquiring a first voltage value and a second voltage value collected in the same preset time period as that for collecting the target EEG signal.

[0043] The first voltage value includes voltage values ​​corresponding to n data points in the first sampling channel, and the second voltage value includes voltage values ​​corresponding to n data points in the second sampling channel.

[0044] Step S302: Calculate the field powers of n data points respectively according to the first voltage value to obtain a first target field power, and calculate the field powers of n data points respectively according to the second voltage value to obtain a second target field power.

[0045] In this embodiment, there are n data points within a preset time period. At a preset collection time, the voltage values ​​corresponding to the n data points in the first sampling channel and the voltage values ​​corresponding to the n data points in the second sampling channel are collected in real time, and each data point has a corresponding voltage value.

[0046] A first voltage value and a second voltage value collected within the same preset time period as the target EEG signal are respectively obtained, the first voltage value including voltage values ​​corresponding to n data points in the first sampling channel, and the second voltage value including voltage values ​​corresponding to n data points in the second sampling channel. Since there may be multiple collection moments within the preset time period, the first voltage value may include multiple voltage values ​​corresponding to multiple data points. Similarly, the second voltage value may include multiple voltage values ​​corresponding to multiple data points.

[0047] The field powers of n data points are calculated respectively according to the first voltage value to obtain the first target field power, and the field powers of n data points are calculated respectively according to the second voltage value to obtain the second target field power. Specifically, the field power corresponding to the i-th data point is calculated according to the average value of the voltage values ​​of the n data points in the same preset time period and the voltage value corresponding to the i-th data point, where i ranges from 1 to n.

[0048] The field power is the field potential power. In this embodiment, the greater the field power, the stronger the EEG signal is, and the more active the corresponding brain area is.

[0049] The calculation formula of field power is as follows:

[0050]

[0051] Among them, i ranges from 1 to n, x i represents the voltage value corresponding to the i-th data point, Represents the mean voltage value of n data points, FP i Represents the field power corresponding to the i-th data point.

[0052] Therefore, within a preset time period, each of the n data points in the first sampling channel corresponds to a field power, and n field powers can be obtained. Similarly, within a preset time period, each of the n data points in the second sampling channel corresponds to a field power, and n field powers can be obtained.

[0053] Through the above-mentioned field power calculation method, the field power of n data points can be calculated respectively according to the first voltage value to obtain the first target field power, and the field power of n data points can be calculated respectively according to the second voltage value to obtain the second target field power.

[0054] In another possible implementation, it can also be specific that, during the first calculation, the field power of a preset fixed time length is first saved. For example, if the fixed time length is 10s, the field power within 0-10s is first saved, and the field power corresponding to the n data points calculated by the first sampling channel within 0-10s is recorded as the first target field power, and the field power corresponding to the n data points calculated by the second sampling channel within 0-10s is recorded as the second target field power. After that, the preset time length is used as the update step length, and the field power of the past fixed time length is continuously updated. For example, the preset time length is 2s and the fixed time length is 10s. After calculating the first target field power and the second target field power corresponding to 0-10s, after 2s, the voltage values ​​corresponding to the n data points at 10-12s are collected, and the first target field power and the second target field power corresponding to 10-12s are respectively calculated, and so on, and the first target field power and the second target field power are continuously updated.

[0055] The first target field power and the second target field power corresponding to the preset time period can be directly acquired.

[0056] Step S101 has been introduced above, and step S102 will be introduced below.

[0057] The maximum value in the first target curve and the maximum value in the second target curve are calculated respectively, and the first maximum value C1 and the second maximum value C2 are obtained correspondingly. The degree to which the target EEG signal is interfered by noise is determined according to the first maximum value C1 and the second maximum value C2.

[0058] The method for judging the degree to which the target EEG signal is interfered by noise according to the first maximum value C1 and the second maximum value C2 is specifically as shown in step S401-step S402:

[0059] Step S401, obtain the maximum values ​​of a preset number of the first target field power to obtain a first maximum value, and calculate the average value of the first maximum value to obtain a first target value, obtain the maximum values ​​of a preset number of the second target field power to obtain a second maximum value, and calculate the average value of the second maximum value to obtain a second target value.

[0060] Step S402: Determine the degree to which the target EEG signal is interfered by noise according to the first maximum value, the second maximum value, the first target value, and the second target value.

[0061] Obtain the field power corresponding to n data points in the first target field power corresponding to the preset time period, arrange them in sequence according to the data point sequence number, and obtain the first data column. There are n field powers in the first data column. Obtain the maximum value of the field power in the first data column, and select a preset number of maximum values ​​from all the maximum values ​​as the first maximum value, where the first maximum value can be selected from all the maximum values. Specifically, sort all the maximum values ​​of the first target field power obtained by size to obtain a set of first sequences. If sorted from small to large, the maximum values ​​of the preset number after the first sequence are determined as the first maximum value. If sorted from large to small, the maximum values ​​of the preset number before the first sequence are determined as the first maximum value. Calculate the average value of the first maximum value to obtain the first target value F1.

[0062] The field power corresponding to n data points in the second target field power corresponding to the preset time period is obtained, and they are arranged in sequence according to the data point sequence number to obtain a corresponding second data column, and there are n field powers in the second data column. The maximum value of the field power in the second data column is obtained, and a preset number of maximum values ​​are selected from all the maximum values ​​as the second maximum value, wherein the second maximum value can be selected from all the maximum values. Specifically, all the maximum values ​​of the second target field power obtained are sorted by size to obtain a set of second sequences. If they are sorted from small to large, the maximum values ​​of the preset number after the second sequence are determined as the second maximum values. If they are sorted from large to small, the maximum values ​​of the preset number before the second sequence are determined as the second maximum values. The average value of the second maximum value is calculated to obtain the second target value F2.

[0063] Among them, the preset number can be 5.

[0064] According to the first maximum value, the second maximum value, the first target value and the second target value, the degree to which the target EEG signal is interfered by noise is determined, as shown in steps S501 to S503:

[0065] Step S501: If the first target value is less than the first threshold value and the first maximum value is less than the second threshold value, it is determined that the degree to which the target EEG signal is interfered by noise is at the first level.

[0066] Among them, the first threshold is the product of the second target value F2 and the first preset value a, the second threshold is the product of the second maximum value C2 and the second preset value b, the first preset value a and the second preset value b are both less than 1, and the first level is that the credibility of the preset parameters calculated based on the target EEG signal is strong.

[0067] Step S502: If the first target value is less than the first threshold value and the first maximum value is greater than the second threshold value, it is determined that the degree to which the target EEG signal is interfered by noise is at the second level.

[0068] The second level is that the credibility of the preset parameters calculated based on the target EEG signal is medium.

[0069] Step S503: If the first target value is greater than the first threshold value and the first maximum value is greater than the second threshold value, it is determined that the degree to which the target EEG signal is interfered by noise is at the third level.

[0070] Among them, the third level is that the credibility of the preset parameters calculated based on the target EEG signal is low.

[0071] If F1<a*F2 and C1<b*C2, it is proved that the EEG signal noise interference used to calculate the preset parameters in the preset time period is small, and the calculation result of the preset parameters has good credibility. The preset parameters can be BIS values.

[0072] If F1<a*F2 and C1>b*C2, it indicates that the EEG signal used to calculate the preset parameters within the preset time period has certain noise interference, and the calculation result of the preset parameters has medium credibility.

[0073] If F1>a*F2 and C1>b*C2, it indicates that the EEG signal noise interference used to calculate the preset parameters within the preset time period is large, and the reliability of the preset parameter calculation result is poor.

[0074] Based on the above method, the first target curve and the second target curve corresponding to the target EEG signal are obtained respectively, and the field power of n data points is calculated respectively according to the first voltage value to obtain the first target field power, and the field power of n data points is calculated respectively according to the second voltage value to obtain the second target field power, and the maximum value in the first target curve and the maximum value in the second target curve are calculated respectively, and the first maximum value and the second maximum value are obtained correspondingly, and the first target value of the first target field power is obtained, and the second target value of the second target field power is obtained. According to the first maximum value, the second maximum value, the first target value and the second target value, the degree of noise interference of the target EEG signal is judged. The present invention calculates the characteristic parameters of the collected EEG signal (Electroencephalogram, EEG), and uses a unilateral spectrum curve and field power to assist in judging the degree of noise interference in the process of collecting EEG signals. On the one hand, the parameters for judging noise interference are increased to make the results more credible, and on the other hand, the spectrum characteristics of the measured EEG signal can be more intuitively displayed to assist doctors in better judging the patient's brain state.

[0075] In order to better implement the above method, the embodiment of the present invention also provides a monitor, referring to Figure 2 , Figure 2 A flow chart of a monitor provided by an embodiment of the present invention is as follows: Figure 2 As shown, the monitor includes an EEG signal acquisition device 10 , a processor 20 and an output device 30 .

[0076] The EEG signal acquisition device 10 is used to acquire EEG signals. The EEG signal acquisition device 10 is also used to acquire the voltage values ​​of n data points in the first sampling channel and the voltage values ​​of n data points in the second sampling channel. The EEG signal acquisition device 10 is specifically used to acquire the EEG signals collected by the first sampling channel and the second sampling channel in a preset time period at each preset time interval, and obtain the first EEG signal and the second EEG signal accordingly.

[0077] The processor 20 is used to perform Fourier calculations on the first EEG data corresponding to the first EEG signal and the second EEG data corresponding to the second EEG signal, respectively, to obtain a plurality of first unilateral spectrum curves corresponding to the first EEG signal and a plurality of second unilateral spectrum curves corresponding to the second EEG signal; to obtain a first target curve and a second target curve corresponding to the target EEG signal, respectively, wherein the target EEG signal includes the first EEG signal and the second EEG signal, the first EEG signal is the EEG signal collected by the first sampling channel within a preset time period, and the second EEG signal is the EEG signal collected by the second sampling channel within the preset time period; the first sampling channel and the second sampling channel are two different channels for sampling EEG signals; the first target curve includes a plurality of first unilateral spectrum curves corresponding to the first EEG signal in the target EEG signal, and the second target curve includes a plurality of second unilateral spectrum curves corresponding to the second EEG signal in the target EEG signal; the maximum value in the first target curve and the maximum value in the second target curve are respectively calculated to obtain the first maximum value and the second maximum value, and the degree to which the target EEG signal is interfered by noise is judged according to the first maximum value and the second maximum value.

[0078] At each preset time interval, the EEG signals collected by the first sampling channel and the second sampling channel within a preset time period are respectively obtained, and the first EEG signal and the second EEG signal are correspondingly obtained;

[0079] The processor 20 is also specifically used to perform Fourier calculation on the first EEG data corresponding to the first EEG signal and the second EEG data corresponding to the second EEG signal, respectively, to obtain a first unilateral spectrum curve corresponding to the first EEG signal and a second unilateral spectrum curve corresponding to the second EEG signal.

[0080] The processor 20 is also specifically used to respectively obtain the first voltage value and the second voltage value collected in the same preset time period as the target EEG signal; wherein the first voltage value includes the voltage values ​​corresponding to the n data points in the first sampling channel, and the second voltage value includes the voltage values ​​corresponding to the n data points in the second sampling channel; the field power of the n data points is calculated according to the first voltage value to obtain the first target field power, and the field power of the n data points is calculated according to the second voltage value to obtain the second target field power. The calculation formulas for the first target field power and the second target field power are as follows:

[0081]

[0082] Among them, i ranges from 1 to n, x i represents the voltage value corresponding to the i-th data point, Represents the mean voltage value of n data points, FP iRepresents the field power corresponding to the i-th data point.

[0083] The processor 20 is also specifically used to obtain a preset number of maximum values ​​of the first target field power to obtain a first maximum value, and calculate the average value of the first maximum value to obtain a first target value, obtain a preset number of maximum values ​​of the second target field power to obtain a second maximum value, and calculate the average value of the second maximum value to obtain a second target value; according to the first maximum value, the second maximum value, the first target value and the second target value, the degree to which the target EEG signal is interfered by noise is judged. The processor 20 is also specifically used to obtain all the maximum values ​​of the first target field power, sort all the maximum values ​​of the first target field power according to size, and obtain a set of first sequences; if sorted from small to large, the maximum values ​​of the preset number after the first sequence are determined as the first maximum value; if sorted from large to small, the maximum values ​​of the preset number before the first sequence are determined as the first maximum value. The processor 20 is also specifically used to obtain all maximum values ​​of the second target field power, sort all maximum values ​​of the second target field power according to size, and obtain a set of second sequences; if sorted from small to large, the maximum values ​​of a preset number after the second sequence are determined as the second maximum values; if sorted from large to small, the maximum values ​​of a preset number before the second sequence are determined as the second maximum values.

[0084] The processor 20 is also specifically used to determine that the degree to which the target EEG signal is interfered with by noise is at the first level if the first target value is less than the first threshold value and the first maximum value is less than the second threshold value; wherein the first threshold value is the product of the second target value and the first preset value, the second threshold value is the product of the second maximum value and the second preset value, the first preset value and the second preset value are both less than 1, and the first level is that the credibility of the preset parameters calculated according to the target EEG signal is strong; if the first target value is less than the first threshold value and the first maximum value is greater than the second threshold value, then the degree to which the target EEG signal is interfered with by noise is determined to be at the second level; the second level is that the credibility of the preset parameters calculated according to the target EEG signal is medium; if the first target value is greater than the first threshold value and the first maximum value is greater than the second threshold value, then the degree to which the target EEG signal is interfered with by noise is determined to be at the third level; wherein the third level is that the credibility of the preset parameters calculated according to the target EEG signal is low.

[0085] The output device 30 is used to output a signal representing the degree to which the target EEG signal is interfered by noise.

[0086] The output device 30 can also be used to display the first unilateral spectrum curve and the second unilateral spectrum curve, and display the first target field power and the second target field power, so as to provide doctors with more clinical monitoring parameters.

[0087] Based on the above device, the characteristic parameters of the collected electroencephalogram (EEG) signal can be calculated, and a unilateral spectrum curve is used to assist in judging the degree of noise interference in the process of collecting the EEG signal. On the one hand, the parameters for judging the noise interference are increased to make the result more credible. On the other hand, the spectral characteristics of the measured EEG signal can be more intuitively displayed to assist doctors in better judging the patient's brain state.

[0088] Figure 3 FIG. 1 shows an internal structure diagram of a computer device in an embodiment. The computer device may be a terminal or a server. Figure 3 As shown, the computer device includes a processor, a memory and a network interface connected via a system bus. The memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium of the computer device stores an operating system and may also store a computer program. When the computer program is executed by the processor, the processor can implement all the steps of the above method. The internal memory may also store a computer program. When the computer program is executed by the processor, the processor can implement all the steps of the above method. Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0089] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes each step of the aforementioned method.

[0090] In one embodiment, a computer-readable storage medium is provided, which stores a computer program. When the computer program is executed by a processor, the processor executes each step of the aforementioned method.

[0091] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0092] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0093] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for determining the degree of noise interference of an EEG signal, characterized in that: The method comprises: Respectively acquiring a first target curve and a second target curve corresponding to a target EEG signal, wherein the target EEG signal includes a first EEG signal and a second EEG signal, the first EEG signal is an EEG signal collected by the first sampling channel within a preset time period, and the second EEG signal is an EEG signal collected by the second sampling channel within the preset time period; the first sampling channel and the second sampling channel are two different channels for sampling EEG signals; the first target curve includes a plurality of first unilateral spectrum curves corresponding to the first EEG signal in the target EEG signal, and the second target curve includes a plurality of second unilateral spectrum curves corresponding to the second EEG signal in the target EEG signal; The maximum value in the first target curve and the maximum value in the second target curve are calculated respectively to obtain a first maximum value and a second maximum value correspondingly, and the degree to which the target EEG signal is interfered by noise is determined according to the first maximum value and the second maximum value.

2. The method according to claim 1, characterized in that The method further comprises: Respectively acquiring a first voltage value and a second voltage value collected in the same preset time period as the target EEG signal; wherein the first voltage value includes voltage values ​​corresponding to n data points in the first sampling channel, and the second voltage value includes voltage values ​​corresponding to n data points in the second sampling channel; The field powers of n data points are calculated respectively according to the first voltage value to obtain a first target field power, and the field powers of n data points are calculated respectively according to the second voltage value to obtain a second target field power.

3. The method according to claim 2, characterized in that The step of judging the degree to which the target EEG signal is interfered by noise according to the first maximum value and the second maximum value includes: Obtaining a preset number of maximum values ​​of the first target field power to obtain a first maximum value, and calculating an average value of the first maximum value to obtain a first target value, obtaining a preset number of maximum values ​​of the second target field power to obtain a second maximum value, and calculating an average value of the second maximum value to obtain a second target value; The degree to which the target EEG signal is interfered by noise is determined according to the first maximum value, the second maximum value, the first target value, and the second target value.

4. The method according to claim 3, characterized in that The step of judging the degree to which the target electroencephalogram signal is interfered by noise according to the first maximum value, the second maximum value, the first target value, and the second target value includes: If the first target value is less than the first threshold value and the first maximum value is less than the second threshold value, it is determined that the degree to which the target EEG signal is interfered by noise is at the first level; wherein the first threshold value is the product of the second target value and the first preset value, the second threshold value is the product of the second maximum value and the second preset value, the first preset value and the second preset value are both less than 1, and the first level is that the credibility of the preset parameters calculated according to the target EEG signal is strong; If the first target value is less than the first threshold value and the first maximum value is greater than the second threshold value, it is determined that the degree to which the target EEG signal is interfered by noise is at the second level; the second level is that the credibility of the preset parameters calculated according to the target EEG signal is medium; If the first target value is greater than the first threshold and the first maximum value is greater than the second threshold, it is determined that the degree to which the target EEG signal is interfered by noise is at the third level; wherein the third level means that the credibility of the preset parameters calculated based on the target EEG signal is low.

5. The method according to claim 1, characterized in that Before respectively acquiring the first target curve and the second target curve corresponding to the target EEG signal, the method includes: At each preset time interval, the EEG signals collected by the first sampling channel and the second sampling channel within a preset time period are respectively obtained, and the first EEG signal and the second EEG signal are correspondingly obtained; Fourier calculation is performed on the first EEG data corresponding to the first EEG signal and the second EEG data corresponding to the second EEG signal respectively to obtain a plurality of first unilateral frequency spectrum curves corresponding to the first EEG signal and a plurality of second unilateral frequency spectrum curves corresponding to the second EEG signal.

6. The method according to claim 2, characterized in that The calculation formulas of the first target field power and the second target field power are as follows: FP i =(x i -x) 2 Among them, i ranges from 1 to n, x i represents the voltage value corresponding to the i-th data point, x represents the mean voltage value of n data points, FP i Represents the field power corresponding to the i-th data point.

7. The method according to claim 3, characterized in that The step of obtaining a preset number of maximum values ​​of the first target field power to obtain a first maximum value includes: Acquire all maximum values ​​of the first target field power, sort all maximum values ​​of the first target field power according to size, and obtain a set of first sequences; If the order is from small to large, the maximum value of the preset number after the first sequence is determined as the first maximum value; if the order is from large to small, the maximum value of the preset number before the first sequence is determined as the first maximum value.

8. The method according to claim 3, characterized in that The obtaining of the maximum value of the preset number of the second target field powers to obtain the second maximum value includes: Acquire all maximum values ​​of the second target field power, sort all maximum values ​​of the second target field power according to size, and obtain a set of second sequences; If the sorting is from small to large, the maximum value of the preset number after the second sequence is determined as the second maximum value; if the sorting is from large to small, the maximum value of the preset number before the second sequence is determined as the second maximum value.

9. A monitor, characterized in that: The monitor includes an EEG signal acquisition device, a processor and an output device; The EEG signal acquisition device is used to acquire EEG signals; The processor is used to respectively obtain a first target curve and a second target curve corresponding to a target EEG signal, wherein the target EEG signal includes a first EEG signal and a second EEG signal, the first EEG signal is an EEG signal collected by the first sampling channel within a preset time period, and the second EEG signal is an EEG signal collected by the second sampling channel within the preset time period; the first sampling channel and the second sampling channel are two different channels for sampling EEG signals; the first target curve includes a plurality of first unilateral spectrum curves corresponding to the first EEG signal in the target EEG signal, and the second target curve includes a plurality of second unilateral spectrum curves corresponding to the second EEG signal in the target EEG signal; the maximum value in the first target curve and the maximum value in the second target curve are respectively calculated to obtain a first maximum value and a second maximum value, and the degree to which the target EEG signal is interfered by noise is judged according to the first maximum value and the second maximum value; The output device is used to output a signal representing the degree to which the target EEG signal is interfered by noise.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the processor is caused to perform the steps of the method according to any one of claims 1 to 8.

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