A method for judging the degree of noise interference on an electroencephalogram signal and a related device

Abstract

The application discloses a method for judging the degree of noise interference on an electroencephalogram signal and related devices, wherein the method comprises: obtaining a first target curve and a second target curve corresponding to a target electroencephalogram signal respectively, wherein the first target curve and the second target curve are a plurality of single-side spectrum curves corresponding to electroencephalogram signals collected by different electroencephalogram signal sampling channels; calculating the maximum value in the first target curve and the maximum value in the second target curve respectively, and obtaining a first maximum value and a second maximum value; and judging the degree of noise interference on the target electroencephalogram signal according to the first maximum value and the second maximum value. The application calculates characteristic parameters of the electroencephalogram signal, uses single-side spectrum curves to assist in judging the degree of noise interference in the process of collecting the electroencephalogram signal, increases the parameters for judging the noise interference, makes the result more reliable, and can more directly display the spectrum characteristics of the measured electroencephalogram signal to assist doctors in better judging the state of the brain of a patient.

Description

Technical Field

[0001] This invention relates to the field of electroencephalogram (EEG) signal processing technology, and in particular to a method and related apparatus for judging the degree of noise interference in EEG signals. Background Technology

[0002] The bispectral index (BIS) is a commonly used clinical index for monitoring the depth of anesthesia in patients. Using BIS can effectively reduce the amount of anesthetic used during surgery and the incidence of patient awakening. However, BIS is also easily affected by various interferences, such as changes in electrooculography (EOG) signals caused by eye movements, changes in electromyography (EMG) signals caused by muscle activity, and the patient's mental state. EEG signal acquisition equipment itself incorporates many signal preprocessing techniques to reduce noise interference and uses the Signal Quality Index (SQI) to represent the quality of the current EEG signal. However, using only one index as a basis for noise judgment is rather simplistic. Summary of the Invention

[0003] The main objective of this invention is to provide a method and related device for judging the degree of noise interference in electroencephalogram (EEG) signals, which can solve the problem that the existing technology has a relatively singular basis for noise judgment. The related device includes at least a monitor and a storage medium.

[0004] To achieve the above objectives, the first aspect of the present invention provides a method for judging the degree of noise interference in electroencephalogram (EEG) signals, the method comprising:

[0005] A first target curve and a second target curve corresponding to the target EEG signal are acquired respectively. 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, 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 used for sampling the EEG signal. The first target curve includes multiple first unilateral spectral curves corresponding to the first EEG signal in the target EEG signal, and the second target curve includes multiple second unilateral spectral 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 the first maximum value and the second maximum value. The degree of noise interference on the target EEG signal is determined based on the first maximum value and the second maximum value.

[0007] In conjunction with the first aspect, in one possible implementation, the above method further includes: acquiring a first voltage value and a second voltage value 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 based on the first voltage value to obtain a first target field power, and calculating the field power of the n data points based on the second voltage value to obtain a second target field power.

[0008] In conjunction with the first aspect, in one possible implementation, the determination of the degree of noise interference on the target EEG signal 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; and determining the degree of noise interference on the target EEG signal based on the first maximum value, the second maximum value, the first target value, and the second target value.

[0009] In conjunction with the first aspect, in one possible implementation, determining the degree of noise interference on the target EEG signal 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 a first threshold and the first maximum value is less than a second threshold, then the degree of noise interference on the target EEG signal is determined to be a first level; wherein, the first threshold is the product of the second target value and a first preset value, the second threshold is the product of the second maximum value and a second preset value, both the first preset value and the second preset value are less than 1, and the first level indicates that the preset parameters calculated based on the target EEG signal have high reliability; if the first target value is less than the first threshold and the first maximum value is greater than the second threshold, then the degree of noise interference on the target EEG signal is determined to be a second level; the second level indicates that the preset parameters calculated based on the target EEG signal have medium reliability; if the first target value is greater than the first threshold and the first maximum value is greater than the second threshold, then the degree of noise interference on the target EEG signal is determined to be a third level; wherein, the third level indicates that the preset parameters calculated based on the target EEG signal have low reliability.

[0010] In conjunction with the first aspect, in one possible implementation, before acquiring the first target curve and the second target curve corresponding to the target EEG signal respectively, the method includes: acquiring EEG signals collected by the first sampling channel and the second sampling channel within a preset time period at preset intervals, thereby 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 respectively, to obtain multiple first single-sided spectral curves corresponding to the first EEG signal and multiple second single-sided spectral curves corresponding to the second EEG signal.

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

[0012]

[0013] Where i takes values ​​from 1 to n, x i This represents the voltage value corresponding to the i-th data point. FP represents the average voltage value of n data points. i This represents the field power corresponding to the i-th data point.

[0014] In conjunction with the first aspect, in one possible implementation, obtaining the first maximum value by acquiring a preset number of maximum values ​​of the first target field power includes: acquiring all maximum values ​​of the first target field power; sorting all maximum values ​​of the first target field power by size to obtain a first sequence; if sorted from smallest to largest, then determining the preset number of maximum values ​​after the first sequence as the first maximum value; if sorted from largest to smallest, then determining the preset number of maximum values ​​before the first sequence as the first maximum value.

[0015] In conjunction with the first aspect, in one possible implementation, obtaining the second maximum value by acquiring a preset number of maximum values ​​of the second target field power includes: acquiring all maximum values ​​of the second target field power; sorting all maximum values ​​of the second target field power by size to obtain a second sequence; if sorted from smallest to largest, then the preset number of maximum values ​​after the second sequence are determined as the second maximum value; if sorted from largest to smallest, then the preset number of maximum values ​​before the second sequence are determined as the second maximum value.

[0016] To achieve the above objectives, a second aspect of the present invention provides a patient monitor, the patient monitor including an electroencephalogram (EEG) signal acquisition device, a processor, and an output device;

[0017] The electroencephalogram (EEG) signal acquisition device is used to acquire EEG signals.

[0018] The processor is configured to acquire a first target curve and a second target curve corresponding to the target EEG signal, wherein the target EEG signal includes a first EEG signal and a second EEG signal, the first EEG signal being the EEG signal acquired by the first sampling channel within a preset time period, and the second EEG signal being the EEG signal acquired 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 the EEG signal; the first target curve includes multiple first single-sided spectral curves corresponding to the first EEG signal in the target EEG signal, and the second target curve includes multiple second single-sided spectral curves corresponding to the second EEG signal in the target EEG signal; the processor calculates the maximum value in the first target curve and the maximum value in the second target curve, respectively, to obtain the first maximum value and the second maximum value, and determines the degree of noise interference affecting the target EEG signal based on the first maximum value and the second maximum value;

[0019] The output device is used to output a signal characterizing the degree of noise interference affecting the target EEG signal.

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

[0021] To achieve the above objectives, a fourth aspect of the present invention provides a computer device including a memory and a processor, the memory storing a computer program, which, when executed by the processor, causes the processor to perform the steps in any of the above methods.

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

[0023] This invention provides a method for judging the degree of noise interference in electroencephalogram (EEG) signals. The method involves acquiring a first target curve and a second target curve corresponding to a target EEG signal. The target EEG signal includes a first EEG signal and a second EEG signal. The first EEG signal is the EEG signal acquired by the first sampling channel within a preset time period, and the second EEG signal is the EEG signal acquired by the second sampling channel within the preset time period. The first target curve includes multiple first single-sided spectral curves corresponding to the first EEG signal in the target EEG signal, and the second target curve includes multiple second single-sided spectral 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 to obtain the first maximum value and the second maximum value. The degree of noise interference in the target EEG signal is judged based on the first maximum value and the second maximum value. This invention calculates characteristic parameters of the acquired EEG signal and uses single-sided spectral curves to assist in judging the degree of noise interference during the EEG signal acquisition process. On the one hand, it increases the parameters for judging noise interference, making the results more reliable; on the other hand, it can more intuitively display the spectral characteristics of the measured EEG signal, assisting doctors in better judging the patient's brain state. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] in:

[0026] Figure 1 This is a flowchart illustrating a method for determining the degree of noise interference in electroencephalogram (EEG) signals according to an embodiment of the present invention.

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

[0028] Figure 3 This is a structural block diagram of a computer device in an embodiment of the present invention. Detailed Implementation

[0029] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0030] This invention provides a method for judging the degree of noise interference in electroencephalogram (EEG) signals. This method is applicable to any scenario requiring judgment of the degree of noise interference in EEG signals. (Refer to...) Figure 1 , Figure 1 This is a flowchart illustrating a method for determining the degree of noise interference in electroencephalogram (EEG) signals according to an embodiment of the present invention. Figure 1 As shown, the specific steps of this method are as follows:

[0031] Step S101: Obtain the first target curve and the second target curve corresponding to the target EEG signal respectively.

[0032] 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, and 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 used for sampling EEG signals. The first target curve includes multiple first unilateral spectral curves corresponding to the first EEG signal in the target EEG signal, and the second target curve includes multiple second unilateral spectral curves corresponding to the second EEG signal in the target EEG signal.

[0033] Step S102: Calculate the maximum value in the first target curve and the maximum value in the second target curve respectively to obtain the first maximum value and the second maximum value. Determine the degree of noise interference on the target EEG signal based on 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 ease of description, the two preset EEG signal sampling channels are referred to as the first sampling channel and the second sampling channel, respectively. The first sampling channel and the second sampling channel acquire EEG signals in real time, as shown in steps S201-S202:

[0035] Step S201: At each preset time interval, acquire the EEG signals collected by the first sampling channel and the second sampling channel within the preset time period, and obtain the first EEG signal and the second EEG signal accordingly.

[0036] Step S202: 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 to obtain multiple first single-sided spectrum curves corresponding to the first EEG signal and multiple second single-sided spectrum curves corresponding to the second EEG signal.

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

[0038] Then, using a preset duration as the update step size, data of past fixed durations is continuously updated. For example, if the preset duration is 2 seconds and the fixed duration is 10 seconds, the single-sided spectral curve corresponding to the EEG signal collected within the most recent 10 seconds is updated every 2 seconds. For instance, after calculating the 5 first-sided spectral curves and 5 second-sided spectral curves corresponding to the EEG signal within 0-10 seconds, if 2 seconds have passed and the EEG signal for 10-12 seconds has been collected, the first-sided and second-sided spectral curves corresponding to the first and second EEG signals within 10-12 seconds are calculated respectively. This process continues, continuously updating the first and second-sided spectral curves. In other words, the time dimension of the single-sided spectral curve is 2 seconds, meaning one single-sided spectral curve is generated every 2 seconds. For example, if the fixed duration is 1 minute, the target curve (CSA curve) will consist of 30 single-sided spectral curves. The preset and fixed durations can be adjusted according to actual needs.

[0039] In this embodiment, the noise interference level of the EEG signals collected within a 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 obtained respectively. The preset time period can be the time period corresponding to the latest fixed duration among the fixed durations of the collected EEG signals. For example, if the preset duration is 2s and the fixed duration is 10s, and the currently collected EEG signals include EEG signals within 30s, then the preset time period is 20-30s. The target EEG signals include 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 the first single-sided spectrum curve corresponding to the first EEG signal collected by the first sampling channel within 20-30s, and the second target curve includes the second single-sided 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 chronological order.

[0041] Furthermore, this embodiment of the invention also performs steps S301-S302:

[0042] Step S301: Acquire the first voltage value and the second voltage value within the same preset time period as the target EEG signal.

[0043] 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.

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

[0045] In this embodiment, there are n data points within a preset time period. At a preset acquisition 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 acquired in real time. Each data point has a corresponding voltage value.

[0046] The first voltage value and the second voltage value are acquired within the same preset time period as the target EEG signal. The first voltage value includes the voltage values ​​corresponding to n data points in the first sampling channel, and the second voltage value includes the voltage values ​​corresponding to n data points in the second sampling channel. Since there may be multiple acquisition times within the preset time period, the first voltage value may contain multiple voltage values ​​corresponding to multiple data points. Similarly, the second voltage value may contain multiple voltage values ​​corresponding to multiple data points.

[0047] The first target field power is obtained by calculating the field power of n data points corresponding to the first voltage value, and the second target field power is obtained by calculating the field power of n data points corresponding to the second voltage value. Specifically, the field power corresponding to the i-th data point is calculated based on the average of the voltage values ​​of n data points within the same preset time period and the voltage value corresponding to the i-th data point, where i takes values ​​from 1 to n.

[0048] Field power refers to field potential power. In this embodiment, the greater the field power, the stronger the EEG signal and the more active the corresponding brain region.

[0049] The formula for calculating field power is as follows:

[0050]

[0051] Where i takes values ​​from 1 to n, x i This represents the voltage value corresponding to the i-th data point. FP represents the average voltage value of n data points. i This 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 thus 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 thus n field powers can be obtained.

[0053] Using the above method for calculating field power, the field power of n data points can be calculated based on the first voltage value to obtain the first target field power, and the field power of n data points can be calculated based on the second voltage value to obtain the second target field power.

[0054] In another possible implementation, specifically, during the first calculation, the field power for a preset fixed duration is saved first. For example, if the fixed duration is 10s, the field power within 0-10s is saved first. 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. Then, the field power of the past fixed duration is continuously updated with the preset duration as the update step size. For example, if the preset duration is 2s and the fixed duration is 10s, after calculating the first and second target field powers corresponding to 0-10s, after 2s, the voltage values ​​corresponding to the n data points at 10-12s are collected, and the first and second target field powers corresponding to 10-12s are calculated respectively. This process is repeated to continuously update the first and second target field powers.

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

[0056] Step S101 has been described above. Step S102 will be described 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 accordingly. The degree of noise interference on the target EEG signal is determined based on the first maximum value C1 and the second maximum value C2.

[0058] The method for determining the degree of noise interference in the target EEG signal based on the first maximum value C1 and the second maximum value C2 is as shown in steps S401-S402:

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

[0060] Step S402: Determine the degree of noise interference on the target EEG signal based on 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 within a preset time period. Arrange these data points sequentially according to their data point numbers to obtain a first data column containing n field power values. Obtain the maximum values ​​of the field power in the first data column and select a preset number of maximum values ​​from all maximum values ​​as the first maximum value. The first maximum value can be selected from the largest of all maximum values. Specifically, sort all the maximum values ​​of the obtained first target field power in ascending order to obtain a first sequence. If sorted in ascending order, the preset number of maximum values ​​after the first sequence are determined as the first maximum value; if sorted in descending order, the preset number of maximum values ​​before the first sequence are determined as the first maximum value. Calculate the average of the first maximum values ​​to obtain the first target value F1.

[0062] Obtain the field power corresponding to n data points in the second target field power within a preset time period. Arrange these data points sequentially according to their data point numbers to obtain a second data column containing n field power values. Obtain the maximum values ​​of the field power in the second data column and select a preset number of maximum values ​​from all maximum values ​​as the second maximum value. The second maximum value can be selected from the largest of all maximum values. Specifically, sort all the maximum values ​​of the obtained second target field power in ascending order to obtain a second sequence. If sorted in ascending order, the preset number of maximum values ​​after the second sequence are determined as the second maximum value. If sorted in descending order, the preset number of maximum values ​​before the second sequence are determined as the second maximum value. Calculate the average of the second maximum values ​​to obtain the second target value F2.

[0063] The preset number can be 5.

[0064] Based on the first maximum value, the second maximum value, the first target value, and the second target value, determine the degree of noise interference affecting the target EEG signal, as shown in steps S501-S503:

[0065] Step S501: If the first target value is less than the first threshold and the first maximum value is less than the second threshold, then the degree of noise interference on the target EEG signal is determined to be the first level.

[0066] 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 the high reliability of the preset parameters calculated based on the target EEG signal.

[0067] Step S502: If the first target value is less than the first threshold and the first maximum value is greater than the second threshold, then the degree of noise interference on the target EEG signal is determined to be the second level.

[0068] The second level indicates that the reliability of the preset parameters calculated based on the target EEG signal is moderate.

[0069] Step S503: If the first target value is greater than the first threshold and the first maximum value is greater than the second threshold, then the degree of noise interference on the target EEG signal is determined to be level three.

[0070] The third level is characterized by low reliability of preset parameters calculated based on the target EEG signal.

[0071] If F1 < a*F2 and C1 < b*C2, it proves that the EEG signal noise interference used to calculate the preset parameters within the preset time period is small, and the calculation results of the preset parameters have excellent reliability. 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 reliability of the preset parameter calculation results is moderate.

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

[0074] Based on the above method, a first target curve and a second target curve corresponding to the target EEG signal are obtained respectively. The field power of n data points is calculated based on the first voltage value to obtain the first target field power. The field power of n data points is calculated based on the second voltage value to obtain the second target field power. The maximum value in the first target curve and the maximum value in the second target curve are calculated respectively to obtain the first maximum value and the second maximum value. The first target value of the first target field power and the second target field power are obtained. Based on the first maximum value, the second maximum value, the first target value, and the second target value, the degree of noise interference on the target EEG signal is determined. This invention calculates characteristic parameters of the acquired Electroencephalogram (EEG) signal and uses a combination of unilateral spectral curves and field power to assist in judging the degree of noise interference during the acquisition of EEG signals. On the one hand, this increases the parameters for judging noise interference, making the results more reliable; on the other hand, it can more intuitively display the spectral characteristics of the measured EEG signal to assist doctors in better judging the patient's brain state.

[0075] To better implement the above method, embodiments of the present invention also provide a patient monitor, referring to... Figure 2 , Figure 2 This is a flowchart illustrating a patient monitor provided in an embodiment of the present invention, as shown below. 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. Specifically, 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. At preset time intervals, the EEG signal acquisition device 10 acquires the EEG signals acquired by the first and second sampling channels within a preset time period, respectively, to obtain the first EEG signal and the second EEG signal.

[0077] The processor 20 is configured to perform Fourier transform 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 multiple first single-sided spectral curves corresponding to the first EEG signal and multiple second single-sided spectral curves corresponding to the second EEG signal; to acquire the first target curve and the second target curve corresponding to the target EEG signal, wherein the target EEG signal includes the first EEG signal and the second EEG signal, the first EEG signal being the EEG signal collected by the first sampling channel within a preset time period, and the second EEG signal being 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 used for sampling EEG signals; the first target curve includes multiple first single-sided spectral curves corresponding to the first EEG signal in the target EEG signal, and the second target curve includes multiple second single-sided spectral curves corresponding to the second EEG signal in the target EEG signal; to calculate the maximum value in the first target curve and the maximum value in the second target curve, respectively, to obtain the first maximum value and the second maximum value, and to determine the degree of noise interference on the target EEG signal based on the first maximum value and the second maximum value.

[0078] At preset time intervals, the EEG signals collected by the first sampling channel and the second sampling channel within the preset time period are obtained respectively, and the first EEG signal and the second EEG signal are obtained accordingly.

[0079] The processor 20 is further configured 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 the first single-sided spectrum curve corresponding to the first EEG signal and the second single-sided spectrum curve corresponding to the second EEG signal.

[0080] The processor 20 is further configured to acquire a first voltage value and a second voltage value within the same preset time period as the target EEG signal acquisition; wherein the first voltage value includes the voltage values ​​corresponding to n data points in the first sampling channel, and the second voltage value includes the voltage values ​​corresponding to n data points in the second sampling channel; the field power of the n data points is calculated based on the first voltage value to obtain the first target field power, and the field power of the n data points is calculated based on 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] Where i takes values ​​from 1 to n, x i This represents the voltage value corresponding to the i-th data point. FP represents the average voltage value of n data points. iThis represents the field power corresponding to the i-th data point.

[0083] The processor 20 is further configured to acquire 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; acquire 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; and determine the degree of noise interference on the target EEG signal based on the first maximum value, the second maximum value, the first target value, and the second target value. The processor 20 is also configured to acquire all maximum values ​​of the first target field power, sort all maximum values ​​of the first target field power by size to obtain a first sequence; if sorted from smallest to largest, the preset number of maximum values ​​after the first sequence are determined as the first maximum value; if sorted from largest to smallest, the preset number of maximum values ​​before the first sequence are determined as the first maximum value. The processor 20 is further configured to obtain all the maximum values ​​of the second target field power, sort all the maximum values ​​of the second target field power according to their size, and obtain a second sequence; if sorted from smallest to largest, the maximum values ​​of the second sequence after a preset number of maximum values ​​are determined as the second maximum values; if sorted from largest to smallest, the maximum values ​​of the second sequence before a preset number of maximum values ​​are determined as the second maximum values.

[0084] The processor 20 is further configured to determine the degree of noise interference on the target EEG signal as a first level if the first target value is less than a first threshold and the first maximum value is less than a second threshold; wherein, the first threshold is the product of the second target value and a first preset value, the second threshold is the product of the second maximum value and a second preset value, both the first preset value and the second preset value are less than 1, and the first level indicates that the preset parameters calculated based on the target EEG signal have high reliability; if the first target value is less than the first threshold and the first maximum value is greater than the second threshold, the degree of noise interference on the target EEG signal is determined to be a second level; the second level indicates that the preset parameters calculated based on the target EEG signal have medium reliability; if the first target value is greater than the first threshold and the first maximum value is greater than the second threshold, the degree of noise interference on the target EEG signal is determined to be a third level; wherein, the third level indicates that the preset parameters calculated based on the target EEG signal have low reliability.

[0085] The output device 30 is used to output a signal that characterizes the degree of noise interference to the target EEG signal.

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

[0087] Based on the above device, characteristic parameters of the acquired electroencephalogram (EEG) signal can be calculated. A single-sided spectral curve is used to assist in judging the degree of noise interference during the acquisition of the EEG signal. On the one hand, it increases the parameters for judging noise interference, making the results more reliable. On the other hand, it can more intuitively display the spectral characteristics of the measured EEG signal to help doctors better judge the patient's brain state.

[0088] Figure 3 An internal structural diagram of a computer device in one embodiment is shown. This computer device can specifically be a terminal or a server. Figure 3 As shown, the computer device includes a processor, memory, and network interface connected via a system bus. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and may also store a computer program. When executed by the processor, this computer program causes the processor to perform all the steps of the above-described method. The internal memory may also store a computer program, which, when executed by the processor, causes the processor to perform all the steps of the above-described method. Those skilled in the art will understand that... Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0089] In one embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the steps of the aforementioned method.

[0090] In one embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, causes the processor to perform the steps of the aforementioned method.

[0091] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. 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 various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0092] The technical features of the above embodiments can be combined in any way. For the sake of brevity, 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 embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for determining the degree of noise interference in electroencephalogram (EEG) signals, characterized in that, The method includes: A first target curve and a second target curve corresponding to the target EEG signal are acquired respectively. The target EEG signal includes a first EEG signal and a second EEG signal. The first EEG signal is the EEG signal collected by a first sampling channel within a preset time period, and the second EEG signal is the EEG signal collected by a second sampling channel within the preset time period. The first sampling channel and the second sampling channel are two different channels used for sampling the EEG signal. The first target curve includes multiple first unilateral spectral curves corresponding to the first EEG signal in the target EEG signal, and the second target curve includes multiple second unilateral spectral 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 the first maximum value and the second maximum value. The degree of noise interference on the target EEG signal is determined based on the first maximum value and the second maximum value. The first voltage value and the second voltage value are acquired within the same preset time period as the target EEG signal; wherein, the first voltage value includes the voltage values ​​corresponding to n data points in the first sampling channel, and the second voltage value includes the voltage values ​​corresponding to n data points in the second sampling channel. The field power of n data points is calculated based on the first voltage value to obtain the first target field power, and the field power of n data points is calculated based on the second voltage value to obtain the second target field power. The step of determining the degree of noise interference to the target EEG signal based on the first maximum value and the second maximum value includes: Obtain the maximum value of a preset number of the first target field power to obtain the first maximum value, and calculate the average value of the first maximum value to obtain the first target value. Obtain the maximum value of a preset number of the second target field power to obtain the second maximum value, and calculate the average value of the second maximum value to obtain the second target value. Based on the first maximum value, the second maximum value, the first target value, and the second target value, determine the degree of noise interference affecting the target EEG signal; The step of determining the degree of noise interference on the target EEG signal 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 and the first maximum value is less than the second threshold, then the degree of noise interference on the target EEG signal is determined to be level one; wherein, the first threshold is the product of the second target value and the first preset value, the second threshold 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 the high reliability of the preset parameters calculated based on the target EEG signal; If the first target value is less than the first threshold and the first maximum value is greater than the second threshold, then the degree of noise interference on the target EEG signal is determined to be level two; the second level is a medium level of confidence of the preset parameters calculated based on the target EEG signal. If the first target value is greater than the first threshold and the first maximum value is greater than the second threshold, then the degree of noise interference on the target EEG signal is determined to be level three; wherein, the level three is the low reliability of the preset parameters calculated based on the target EEG signal.

2. The method according to claim 1, characterized in that, Before acquiring the first target curve and the second target curve corresponding to the target EEG signal respectively, the method includes: At preset time intervals, the EEG signals collected by the first sampling channel and the second sampling channel within the preset time period are obtained respectively, and the first EEG signal and the second EEG signal are obtained accordingly. Fourier transform calculations were 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 multiple first single-sided spectral curves corresponding to the first EEG signal and multiple second single-sided spectral curves corresponding to the second EEG signal.

3. The method according to claim 1, characterized in that, The calculation formulas for the first target field power and the second target field power are as follows: Where i takes values ​​from 1 to n, This represents the voltage value corresponding to the i-th data point. This represents the average voltage value of n data points. This represents the field power corresponding to the i-th data point.

4. The method according to claim 1, characterized in that, The step of obtaining a preset number of maximum values ​​of the power of the first target field to obtain the first maximum value includes: Obtain all maxima of the power of the first target field, and sort all maxima of the power of the first target field according to their size to obtain a first sequence; If sorted in ascending order, the maximum value of the preset number of items after the first sequence is determined as the first maximum value; if sorted in descending order, the maximum value of the preset number of items before the first sequence is determined as the first maximum value.

5. The method according to claim 1, characterized in that, The step of obtaining a preset number of maximum values ​​of the power of the second target field to obtain the second maximum value includes: Obtain all maxima of the power of the second target field, and sort all maxima of the power of the second target field in order of magnitude to obtain a second sequence; If sorted in ascending order, the maximum value of the preset number of items after the second sequence is determined as the second maximum value; if sorted in descending order, the maximum value of the preset number of items before the second sequence is determined as the second maximum value.

6. A patient monitor, characterized in that, The monitor includes an electroencephalogram (EEG) signal acquisition device, a processor, and an output device; The electroencephalogram (EEG) signal acquisition device is used to acquire EEG signals. The processor is configured to perform the steps of the method as described in any one of claims 1 to 5; The output device is used to output a signal characterizing the degree of noise interference affecting the target EEG signal.

7. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, the processor performs the steps of the method as described in any one of claims 1 to 5.

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