Intraoperative electroencephalogram signal quality evaluation method, storage medium, electronic equipment and product

Abstract

The invention relates to the technical field of signal processing, and particularly provides an intra-operative electroencephalogram signal quality evaluation method, a storage medium, electronic equipment and a product, and the method can comprise the following steps: obtaining a characteristic value set corresponding to an electroencephalogram signal to be evaluated, the characteristic value set comprises a contact impedance characteristic value, a power difference characteristic value and a phase lag entropy characteristic value; determining a standard eigenvalue and an entropy weight value of each eigenvalue in the eigenvalue set; and calculating the standard characteristic value and the entropy weight value of each characteristic value to obtain an electroencephalogram signal quality evaluation result. Some embodiments of the invention can improve the accuracy of quality evaluation of the intra-operative electroencephalogram signals.

Description

Technical Field

[0001] The present application relates to the field of signal processing technology, and in particular to a method, storage medium, electronic device and product for evaluating the quality of intraoperative electroencephalogram (EEG) signals. Background Art

[0002] Electroencephalogram (EEG) is a physiological signal that is highly sensitive to time variation and has a weak amplitude. It is easily contaminated by various irrelevant interference sources during the acquisition process, which causes great trouble for the subsequent processing and analysis of EEG signals.

[0003] At present, in order to obtain better quality EEG signals, the existing technology evaluates the quality of EEG signals based on the distinction of time domain features, such as peak-to-peak value or envelope discrimination. However, since different acquisition devices have different sensitivities to noise, evaluation based only on time domain features is prone to misjudgment, which affects the safety of surgery during surgery.

[0004] Therefore, how to provide a more accurate technical solution for intraoperative EEG signal quality assessment has become a technical problem that needs to be solved urgently. Summary of the invention

[0005] The purpose of some embodiments of the present application is to provide a method, storage medium, electronic device and product for evaluating the quality of intraoperative EEG signals. Through the technical solutions of the embodiments of the present application, the quality of EEG signals can be evaluated in real time and accurately with high reliability.

[0006] In a first aspect, some embodiments of the present application provide a method for intraoperative EEG signal quality assessment, comprising: obtaining a set of eigenvalues ​​corresponding to the EEG signal to be evaluated, wherein the set of eigenvalues ​​includes: contact impedance eigenvalues, power difference eigenvalues, and phase lag entropy eigenvalues; determining a standard eigenvalue and an entropy weight value for each eigenvalue in the set of eigenvalues; calculating the standard eigenvalue and the entropy weight value for each eigenvalue to obtain an EEG signal quality assessment result.

[0007] Some embodiments of the present application obtain multiple eigenvalues ​​related to the EEG signal, determine the information entropy and entropy weight value corresponding to each eigenvalue, and then calculate the EEG signal quality assessment result. Some embodiments of the present application can accurately assess the quality of EEG signals in real time, improve the accuracy of EEG signal data processing and analysis during surgery, ensure safety during surgery, and have high reliability.

[0008] In some embodiments, obtaining a set of characteristic values ​​corresponding to the EEG signal to be evaluated includes: performing statistics and calculations on the contact impedance value of the EEG signal to obtain the contact impedance characteristic value; preprocessing the EEG signal to obtain a processed EEG signal; and obtaining the power difference characteristic value and the phase lag entropy characteristic value corresponding to the processed EEG signal.

[0009] Some embodiments of the present application obtain three characteristic values ​​by correspondingly processing the EEG signal, thereby providing a data analysis basis for subsequent EEG signal quality assessment.

[0010] In some embodiments, the power difference characteristic value is obtained by the following method: decomposing the processed EEG signal based on wavelet transform to obtain a first frequency band and a second frequency band; calculating the power difference between the first frequency band and the second frequency band; and taking the ratio of the data length in which the power difference is less than a preset threshold to the window length of the EEG signal as the power difference characteristic value.

[0011] Some embodiments of the present application decompose the processed EEG signal in the frequency domain to obtain a first frequency band and a second frequency band, and then determine the power difference between the frequency bands to calculate the power difference characteristic value. The reliability and applicability of EEG signal quality assessment are improved by making the performance of different devices in the frequency domain information more consistent and stable.

[0012] In some embodiments, the phase lag entropy eigenvalue is obtained by the following method: obtaining the first phase lag entropy and the second phase lag entropy corresponding to the first frequency range and the second frequency range of the processed EEG signal respectively; fusing the first phase lag entropy and the second phase lag entropy to obtain a phase lag entropy fusion value; taking the ratio of the data length of the phase lag entropy fusion value greater than the phase lag entropy threshold to the window length of the EEG signal as the phase lag entropy eigenvalue.

[0013] Some embodiments of the present application calculate the phase lag entropy eigenvalue by fusion calculation of phase lag entropy in different frequency ranges and comparing it with the phase lag entropy threshold, thereby providing data support for the subsequent accurate evaluation of EEG signal quality.

[0014] In some embodiments, before using the ratio of the data length in which the phase lag entropy fusion value is greater than the phase lag entropy threshold to the window length of the EEG signal as the phase lag entropy characteristic value, the method further includes: setting an initial clustering number of EEG signal quality, and determining the center point of each clustering category in the initial clustering number; determining the final clustering category of the EEG signal quality based on the distance between the phase lag entropy fusion value and the center point; selecting two clustering centers from the clustering categories of the EEG signal quality; and using the midpoint of the two clustering centers as the phase lag entropy threshold.

[0015] Some embodiments of the present application cluster the EEG signal quality through the idea of ​​clustering, and determine the phase lag entropy threshold by the midpoint of the distance between the two cluster centers in the final cluster category, so as to achieve accurate calculation of the phase lag entropy eigenvalue and improve the accuracy of the final EEG signal quality assessment.

[0016] In some embodiments, determining the standard eigenvalue and entropy weight value of each eigenvalue in the eigenvalue set includes: standardizing each eigenvalue to obtain each standard eigenvalue; calculating the standard weight of each standard eigenvalue based on each standard eigenvalue; calculating the standard weight of each standard eigenvalue to obtain the information entropy of each eigenvalue; and calculating the entropy weight value of each eigenvalue based on the information entropy of each eigenvalue.

[0017] Some embodiments of the present application calculate the information entropy and entropy weight value after standardizing each feature value and determining the standard weight value, so as to achieve accurate fusion of the three features, avoid the subjective influence caused by human settings, and have high reliability.

[0018] In some embodiments, the standard eigenvalue and the entropy weight value of each eigenvalue are calculated to obtain an EEG signal quality assessment result, including: performing weighted calculation on the standard eigenvalue and the entropy weight value of each eigenvalue to obtain an EEG signal quality assessment value; and determining the EEG signal quality assessment result by determining the quality level interval to which the EEG signal quality assessment value belongs.

[0019] Some embodiments of the present application obtain EEG signal quality assessment results with high accuracy and reliability by confirming the quality level interval to which the EEG signal quality assessment value after weighted calculation of information entropy and entropy weight value belongs, thereby providing reliable data support for intraoperative safety.

[0020] In a second aspect, some embodiments of the present application provide a device for intraoperative EEG signal quality assessment, comprising: a feature acquisition module, configured to acquire a set of eigenvalues ​​corresponding to the EEG signal to be evaluated, wherein the set of eigenvalues ​​includes: contact impedance eigenvalues, power difference eigenvalues ​​and phase lag entropy eigenvalues; an entropy information determination module, configured to determine a standard eigenvalue and an entropy weight value of each eigenvalue in the eigenvalue set; and a quality assessment module, configured to calculate the standard eigenvalue and the entropy weight value of each eigenvalue to obtain an EEG signal quality assessment result.

[0021] In a third aspect, some embodiments of the present application provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, can implement the method described in any embodiment of the first aspect.

[0022] In a fourth aspect, some embodiments of the present application provide an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor can implement a method as described in any embodiment of the first aspect when executing the program.

[0023] In a fifth aspect, some embodiments of the present application provide a computer program product, wherein the computer program product comprises a computer program, wherein the computer program, when executed by a processor, can implement the method described in any embodiment of the first aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions of some embodiments of the present application, the drawings required for use in some embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0025] Figure 1 System diagram for intraoperative EEG signal quality assessment provided for some embodiments of the present application;

[0026] Figure 2 One of the flow charts of the method for intraoperative EEG signal quality assessment provided in some embodiments of the present application;

[0027] Figure 3 A second flow chart of a method for evaluating the quality of intraoperative EEG signals provided in some embodiments of the present application;

[0028] Figure 4 A block diagram of the apparatus for evaluating the quality of intraoperative EEG signals provided in some embodiments of the present application;

[0029] Figure 5 A schematic diagram of an electronic device is provided for some embodiments of the present application. DETAILED DESCRIPTION

[0030] The technical solutions in some embodiments of the present application will be described below in conjunction with the drawings in some embodiments of the present application.

[0031] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0032] In the related technology, during clinical surgery, the environment for collecting EEG signals is very complex. In addition to being affected by the subject itself, the signal is often also affected by electromagnetic interference from the environment. For example, radio frequency interference is generated by electronic surgical instruments and other surgical equipment. The various EEG artifacts caused by these interferences may mask the essential characteristics of EEG signals, thereby causing great difficulties in the processing and analysis of EEG signals. For example, when using EEG signals to monitor the depth of anesthesia during surgery, excessive noise may cause the instrument to misjudge and affect the safe conduct of the operation. Therefore, it is very important to use effective artifact and interference identification methods to conduct real-time and accurate evaluation of the quality of EEG signals.

[0033] There are many ways to evaluate the quality of EEG signals in the current prior art. For example, one way is to evaluate the quality of EEG signals based on the distinction of time domain features (such as peak-to-peak value or envelope discrimination), but different acquisition devices have different sensitivities to noise, and the reliability of this analysis method needs to be improved. Furthermore, the EEG signals collected in the intraoperative environment are superimposed with the background noise in the full-band signal, and it is difficult to judge the quality of the EEG signal only by the signal amplitude and power spectrum density. Moreover, the existing EEG signal quality assessment methods are mostly focused on the identification and evaluation of traditional noise and artifacts, and lack of identification methods for abnormal interference in complex intraoperative environments. For example, the electromagnetic interference caused by surgical instruments such as electric knives used during surgery has not been fully identified and evaluated. It can be seen from the above-mentioned related technologies that the accuracy of the existing technology for evaluating the quality of EEG signals is low.

[0034] In view of this, some embodiments of the present application provide a method for evaluating the quality of EEG signals during surgery. The method is based on the frequency domain of the signal under a specific frequency band, and obtains a set of eigenvalues ​​corresponding to the EEG signal by identifying multiple interference features; then, the entropy weight method is used to objectively weight each eigenvalue indicator, and its entropy weight value is determined by the information entropy of each eigenvalue, thereby avoiding the influence of human subjective factors. Finally, the EEG signal quality evaluation result is determined based on the information entropy of each eigenvalue and its corresponding entropy weight value. Some embodiments of the present application can improve the accuracy of EEG signal quality evaluation.

[0035] The following is combined with Figure 1 The overall composition structure of the system for intraoperative EEG signal quality assessment provided by some embodiments of the present application is exemplified.

[0036] like Figure 1As shown, some embodiments of the present application provide a system for intraoperative EEG signal quality assessment, which may include: an EEG acquisition module 110, a data preprocessing module 120, and a quality assessment module 130. The EEG acquisition module 110 includes a hardware acquisition device 111 and a software receiving platform 112. The quality assessment module 130 includes a feature calculation module 131 and a feature fusion module 132.

[0037] Specifically, the EEG acquisition module 110 includes an EEG signal collector, a transmission and storage medium (as a specific example of the hardware acquisition device 111). The EEG signal collector is used to collect the EEG signal of the subject's current head, and the data is transmitted to the host computer software receiving platform 112 in real time through the transmission and storage medium for reception and storage. The data preprocessing module 120 can preprocess the EEG signal (for example, filtering, downsampling, division, etc.) so that the feature calculation module 131 of the quality assessment module 130 can analyze and calculate the EEG signal to obtain a set of related feature values. That is, the feature calculation module 131 is responsible for calculating the features of the preprocessed EEG signal data, and its internal part can also be divided into three sub-modules (not shown in the figure), which respectively calculate the feature indicators required for the quality assessment of the three EEG signals. These three feature indicators include: the contact impedance feature value, the power difference feature value and the phase lag entropy feature value of each EEG signal processing unit (that is, the EEG signal). The feature fusion module 132 is responsible for fusing and analyzing the three feature values ​​to obtain the fused EEG quality index (EQI, or EEG signal quality evaluation value), and then confirm the EEG quality level.

[0038] The EEG signal quality assessment system adopted in some embodiments of the present application can assess the quality of the current EEG signal, effectively determine the quality of the EEG signal, and judge whether the EEG signal collector is worn correctly, so as to facilitate the subsequent processing of the EEG signal and evaluate whether the EEG signal is credible, thereby providing a basis for the subsequent EEG signal processing and the implementation of related algorithms.

[0039] The following is combined with Figure 2 Exemplary description of some embodiments of the present application provided by Figure 1 The implementation process of intraoperative EEG signal quality assessment performed by the EEG signal quality assessment system.

[0040] Please see attached Figure 2 , Figure 2 A flow chart of a method for evaluating the quality of intraoperative EEG signals provided in some embodiments of the present application, the method for evaluating the quality of intraoperative EEG signals may include:

[0041] S210, obtaining a set of eigenvalues ​​corresponding to the EEG signal to be evaluated, wherein the set of eigenvalues ​​includes: a contact impedance eigenvalue, a power difference eigenvalue, and a phase lag entropy eigenvalue.

[0042] For example, in some embodiments of the present application, the dual-channel EEG signal acquisition of the person under test during the operation is performed by the EEG acquisition module 110, and the signal acquisition can be completed by connecting the electrodes to the human head in a traditional manner. For example, the EEG acquisition device can obtain the electrophysiological signal of the person under test by attaching the acquisition electrodes to the head of the person under test. Usually, the process of pre-filtering, amplification and analog-to-digital conversion is required. The metal electrodes of the head-mounted device in the EEG acquisition module 110 are placed at the frontal lobe Fp1 and Fp2 of the person under test, and the reference electrode and the ground electrode are connected at the same time. The acquisition device is turned on and connected to the software receiving platform 112 to start data acquisition. The collected original signal contains EEG signals, artifacts and interference signals, wherein the artifacts may include partial electromyographic artifacts, background noise signals, etc., and the interference signals may include electromagnetic interference caused by surgical instruments such as electric knives and defibrillators. Then, the collected EEG signals are analyzed to obtain the corresponding characteristic values. It should be understood that when the present application collects the EEG signals of the person being tested, there are two groups of signals, Fp1 and Fp2. In the subsequent quality assessment, one of the groups can be selected or the quality assessment of both groups of signals can be performed separately. The embodiments of the present application are not specifically limited here.

[0043] In some embodiments of the present application, S210 may include:

[0044] S211, statistics and calculation are performed on the contact impedance value of the electroencephalogram signal to obtain the contact impedance characteristic value.

[0045] For example, in some embodiments of the present application, the collected EEG signal is analyzed, and the electrode contact impedance (CI) index is calculated as the first feature of the quality assessment module 130 (as a specific example of the contact impedance feature value).

[0046] Specifically, since EEG signals are easily affected by the internal impedance of the skin and tissue in the acquisition area, the stability and accuracy of the impedance of the connection between the EEG acquisition front end and the brain will directly affect the reliability and accuracy of EEG signal acquisition. Therefore, the impedance stability in the connection state is evaluated by calculating the sensor impedance. The process of evaluating the electrode contact impedance of the collected EEG signal is as follows:

[0047] Step 1: divide the collected original signal into data and use the sliding window to calculate the scalp contact impedance value CI at the i-th time point within time T i(i=1,2,…,T). It should be noted that the present application divides the original signal into multiple processing units with a time length (or window) of T through a sliding window, and the "brain electric signal" in S210 may refer to one of the multiple processing units. In other words, the present application performs quality assessment on the brain electric signal of a single processing unit.

[0048] Step 2: Set the upper threshold th1. If the CI value is greater than th1 for 5 consecutive seconds within the acquisition window, execute step 3; otherwise, execute step 4.

[0049] Step 3: The EEG quality index (EQI) is set to 0, and it is prompted that the impedance value is too large. It is necessary to immediately find out the cause and adjust the acquisition method until the impedance value CI is less than the upper threshold.

[0050] Step 4, set the lower limit threshold th2, count the contact impedance score during the window T, check the impedance ratio in the lead that does not exceed the threshold th2, and calculate the contact impedance score (that is, the first feature). That is, calculate the percentage of the number of sampling points that do not exceed th2 in the signal window T to the total number of sampling points in the entire window T, and use it as the first feature F1 of the quality assessment module. The more sampling points that exceed th2, the worse the signal quality is considered to be.

[0051] S212, pre-processing the EEG signal to obtain a processed EEG signal.

[0052] For example, in some embodiments of the present application, a bandpass filter is used to filter out baseline drift of the EEG signal, retaining the signal in the range of 0.1 Hz-100 Hz; a 50 Hz notch filter is used to filter out power frequency interference; further, the filtered EEG signal is downsampled, and the sampling rate is adjusted from 1000 Hz to 128 Hz; the processed EEG signal is also processed at one unit per T seconds.

[0053] It should be noted that, in another embodiment, the original signal corresponding to the EEG signal can be first filtered and downsampled, and then divided into multiple processing units with a period of T, and the quality of the EEG signal in a processing unit is subsequently evaluated. Specifically, the division method of multiple processing units and the execution stage of the division step can be selected according to actual needs, and the embodiment of the present application is not specifically limited here.

[0054] S213, obtaining the power difference characteristic value and the phase lag entropy characteristic value corresponding to the processed EEG signal.

[0055] For example, in some embodiments of the present application, a power difference index (as a specific example of a power difference characteristic value) is calculated for the processed EEG signal (as a specific example of a processed EEG signal) to detect the influence of background noise. A phase lag entropy PLE characteristic value is calculated for the processed EEG signal to detect the influence of electrosurgical interference.

[0056] In some embodiments of the present application, S213 may include: decomposing the processed EEG signal based on wavelet transform to obtain a first frequency band and a second frequency band; calculating the power difference between the first frequency band and the second frequency band; and taking the ratio of the data length in which the power difference is less than a preset threshold to the window length of the EEG signal as the power difference characteristic value.

[0057] For example, in some embodiments of the present application, wavelet transform is used to extract effective signal frequency bands and main noise frequency bands. Specifically, discrete wavelet transform (DWT) is used to decompose the EEG signal to obtain wavelet coefficients of multiple scales; based on the obtained wavelet coefficients, the main frequency range of the effective signal is selected as the first frequency band, and the main frequency range of the noise signal is selected as the second frequency band. The power difference is calculated using a sliding window. Calculate the signal power P1 in the first frequency band and the signal power P2 in the second frequency band. Take the logarithm of the signal power P1 and the signal power P2 respectively, and calculate the difference SPD, that is, SPD = 10log 10 P1-10log 10 P2. Set a preset threshold th2, and count the cumulative data length L of the SPD value in the signal unit with a length of T that is less than th2 SPD , and calculate its proportion in the window Lwindow (that is, the window length), and obtain the second feature F2 (that is, the power difference feature value), that is, F2 = L SPD / L window .

[0058] In some embodiments of the present application, S213 may include: obtaining the first phase lag entropy and the second phase lag entropy corresponding to the first frequency range and the second frequency range of the processed EEG signal respectively; fusing the first phase lag entropy and the second phase lag entropy to obtain a phase lag entropy fusion value; and taking the ratio of the data length of the phase lag entropy fusion value greater than the phase lag entropy threshold to the window length of the EEG signal as the phase lag entropy characteristic value.

[0059] For example, in some embodiments of the present application, the process of obtaining the phase lag entropy PLE eigenvalue is as follows:

[0060] It should be noted that when collecting data from the person under test, processed EEG signals of two channels (ie, the first channel and the second channel) corresponding to Fp1 and Fp2 can be collected.

[0061] Step 1: Use a bandpass filter to extract from the EEG signal the channel signals x1(t) and x2(t) corresponding to the first channel and the second channel respectively within the range of 0.1-1 Hz (as a specific example of the first frequency range), and the channel signals x1(t) and x2(t) corresponding to the first channel and the second channel respectively within the frequency range of 30-45 Hz (as a specific example of the second frequency range).

[0062] Step 2: Calculate the instantaneous phase of the analytical signal Perform Hilbert transform on the above signals x1(t) and x2(t) respectively to construct the corresponding complex analytic signals Where A(t) is the envelope of the analytical signal, The instantaneous phase of the signal. This can be used to obtain the corresponding phase of x1(t): and x2(t) corresponds to

[0063] Step 3: and Calculate the time phase difference Δ t (t=1, 2, ...N), N is the number of data points sampled in the EEG signal.

[0064] Step 4: Binarize the phase difference to generate a symbol sequence, which is a vector of the time pattern of the phase relationship between the signals, that is, Φ(t) = {Δ t ,Δ t+τ ,...,Δ t+(m-1)τ}, t=1,2,...N-(m-1)τ;

[0065] Where τ represents the time lag with a resolution of 1 / fs, and m is the dimension of the temporal pattern of the phase relationship to be extracted. If the phase of the first signal leads the phase of the second signal, the temporal phase difference is positive, and Δ t =1. When the phase of the first signal lags behind the second signal, the time difference becomes negative, Δ t =0.

[0066] Step 5: Apply the standard Shannon entropy formula to the distribution of the phase diagram to obtain the phase lag entropy PLE:

[0067]

[0068] P j is the probability of the jth mode in a given epoch, calculated as:

[0069]

[0070] Specifically, within the range of 0.1-1 Hz, PLE1 is obtained by using the above formula for the channel signals x1(t) and x2(t) corresponding to the first channel and the second channel respectively; similarly, within the range of 30-45 Hz, PLE2 is obtained by using the above formula for the channel signals x1(t) and x2(t) corresponding to the first channel and the second channel respectively.

[0071] Step 6: Fuse the phase hysteresis entropies PLE1 and PLE2 calculated above to obtain a phase hysteresis entropy fusion value mPLE: mPLE=α*PLE1+β*PLE2, where α and β are fusion coefficients of the first frequency band PLE1 and the second frequency band PLE2, respectively.

[0072] Step 7: Count the cumulative length L of data whose mPLE value is greater than th3 (as a specific example of the phase hysteresis entropy threshold) PLE , and calculate its proportion in the window Lwindow, and obtain the third feature F3 (that is, the phase lag entropy eigenvalue): F3 = L PLE / L window .

[0073] It should be noted that th3 is obtained by clustering the EEG signal quality and the cluster center; the distance from mPLE to the cluster center can be compared with th3. When the distance is less than th3, it is considered that electrosurgical interference occurs in the signal, and the smaller the value, the stronger the interference.

[0074] In some embodiments of the present application, S213 may include: setting an initial clustering number of EEG signal quality, and determining the center point of each clustering category in the initial clustering number; determining the final clustering category of the EEG signal quality based on the distance between the phase lag entropy fusion value and the center point; selecting two clustering centers from the clustering categories of the EEG signal quality; and using the midpoint of the two clustering centers as the phase lag entropy threshold.

[0075] For example, in some embodiments of the present application, the phase lag entropy threshold is determined based on K-means clustering.

[0076] For example, set the initial number of clusters K = 2. Randomly initialize K cluster centers. According to the distance between each calculated phase lag entropy fusion value mPLE and the K cluster centers, assign it to the nearest cluster category; then recalculate the center point of each cluster category and update the cluster center. Repeat the above assignment and update steps until the cluster center no longer changes significantly (for example, the difference between the current cluster center and the previous cluster center is less than the threshold) or the maximum number of iterations is reached, and the EEG data is divided into K categories. Select the midpoint between two cluster centers as the threshold th3.

[0077] S220, determining a standard eigenvalue and an entropy weight value of each eigenvalue in the eigenvalue set.

[0078] For example, in some embodiments of the present application, the entropy weight method is used to objectively weight each feature (ie, F1, F2, and F3), and the weight of each feature is determined by the information entropy of each feature, thereby avoiding the influence of human subjective factors.

[0079] In some embodiments of the present application, S220 may include: standardizing each eigenvalue to obtain each standard eigenvalue; based on each standard eigenvalue, calculating the standard weight of each standard eigenvalue; calculating the standard weight of each standard eigenvalue to obtain the information entropy of each eigenvalue; based on the information entropy of each eigenvalue, calculating the entropy weight value of each eigenvalue.

[0080] For example, in some embodiments of the present application, since the dimensions of the characteristic indicators are different, the data needs to be standardized to eliminate the influence of the dimensions.

[0081] First, normalize F1, F2, and F3 to the interval [0,1]. The normalization formula is:

[0082]

[0083] Among them, F ij is the value of the jth window sample on the i-th feature index (i = 1, 2, 3), Z ij is the standardized value (i.e., each standard eigenvalue). The characteristic indexes are the above-mentioned F1, F2, and F3. The jth window sample is a signal processing unit.

[0084] It should be noted that in actual applications, the original signals of the tested person collected may be divided into multiple processing units, so this application takes any one of the multiple processing units, that is, the jth window, as an example for explanation. The jth window corresponds to the "electroencephalogram signal to be evaluated" in S210 above.

[0085] Secondly, calculate the proportion of each standardized indicator in the sample (that is, the standard weight of each standard feature value):

[0086]

[0087] Among them, P ij It represents the weight of the j-th window sample on the i-th feature index, and L is the window length.

[0088] Afterwards, according to the specific gravity P ij, calculate the information entropy of the ith characteristic index (that is, the information entropy of each characteristic value); information entropy is used to characterize the amount of information of the characteristic. The smaller the information entropy value, the greater the discrete degree of the index, and the greater the influence of the index on the comprehensive evaluation (that is, the weight). Specifically, the calculation formula of information entropy is as follows:

[0089]

[0090] Among them, E i represents the information entropy of the i-th feature index. In order to avoid P ij When ε is zero, there is a problem in the calculation, so a very small constant ε is introduced to replace the zero value.

[0091] Finally, according to the information entropy of each feature indicator, its entropy weight (that is, the entropy weight value of each feature value) is calculated:

[0092]

[0093] S230, calculating the standard eigenvalue and the entropy weight value of each eigenvalue to obtain an EEG signal quality assessment result.

[0094] For example, in some embodiments of the present application, Z obtained by the above formula ij and W i , the EEG signal quality assessment result can be obtained.

[0095] In some embodiments of the present application, S230 may include: performing weighted calculation on the standard eigenvalue and the entropy weight value of each eigenvalue to obtain an EEG signal quality assessment value; and determining the EEG signal quality assessment result by determining the quality level interval to which the EEG signal quality assessment value belongs.

[0096] For example, in some embodiments of the present application, the EEG signal quality evaluation value EQI of the jth window is obtained by weighted summing the entropy weights of each feature index and the standardized standard feature value. j .

[0097]

[0098] EQI j The value and the defined grade standard (as a specific example of the quality grade area) are used to judge the quality grade of the EEG signal (as a specific example of the EEG signal quality evaluation result). For example, when EQI≤50, the data is unreliable, and the grade standard may include the following intervals: 0≤EQI≤50: poor; 50<EQI≤70: general; 70<EQI≤90: good; 90<EQI≤100: excellent. It can be understood that the grade standard can be set according to actual needs, and the embodiments of the present application are not limited to this.

[0099] Through some of the above-mentioned embodiments of the present application, it can be known that for signals across devices, different devices have more consistent and stable characteristics in frequency domain information. The embodiments of the present application use frequency domain calculations based on specific frequency bands of signals to identify interference and evaluate the quality of EEG signals, and have wider applicability. The embodiments of the present application use the power difference in a specific frequency band to quantify the background noise to identify environmental noise and determine the noise intensity, and determine the power difference characteristic value with high accuracy. In addition, the embodiments of the present application accurately identify the possibility of electrosurgical interference through the phase lag entropy characteristic value indicator, and quantify the electrosurgical interference as one of the evaluation features, which can more comprehensively evaluate the quality of EEG signals in complex scenarios.

[0100] The following is combined with Figure 3 The specific process of intraoperative EEG signal quality assessment provided by some embodiments of the present application is exemplified.

[0101] Please see attached Figure 3 , Figure 3 A flow chart of a method for intraoperative EEG signal quality assessment provided for some embodiments of the present application.

[0102] The above process is explained below as an example.

[0103] S310, collecting data from the person under test using an electroencephalogram (EEG) acquisition device to obtain the entire EEG signal.

[0104] S320, determining the EEG signal to be evaluated, performing statistics and calculation on the contact impedance value of the EEG signal, and obtaining a contact impedance characteristic value.

[0105] It should be understood that the EEG signal to be evaluated is a processing unit in the entire EEG signal collected from the person being tested. The entire EEG signal is divided into multiple processing units according to time T.

[0106] S330, pre-processing the entire EEG signal to obtain a processed EEG signal.

[0107] It should be understood that preprocessing includes: filtering, downsampling, and dividing the filtered and downsampled signals into multiple processing units according to time T. Among them, the length of the EEG signal to be evaluated in S320 and the processed EEG signal in S330 are both T, and the two are aligned in the time dimension.

[0108] S340, obtaining a power difference characteristic value and a phase lag entropy characteristic value corresponding to the processed EEG signal.

[0109] S350, determining standard eigenvalues ​​and entropy weight values ​​corresponding to the contact impedance eigenvalue, the power difference eigenvalue, and the phase lag entropy eigenvalue, respectively.

[0110] S360, using a weighted method, the standard eigenvalues ​​and entropy weight values ​​corresponding to the contact impedance eigenvalue, the power difference eigenvalue, and the phase lag entropy eigenvalue are calculated to obtain an EEG signal quality index EQI.

[0111] S370, determining the grade standard interval to which the EQI belongs, and obtaining the quality grade of the EEG signal.

[0112] It should be noted that the specific implementation process of S310 to S370 can refer to the method embodiment provided above. To avoid repetition, the detailed description is appropriately omitted here.

[0113] Through some of the above embodiments of the present application, it can be seen that the embodiments of the present application can achieve the following technical effects:

[0114] This application analyzes the EEG signal and noise characteristics, extracts and calculates three key indicators that affect signal quality from three aspects: electrode contact impedance, external environmental noise, and electrosurgical unit interference recognition, and establishes a more complete signal quality evaluation standard.

[0115] Electrode contact impedance detection plays a vital role in EEG signal acquisition. Specifically, it can effectively evaluate the contact quality between the electrode and the skin, which is crucial for obtaining high-quality, low-noise EEG signals and is the first guarantee for ensuring signal quality during the EEG signal acquisition process.

[0116] Secondly, in the operating room environment, the background noise that occurs during the acquisition process usually covers the entire frequency band of the signal, which is difficult to clearly define through traditional signal amplitude and power spectrum density calculations. This application quantifies the background noise by extracting the signal in a specific frequency band and calculating the power difference, and identifies the environmental noise and determines its intensity by setting a threshold.

[0117] In addition, the frequent use of instruments such as electrosurgery during surgical operations will produce significant electromagnetic interference, which will have a great impact on EEG signals. Due to the different sensitivities of different acquisition devices to noise and artifacts, traditional time domain methods such as peak-to-peak value calculations are difficult to achieve standardized evaluation. Through preliminary research, this application discovered and verified the feasibility of accurately identifying electrosurgery interference through phase lag entropy. Through this feature, the occurrence of electrosurgery interference can be identified and quantified, thereby more accurately evaluating the current state of EEG signals.

[0118] The three features calculated in this application not only expand the scope of existing quality assessment methods, but also cover external environmental influences and electromagnetic equipment interference beyond traditional noise and artifact recognition, and are more suitable for operating room environments; in addition, the selected parameters are more focused on the frequency domain information of the signal. Compared with time domain features, frequency domain features are more stable and more suitable for EEG signals collected by different devices. By applying the entropy weight method for feature fusion, the weights of each indicator are determined based on its information entropy, thereby avoiding the influence of human subjective factors.

[0119] In summary, this application has built a more complete EEG signal quality assessment system by comprehensively considering various interference factors in the operating room environment. This method not only improves the judgment accuracy of signal quality, but also expands the application scope of traditional assessment methods, providing a more reliable guarantee for EEG signal acquisition and analysis in complex clinical environments.

[0120] Please refer to Figure 4 , Figure 4 The block diagram of the composition of the device for evaluating the quality of intraoperative EEG signals provided by some embodiments of the present application is shown. It should be understood that the device for evaluating the quality of intraoperative EEG signals corresponds to the above-mentioned method embodiment and can perform each step involved in the above-mentioned method embodiment. The specific functions of the device for evaluating the quality of intraoperative EEG signals can be found in the description above. To avoid repetition, the detailed description is appropriately omitted here.

[0121] Figure 4 The device for evaluating the quality of intraoperative EEG signals includes at least one software function module that can be stored in a memory in the form of software or firmware or solidified in the device for evaluating the quality of intraoperative EEG signals, and the device for evaluating the quality of intraoperative EEG signals includes: a feature acquisition module 410, configured to obtain a set of eigenvalues ​​corresponding to the EEG signal to be evaluated, wherein the set of eigenvalues ​​includes: a contact impedance eigenvalue, a power difference eigenvalue, and a phase lag entropy eigenvalue; an entropy information determination module 420, configured to determine a standard eigenvalue and an entropy weight value for each eigenvalue in the set of eigenvalues; and a quality assessment module 430, configured to calculate the standard eigenvalue and the entropy weight value for each eigenvalue to obtain an EEG signal quality assessment result.

[0122] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the device described above can refer to the corresponding process in the aforementioned method, and will not be described in detail here.

[0123] Some embodiments of the present application further provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, can implement the operations of the method corresponding to any of the above methods provided in the above embodiments.

[0124] Some embodiments of the present application further provide a computer program product, which includes a computer program, wherein when the computer program is executed by a processor, it can implement the operations corresponding to any of the above methods provided in the above embodiments.

[0125] like Figure 5 As shown, some embodiments of the present application provide an electronic device 500, which includes: a memory 510, a processor 520, and a computer program stored in the memory 510 and executable on the processor 520, wherein the processor 520 can implement a method as described in any of the above embodiments when reading the program from the memory 510 through a bus 530 and executing the program.

[0126] Processor 520 can process digital signals and can include various computing structures, such as complex instruction set computer structure, reduced instruction set computer structure, or a structure that implements a combination of multiple instruction sets. In some examples, processor 520 can be a microprocessor.

[0127] The memory 510 may be used to store instructions executed by the processor 520 or data related to the execution of instructions. These instructions and / or data may include codes for implementing some or all functions of one or more modules described in the embodiments of the present application. The processor 520 of the disclosed embodiment may be used to execute instructions in the memory 510 to implement the method shown above. The memory 510 includes a dynamic random access memory, a static random access memory, a flash memory, an optical memory, or other memory known to those skilled in the art.

[0128] The above description is only an embodiment of the present application and is not intended to limit the scope of protection of the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application. It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings.

[0129] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0130] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

Claims

1. A method for evaluating the quality of intraoperative electroencephalogram signals, characterized in that: include: Acquire a set of eigenvalues ​​corresponding to the EEG signal to be evaluated, wherein the set of eigenvalues ​​includes: a contact impedance eigenvalue, a power difference eigenvalue, and a phase lag entropy eigenvalue; Determining a standard eigenvalue and an entropy weight value of each eigenvalue in the eigenvalue set; The standard eigenvalue and the entropy weight value of each eigenvalue are calculated to obtain an electroencephalogram signal quality assessment result.

2. The method according to claim 1, characterized in that The step of obtaining a set of characteristic values ​​corresponding to the electroencephalogram signal to be evaluated includes: Performing statistics and calculation on the contact impedance value of the EEG signal to obtain the contact impedance characteristic value; Preprocessing the EEG signal to obtain a processed EEG signal; The power difference characteristic value and the phase lag entropy characteristic value corresponding to the processed EEG signal are obtained.

3. The method according to claim 2, characterized in that The power difference characteristic value is obtained by the following method: Decomposing the processed EEG signal based on wavelet transform to obtain a first frequency band and a second frequency band; calculating a power difference between the first frequency band and the second frequency band; The ratio of the data length in which the power difference is less than a preset threshold to the window length of the electroencephalogram signal is used as the power difference characteristic value.

4. The method according to claim 2 or 3, characterized in that The phase lag entropy eigenvalue is obtained by the following method: Acquire a first phase lag entropy and a second phase lag entropy corresponding to a first frequency range and a second frequency range of the processed EEG signal, respectively; The first phase hysteresis entropy and the second phase hysteresis entropy are fused to obtain a phase hysteresis entropy fusion value; The ratio of the data length whose phase lag entropy fusion value is greater than the phase lag entropy threshold to the window length of the electroencephalogram signal is used as the phase lag entropy characteristic value.

5. The method according to claim 4, characterized in that Before taking the ratio of the data length whose phase lag entropy fusion value is greater than the phase lag entropy threshold to the window length of the EEG signal as the phase lag entropy feature value, the method further comprises: Setting an initial number of clusters of EEG signal quality, and determining a center point of each cluster category in the initial number of clusters; Determining a final clustering category of the EEG signal quality according to the distance between the phase lag entropy fusion value and the center point; Selecting two cluster centers from the cluster categories of the EEG signal quality; The midpoint of the two cluster centers is used as the phase hysteresis entropy threshold.

6. The method according to any one of claims 1 to 3 and 5, characterized in that: The determining of the standard eigenvalue and entropy weight value of each eigenvalue in the eigenvalue set includes: Performing standardization on each of the eigenvalues ​​to obtain each standard eigenvalue; Based on each standard feature value, calculating a standard weight for each standard feature value; The standard weight of each standard eigenvalue is calculated to obtain the information entropy of each eigenvalue; Based on the information entropy of each eigenvalue, the entropy weight value of each eigenvalue is calculated.

7. The method according to any one of claims 1 to 3 and 5, characterized in that: The step of calculating the standard eigenvalue and the entropy weight value of each eigenvalue to obtain an EEG signal quality assessment result includes: Performing weighted calculation on the standard eigenvalue and the entropy weight value of each eigenvalue to obtain an electroencephalogram signal quality assessment value; The EEG signal quality evaluation result is determined by determining the quality level interval to which the EEG signal quality evaluation value belongs.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program executes the method according to any one of claims 1 to 7 when executed by a processor.

9. An electronic device, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the computer program executes the method according to any one of claims 1 to 7 when being run by the processor.

10. A computer program product, characterized in that The computer program product comprises a computer program, wherein the computer program executes the method according to any one of claims 1 to 7 when executed by a processor.

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