CN112244872B - Electroencephalogram signal artifact identification, removal and evaluation method and device and electronic equipment - Google Patents
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Abstract
The invention discloses electroencephalogram signal artifact identification, removal and evaluation methods, devices and electronic equipment, wherein the identification method comprises the following steps: acquiring electroencephalogram signals, and preprocessing the electroencephalogram signals; calculating the signal energy and/or wire length of all leads of the preprocessed electroencephalogram signal; detecting when the signal energy of the lead exceeds a signal energy lead threshold and/or the cord length exceeds a cord length lead threshold and when the signal energy of the electroencephalogram signal exceeds a signal energy global threshold and/or the cord length exceeds a cord length global threshold; acquiring two adjacent moments with an interval smaller than a preset time threshold; and determining that the signals of the time periods corresponding to the two moments are pseudo-errors. The method can accurately identify and remove artifact components mixed in the electroencephalogram signal, and evaluate the signal quality of the electroencephalogram signal.
Description
Technical Field
The invention relates to the technical field of biological signal processing, in particular to electroencephalogram signal artifact identification, removal and evaluation methods, devices and electronic equipment.
Background
Electroencephalography (EEG) is a commonly used graph for recording electrical activity generated by the cortex, has the characteristics of high resolution, non-invasiveness and low cost, and is therefore widely used for preoperative evaluation of epileptic lesions. Electroencephalogram activity is a very weak bioelectric signal that needs to be amplified millions of times before being recorded on the scalp, but electroencephalograms are signals that are easily contaminated by artifacts.
In electroencephalograms acquired in the clinical epilepsy diagnosis and treatment process, the most common artifact sources influencing the electroencephalogram signal quality comprise electromyographic activities, electrode movement, power frequency interference, white noise and the like. The existing electroencephalogram artifact identification and removal has the following problems:
1. some noise sources (e.g., white noise) are difficult to remove by typical band-pass Finite Impulse Response (FIR) or Infinite Impulse Response (IIR) filtering because the Power Spectral Density (PSD) of white noise completely overlaps the PSD of an electroencephalogram signal, making it almost impossible to separate a clean electroencephalogram signal from the white noise component;
2. eye movements (including eye movements or blinking), muscle noise, electrocardiosignals, etc. generally produce large-amplitude and distracting artifacts in electroencephalograms. The electroencephalogram signal segments with artifacts completely removed can lose data volume, and the method is not generally adopted in practical application. The electroencephalogram signal contains epileptic abnormal discharge information with frequency lower than 100Hz, although a filtering method can be used to remove power frequency interference and other higher frequency components. However, artifacts caused by eye movement and muscle movement also overlap with the electroencephalogram signal related to epilepsy, and thus these artifacts cannot be removed by conventional filtering methods.
3. The pseudoerrors caused by muscle movements and the like have the following characteristics: the amplitude of the artifact component is high, possibly several times larger than the electroencephalogram signal; the frequency band distribution range of the artifact component is wide, and artifacts with lower frequency and higher frequency can appear; the spatial distribution of the artifact varies widely, and some spread over a large range or even over all leads. These artifact components are therefore difficult to eliminate.
The identification of the artifact is crucial to the accuracy of lesion judgment, and some artifacts can cover the identification of pathological spikes. In clinical applications, various artifacts can lead to false positive results, so that the neurologist cannot make a correct judgment on the lesion unless the neurologist can recognize the artifacts.
Disclosure of Invention
The invention provides electroencephalogram signal artifact identification, removal and evaluation methods, devices and electronic equipment, and aims to solve the problem that artifact identification of electroencephalograms is difficult in the prior art.
In a first aspect of the present invention, there is provided an artifact recognition method of an electroencephalogram signal, including:
acquiring electroencephalogram signals, and preprocessing the electroencephalogram signals;
calculating the signal energy and/or wire length of all leads of the preprocessed electroencephalogram signal;
detecting when the signal energy of the lead exceeds a signal energy lead threshold and/or the cord length exceeds a cord length lead threshold and when the signal energy of the electroencephalogram signal exceeds a signal energy global threshold and/or the cord length exceeds a cord length global threshold;
acquiring two adjacent moments with an interval smaller than a preset time threshold;
and determining that the signals of the time periods corresponding to the two moments are pseudo-errors.
Further, preprocessing the electroencephalogram signal, including:
high pass filtering the electroencephalogram signal;
denoising the electroencephalogram signal after the high-pass filtering;
performing low-pass filtering on the electroencephalogram signals subjected to denoising processing;
the low-pass filtered electroencephalogram signal is resampled.
Further, the signal energy is calculated as follows:
energyeeg＝(x(t))2；
where x (t) represents the amplitude of the pre-processed electroencephalogram signal, t represents the time sampling point, energyeegSignal energy representing an electroencephalogram signal;
the wire length is calculated as follows:
wherein y (t) represents the waveform of the preprocessed electroencephalogram signal, t represents a time sampling point, and NllIs the number of sampling points in the window width used in the line length calculation, abs]For calculating absolute value functions, LlIndicating the wire length.
Further, the signal energy lead threshold is calculated as follows:
energyChi＝achi·median1chi；
wherein, mean 1chiSignal energy median representing the chi-th lead, chi-1, 2, … Nchanel，NchanelRepresenting the total number of leads of the electroencephalogram signal, achiExpressing fold, energychiIs a signal energy lead threshold;
the signal energy global threshold is calculated as follows:
Thresholdenergy＝a·Median1；
wherein, Median1Representing the median signal energy of all lead electroencephalographic signals, a represents the multiple, ThresholdenergyRepresenting a signal energy global threshold.
Further, the wire long lead threshold is calculated as follows:
llChi＝achi·median2chi；
wherein, mean 2chiMedian length of signal line, a, representing the chi leadchiDenotes a multiple, chi 1, 2, … Nchanel，NchanelRepresenting the total number of leads, ll, of the electroencephalogram signalchiRepresenting a line length lead threshold;
calculating a line length global threshold according to the following formula:
Thresholdll＝b·Median2；
wherein, Median2Represents the median of the line length, b represents the multiple, ThresholdllRepresenting a line length global threshold.
Further, the method further comprises:
calculating a waveform envelope of the preprocessed electroencephalogram signal;
dividing the envelope into a plurality of short-time segments according to a preset sliding window;
calculating the correlation value of the waveform envelopes of every two lead signals in the electroencephalogram signal of each short-time segment to obtain a pair-wise correlation matrix;
calculating a sum of each pair-wise correlation matrix;
detecting when the sum of the pair-wise correlation matrices exceeds a threshold;
acquiring two adjacent moments with an interval smaller than a preset time threshold;
and determining that the signals of the time periods corresponding to the two moments are pseudo-errors.
Further, the waveform envelope is calculated as follows:
wherein y (t) represents a waveform of the electroencephalogram signal after the preprocessing; t represents a time sampling point; enveeegRepresenting a waveform envelope; denotes convolution operation.
Further, the threshold for the sum of the pairwise correlation matrices is calculated as follows:
Thresholdcorr＝c·median3；
wherein, mean3Represents the median of the sum of the pairwise correlation matrices, c represents the multiple; threshold (Threshold)corrA threshold representing the sum of the pairwise correlation matrices.
In a second aspect of the present invention, there is provided an artifact removing method of an electroencephalogram signal, including:
identifying artifacts according to the method described above;
and removing the electroencephalogram signals of the time period in which the artifact exists.
In a third aspect of the present invention, there is provided a quality evaluation method of an electroencephalogram signal, including:
identifying artifacts according to the method described above;
calculating a single derivative signal-to-noise ratio R1 of higher amplitude artifacts from the identified artifactschiAnd overall signal-to-noise ratio Rnoise1Single lead SNR R2 for needle HF potentiometric artifactchiAnd overall signal-to-noise ratio Rnoise2And signal-to-noise ratio R of multi-lead artifactsn3；
The overall signal-to-noise ratio of the electroencephalogram signal is calculated as follows:
RatioSN＝(Rsn1+Rsn2+Rsn3)/3；
wherein, when detecting higher amplitude artifacts in a single lead,
when higher amplitude artifacts are detected as a whole, Rsn1＝Rnoise1；
When detecting the artifacts issued by the needle-shaped high-frequency potential according to the single lead,
when detecting integrally needle-shaped high-frequency potential-emitting artifacts, Rsn2＝Rnoise2。
Further, higher amplitude artifact single derivative signal-to-noise ratio R1chiThe calculation is made by the following formula:
wherein, R1chiSingle derivative signal-to-noise ratio, N, representing higher amplitude artifactshighHigher amplitude artifact event A representing the chi leadhighNumber of (2), ThighRepresenting higher amplitude artifact events AhighDuration of (D), TEEGRepresents the total length of time of the electroencephalogram signal, j 1, 2high，chi＝1，2，…Nchanel；
Overall signal-to-noise ratio R of higher amplitude artifactsnoise1The calculation is made by the following formula:
wherein R isnoise1Overall signal-to-noise ratio representing higher amplitude artifact, N' representing higher amplitude artifact events A for all leadshighNumber of (2), ThighRepresenting higher amplitude artifact events AhighJ ═ 1, 2.. N', TEEGRepresenting the total length of time of the electroencephalogram signal.
Further, the needle-shaped high-frequency potential distribution pseudodifferential single lead signal-to-noise ratio R2chiThe calculation is made by the following formula:
wherein, R2chiSingle lead signal-to-noise ratio, N, representing artifacts of needle high-frequency potentiometry of each leadneedleLine length artifact event A representing the chi leadneedleNumber of (2), TneedleIndicating line length artifact event AneedleDuration of (D), TEEGN is 1, 2needle，chi＝1，2，…Nchanel；
Overall signal-to-noise ratio R of needle-shaped high-frequency potential distribution artifactnoise2The calculation is made by the following formula:
wherein R isnisee2Overall signal-to-noise ratio, N, representing needle potential dispensing artefactallLine length artifact events A representing all leadsneedleNumber of (2), TneedleIndicating line length artifact event AneedleN ═ 1, 2.. Nall，TEEGRepresenting the total length of time of the electroencephalogram signal.
Further, the signal-to-noise ratio R of the artifact of multi-lead is sweptsn3The calculation is made by the following formula:
wherein R issn3Signal-to-noise ratio, N, representing the artifact of swept multiple leadsmultipleRepresenting multiple lead artifact events AmultipleNumber of (2), TmultipleRepresenting multiple lead artifact events AmultipleN, m 1, 2multiple，TEEGRepresenting the total length of time of the electroencephalogram signal.
In a fourth aspect of the present invention, there is provided an electronic device comprising a processor and a storage medium storing a plurality of instructions, the processor being configured to read the instructions and execute the artifact identification method for electroencephalogram signals, the artifact removal method for electroencephalogram signals, and/or the quality evaluation method for electroencephalogram signals as described above.
In a fifth aspect of the present invention, there is provided a computer-readable storage medium having stored thereon computer instructions which, when executed by a processor, implement the artifact identification method of an electroencephalogram signal, the artifact removal method of an electroencephalogram signal, and/or the quality evaluation method of an electroencephalogram signal as described above.
The electroencephalogram signal artifact identification, removal and evaluation method, device and electronic equipment provided by the invention can accurately and efficiently identify and remove various artifact components mixed in the electroencephalogram signal, and can evaluate the signal quality of the electroencephalogram signal based on artifact information.
Drawings
Fig. 1 is a flowchart of an embodiment of a method for identifying artifact of an electroencephalogram signal according to the present invention.
Fig. 2 is a flowchart of an embodiment of electroencephalogram signal preprocessing in the method for identifying artifact of electroencephalogram signals provided by the present invention.
Fig. 3 is a flowchart of another embodiment of the artifact identification method for electroencephalogram signals provided by the present invention.
Fig. 4 is a flowchart of an embodiment of a method for evaluating the quality of an electroencephalogram signal according to the present invention.
Fig. 5 is a schematic structural diagram of an embodiment of an electroencephalogram signal artifact identification device provided by the present invention.
Fig. 6 is a schematic structural diagram of an electroencephalogram signal artifact identification apparatus according to another embodiment of the present invention.
Fig. 7 is a schematic structural diagram of an embodiment of an electroencephalogram signal artifact removal apparatus according to the present invention.
Fig. 8 is a schematic structural diagram of an electroencephalogram signal quality evaluation apparatus according to an embodiment of the present invention.
FIG. 9 is a schematic illustration of a higher amplitude artifact event of the present invention;
FIG. 10 is a schematic diagram of a line length artifact event of needle shaped high frequency potential dispense of the present invention;
FIG. 11 is a schematic representation of the artifact events of the swept multi-leads of the present invention.
Detailed description of the preferred embodiments
In order to better understand the technical solution, the technical solution will be described in detail with reference to the drawings and the specific embodiments.
Referring to fig. 1, in some embodiments, there is provided a method of artifact identification of an electroencephalogram signal, including:
step S101, collecting electroencephalogram signals and preprocessing the electroencephalogram signals;
step S102, calculating signal energy and/or wire length of all leads of the preprocessed electroencephalogram signals;
step S103, detecting the time when the signal energy of the lead exceeds the signal energy lead threshold and/or the wire length exceeds the wire length lead threshold and the time when the signal energy of the electroencephalogram signal exceeds the signal energy global threshold and/or the wire length exceeds the wire length global threshold;
step S104, two adjacent moments with the interval smaller than a preset time threshold are obtained;
and step S105, determining that the signals of the time periods corresponding to the two moments are pseudo-errors.
Specifically, in step S101, acquiring electroencephalogram signals according to a clinically standard electroencephalogram amplifier, wherein a sampling rate range is usually 500-1000 Hz, a channel number range is usually 16-64 leads, and a monitoring duration of the electroencephalogram signals is usually half an hour to 1 day or more clinically.
Referring to fig. 2, in some embodiments, the electroencephalographic signal is pre-processed, including:
step S1011, performing high-pass filtering on the electroencephalogram signal;
step S1012, performing denoising processing on the electroencephalogram signal after the high-pass filtering;
in step S1013, the electroencephalogram signal after the denoising process is low-pass filtered.
In step S1011, in order to eliminate the influence of the data drift, high-pass filtering with a lower frequency limit of 0.1 to 1Hz is performed on the acquired electroencephalogram signals, so as to obtain electroencephalogram signals after the base drift is removed.
In step S1012, a 50Hz notch filter is used to eliminate power frequency noise interference, and an electroencephalogram signal without power frequency noise is obtained.
In step S1013, since the frequency band in which abnormal epileptic discharge is common in electroencephalogram signals is 70Hz or less, a low-pass filter with an upper frequency limit of 40-70Hz is used to remove higher frequency components, and an electroencephalogram signal after filtering is obtained.
In some embodiments, after the low-pass filtering the de-noised electroencephalogram signal, the method further comprises:
in step S1014, the low-pass filtered electroencephalogram signal is resampled.
In order to reduce the amount of data and increase the calculation speed, the electroencephalogram signal is resampled. The sampling rate after resampling sampling meets the Nyquist sampling law, the range of the resampling sampling rate can be set as 100-200Hz, and the electroencephalogram signal after resampling is obtained.
Further, in step S102, the signal energy is calculated according to the following formula:
energyeeg＝(x(t))2； (1)
where x (t) represents the amplitude of the pre-processed electroencephalogram signal, t represents the time sampling point, energyeegSignal energy representing an electroencephalogram signal;
the wire length is calculated as follows:
wherein y (t) represents the waveform of the preprocessed electroencephalogram signal, t represents a time sampling point, NllThe number of sampling points in the window width used in the line length calculation, the window width being, for example, 0.5 to 1 second; abs [ alpha ], [ beta ], [ alpha ] and a]For calculating absolute value functions, LlIndicating the wire length. The linear length refers to the length of the waveform of the brain wave signal, i.e., the length of the waveform developed into a straight line state.
Further, in step S103, for each lead of the electroencephalogram signal, the signal energy lead threshold is calculated as follows:
energyChi＝achi·median1chi； (3)
wherein, mean 1chiSignal energy median representing the chi-th lead, chi-1, 2, … Nchanel，NchanelRepresenting the total number of leads of the electroencephalogram signal, achiThe expression multiple is in the range of 4-20, energychiIs a signal energy lead threshold;
the signal energy global threshold is calculated as follows:
Thresholdenergy＝a·Median1； (4)
wherein, Median1Representing the signal energy median of all lead electroencephalogram signals, a represents a multiple, and the range is 4-20, ThresholdenergyRepresenting a signal energy global threshold.
Further, for each lead of the electroencephalogram signal, the wire-long lead threshold is calculated as follows:
llChi＝achi·median2chi； (5)
wherein, mean 2chiMedian length of signal line, a, representing the chi leadchiThe expression multiple is in the range of 4-20, chi is 1, 2, … Nchanel，NchanelRepresenting the total number of leads, ll, of the electroencephalogram signalchiRepresenting line length lead threshold。
Calculating a line length global threshold according to the following formula:
Thresholdll＝b·Median2； (6)
wherein, Median2Represents the median of the line length, b represents the multiple, and the range is 2-10, ThresholdllRepresenting a line length global threshold.
Further, detecting the moment when the signal energy of the lead exceeds the lead threshold of the signal energy, and simultaneously, detecting the moment when the signal energy exceeds the global threshold of the signal energy; and/or the presence of a gas in the gas,
and detecting the time when the wire length of the lead exceeds the wire length lead threshold, and simultaneously, detecting the time when the wire length exceeds the wire length global threshold.
Further, in step S104 and step S105, two times when the signal energy with an interval smaller than the first time threshold (e.g. 500 ms) exceeds the signal energy lead threshold or the signal energy global threshold are obtained, and the signal of the time period corresponding to the two times is a higher amplitude artifact event ahigh(as shown in FIG. 9), save NhighDuration T of higher amplitude artifact eventhigh. Since the signal energy exceeds the threshold, it is named as a high amplitude artifact, indicating a relatively high amplitude with respect to the entire brain wave signal.
Acquiring the time when the line length of two leads with the interval less than a second time threshold (for example, 500 milliseconds) exceeds a line length lead threshold or a line length global threshold, wherein signals of time periods corresponding to the two times are line length pseudo-error events A for one needle type high-frequency potential distributionneedle(as shown in FIG. 10), save NneedleThread length pseudo-error event A for single-needle type high-frequency potential distributionneedleDuration of (T)needle。
In some embodiments, an artifact removal method for electroencephalogram signals is also provided, which removes higher amplitude artifact events AhighAnd obtaining the electroencephalogram signal with the high-amplitude artifact component removed from the signal of the time position.
In some embodiments, the wire length artifact event A of needle type high frequency potential release is removedneedleAt a time positionAnd obtaining the electroencephalogram signal after removing the pseudo difference generated by the needle type high-frequency potential.
Further, with reference to fig. 3, in some embodiments, the provided method further comprises:
step S201, calculating the waveform envelope of the preprocessed electroencephalogram signal;
step S202, dividing the envelope into a plurality of short-time segments according to a preset sliding window;
step S203, calculating the correlation value of the waveform envelopes of the signals of every two leads in the electroencephalogram signal of each short-time segment to obtain a pair-wise correlation matrix; the correlation value is, for example, a pearson correlation value;
step S204, calculating the sum of each pair of correlation matrixes;
step S205, detecting the moment when the sum of the paired correlation matrixes exceeds a threshold value;
step S206, two adjacent moments with the interval smaller than a preset time threshold are obtained;
step S207, determining that the signals of the time periods corresponding to the two moments are false differences.
Further, in step S201, the waveform envelope is calculated according to the following formula:
wherein y (t) represents a waveform of the electroencephalogram signal after the preprocessing; t represents a time sampling point; enveeegRepresenting a waveform envelope; denotes convolution operation.
In step S202, a window width time length W is setbinDividing the signal envelope into N by a sliding time window having a step size of half the window width time lengthbinA fragment of, wherein WbinThe range of (1) is 200 to 1500 milliseconds.
In step S203, the pairwise correlation matrix is a square matrix with a matrix dimension equal to the number of leads of the electroencephalogram signal.
In step S204, the sum of each pairwise correlation matrix is calculatediI is 1, 2, … Nbin。
In step S205, the threshold value of the sum of the pair-wise correlation matrices is calculated as follows:
Thresholdcorr＝c·median3； (8)
wherein, mean3Representing the median of the sum of the pairwise correlation matrices, c represents a multiple, and ranges from 2 to 12; threshold (Threshold)corrA threshold representing the sum of the pairwise correlation matrices.
In step S206, a time at which the sum of two paired correlation matrices whose interval is smaller than a third time threshold (e.g., 500 msec) exceeds the threshold is acquired.
In step S207, a time period corresponding to the time when the sum of the two pairwise correlation matrices separated by less than a third time threshold (e.g., 500 ms) exceeds the threshold is determined as a multi-lead artifact event Amultiple(as shown in FIG. 11), save NmultipleDuration T of multiple multi-lead artifact eventsmultiple。
In some embodiments, there is also provided an artifact removal method for electroencephalogram signals, removing multi-lead artifact events AmultipleAnd obtaining the electroencephalogram signal with the multi-lead artifact component removed from the signal of the time position.
By performing the methods of fig. 1 and 3, higher amplitude artifact events, needle high frequency potential dispensing line length artifact events, and multi-lead artifact events in brain wave signals can be identified and removed.
Referring to fig. 4, in some embodiments, there is also provided a method of quality assessment of an electroencephalogram signal, including:
step S301, identifying artifact according to the method;
step S302, calculating a single derivative signal-to-noise ratio R1 of higher amplitude artifact based on the identified artifactchiAnd overall signal-to-noise ratio Rnoise1Single lead SNR R2 for needle HF potentiometric artifactchiAnd overall signal-to-noise ratio Rnoise2And signal-to-noise ratio R of multi-lead artifactsn3；
Step S303, calculating the overall signal-to-noise ratio of the electroencephalogram signal according to the following formula:
RatioSN＝(Rsn1+Rsn2+Rsn3)/3； (9)
wherein, when detecting higher amplitude artifact according to single lead,
when higher amplitude artifacts are detected as a whole, Rsn1＝Rnoise1；
When detecting the artifacts issued by the needle-shaped high-frequency potential according to the single lead,
when detecting integrally needle-shaped high-frequency potential-emitting artifacts, Rsn2＝Rnoise2。
In step S301, the identified artifacts include: the specific identification method of the line length artifact and the multi-lead artifact provided by the high-amplitude artifact and the needle type high-frequency potential is referred to above and is not repeated herein.
Further, in step S302, the higher amplitude artifact single derivative SNR R1chiThe calculation is made by the following formula:
wherein, R1chiSingle derivative signal-to-noise ratio, N, representing higher amplitude artifactshighHigher amplitude artifact event A representing the chi leadhighNumber of (2), ThighRepresenting higher amplitude artifact events AhighDuration of (D), TEEGRepresents the total length of time of the electroencephalogram signal, j 1, 2high，chi＝1，2，…Nchanel；
Overall signal-to-noise ratio R of higher amplitude artifactsnoise1The calculation is made by the following formula:
wherein R isnoise1Overall signal-to-noise ratio representing higher amplitude artifact, N' representing higher amplitude artifact events A for all leadshighNumber of (2), ThighRepresenting higher amplitude artifact events AhighJ ═ 1, 2.. N', TEEGRepresenting the total length of time of the electroencephalogram signal.
Further, the needle-shaped high-frequency potential distribution pseudodifferential single lead signal-to-noise ratio R2chiThe calculation is made by the following formula:
wherein, R2chiSingle lead signal-to-noise ratio, N, representing artifacts of needle high-frequency potentiometry of each leadneedleLine length artifact event A representing the chi leadneedleNumber of (2), TneedleIndicating line length artifact event AneedleDuration of (D), TEEGN is 1, 2needle，chi＝1，2，…Nchanel；
Overall signal-to-noise ratio R of needle-shaped high-frequency potential distribution artifactnoise2The calculation is made by the following formula:
wherein R isnoise2Overall signal-to-noise ratio, N, representing needle potential dispensing artefactallLine length artifact events A representing all leadsneedleNumber of (2), TneedleIndicating line length artifact event AneedleN ═ 1, 2.. Nall，TEEGRepresenting the total length of time of the electroencephalogram signal.
Further, the signal-to-noise ratio R of the artifact of multi-lead is sweptsn3The calculation is made by the following formula:
wherein R issn3Signal-to-noise ratio, N, representing multi-lead artefactsmultipleRepresenting multiple lead artifact events AmultipleNumber of (2), TmultipleRepresenting multiple lead artifact events AmultipleN, m 1, 2multiple，TEEGRepresenting the total length of time of the electroencephalogram signal.
In step S303, the obtained overall signal-to-noise ratio can assist the neurologist in interpretation analysis of the abnormal electroencephalogram of epilepsy, and false positive results caused by artifacts are avoided as much as possible, so that the accuracy of electroencephalogram signal interpretation is improved. Meanwhile, the signal-to-noise ratio index of the electroencephalogram signal can help to evaluate the signal quality of the electroencephalogram.
Referring to fig. 5, in some embodiments, there is also provided an artifact recognition apparatus of an electroencephalogram signal, including:
the preprocessing module 401 is configured to acquire an electroencephalogram signal and preprocess the electroencephalogram signal;
a calculation module 402 for calculating the signal energy and/or wire length of all leads of the preprocessed electroencephalogram signal;
a first detection module 403, configured to detect a time when the signal energy of the lead exceeds the signal energy lead threshold and/or the cord length exceeds the cord length lead threshold and a time when the signal energy of the electroencephalogram signal exceeds the signal energy global threshold and/or the cord length exceeds the cord length global threshold;
a first obtaining module 404, configured to obtain two adjacent moments whose interval is smaller than a preset time threshold;
a first artifact determining module 405, configured to determine that the signals of the time periods corresponding to the two moments are artifacts.
Specifically, the preprocessing module 401 is configured to:
high pass filtering the electroencephalogram signal;
denoising the electroencephalogram signal after the high-pass filtering;
performing low-pass filtering on the electroencephalogram signals subjected to denoising processing;
the low-pass filtered electroencephalogram signal is resampled.
Further, the calculating module 402 calculates the signal energy according to equation (1).
The calculation module 402 calculates the line length according to equation (2).
The first detection module 403 calculates the signal energy lead threshold according to equation (3).
The first detection module 403 calculates a signal energy global threshold according to equation (4).
The first detection module 403 calculates the line long lead threshold according to equation (5).
The first detection module 403 calculates a line length global threshold according to equation (6).
Further, referring to fig. 6, the apparatus further includes:
an envelope calculation module 406 for calculating an envelope of the preprocessed electroencephalogram signal;
a dividing module 407, configured to divide the envelope into a plurality of short-time segments according to a preset sliding window;
a matrix determining module 408, configured to calculate correlation values of signal envelopes of every two leads in the electroencephalogram signal of each short-time segment, so as to obtain a pairwise correlation matrix;
and a calculation module 409 for calculating the sum of each pair-wise correlation matrix;
a second detection module 410 for detecting when the sum of the pair of correlation matrices exceeds a threshold;
a second obtaining module 411, configured to obtain two adjacent moments whose intervals are smaller than a preset time threshold;
and a second artifact determining module 412, configured to determine that the signals of the time periods corresponding to the two moments are artifacts.
The envelope calculation module 406 calculates the waveform envelope according to equation (7).
The second detection module 410 calculates a threshold value for the sum of the pairwise correlation matrices according to equation (8).
Referring to fig. 7, in some embodiments, there is also provided an artifact removing apparatus for an electroencephalogram signal, including the artifact identifying apparatus 501 for an electroencephalogram signal described above, further including:
and the artifact removing module 502 is used for removing the electroencephalogram signal of the time period in which the artifact is positioned.
The artifacts identified by the artifact identifying means 501 include: the specific identification method refers to the above, and is not repeated herein.
Referring to fig. 8, in some embodiments, there is further provided an electroencephalogram signal quality evaluation apparatus including the above-described artifact recognition apparatus 601 for an electroencephalogram signal, further including:
a signal-to-noise ratio calculation module 602 for calculating a single derivative signal-to-noise ratio R1 of higher amplitude artifacts based on the identified artifactschiAnd overall signal-to-noise ratio Rnoise1Single lead SNR R2 for needle HF potentiometric artifactchiAnd overall signal-to-noise ratio Rnoise2And signal-to-noise ratio R of multi-lead artifactsn3；
An overall evaluation module 603 configured to calculate an overall signal-to-noise ratio of the electroencephalogram signal according to the following formula:
RatioSN＝(Rsn1+Rsn2+Rsn3)/3；
wherein, when detecting higher amplitude artifacts in a single lead,
when higher amplitude artifacts are detected as a whole, Rsn1＝Rnoise1；
When detecting the artifacts issued by the needle-shaped high-frequency potential according to the single lead,
when detecting integrally needle-shaped high-frequency potential-emitting artifacts, Rsn2＝Rnoise2。
The SNR calculation module 602 calculates a higher amplitude artifact single derivative SNR R1 according to equation (10)chiCalculating the overall signal-to-noise ratio R of higher amplitude artifacts according to equation (11)noise1According to equation (12), calculating the needleSingle-lead SNR R2 of high-frequency potential-distribution-shaped pseudo-errorschiCalculating the overall SNR R of the needle-shaped high-frequency potential distribution artifact according to the formula (13)noise2Calculating the SNR R of the swept multi-lead artifact according to equation (14)sn3。
In some embodiments, there is also provided an electronic device comprising a processor and a storage medium storing a plurality of instructions, the processor being configured to read the instructions and execute the artifact identification method, the removal method and/or the quality assessment method of electroencephalography signals described above.
In some embodiments, there is also provided a computer readable storage medium having stored thereon computer instructions which, when executed by a processor, implement the artifact identification method, the removal method, and/or the quality assessment method of electroencephalographic signals described above.
The electroencephalogram signal quality evaluation method, device and system provided by the invention can identify the artifact component mixed in the electroencephalogram signal, evaluate the signal quality of the electroencephalogram signal and are beneficial to improving the accuracy of lesion judgment for medical staff.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention. It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (14)
1. A method for artifact identification of an electroencephalogram signal, comprising:
acquiring electroencephalogram signals, and preprocessing the electroencephalogram signals;
calculating the signal energy and wire length of all leads of the preprocessed electroencephalogram signals;
detecting a first moment when the signal energy of each lead exceeds a lead energy threshold, and simultaneously detecting a second moment when the sum of the signal energy of all leads of the electroencephalogram signal exceeds a global threshold of signal energy; acquiring two adjacent moments with the interval smaller than a first time threshold from a set comprising the first moment and the second moment, and determining that signals of time periods corresponding to the two moments are pseudo-differences with higher amplitudes;
detecting a third moment when the line length of each lead exceeds the line length lead threshold, and simultaneously detecting a fourth moment when the sum of the line lengths of all leads exceeds the line length global threshold; acquiring two adjacent moments with an interval smaller than a second time threshold from a set comprising a third moment and a fourth moment, and determining signals of time periods corresponding to the two moments acquired based on the line length as pseudo-differences issued by the high-frequency potential;
calculating a waveform envelope of the preprocessed electroencephalogram signal;
the envelope is divided into a plurality of short-time segments according to a preset sliding window, and the short-time segments are segments formed by dividing the envelope by a sliding time window with half of the time length of a set window width;
calculating the correlation value of the waveform envelope of each short-time segment of electroencephalogram signal between every two lead signals to obtain a pair-wise correlation matrix;
calculating a sum of each pair-wise correlation matrix;
detecting when the sum of the pair-wise correlation matrices exceeds a threshold;
acquiring the moment when the sum of two adjacent paired correlation matrixes with the interval smaller than a third time threshold exceeds the threshold; and determining the time period corresponding to the moment when the sum of the two paired correlation matrixes with the interval smaller than the third time threshold exceeds the threshold as the artifact of the multi-lead.
2. The method for artifact identification of electroencephalogram signals according to claim 1, wherein preprocessing the electroencephalogram signals comprises:
high pass filtering the electroencephalogram signal;
denoising the electroencephalogram signal after the high-pass filtering;
performing low-pass filtering on the electroencephalogram signals subjected to denoising processing;
the low-pass filtered electroencephalogram signal is resampled.
3. The method for artifact identification of electroencephalogram signals according to claim 1, wherein the signal energy is calculated according to the following formula:
energyeeg＝(x(t))2；
where x (t) represents the amplitude of the pre-processed electroencephalogram signal, t represents the time sampling point, energyeegSignal energy representing an electroencephalogram signal;
the wire length is calculated as follows:
wherein y (t) represents the waveform of the preprocessed electroencephalogram signal, t represents a time sampling point, and NllIs the number of sampling points in the window width used in the line length calculation, abs]For calculating absolute value functions, LlIndicating the wire length.
4. The method of artifact identification of electroencephalogram signals according to claim 3, wherein said signal energy lead threshold is calculated as follows:
energychi＝a1chi·median1chi；
wherein, mean 1chiSignal energy median representing the chi-th lead, chi-1, 2, … Nchanel，NchanelRepresenting the total number of leads of the electroencephalogram signal, a1chiExpressing fold, energychiIs a signal energy lead threshold;
the signal energy global threshold is calculated as follows:
Thresholdenergy＝a·Median1；
wherein, Median1Representing the median signal energy of all lead electroencephalographic signals, a represents the multiple, ThresholdenergyRepresenting a signal energy global threshold.
5. The method for artifact identification of electroencephalogram signals according to claim 4, wherein said wire-long lead threshold is calculated according to the following formula:
llchi＝a2chi·median2chi；
wherein, mean 2chiMedian length of signal line, a, representing the chi lead2chiDenotes a multiple, chi 1, 2, … Nchanel，NchanelRepresenting the total number of leads, ll, of the electroencephalogram signalchiRepresenting a line length lead threshold;
calculating a line length global threshold according to the following formula:
Thresholdll＝b·Median2；
wherein, Median2Represents the median of the line lengths of all lead electroencephalogram signals, b represents the multiple, ThresholdllRepresenting a line length global threshold.
6. The method for artifact identification of electroencephalogram signals according to claim 1, wherein the waveform envelope is calculated as follows:
wherein y (t) represents a waveform of the electroencephalogram signal after the preprocessing; t represents a time sampling point; enveeegRepresenting a waveform envelope; denotes convolution operation.
7. The method for artifact identification of electroencephalogram signals according to claim 6, wherein the threshold value of the sum of the pairwise correlation matrices is calculated as follows:
Thresholdcorr＝c·median3；
wherein, mean3Representing the median of the sum of the pairwise correlation matrices, c represents a multiple, and ranges from 2 to 12; threshold (Threshold)corrA threshold representing the sum of the pairwise correlation matrices.
8. A method for artifact removal of an electroencephalogram signal, comprising:
identifying artefacts according to the method of any one of claims 1 to 7;
and removing the electroencephalogram signals of the time period in which the artifact exists.
9. A method of quality assessment of an electroencephalogram signal, comprising:
identifying artifacts according to the method of any one of claims 1, 6, 7;
calculating a single derivative noise ratio of higher amplitude artifacts from the identified artifacts R1chiAnd the overall noise ratio Rnoise1Single lead noise ratio R2 of needle shape high frequency potential distribution pseudo errorchiAnd the overall noise ratio Rnoise2And noise ratio R of multi-lead artifactsn3；
The overall noise occupancy of the electroencephalogram signal is calculated as follows:
RatioSN＝(Rsn1+Rsn2+Rsn3)/3；
wherein, when detecting higher amplitude artifacts in a single lead,
when higher amplitude artifacts are detected as a whole, Rsn1＝Rnoise1；
When detecting the artifacts issued by the needle-shaped high-frequency potential according to the single lead,
when detecting integrally needle-shaped high-frequency potential-emitting artifacts, Rsn2＝Rnoise2；
chi＝1，2，…Nchanel，NchanelRepresenting the total number of leads of the electroencephalogram signal.
10. The method for quality assessment of electroencephalogram signals of claim 9, characterized in that the single-lead noise occupation ratio of higher-amplitude artifacts R1chiThe calculation is made by the following formula:
wherein, R1chiSingle derivative noise ratio, N, representing higher amplitude artifactshighHigher amplitude artifact event A representing the chi leadhighNumber of (2), ThighRepresenting higher amplitude artifact events AhighDuration of (D), TEEGRepresents the total length of time of the electroencephalogram signal, j 1, 2high，chi＝1，2，…Nchanel；
Integral noise ratio R of higher amplitude artifactnoise1The calculation is made by the following formula:
wherein R isnoise1Overall noise ratio representing higher amplitude artifact, N' representing higher amplitude artifact events for all leads AhighNumber of (2), ThighRepresenting higher amplitude artifact events AhighJ ═ 1, 2.. N', TEEGRepresenting the total length of time of the electroencephalogram signal.
11. The method for quality assessment of electroencephalogram signals according to claim 9, characterized in that the needle-shaped high-frequency potential-dispensing pseudoscopic single-lead noise ratio R2chiThe calculation is made by the following formula:
wherein, R2chiNoise ratio representing the artifacts of the needle high-frequency potential distribution of each lead, NneedleHigh frequency potentiometric artifact A representing the chi leadneedleNumber of (2), TneedleRepresenting high-frequency potential-emitting artefact events aneedleDuration of (D), TEEGN is 1, 2needle，chi＝1，2，…Nchanel；
Overall noise ratio R of needle-shaped high-frequency potential distribution artifactnoise2The calculation is made by the following formula:
wherein R isnoise2Integral noise ratio, N, representing artifacts of needle-shaped high-frequency potential distributionallHigh frequency potentiometric emission artifact A representing all leadsneedleNumber of (2), TneedleRepresenting high-frequency potential-emitting artefact events aneedleN ═ 1, 2.. Nall，TEEGRepresenting the total length of time of the electroencephalogram signal.
12. The method for quality assessment of electroencephalogram signals according to claim 9, characterized in that the noise ratio R of the artifact of multi-lead is sweptsn3The calculation is made by the following formula:
wherein R issn3Noise ratio representing the artifact of swept multiple leads, NmultipleRepresenting multiple lead artifact events AmultipleNumber of (2), TmultipleRepresenting each multiple lead artifact event AmultipleIs heldDuration, m ═ 1, 2.. Nmultiple，TEEGRepresenting the total length of time of the electroencephalogram signal.
13. An electronic device comprising a processor and a storage medium, the storage medium storing a plurality of instructions, the processor being configured to read the instructions and execute the method for artifact identification of an electroencephalogram signal according to any one of claims 1 to 7, the method for artifact removal of an electroencephalogram signal according to claim 8, and/or the method for quality evaluation of an electroencephalogram signal according to any one of claims 9 to 12.
14. A computer-readable storage medium, characterized in that it has stored thereon computer instructions which, when executed by a processor, implement the method of artifact identification of electroencephalogram signals according to any one of claims 1 to 7, the method of artifact removal of electroencephalogram signals according to claim 8, and/or the method of quality assessment of electroencephalogram signals according to any one of claims 9 to 12.
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