CN111990991A - Electroencephalogram signal analysis method based on complex network and application - Google Patents
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Abstract
The invention discloses an electroencephalogram signal analysis method based on a complex network and application thereof, and particularly relates to the technical field of biological signal processing, and the method specifically comprises the following steps: the method comprises the following steps: collecting signals, and step two: constructing an electroencephalogram signal network, and the third step: quantitative analysis, step four: matching and tracking, and step five: wavelet transformation, step six: and (6) analyzing the data. According to the method, resolution analysis is carried out on signal data which are obviously but difficult to process in local characteristics in a time domain and a frequency domain, a rapid transient phenomenon displayed by the data can be distinguished more accurately and effectively, an original complicated electroencephalogram signal network can be extracted quickly and effectively by utilizing multi-scale analysis, and electroencephalograms required by researchers are extracted so as to deal with the phenomenon that real-time electroencephalograms need to be captured when the occurrence time of stimulation cannot be known exactly.
Description
Technical Field
The invention relates to the technical field of biological signal processing, in particular to an electroencephalogram signal analysis method based on a complex network and application thereof.
Background
Electroencephalography (EEG) is a general reflection of electrophysiological activity of brain neurons on the surface of the cerebral cortex or scalp. The electroencephalogram signals contain a large amount of physiological and disease information, and in the aspect of clinical medicine, electroencephalogram signal processing not only can provide diagnosis basis for certain brain diseases, but also provides effective treatment means for certain brain diseases. In engineering applications, people also try to realize a brain-computer interface (BCI) by using electroencephalogram signals, and achieve a certain control purpose by effectively extracting and classifying the electroencephalogram signals by using the difference of electroencephalograms of people on different senses, motions or cognitive activities.
However, because the electroencephalogram signal is a non-stationary random signal without ergodicity, and the background noise is very strong, especially the time-frequency data of the electroencephalogram signal is extremely unstable, and the resolution analysis is difficult to be carried out.
Disclosure of Invention
In order to overcome the above defects in the prior art, embodiments of the present invention provide an electroencephalogram signal analysis method based on a complex network and an application thereof, by performing resolution analysis on signal data with obvious local characteristics in a time domain and a frequency domain but difficult processing, a fast transient phenomenon displayed by the data can be distinguished more accurately and effectively, an electroencephalogram signal network with original complex and complicated characteristics can be extracted quickly and effectively by using multi-scale analysis, and electroencephalogram signals required by researchers can be extracted, so as to deal with the phenomenon that real-time electroencephalogram signals need to be captured when the occurrence of stimulation cannot be known exactly.
In order to achieve the purpose, the invention provides the following technical scheme: an electroencephalogram signal analysis method based on a complex network and an application thereof specifically comprise the following steps:
the method comprises the following steps: collecting signals, randomly selecting an appropriate X number of research objects, placing the X research objects in the same environment, collecting digital electroencephalogram signals of the research objects in a quiet, awake and eye-closed state, controlling sampling frequency, obtaining one data every time period, and using the data as a data module;
step two: constructing an electroencephalogram network, storing a plurality of sets of data collected in the first step into a computer, and preprocessing to obtain effective data;
step three: quantitative analysis, namely performing continuous wavelet analysis on an electroencephalogram signal of a research object by adopting a continuous wavelet transform method, analyzing 30-40 scales in total, drawing a wavelet coefficient contour map, displaying time-frequency characteristics of the electroencephalogram signal, taking the wavelet coefficient as a detection object, detecting the electroencephalogram signal of each scale by using a conditional sampling method, and performing phase alignment, superposition and averaging on similar events of the electroencephalogram signal to obtain a phase average waveform of the electroencephalogram signal of the scale;
step four: matching and tracking, namely selecting a fundamental wave which is best matched with the electroencephalogram signal to be researched, matching the fundamental wave to a residual signal in each subsequent step of the matching, subtracting the repeated result of the previous time, and obtaining a fundamental wave numerical value and a function time-frequency library through Cohen transformation;
step five: wavelet transformation, namely performing detailed observation on a part with a small observation range on the time axis and higher frequency on the frequency domain by adopting high-frequency wavelets, and performing general view observation on a part with a larger observation range on the time axis and lower frequency on the frequency domain by adopting low-frequency wavelets;
step six: and D, data analysis, namely classifying the electroencephalogram signal data obtained through the processing of the steps five to five, analyzing the result, and sending the analyzed result to a receiving terminal.
In a preferred embodiment, in the first step, the value range of X is set to 30-100, the sampling frequency is set to 200-300Hz, and the time period included in the data module is set to 15-25 seconds.
In a preferred embodiment, the preprocessing of the acquired electroencephalogram signals in the second step is as follows: firstly, band-pass filtering is carried out on collected electroencephalogram data, high and low frequency interference components in the electroencephalogram data are removed, and then artifact data are manually removed from the electroencephalogram data, so that preprocessed electroencephalogram data are obtained.
In a preferred embodiment, the measure for removing the high and low frequency interference in the second step is specifically: the method comprises the steps of training an adaptive AR model by using a Kalman filter to remove electromyographic signals and transient high-amplitude signal interference, determining a threshold value to remove abnormal signal points or sections according to the statistical characteristics of the signals, decomposing the electroencephalographic signals by using a wavelet transform method, setting the coefficient of the lowest frequency band to zero to remove the influence of baseline drift and respiratory artifact caused by electrode impedance change, and removing Gaussian white noise contained in the signals by using a thresholding method based on empirical Bayesian estimation on other frequency bands.
In a preferred embodiment, said step five wavelet transform uses a mother wavelet function, by which the signal portion to be analyzed is selected by its transformation in the time domain.
The invention has the technical effects and advantages that:
1. according to the method, resolution analysis is carried out on signal data which are obviously but difficult to process in local characteristics of a time domain and a frequency domain, a rapid transient phenomenon displayed by the data can be distinguished more accurately and effectively, an original complicated electroencephalogram signal network can be extracted quickly and effectively by utilizing multi-scale analysis, and electroencephalograms required by researchers are extracted so as to deal with the phenomenon that real-time electroencephalograms need to be captured when the occurrence time of stimulation cannot be known exactly;
2. the invention can not only obtain high time-frequency resolution, but also represent all signals by using parameters, convert the electroencephalogram digital signals into function digital signals without cross terms, and then process the obtained data by using a mathematical method, thereby being a reasonable and effective method for performing dynamic analysis of electroencephalogram activity.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1:
the invention provides an electroencephalogram signal analysis method based on a complex network and application thereof, and specifically comprises the following steps:
the method comprises the following steps: collecting signals, randomly selecting 30 suitable study objects, placing the 30 study objects in the same environment, collecting digital electroencephalogram signals of the study objects in a quiet, awake and eye-closed state, controlling sampling frequency, obtaining one data in each time period, and using the data as a data module, wherein the sampling frequency is set to be 200Hz, and the time period contained in the data module is set to be 15 seconds;
step two: the method comprises the following steps of constructing an electroencephalogram signal network, storing a plurality of groups of data acquired through step one into a computer, preprocessing, carrying out band-pass filtering on collected electroencephalogram data, removing high and low frequency interference components in the electroencephalogram data, manually removing artifact data from the electroencephalogram data, and acquiring the preprocessed electroencephalogram data, wherein the measures for removing the high and low frequency interference are specifically as follows: training an adaptive AR model by using a Kalman filter to remove electromyographic signals and transient high-amplitude signal interference, determining a threshold value to remove abnormal signal points or sections according to the statistical characteristics of the signals, decomposing the electroencephalographic signals by using a wavelet transform method, setting the coefficient of the lowest frequency band to zero to remove the influence of baseline drift and respiratory artifact caused by electrode impedance change, and removing Gaussian white noise contained in the signals by using a thresholding method based on empirical Bayesian estimation on other frequency bands;
step three: quantitative analysis, namely performing continuous wavelet analysis on an electroencephalogram signal of a research object by adopting a continuous sub-transform method, analyzing 30 scales in total, drawing a wavelet coefficient contour map, displaying the time-frequency characteristics of the electroencephalogram signal, taking the wavelet coefficient as a detection object, detecting the electroencephalogram signal of each scale by using a conditional sampling method, performing phase alignment, superposition and averaging on similar events of the electroencephalogram signal, and obtaining a phase average waveform of the electroencephalogram signal of the scale;
step four: matching and tracking, namely selecting a fundamental wave which is best matched with the electroencephalogram signal to be researched, matching the fundamental wave to a residual signal in each subsequent step of the matching, subtracting the repeated result of the previous time, and obtaining a fundamental wave numerical value and a function time-frequency library through Cohen transformation;
step five: wavelet transformation, wherein the wavelet transformation adopts a mother wavelet function, a signal part to be analyzed is selected through the transformation of the function in a time domain, high-frequency wavelets are adopted for carrying out detailed observation on a part with a small observation range on a time axis and a higher frequency on a frequency domain, and low-frequency wavelets are adopted for carrying out profile observation on a part with a larger observation range on the time axis and a part with a lower frequency on the frequency domain;
step six: and D, data analysis, namely classifying the electroencephalogram signal data obtained through the processing of the steps five to five, analyzing the result, and sending the analyzed result to a receiving terminal.
Example 2:
the invention provides an electroencephalogram signal analysis method based on a complex network and application thereof, and specifically comprises the following steps:
the method comprises the following steps: collecting signals, randomly selecting 40 suitable study objects, placing the 40 study objects in the same environment, collecting digital electroencephalogram signals of the study objects in a quiet, awake and eye-closed state, controlling sampling frequency, obtaining one data in each time period, and using the data as a data module, wherein the sampling frequency is set to be 220Hz, and the time period contained in the data module is set to be 17 seconds;
step two: the method comprises the following steps of constructing an electroencephalogram signal network, storing a plurality of groups of data acquired through step one into a computer, preprocessing, carrying out band-pass filtering on collected electroencephalogram data, removing high and low frequency interference components in the electroencephalogram data, manually removing artifact data from the electroencephalogram data, and acquiring the preprocessed electroencephalogram data, wherein the measures for removing the high and low frequency interference are specifically as follows: training an adaptive AR model by using a Kalman filter to remove electromyographic signals and transient high-amplitude signal interference, determining a threshold value to remove abnormal signal points or sections according to the statistical characteristics of the signals, decomposing the electroencephalographic signals by using a wavelet transform method, setting the coefficient of the lowest frequency band to zero to remove the influence of baseline drift and respiratory artifact caused by electrode impedance change, and removing Gaussian white noise contained in the signals by using a thresholding method based on empirical Bayesian estimation on other frequency bands;
step three: quantitative analysis, namely performing continuous wavelet analysis on an electroencephalogram signal of a research object by adopting a continuous sub-transform method, analyzing 32 scales in total, drawing a wavelet coefficient contour map, displaying the time-frequency characteristics of the electroencephalogram signal, taking the wavelet coefficient as a detection object, detecting the electroencephalogram signal of each scale by using a conditional sampling method, performing phase alignment, superposition and averaging on similar events of the electroencephalogram signal, and obtaining a phase average waveform of the electroencephalogram signal of the scale;
step four: matching and tracking, namely selecting a fundamental wave which is best matched with the electroencephalogram signal to be researched, matching the fundamental wave to a residual signal in each subsequent step of the matching, subtracting the repeated result of the previous time, and obtaining a fundamental wave numerical value and a function time-frequency library through Cohen transformation;
step five: wavelet transformation, wherein the wavelet transformation adopts a mother wavelet function, a signal part to be analyzed is selected through the transformation of the function in a time domain, high-frequency wavelets are adopted for carrying out detailed observation on a part with a small observation range on a time axis and a higher frequency on a frequency domain, and low-frequency wavelets are adopted for carrying out profile observation on a part with a larger observation range on the time axis and a part with a lower frequency on the frequency domain;
step six: and D, data analysis, namely classifying the electroencephalogram signal data obtained through the processing of the steps five to five, analyzing the result, and sending the analyzed result to a receiving terminal.
Example 3:
the invention provides an electroencephalogram signal analysis method based on a complex network and application thereof, and specifically comprises the following steps:
the method comprises the following steps: collecting signals, randomly selecting 50 suitable study objects, placing the 50 study objects in the same environment, collecting digital electroencephalogram signals of the study objects in a quiet, awake and eye-closed state, controlling sampling frequency, obtaining one data every time period, and using the data as a data module, wherein the sampling frequency is set to be 240Hz, and the time period contained in the data module is set to be 20 seconds;
step two: the method comprises the following steps of constructing an electroencephalogram signal network, storing a plurality of groups of data acquired through step one into a computer, preprocessing, carrying out band-pass filtering on collected electroencephalogram data, removing high and low frequency interference components in the electroencephalogram data, manually removing artifact data from the electroencephalogram data, and acquiring the preprocessed electroencephalogram data, wherein the measures for removing the high and low frequency interference are specifically as follows: training an adaptive AR model by using a Kalman filter to remove electromyographic signals and transient high-amplitude signal interference, determining a threshold value to remove abnormal signal points or sections according to the statistical characteristics of the signals, decomposing the electroencephalographic signals by using a wavelet transform method, setting the coefficient of the lowest frequency band to zero to remove the influence of baseline drift and respiratory artifact caused by electrode impedance change, and removing Gaussian white noise contained in the signals by using a thresholding method based on empirical Bayesian estimation on other frequency bands;
step three: quantitative analysis, namely performing continuous wavelet analysis on an electroencephalogram signal of a research object by adopting a continuous sub-transform method, analyzing 35 scales in total, drawing a wavelet coefficient contour map, displaying the time-frequency characteristics of the electroencephalogram signal, taking the wavelet coefficient as a detection object, detecting the electroencephalogram signal of each scale by using a conditional sampling method, performing phase alignment, superposition and averaging on similar events of the electroencephalogram signal, and obtaining a phase average waveform of the electroencephalogram signal of the scale;
step four: matching and tracking, namely selecting a fundamental wave which is best matched with the electroencephalogram signal to be researched, matching the fundamental wave to a residual signal in each subsequent step of the matching, subtracting the repeated result of the previous time, and obtaining a fundamental wave numerical value and a function time-frequency library through Cohen transformation;
step five: wavelet transformation, wherein the wavelet transformation adopts a mother wavelet function, a signal part to be analyzed is selected through the transformation of the function in a time domain, high-frequency wavelets are adopted for carrying out detailed observation on a part with a small observation range on a time axis and a higher frequency on a frequency domain, and low-frequency wavelets are adopted for carrying out profile observation on a part with a larger observation range on the time axis and a part with a lower frequency on the frequency domain;
step six: and D, data analysis, namely classifying the electroencephalogram signal data obtained through the processing of the steps five to five, analyzing the result, and sending the analyzed result to a receiving terminal.
Example 4:
the invention provides an electroencephalogram signal analysis method based on a complex network and application thereof, and specifically comprises the following steps:
the method comprises the following steps: collecting signals, randomly selecting 70 suitable study objects, placing the 70 study objects in the same environment, collecting digital electroencephalogram signals of the study objects in a quiet, awake and eye-closed state, controlling sampling frequency, obtaining one data every time period, and using the data as a data module, wherein the sampling frequency is set to be 280Hz, and the time period contained in the data module is set to be 22 seconds;
step two: the method comprises the following steps of constructing an electroencephalogram signal network, storing a plurality of groups of data acquired through step one into a computer, preprocessing, carrying out band-pass filtering on collected electroencephalogram data, removing high and low frequency interference components in the electroencephalogram data, manually removing artifact data from the electroencephalogram data, and acquiring the preprocessed electroencephalogram data, wherein the measures for removing the high and low frequency interference are specifically as follows: training an adaptive AR model by using a Kalman filter to remove electromyographic signals and transient high-amplitude signal interference, determining a threshold value to remove abnormal signal points or sections according to the statistical characteristics of the signals, decomposing the electroencephalographic signals by using a wavelet transform method, setting the coefficient of the lowest frequency band to zero to remove the influence of baseline drift and respiratory artifact caused by electrode impedance change, and removing Gaussian white noise contained in the signals by using a thresholding method based on empirical Bayesian estimation on other frequency bands;
step three: quantitative analysis, namely performing continuous wavelet analysis on an electroencephalogram signal of a research object by adopting a continuous wavelet transform method, analyzing 38 scales in total, drawing a wavelet coefficient contour map, displaying time-frequency characteristics of the electroencephalogram signal, taking the wavelet coefficient as a detection object, detecting the electroencephalogram signal of each scale by using a conditional sampling method, performing phase alignment, superposition and averaging on similar events of the electroencephalogram signal, and obtaining a phase average waveform of the electroencephalogram signal of the scale;
step four: matching and tracking, namely selecting a fundamental wave which is best matched with the electroencephalogram signal to be researched, matching the fundamental wave to a residual signal in each subsequent step of the matching, subtracting the repeated result of the previous time, and obtaining a fundamental wave numerical value and a function time-frequency library through Cohen transformation;
step five: wavelet transformation, wherein the wavelet transformation adopts a mother wavelet function, a signal part to be analyzed is selected through the transformation of the function in a time domain, high-frequency wavelets are adopted for carrying out detailed observation on a part with a small observation range on a time axis and a higher frequency on a frequency domain, and low-frequency wavelets are adopted for carrying out profile observation on a part with a larger observation range on the time axis and a part with a lower frequency on the frequency domain;
step six: and D, data analysis, namely classifying the electroencephalogram signal data obtained through the processing of the steps five to five, analyzing the result, and sending the analyzed result to a receiving terminal.
Example 5:
the invention provides an electroencephalogram signal analysis method based on a complex network and application thereof, which are characterized in that: the method specifically comprises the following steps:
the method comprises the following steps: collecting signals, randomly selecting 100 suitable study objects, placing the 100 study objects in the same environment, collecting digital electroencephalogram signals of the study objects in a quiet, awake and eye-closed state, controlling sampling frequency, obtaining one data in each time period, and using the data as a data module, wherein the sampling frequency is set to be 300Hz, and the time period contained in the data module is set to be 25 seconds;
step two: the method comprises the following steps of constructing an electroencephalogram signal network, storing a plurality of groups of data acquired through step one into a computer, preprocessing, carrying out band-pass filtering on collected electroencephalogram data, removing high and low frequency interference components in the electroencephalogram data, manually removing artifact data from the electroencephalogram data, and acquiring the preprocessed electroencephalogram data, wherein the measures for removing the high and low frequency interference are specifically as follows: training an adaptive AR model by using a Kalman filter to remove electromyographic signals and transient high-amplitude signal interference, determining a threshold value to remove abnormal signal points or sections according to the statistical characteristics of the signals, decomposing the electroencephalographic signals by using a wavelet transform method, setting the coefficient of the lowest frequency band to zero to remove the influence of baseline drift and respiratory artifact caused by electrode impedance change, and removing Gaussian white noise contained in the signals by using a thresholding method based on empirical Bayesian estimation on other frequency bands;
step three: quantitative analysis, namely performing continuous wavelet analysis on an electroencephalogram signal of a research object by adopting a continuous sub-transform method, analyzing 40 scales in total, drawing a wavelet coefficient contour map, displaying the time-frequency characteristics of the electroencephalogram signal, taking the wavelet coefficient as a detection object, detecting the electroencephalogram signal of each scale by using a conditional sampling method, performing phase alignment, superposition and averaging on similar events of the electroencephalogram signal, and obtaining a phase average waveform of the electroencephalogram signal of the scale;
step four: matching and tracking, namely selecting a fundamental wave which is best matched with the electroencephalogram signal to be researched, matching the fundamental wave to a residual signal in each subsequent step of the matching, subtracting the repeated result of the previous time, and obtaining a fundamental wave numerical value and a function time-frequency library through Cohen transformation;
step five: wavelet transformation, wherein the wavelet transformation adopts a mother wavelet function, a signal part to be analyzed is selected through the transformation of the function in a time domain, high-frequency wavelets are adopted for carrying out detailed observation on a part with a small observation range on a time axis and a higher frequency on a frequency domain, and low-frequency wavelets are adopted for carrying out profile observation on a part with a larger observation range on the time axis and a part with a lower frequency on the frequency domain;
step six: and D, data analysis, namely classifying the electroencephalogram signal data obtained through the processing of the steps five to five, analyzing the result, and sending the analyzed result to a receiving terminal.
The following data were obtained by applying the method designed according to the present invention to the study population by taking the analysis methods used in examples 1 to 5 above for the study subjects selected in each method, respectively:
five electroencephalogram signal analysis methods based on a complex network and applications thereof can be obtained through the five groups of embodiments, and the five electroencephalogram signal analysis methods are practically applied, wherein the data of the electroencephalogram signal analysis method in embodiment 3 is least in artifact and most clear in data, and in the test process, the obtained parameter pairs are as follows:
| ratio of artifact data | Filtering | With or without cross terms | Resolution ratio | |
| Example 1 | 17％ | 14％ | Is free of | Height of |
| Example 2 | 17％ | 16％ | Is free of | Height of |
| Example 3 | 15％ | 14％ | Is free of | Height of |
| Example 4 | 16％ | 15％ | Is free of | Height of |
| Example 5 | 18％ | 16％ | Is free of | Height of |
As can be seen from the above table, in the embodiment 3, the study object is most reasonably selected, and the present invention analyzes the resolution of the signal data which is obviously difficult to process by the local features in the time domain and the frequency domain, the rapid transient phenomenon displayed by the data can be distinguished more accurately and effectively, the original complex electroencephalogram signal network can be extracted quickly and effectively by utilizing multi-scale analysis, the electroencephalogram signals required by researchers are extracted, so as to deal with the phenomenon that the real-time electroencephalogram signal needs to be captured when the stimulation moment can not be known exactly, not only can very high time-frequency resolution be obtained, all signals are expressed by using parameters, the electroencephalogram digital signals are converted into function digital signals without cross terms, and then the obtained data are processed by using a mathematical method, so that the method is a reasonable and effective method for performing dynamic analysis on the electroencephalogram activity.
And finally: the above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that are within the spirit and principle of the present invention are intended to be included in the scope of the present invention.
Claims (5)
1. An electroencephalogram signal analysis method based on a complex network and application thereof are characterized in that: the method specifically comprises the following steps:
the method comprises the following steps: collecting signals, randomly selecting an appropriate X number of research objects, placing the X research objects in the same environment, collecting digital electroencephalogram signals of the research objects in a quiet, awake and eye-closed state, controlling sampling frequency, obtaining one data every time period, and using the data as a data module;
step two: constructing an electroencephalogram network, storing a plurality of sets of data collected in the first step into a computer, and preprocessing to obtain effective data;
step three: quantitative analysis, namely performing continuous wavelet analysis on an electroencephalogram signal of a research object by adopting a continuous wavelet transform method, analyzing 30-40 scales in total, drawing a wavelet coefficient contour map, displaying time-frequency characteristics of the electroencephalogram signal, taking the wavelet coefficient as a detection object, detecting the electroencephalogram signal of each scale by using a conditional sampling method, and performing phase alignment, superposition and averaging on similar events of the electroencephalogram signal to obtain a phase average waveform of the electroencephalogram signal of the scale;
step four: matching and tracking, namely selecting a fundamental wave which is best matched with the electroencephalogram signal to be researched, matching the fundamental wave to a residual signal in each subsequent step of the matching, subtracting the repeated result of the previous time, and obtaining a fundamental wave numerical value and a function time-frequency library through Cohen transformation;
step five: wavelet transformation, namely performing detailed observation on a part with a small observation range on the time axis and higher frequency on the frequency domain by adopting high-frequency wavelets, and performing general view observation on a part with a larger observation range on the time axis and lower frequency on the frequency domain by adopting low-frequency wavelets;
step six: and D, data analysis, namely classifying the electroencephalogram signal data obtained through the processing of the steps five to five, analyzing the result, and sending the analyzed result to a receiving terminal.
2. The electroencephalogram signal analysis method and application based on the complex network as claimed in claim 1, wherein the method comprises the following steps: in the first step, the value range of X is set to be 30-100, the sampling frequency is set to be 200-300Hz, and the time period contained in the data module is set to be 15-25 seconds.
3. The electroencephalogram signal analysis method and application based on the complex network as claimed in claim 1, wherein the method comprises the following steps: the step two of preprocessing the acquired electroencephalogram signals comprises the following steps: firstly, band-pass filtering is carried out on collected electroencephalogram data, high and low frequency interference components in the electroencephalogram data are removed, and then artifact data are manually removed from the electroencephalogram data, so that preprocessed electroencephalogram data are obtained.
4. The electroencephalogram signal analysis method and application based on the complex network as claimed in claim 3, wherein the method comprises the following steps: the measures for removing the high and low frequency interference in the second step are specifically as follows: the method comprises the steps of training an adaptive AR model by using a Kalman filter to remove electromyographic signals and transient high-amplitude signal interference, determining a threshold value to remove abnormal signal points or sections according to the statistical characteristics of the signals, decomposing the electroencephalographic signals by using a wavelet transform method, setting the coefficient of the lowest frequency band to zero to remove the influence of baseline drift and respiratory artifact caused by electrode impedance change, and removing Gaussian white noise contained in the signals by using a thresholding method based on empirical Bayesian estimation on other frequency bands.
5. The electroencephalogram signal analysis method and application based on the complex network as claimed in claim 1, wherein the method comprises the following steps: in said step five, the wavelet transform uses a mother wavelet function, and the signal portion to be analyzed is selected by the transformation of the function in the time domain.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| TWI767447B (en) * | 2020-12-11 | 2022-06-11 | 國立成功大學 | Cognition evaluation system and method |
| CN116491960A (en) * | 2023-06-28 | 2023-07-28 | 南昌大学第一附属医院 | Brain transient monitoring device, electronic device, and storage medium |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101449974A (en) * | 2007-12-05 | 2009-06-10 | 李小俚 | Method for automatic real-time estimating anesthesia depth |
| CN102488516A (en) * | 2011-12-13 | 2012-06-13 | 湖州康普医疗器械科技有限公司 | Nonlinear electroencephalogram signal analysis method and device |
| CN104586387A (en) * | 2015-01-19 | 2015-05-06 | 秦皇岛市惠斯安普医学系统有限公司 | Method for extracting and fusing time, frequency and space domain multi-parameter electroencephalogram characters |
| CN106473736A (en) * | 2016-10-11 | 2017-03-08 | 天津大学 | Electroencephalogramsignal signal analysis method and application based on complex network |
| CN106821318A (en) * | 2016-08-31 | 2017-06-13 | 天津市人民医院 | A kind of multiple dimensioned quantitative analysis method of EEG signals |
| CN106859673A (en) * | 2017-01-13 | 2017-06-20 | 兰州大学 | A kind of depression Risk Screening system based on sleep cerebral electricity |
| CN109002798A (en) * | 2018-07-19 | 2018-12-14 | 大连理工大学 | It is a kind of singly to lead visual evoked potential extracting method based on convolutional neural networks |
-
2020
- 2020-08-22 CN CN202010881666.8A patent/CN111990991A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101449974A (en) * | 2007-12-05 | 2009-06-10 | 李小俚 | Method for automatic real-time estimating anesthesia depth |
| CN102488516A (en) * | 2011-12-13 | 2012-06-13 | 湖州康普医疗器械科技有限公司 | Nonlinear electroencephalogram signal analysis method and device |
| CN104586387A (en) * | 2015-01-19 | 2015-05-06 | 秦皇岛市惠斯安普医学系统有限公司 | Method for extracting and fusing time, frequency and space domain multi-parameter electroencephalogram characters |
| CN106821318A (en) * | 2016-08-31 | 2017-06-13 | 天津市人民医院 | A kind of multiple dimensioned quantitative analysis method of EEG signals |
| CN106473736A (en) * | 2016-10-11 | 2017-03-08 | 天津大学 | Electroencephalogramsignal signal analysis method and application based on complex network |
| CN106859673A (en) * | 2017-01-13 | 2017-06-20 | 兰州大学 | A kind of depression Risk Screening system based on sleep cerebral electricity |
| CN109002798A (en) * | 2018-07-19 | 2018-12-14 | 大连理工大学 | It is a kind of singly to lead visual evoked potential extracting method based on convolutional neural networks |
Non-Patent Citations (1)
| Title |
|---|
| 季忠, 秦树人, 彭丽玲: "脑电信号的现代分析方法", 重庆大学学报(自然科学版), no. 09, pages 108 - 112 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| TWI767447B (en) * | 2020-12-11 | 2022-06-11 | 國立成功大學 | Cognition evaluation system and method |
| CN116491960A (en) * | 2023-06-28 | 2023-07-28 | 南昌大学第一附属医院 | Brain transient monitoring device, electronic device, and storage medium |
| CN116491960B (en) * | 2023-06-28 | 2023-09-19 | 南昌大学第一附属医院 | Brain transient monitoring device, electronic device, and storage medium |
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