CN115299964A - Electroencephalogram complexity analysis method for Alzheimer disease patient - Google Patents
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Abstract
The invention discloses an electroencephalogram complexity analysis method for patients with Alzheimer's disease, which comprises the following steps: s1, acquiring electroencephalogram signals of a subject, wherein the subject comprises three groups of AD, MCI and HOA; s2, preprocessing the acquired electroencephalogram signals, taking artifacts of the electroencephalogram signals out through an independent component analysis method, and then performing band-pass filtering on the electroencephalogram signals; s3, performing correlation complexity calculation on the preprocessed electroencephalogram signals, and calculating corresponding complexity through an LEMPEL-ZIV algorithm and a fuzzy approximate entropy algorithm respectively; and S4, analyzing the calculated complexity. The method can be used to assess neuronal degeneration caused by AD progression prior to changes in brain tissue or the appearance of behavioral symptoms. Due to its advantages of non-invasiveness, portability, and lower cost.
Description
Technical Field
The invention relates to the technical field of bioelectricity signal processing, in particular to an electroencephalogram complexity analysis method for patients with Alzheimer's disease.
Background
The manifestation of dementia caused by Alzheimer's Disease (AD) is preceded by two stages, preclinical AD and Mild Cognitive Impairment (MCI). While in the preclinical AD phase, patients do not show any clinical symptoms, but physiological changes in the brain, blood, cerebrospinal fluid, etc. associated with AD begin to occur in patients, and several years later, dementia may only show clinical symptoms, the possibility of detecting AD in this phase will provide a critical opportunity for therapeutic intervention, and a definitive early diagnosis will also help patients to maintain independence for a longer period of time and prevent symptoms associated with mental illness, such as depression or psychosis, thereby reducing personal and social costs associated with AD. Furthermore, new drugs for treating AD symptoms are likely to be more effective in the early stages of the disease, i.e., before neurodegeneration occurs. Therefore, relevant early diagnosis of AD techniques have been developed in this context.
However, until today, only if the structural brain damage features caused by AD are revealed, it is possible to make a clear diagnosis of AD after the patient dies. Current diagnostic methods, such as neurological tests and medical records, have accuracy as high as 90%. Meanwhile, simple mental state examination (MMSE) is the most commonly used cognitive ability testing tool in actual clinical diagnosis, and montreal cognitive assessment (MoCA) and addenbroke cognitive test revisions are also frequently used in clinical practice. Other examples of neurological tests are the severe cognitive impairment scale, the alzheimer's disease assessment cognitive scale, a combination of neuropsychological tests and a combination of severe cognitive impairment, etc. In addition, since other dementia-causing diseases, such as vascular brain injury, lewy body disease and parkinson's disease, may also occur in some cases in parallel with AD, leading to overlap of some early symptoms, techniques using specific biomarkers may enhance the differential diagnosis of AD and these diseases at early stages.
At present, prevention is a major goal in AD treatment studies. However, relying on neurological tests and medical record assessment requires experienced clinicians and lengthy treatment procedures, which makes AD diagnosis time-consuming and laborious and impossible to popularize and reproduce on a large scale. To address these shortcomings, there has been an increase in the past few years regarding the use, research and development of AD biomarkers, which have played a central role in recent diagnostic criteria for AD research. Biomarkers for AD can be divided into three major categories in related studies: A. t and N, wherein the first two classes include biomarkers that measure brain amyloidosis and allergy, respectively, such as amyloid and tau tracer positron emission tomography, and the cerebrospinal fluid concentration of ACF142 and P-tau; class N includes two disease markers for measuring neurodegeneration or nerve damage (e.g. atrophy in β T-tau, FDGPET and MRI).
The most common cerebrospinal fluid biomarker, beta, was found 42 The values in AD patients are lower than in healthy people. However, lumbar puncture is required to obtain a cerebrospinal fluid sample, which makes the technique invasive and difficult to popularize, thus preventing the technique from being used in daily clinical practice. Alternatively, blood biomarkers, such as plasma T-tau, are under investigation as they can provide information similar to cerebrospinal fluid, a less invasive, more costly technique. In addition, a neural imaging tool such as positron emission tomography, magnetic resonance imaging and computed tomography enables a clinician to study the range of brain injury caused by AD in vivo. However, once the disease-related structural damage is detectable at the current spatial resolution of these current neuroimaging techniques, the AD patient is already severely ill at this time, i.e., the atrophy of the brain of the patient has been exaggerated. In addition, these neuroimaging tools are expensive and time consuming and require expert intervention. Furthermore, not all hospitals are affordable for MRI and PET scanners, particularly in low and medium income countries or remote areas, and are therefore neither comfortable nor practical for patients. Thus, in summary, existing cerebrospinal fluid-derived biomarker and neuroimaging techniques are impractical because they are either invasive or expensive.
Although in recent years, the AD disease monitoring is predicted by a related method for detecting the complexity abnormality of the electroencephalogram signals of the Alzheimer disease, for example, the complexity change hidden in a time sequence is fully reflected by a multivariable multi-scale weighted value sequencing entropy, the detection method still has the problems that noise has unavoidable interference on the signals, and the like.
Disclosure of Invention
According to the defects of the prior art, the invention provides an electroencephalogram complexity analysis method for patients with Alzheimer's disease, which can be used for evaluating neuron degeneration caused by AD progress before brain tissue changes or behavior symptoms appear. Has the advantages of non-invasiveness, portability, lower cost and the like.
In order to solve the technical problems, the technical scheme of the invention is as follows:
an electroencephalogram complexity analysis method for patients with Alzheimer's disease comprises the following steps:
s1, acquiring electroencephalogram signals of a subject, wherein the subject comprises three groups of AD, MCI and HOA;
s2, preprocessing the acquired electroencephalogram signals, taking artifacts of the electroencephalogram signals out through an independent component analysis method, and then performing band-pass filtering on the electroencephalogram signals;
s3, performing correlation complexity calculation on the preprocessed electroencephalogram signals, and calculating corresponding complexity through an LEMPEL-ZIV algorithm and a fuzzy approximate entropy algorithm respectively;
the method for calculating the complexity of the fuzzy approximate entropy algorithm comprises the following steps:
for a given time series of N points { μ (i), i =1,2,.., N }, the set of m-dimensional vectors is formed as X i ＝[μ(i),μ(i+1),...,μ(i+m-1)]I ranges from 1 to N-m +1; two vectors
And
the distance between is defined by m (r)：
The approximate entropy can be defined as:
defining the fuzzy approximation entropy, { μ (i), i =1,2,. Times, N } forms a series of vectors by:
given r and n, the similarity between two vectors is determined by a blur function:
similar to ApEn, function
And phi m Is defined as:
finally, the estimate of the fuzzy approximation entropy fApEn can be expressed by the following equation:
fApEn(m,n,r,N)＝φ m (n,r)-φ m+1 (n,r)
wherein the embedding dimension m, the data length N, the tolerance r, and the exponential function N;
and S4, analyzing the calculated complexity.
Preferably, in step S2-2, a specific method of band-pass filtering is as follows: in the full-band analysis, the EEG signal of the testee in the full band is obtained by adopting 0.5-40HZ band-pass filtering; in multi-band analysis, a wavelet packet algorithm is adopted to respectively extract a plurality of frequency bands, namely a delta frequency band (0.5-4 Hz), a theta frequency band (4-8 Hz), an alpha frequency band (8-13 Hz) and a beta frequency band (13-30 Hz), in an EEG signal.
Preferably, in step S3, the method for calculating the complexity of the LEMPEL-ZIV algorithm is as follows:
setting the time scale as s, taking an integer value of s without exceeding the length of the time sequence, and splitting the data segment into 1s data segments;
the method comprises performing binarization processing on EEG signal, and finding out the middle value m of the EEG signal * x Then use this intermediate value m * x Comparing and judging with each data point in the brain electrical sequence, and is larger than the middle value m * x Is taken to be 1, less than the median value m * x Taking the value as 0, obtaining a time sequence { s (i), i =1,2,.., n } after binarization;
suppose S (S) 1 ,s 2 ,...,s n ) Expressed as a time series of values (0,1) in which S represents the letter S i The numerical values are only 1 and 0, the length of the time sequence S is n, the subcharacter string in the time sequence S is represented by subs (i, j), the character string composition between the ith letter and the jth letter in the time sequence S is represented by the subcharacter string in the time sequence S, the character string is required to satisfy 1 ≦ i ≦ j ≦ n naturally, the word set in the time sequence S is represented by V (S), the word set represents the total set of sub-character strings in the time sequence S, and the S (S) is simulated 1 ,s 2 ,...,s n ) Assume additionally a (0,1) time series Q (Q) 1 ,q 2 ,...,q m ) Defining SQ pi as time series S (S) 1 ,s 2 ,...,s n ) And Q (Q) 1 ,q 2 ,...,q m ) One character in the set is removed, i.e. SQ π is denoted as SQ π =(s) 1 ,s 2 ,...,s n ,q 1 ,q 2 ,...,q m-1 ) And simultaneously V (S) definition can be obtained, wherein V (SQ pi) is expressed as a set of different substrings in a time sequence set SQ pi, and c (n) is defined as a complex measure of the time sequence S, so that the time sequence (S) is calculated 1 ,s 2 ,...,s n ) The complexity of (2);
the initialization is as follows: c (n) =1, S = (S) 1 )、Q＝(s 1 ) Then SQ π =(s) 1 ) Assuming that if Q ∈ V (SQ π), then the sequence of characters in the time series Q can be copied through the sequence S, at which point the next character of the time series to be computed can be concatenated to the sequence Q, assuming
Then the time series Q needs to be inserted into the character and concatenated into the series S, denoted S = SQ, and finally the series Q is emptied, at which time the next character of the sequence to be calculated is concatenated into Q, at which time the time series S is denoted S = (S =) (S) 1 ,s 2 )，Q＝(s 3 ) In the calculation, each time the time series Q is connected to the time series S, c (n) = c (n) +1, and the steps are repeated until the time series Q takes the last bit of the time series S to be calculated;
c (n) is processed by normalization.
Preferably, the expression of the normalization process c (n) is as follows:
LZC＝c(n)/b(n)
the obtained substrings are normalized by the above two equations, when n is close to ∞, b (n) is expressed as one approximate value of c (n), and here, by using b (n) to normalize the sequence c (n), a Lempel-Ziv complexity LZC expression completely independent of n can be obtained.
Preferably, the complexity analysis method in step S4 is feature analysis of LZC and fuzzy approximation entropy.
Preferably, the characteristic analysis method of the LZC and the fuzzy approximate entropy is as follows:
s4-1, when calculating fApEn and LZC values of all the testees in three groups, measuring and calculating the average electroencephalogram complexity of each channel of each tester, and then taking the average value of the LZC and fApEn of the EEG signals of all the channels of the testees as the final LZC and fApEn value of each tester;
s4-2, comparing and analyzing the electroencephalogram complexity of three testees of AD, MCI and HOA under the full frequency band and four frequency bands.
Preferably, in the step S4-1, when calculating the average brain electrical complexity of each channel of each subject, all the data segments are divided into a plurality of data segments of 1S, and each data segment is used as one channel to calculate the average brain electrical complexity.
The invention has the following characteristics and beneficial effects:
by adopting the technical scheme, the complexity characteristic of the full frequency band is verified to be possibly not suitable for quantifying electroencephalogram change caused by AD, meanwhile, the performances of three groups of testees in multiple frequency bands are more diversified, and the significant difference exists among three groups of delta, theta and alpha frequency bands. The method shows that the complexity characteristics of multiple frequency bands are possibly more suitable for quantifying the electroencephalogram change caused by AD, and provides a better theoretical basis for AD diagnosis by utilizing electroencephalogram signals in the future.
Meanwhile, the invention not only shows that the measurement based on the EEG complexity provides a potential useful method for detecting AD, but also shows that the extraction of complexity measure from the multi-band of EEG signal is an important step for obtaining the steady biomarker. In addition, the method for measuring the complexity simultaneously utilizes two complexity measuring methods of LEMPEL-ZIV and fuzzy approximate entropy, and is an efficient and accurate method for analyzing the brain electrical complexity of the Alzheimer disease patient.
The data result shows that the method quantifies the influence of the Alzheimer's disease on the electroencephalogram complexity of a tested person by combining two different nonlinear dynamics methods of fuzzy approximate entropy (fApEn) and Lempel-Ziv complexity (LZC), and is an efficient and accurate electroencephalogram complexity analysis method for patients with Alzheimer's disease. And the delta, theta and alpha frequency bands extracted from the multi-frequency bands are used as main frequency bands of the complexity measurement, so that good detection and analysis of the electroencephalogram complexity can be realized only by using a small number of electroencephalogram channels, data volume and specific frequency bands, a potential useful method is provided for detecting AD, and the extraction of the complexity measurement from the multi-frequency bands of the EEG signal is an important step for obtaining the stable biomarker.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the description of the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of a method according to an embodiment of the present invention.
FIG. 2 is a time domain analysis of different artifacts in brain electrical signals, where
(a) Interference components caused by eye movement in the EEG signals;
(b) Interference components caused by muscles in the electroencephalogram signals;
(c) Interference components caused by the central electricity of the electroencephalogram signals;
(d) The interference component caused by the power frequency power supply in the electroencephalogram signal.
Fig. 3 is a schematic diagram of the overall process of complexity analysis.
FIG. 4 is a schematic diagram of the LZC algorithm.
Detailed Description
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict.
In the description of the present invention, it is to be understood that the terms "center", "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on those shown in the drawings, and are used only for convenience in describing the present invention and for simplicity in description, and do not indicate or imply that the referenced devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and thus, are not to be construed as limiting the present invention. Furthermore, the terms "first", "second", etc. are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first," "second," etc. may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means two or more unless otherwise specified.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art through specific situations.
The invention provides an electroencephalogram complexity analysis method for patients with Alzheimer's disease, which comprises the following steps as shown in figure 1:
s1, acquiring electroencephalogram signals of a subject, wherein the subject comprises three groups of AD, MCI and HOA.
Specifically, 56 subjects were recruited, with AD patients (n =21, 15 males) and amnesic MCI patients (n =15,9 males) and age-matched cognitive normal elderly as a control group (n =20, 12 males). The control group of subjects selected were healthy, each subject had no apparent cognitive impairment, and the subjects had no evidence of dementia or other neuropsychological impairment.
The patient was instructed to remain awake and relaxed during the acquisition and the resting EEG signals for each subject were recorded. The total length of the recording was 5 minutes, with the first 10s being the time the subject was familiar with the environment and the data collected was not included in the final data analysis. When the testee is unconscious, the data acquisition personnel can remind the testee so as to improve the attention. According to the requirement of the research, the electroencephalogram signals of 19 channels are finally selected from 59 electroencephalogram channels, and the selection is as follows: FP1, FP2, FZ, F3, F4, F7, cz, C3, C4, F8, T7, T8, PZ, P3, P4, P8, P7, O1 and O2 electrodes. The electroencephalogram channels of the 19 channels can represent the whole resting state electroencephalogram representation of the testee to a certain extent.
S2, preprocessing the acquired electroencephalogram signals, taking out artifacts of the electroencephalogram signals through an independent component analysis method, and then performing band-pass filtering on the electroencephalogram signals.
Specifically, the original EEG signal is filtered and denoised through wavelet transformation, and then physiological artifacts such as blinking and electrocardio are removed through an EMD-FastICA method based on the combination of EMD and fast independent component analysis (FastICA).
In this embodiment, for example, to remove the blink type, the EMD is first applied to the most affected channels (FP 1 and FP2 channels) to extract the blink artifact as the template. This extracted blink template would then be input to FastICA along with other contaminated EEG channels to separate blinks from other channels. In the whole process, as shown in fig. 2, artifact removal is performed on interference components caused by (a) eye movement, (b) muscle, (c) electrocardiogram, and (d) line-frequency power supply, respectively.
The specific method of the band-pass filtering is as follows, as shown in fig. 3, the obtained data segment is split into 1s data segments, and the full-band EEG signals of three groups of subjects, AD, MCI, and HOA, and the complexity changes of the multi-band EEG signals are analyzed respectively. Obtaining full-band EEG signals of the testee by adopting 0.5-40HZ band-pass filtering; in multi-band analysis, a wavelet packet algorithm is adopted to respectively extract a plurality of frequency bands, namely a delta frequency band (0.5-4 Hz), a theta frequency band (4-8 Hz), an alpha frequency band (8-13 Hz) and a beta frequency band (13-30 Hz), in an EEG signal.
And S3, performing correlation complexity calculation on the preprocessed electroencephalogram signals, and calculating corresponding complexity through an LEMPEL-ZIV algorithm and a fuzzy approximate entropy algorithm respectively.
Further, as shown in fig. 4, the LEMPEL-ZIV algorithm has the following calculation complexity:
setting the time scale as s, taking an integer value of s without exceeding the length of the time sequence, and splitting the data segment into 1s data segments;
the method comprises performing binarization processing on EEG signal, and finding out the middle value m of the EEG signal * x Then use this intermediate value m * x The size of each data point in the brain electrical sequence is compared and judged, and the data point is larger than the middle value m * x Is taken to be 1, less than the median value m * x Taking the value as 0, obtaining a time sequence { s (i), i =1,2,.., n } after binarization;
suppose S (S) 1 ,s 2 ,...,s n ) Expressed as a time series of values (0,1) in which S represents the letter S i The numerical values are only 1 and 0, the length of the time sequence S is n, the subcharacter string in the time sequence S is represented by subs (i, j), the character string composition between the ith letter and the jth letter in the time sequence S is represented by the subcharacter string in the time sequence S, the character string is required to satisfy 1 ≦ i ≦ j ≦ n naturally, the word set in the time sequence S is represented by V (S), the word set represents the total set of sub-character strings in the time sequence S, and the S (S) is simulated 1 ,s 2 ,...,s n ) Assume additionally a (0,1) time series Q (Q) 1 ,q 2 ,...,q m ) Defining SQ pi as time series S (S) 1 ,s 2 ,...,s n ) And Q (Q) 1 ,q 2 ,...,q m ) One character in the set is removed, i.e. SQ pi is expressed as SQ pi =(s) 1 ,s 2 ,...,s n ,q 1 ,q 2 ,...,q m-1 ) And simultaneously V (S) definition can be obtained, wherein V (SQ pi) is expressed as a set of different substrings in a time sequence set SQ pi, and c (n) is defined as a complex measure of the time sequence S, so that the time sequence (S) is calculated 1 ,s 2 ,...,s n ) The complexity of (2);
the initialization is as follows: c (n) =1, S = (S) 1 )、Q＝(s 1 ) Then SQ π =(s) 1 ) Assuming that if Q ∈ V (SQ π), the sequence of characters in the time series Q can be copied by the sequence S, at which point the next character of the time series to be computed can be concatenated to the sequence Q, assuming
Then the time sequence Q needs to be inserted into the character and concatenated into the sequence S, denoted S = SQ, and finally the sequence Q is cleared, at which time the next character of the sequence to be computed is concatenated into Q, at which time the time sequence S is denoted S = (S =) (S) 1 ,s 2 )，Q＝(s 3 ) In the calculation, each time the time series Q is connected to the time series S, c (n) = c (n) +1, and the steps are repeated until the time series Q takes the last bit of the time series S to be calculated;
through the normalization process c (n), the expression of the normalization process c (n) is as follows:
LZC＝c(n)/b(n)
the obtained substrings are normalized by the above two equations, when n is close to ∞, b (n) is expressed as one approximate value of c (n), and here, by using b (n) to normalize the sequence c (n), a Lempel-Ziv complexity LZC expression completely independent of n can be obtained.
Through the steps, we can get(s) 1 ,s 2 ,...,s n ) Sub-strings divided into different c (n), i.e. represented as sequences(s) 1 ,s 2 ,...,s n ) Of the system.
Further, the method for calculating the complexity of the fuzzy approximate entropy algorithm is as follows:
for a given N-point time series { μ (i), i =1,2,.., N }, a set of m-dimensional vectors is formed as X i ＝[μ(i),μ(i+1),...,μ(i+m-1)]I ranges from 1 to N-m + 1; two vectors
And
the distance between is defined by m (r)：
The approximate entropy can be defined as:
defining a fuzzy approximate entropy, for the same time series, { μ (i), i =1,2,.., N } forms a series of vectors by:
given r and n, the similarity between two vectors is determined by a blur function:
similar to ApEn, function
And phi m Is defined as:
finally, the estimate of the fuzzy approximation entropy fApEn can be expressed by the following equation:
fApEn(m,n,r,N)＝φ m (n,r)-φ m+1 (n,r)
and determining parameters and calculating. Four parameters, namely the embedding dimension m, the data length N, the tolerance r, and the gradient in the exponential function N, need to be determined before calculating the entropy, and both m and N have a relatively small effect on the entropy calculation compared to the parameter r and are therefore set as fixed values. In this embodiment, the parameter m is set to 2. The boundary width of the similarity measure here is represented by the tolerance r, small values of r can be significantly affected by noise, while large values of r can lead to loss of useful information, so 0.1 ≦ r ≦ 0.25 is recommended in many studies. The gradient n is a new parameter in fApEn as a weight of the similarity between vectors. When n goes to infinity, the exponential function μ (d, r, n) = exp (- (d/r) n ) Degenerates to the Heaviside function where information near the boundary points is severely lost. Thus, n should be small positive integers, such as 2 and 3.
And S4, analyzing the calculated complexity into characteristic analysis of LZC and fuzzy approximate entropy.
Specifically, the characteristic analysis method of the LZC and the fuzzy approximate entropy is as follows:
s4-1, when calculating fApEn and LZC values of different groups (AD, MCI and HOA) of all testees, calculating the average brain electrical complexity (1S) of each channel of each tester, then taking the average values of LZC and fApEn of EEG signals of all channels of each tester as the final LZC and fApEn values of each tester, when calculating the average brain electrical complexity of each channel of each tester, dividing all data segments into a plurality of data segments of 1S, and taking each data segment as one channel to calculate the average brain electrical complexity.
And S4-2, analyzing the electroencephalogram complexity under the full frequency band and the four frequency bands.
In the complexity analysis of the full-band EEG signal, the complexity index of the AD patient on a specific EEG frequency band and a specific EEG channel is obviously lower than that of other two groups of testees. Although the complexity of the AD group and the MCI group is lower than that of the HOA group, none of the three groups of subjects has significant difference on most brain electrical channels. It was then found in a multi-band complexity analysis that the subjects of the AD, MCI and HOA groups differed more between LZC biomarkers in the four electroencephalogram bands than the full-band EEG signals, particularly in the delta, theta and alpha bands, which seems to indicate that using band-derived biomarkers was better at detecting AD than using full-band EEG signals. Because the complexity measures obtained from a certain frequency band for AD patients and normal subjects differ more than the complexity measure derived from the whole signal, this is an ideal attribute for a good biomarker. This means that it is possible to provide the best biomarker for detecting AD using only a small number of brain electrical channels and specific frequency bands. This can be exploited to achieve good performance in situations where the number of available channels and the amount of data is limited.
In conclusion, compared with other complexity analysis methods, the method combines two different nonlinear dynamics methods of fuzzy approximate entropy (fApEn) and Lempel-Ziv complexity (LZC) to quantify the influence of the Alzheimer's disease on the electroencephalogram complexity of a subject, and delta, theta and alpha frequency bands extracted from the multiple frequency bands are used as main frequency bands of complexity measurement, so that good detection and analysis of the electroencephalogram complexity can be realized only by using a small number of electroencephalogram channels, data volume and specific frequency bands, a potential useful method is provided for AD detection, and the complexity measurement extracted from the multiple frequency bands of EEG signals is an important step for obtaining a robust biomarker.
The embodiments of the present invention have been described in detail with reference to the accompanying drawings, but the present invention is not limited to the described embodiments. It will be apparent to those skilled in the art that various changes, modifications, substitutions and alterations can be made in these embodiments, including the components, without departing from the principles and spirit of the invention, and still fall within the scope of the invention.
Claims (7)
1. An electroencephalogram complexity analysis method for patients with Alzheimer's disease is characterized by comprising the following steps:
s1, acquiring electroencephalogram signals of a subject, wherein the subject comprises three groups of AD, MCI and HOA;
s2, preprocessing the acquired electroencephalogram signals, taking artifacts of the electroencephalogram signals out through an independent component analysis method, and then performing band-pass filtering on the electroencephalogram signals;
s3, performing correlation complexity calculation on the preprocessed electroencephalogram signals, and calculating corresponding complexity through an LEMPEL-ZIV algorithm and a fuzzy approximate entropy algorithm respectively;
the method for calculating the complexity of the fuzzy approximate entropy algorithm comprises the following steps:
for a given N-point time series { μ (i), i =1,2,.., N }, a set of m-dimensional vectors is formed as X i ＝[μ(i),μ(i+1),...,μ(i+m-1)]I ranges from 1 to N-m +1; two vectors
And
the distance between is defined by m (r)：
The approximate entropy can be defined as:
defining a fuzzy approximate entropy, for the same time series, { μ (i), i =1,2,.., N } forms a series of vectors by:
given r and n, the similarity between two vectors is determined by a blur function:
similar to ApEn, apply a function
And phi m Is defined as:
finally, the estimate of the fuzzy approximation entropy fApEn can be expressed by the following equation:
fApEn(m,n,r,N)＝φ m (n,r)-φ m+1 (n,r)
wherein the embedding dimension m, the data length N, the tolerance r, and the exponential function N;
and S4, analyzing the calculated complexity.
2. The method for analyzing the electroencephalogram complexity of the patients with alzheimer' S disease according to claim 1, wherein in the step S2-2, the specific method of band-pass filtering is as follows: in the full-band analysis, the EEG signal of the testee in the full band is obtained by adopting 0.5-40HZ band-pass filtering; in multi-band analysis, a wavelet packet algorithm is adopted to respectively extract a plurality of frequency bands, namely a delta frequency band (0.5-4 Hz), a theta frequency band (4-8 Hz), an alpha frequency band (8-13 Hz) and a beta frequency band (13-30 Hz), in an EEG signal.
3. The method for analyzing the brain electrical complexity of the alzheimer' S disease patient according to claim 1, wherein in said step S3, the method for calculating the complexity by the LEMPEL-ZIV algorithm is as follows:
setting the time scale as s, taking an integer value of s without exceeding the length of the time sequence, and splitting the data segment into 1s data segments;
the method comprises performing binarization processing on EEG signal, and finding out the middle value m of the EEG signal * x Then use this intermediate value m * x The size of each data point in the brain electrical sequence is compared and judged, and the data point is larger than the middle value m * x Is taken as 1, less than the median value m * x Taking the value as 0, obtaining a time sequence { s (i), i =1,2,. Once, n } after binarization;
suppose S (S) 1 ,s 2 ,...,s n ) Expressed as a time series of values (0,1) in which S represents the letter S i The numerical values are only 1 and 0, the length of the time sequence S is n, subs (i, j) is used for representing a sub-character string in the time sequence S, the character string is represented by a character string between the ith letter and the jth letter in the time sequence S, the character string needs to satisfy 1 ≦ i ≦ j ≦ n naturally, V (S) is used for representing a word set in the time sequence S, the word set represents a total set of sub-character strings in the time sequence S, and S (S) is simulated 1 ,s 2 ,...,s n ) Assume additionally a (0,1) time series Q (Q) 1 ,q 2 ,...,q m ) Defining SQ pi as time series S (S) 1 ,s 2 ,...,s n ) And Q (Q) 1 ,q 2 ,...,q m ) One character in the set is removed, i.e. SQ pi is expressed as SQ pi =(s) 1 ,s 2 ,...,s n ,q 1 ,q 2 ,...,q m-1 ) And simultaneously V (S) definition can be obtained, wherein V (SQ pi) is expressed as a set of different substrings in a time sequence set SQ pi, and c (n) is defined as a complex measure of the time sequence S, so that the time sequence (S) is calculated 1 ,s 2 ,...,s n ) The complexity of (2);
the initialization is as follows: c (n) =1, S = (S) 1 )、Q＝(s 1 ) Then SQ π =(s) 1 ) Assuming that if Q ∈ V (SQ π), the sequence of characters in the time series Q can be copied by the sequence S, at which point the next character of the time series to be computed can be concatenated to the sequence Q, assuming
Then the time series Q needs to be inserted into the character and concatenated into the series S, denoted S = SQ, and finally the series Q is emptied, at which time the next character of the sequence to be calculated is concatenated into Q, at which time the time series S is denoted S = (S =) (S) 1 ,s 2 )，Q＝(s 3 ) In the calculation, each time the time series Q is connected to the time series S, c (n) plus 1 needs to be executed once, c (n) = c (n) +1, and the steps are repeated until the time series Q takes the last bit of the time series S to be calculated;
c (n) is processed by normalization.
4. The method of analyzing brain electrical complexity of patients with alzheimer's disease according to claim 3, wherein said normalization process c (n) is expressed as follows:
LZC＝c(n)/b(n)
the obtained substrings are normalized by the above two equations, when n is close to ∞, b (n) is expressed as one of the approximate values of c (n), and here, by using b (n) to normalize the sequence c (n), a Lempel-Ziv complexity LZC expression completely independent of n can be obtained.
5. The method for analyzing the complexity of brain electricity of a patient with Alzheimer' S disease according to claim 4, wherein the complexity analyzing method in the step S4 is the feature analysis of LZC and fuzzy approximate entropy.
6. The method for analyzing the complexity of brain electricity of a patient with Alzheimer's disease according to claim 4, wherein the characteristic analysis method of LZC and fuzzy approximate entropy is as follows:
s4-1, when calculating fApEn and LZC values of all the testees in three groups, measuring and calculating the average electroencephalogram complexity of each channel of each tester, and then taking the average value of the LZC and fApEn of the EEG signals of all the channels of the testees as the final LZC and fApEn value of each tester;
s4-2, comparing and analyzing the electroencephalogram complexity of three testees of AD, MCI and HOA under the full frequency band and four frequency bands.
7. The method for analyzing electroencephalogram complexity of patients with Alzheimer' S disease according to claim 6, wherein in the step S4-1, when calculating the average electroencephalogram complexity of each channel of each subject, all the data segments are divided into a plurality of data segments of 1S, and each data segment is used as one channel to calculate the average electroencephalogram complexity.
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