CN102178514A - Coma degree evaluating method based on multiple indexes of non-linearity and complexity - Google Patents
Coma degree evaluating method based on multiple indexes of non-linearity and complexity Download PDFInfo
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Abstract
The invention discloses a coma degree evaluating method based on multiple indexes of non-linearity and complexity, which is used for monitoring the brain state of a patient in a coma, and can realize continuous coma depth monitoring, and coma phase grading and early warning. Multiple complexity indexes such as complexity, Lyapunov exponent, approximate entropy, related dimensions, and the like are extracted from coma electroencephalographic signals by adopting a nonlinear dynamics analytical method, and a comprehensive coma state exponent is obtained by combining a traditional GOS (Glasgow Outcome Scale) evaluation system and a traditional GCS (Glasgow Coma Scale) evaluation system; the coefficients of the correlation between the parameters are obtained by clinical experiments; a coma state grading database is founded and a parameter fusion coefficient is determined on the basis of clinical effects; and finally, complete conscious state grading evaluation exponents are founded to guide the treatment of a patient and make prognosis.
Description
Technical field
The present invention relates to the signal processing technology field, relate in particular to a kind of feature extraction and information fusion technology of the EEG signals (EEG) under comatose state.
Background technology
Stupor is the most serious stage of disturbance of consciousness, is that highly a kind of state of inhibition takes place in neuromechanism under cerebral cortex and the cortex.Show as the consciousness definition clinically and extremely reduce, stimulate reactionlessly to external world, degree can exist than the lighter's protective reflex and vital sign, and severe patient disappears.Stupor both can be caused (accounting for 70%) by central nervous system pathological change, can be again the consequence of systemic disease, all can cause stupor as acute infectious diseases, endocrine and dysbolismus, cardiovascular disease, poisoning and electric shock, heatstroke, altitude sickness etc.
For the stupor grading, at present most widely used is Glasgow stupor index (GCS, Glasgow Coma Scale).This index is to be delivered institute in 1974 by two neurosurgery professor Graham Teasdale of University of Glasgow and Bryan J. Jennett.By medical personnel the patient is done the test of some language and limbs, with patient's response result as fractionated basis.But traditional GCS, GOS scoring system need patient's active to reply, and the subjectivity, the objective factor that are vulnerable to patient, medical personnel are disturbed deficient in stability and accuracy.
Spontaneous and the rhythmic electrical activity of EEG signals (EEG) reflection brain cell group is generally described with features such as wave amplitude, frequency and phase places.Brain electricity digital assay has been widely used in the clinical monitoring of cerebral cortex function, such as epilepsy, cerebral trauma, cerebrovascular disease, depth of anesthesia, Depth of sleep monitoring etc., and in the auxiliary diagnosis of part psychiatric department disease such as schizophrenia, senile dementia.But still being in, the monitoring that is applied to the stupor degree of depth of comatose patient explores and experimental stage.
According to the research of existing brain neurophysiology EEG generation mechanism, the EEG signal originates from the nonlinear system of a height, not only finds many feedback control loops in each layering of central nervous system, and single neuron self also shows the height non-linear factor.Can observe chaotic behavior on neuron membrane, neural discharge transforms and follows the bifurcated rule, and chaos and bifurcated behavior belong to the category of nonlinear science.Therefore the EEG signal is the non-linear coupling of a large amount of neurocytes, is a complex that highly non-linear multiple-unit connects, and the EEG activity has the definitiveness chaotic characteristic, and brain is a nonlinear kinetics system complicated, self-organizing.
When analyzing EEG signals, complexity measure and nonlinear method have special advantages compared to traditional time-frequency domain analytical method.Therefore, in the comatose state monitoring, complexity and nonlinear analysis method are used in this project plan, extract the multiple indexs such as complexity, lyapunov index, approximate entropy and relevant dimension in the EEG signals, and in conjunction with clinical GCS, GOS marking system, in the hope of more accurately and objectively reflecting the relation between brain electrical acti and the comatose state.
Summary of the invention
The objective of the invention is at the deficiencies in the prior art, provide a kind of based on the non-linear and stupor degree evaluation method multiple index of complexity.
The objective of the invention is to be achieved through the following technical solutions: a kind of based on the non-linear and stupor degree evaluation method multiple index of complexity, this method may further comprise the steps:
(1) gathers the EEG signal;
(2) Signal Pretreatment: adopt the PCA method to carry out the removal of the electric artefact of eye, record EEG and EOG signal when the patient finishes the eye movement and the task of nictation, calculate the main constituent of these signals again, main constituent as the eye movement artefact, from mixed signal, remove this composition, the signal after obtaining proofreading and correct then;
(3) extract signal characteristic: calculate a sub-eigenvalue with per 256 sample points, extract approximate entropy, complexity, lyapunov index and relevant dimension characteristic parameter respectively as EEG signals;
(4) the comatose state index merges: according to the characteristic parameter sequence that step (3) obtains, calculate its serial correlation between in twos respectively; By being normalized to 0 to 100 numeral to the brain death state and dividing from clear-headed, concrete rule is as follows with each characteristic parameter: waking state=100, and brain death state=0, the initial value of all the other intermediatenesses carries out linear fit according to linear function; The relative coefficient of each characteristic sequence is determined its weights by its dependency size (taking absolute value)
, wherein,
, i.e. the weight coefficient of the shared brain state indices of this feature; Initial relative coefficient determined by EEG signals under the waking state, treat that the clinical testing data storehouse is set up after, serve as to instruct to adjust weight coefficient with data base's sample; Then 4 kinds of characteristic parameters are calculated according to weight coefficient
, draw last comatose state index CSI, here
Represent 4 kinds of characteristic parameters respectively.
Further, in the described step (4), described clinical testing data storehouse is set up by following substep:
(A) clinical case sample collection: collection all ages and classes, different sexes, difference cause patient's case of stupor reason, with identical eeg signal acquisition method, note patient's relevant information and EEG signals;
(B) set up the contrasting data storehouse: according to the EEG signals of step (1), calculate its Feature Fusion coefficient respectively, set up and improve the data base at last, establishment all ages and classes section, different sexes, difference cause stupor reason patient fusion coefficients separately.
The invention has the beneficial effects as follows: comatose patient is implemented omnidistance monitoring, the evaluation result that finally draws (the comatose state index of 0-100, no scale numerical value) accurate, objective, can monitor patient's stupor degree in real time, and do not need patient's active to reply, be subjected to patient, medical personnel's subjective factors to influence less.Be convenient to medical personnel and take the corresponding treatment measure according to patient's state true of living in, and the reference of prognosis rehabilitation.
Description of drawings
Fig. 1 is technology path figure of the present invention;
Fig. 2 is an eeg signal acquisition sketch map of the present invention;
Fig. 3 is an approximate entropy algorithm flow chart of the present invention;
Fig. 4 is a complexity algorithm flow chart of the present invention;
Fig. 5 is the algorithm flow chart of relevant dimension of the present invention;
Fig. 6 is a CSI Clinical symptoms parallel tables of the present invention.
The specific embodiment
Human tissue cell is always spontaneously constantly producing very faint bioelectric.Utilization extracts the electrical activity of brain cell and amplifies the back record through electroencephalograph at the electrode of laying on the scalp, promptly draws certain waveform, wave amplitude, frequency and mutually figure, curve, is electroencephalogram.When cerebral tissue generation pathology or changing function, corresponding change promptly takes place in this curve, thereby for clinical diagnosis, curing the disease provides foundation.
Each characteristic parameter parameter principle
(Approximate entropy ApEn) is at first proposed in 1991 by Pincus approximate entropy.Definition according to the KShi entropy is defined as similar vector continues to keep its similarity when increasing to the m+l dimension by the m dimension conditional probability with approximate entropy.Physical significance is the size that produces the probability of new model when dimension changes in the time sequence, and the probability that produces new model is big more, sequence is complicated more.Corresponding approximate entropy is also just big more.Can be used for the mixed signal formed by stochastic signal and definite signal.
Complexity is the algorithm that is proposed by Lempel-Ziv, is widely used in nonlinear science research.Analysis of complexity is a kind of nonlinear dynamic analysis method, is fit to very much analyze non-stationary signal, and a kind of just non-stationary signal of brain electricity.
The Lyapunov index is an important quantitative target weighing the system dynamics characteristic, and it has characterized the average index rate that system restrains or disperses between adjacent orbit in phase space.Supposing the system has n lyapunov index, with they by size sequence arrangement get up, as
, the set that then claims these arrays to become is the lyapunov index spectrum, is designated as
Wherein
Be called as maximum lyapunov index.A lot of about the method for calculating LLE at present, can be divided into two big classes substantially: Wolf method and Jacobian method.At 1993 and 1994, people such as Rosenstein and Kantz independently proposed a kind of computational methods of small data quantity of robustness respectively, directly from the definition construction algorithm of lyapunov index.
The phase space reconfiguration theory: the phase space reconfiguration method at first is that Takens and Packard put forward, and is present the most widely used method, and its theoretical basis is Takens reconstruct theorem.For the state space of an equivalence of reconstruct, only need the variation of a variable in the taking into account system, be univariate data map a vector point on the hyperspace.Like this, just can be by phase space of single argument reconstruct, the vector point on the phase space of reconstruct shows has the characteristic identical with former real space.The inherent definitiveness of chaos system makes it can converge on specific attractor.The parameter of representing this attractor fractals characteristic is exactly relevant dimension
The present invention is based on non-linear and the stupor degree evaluation method multiple index of complexity, may further comprise the steps:
1, comatose state Index for Calculation
1.1:EEG signals collecting
Adopt general electroencephalograph, quietly in the environment patient's head is being laid electrode, and use and use maximum international electroencephalogram association proposed standard electrode at present clinically and lay method, as shown in Figure 2.
Adopt clear-headed human brain data computation initial weight during initial experiment, sample frequency is 256HZ.
1.2: Signal Pretreatment
EEG signals also has very high time sensitize, and its signal is very easily polluted by uncorrelated noise, therefore needs to avoid as much as possible in strict accordance with the surveying record program of standard.
The EEG signals amplitude is very faint, and frequency range is generally at 0.5-50HZ.And nictation, ocular movement are difficult to avoid in EEG measuring, and these athletic meeting form eye movement artefact (EOG), very easily with the EEG signals frequency overlap, are interference noises main in the EEG signals therefore.
Here the PCA(principal component analysis that adopts people such as Lins in 1993 to propose) method is carried out the removal of the electric artefact of eye.Record EEG and EOG signal calculate the main constituent of these signals again when the patient finishes the eye movement and the task of nictation, as the main constituent of eye movement artefact, remove this composition, the signal after obtaining proofreading and correct then from mixed signal.
1.3: signal characteristic extracts
Because the frequency acquisition of EEG signals is 256HZ, still calculate a sub-eigenvalue with per 256 sample points.And extract approximate entropy, complexity, lyapunov index and relevant dimension characteristic parameter respectively as EEG signals according to method shown in hereinafter.
Approximate entropy algorithm (see figure 3) is as follows:
1, right
Point sequence
, reconstitute one group by the sequence order
N dimensional vector n (
Be the pattern dimension):
2, definition
With
Between distance
Be of difference maximum in both corresponding elements, that is:
3, given threshold value
, to each
The primary system meter
Less than
Number and this number and distance sum
Ratio, note is done
, that is:
4, earlier will
Take the logarithm, ask it again all
Meansigma methods, note is done
, that is:
5, again dimension is added 1, become
Dimension repeats above-mentioned steps, calculates
With
6, in theory, the approximate entropy of this sequence is:
, in general, this limit one probability 1 exists.But in the Practical Calculation, often with the sequence of certain-length as
Estimated value.Note is done
Value obviously with
Value relevant.Pincus rule of thumb, suggestion is got
, {
Be the standard deviation (standard deviation) of original series }.
Complexity algorithm (see figure 4) is simple, quick and be easy to realization, therefore can satisfy clinical in the depth of anesthesia requirement of monitoring in real time.
The first step of computation complexity is sequence to be carried out coarse handle, and obtains a symbol sebolic addressing that length is identical with former sequence, and at present common have two-value coarse and a many-valued coarse method, and the threshold value of two-value coarse is chosen the meansigma methods of studying sequence usually.In addition; also there is the scholar to propose to go out the research method of the maximum of complexity as the complexity index of this sequence with different threshold search; or at the serious sequence of baseline drift; having proposed to adopt matched curve is the two-value coarse method on boundary, and these all are the improvement that two-value coarse method is proposed.And the cut off value of many-valued coarse mostly is the equivalent cut-point of getting between research sequence minima and the maximum.The complexity that this many-valued coarse method is calculated is subject to the influence of impulse disturbances data; when causing the complexity that same dynamical system adopts different coarse hop counts to calculate analyzed; the evolution rule that is reflected there are differences sometimes, is difficult to obtain stable and consistent result.
Second step of computation complexity is the reproducing sequence after the scanning coarse is handled, and according to the new substring that specific algorithm counts did not occur in the past, the number of all substrings is the absolute value of complexity; With the complexity value of this value with stochastic signal
(
Be sequence length) carry out normalization, eliminate the influence of data length, get complexity to the end.
In actual calculation, for fear of the influence of coarse processing, so adopt a kind of modified model account form of complexity to former sequence information:
Complexity.
Note
Be that a length is
Time series, then
Constitute corresponding Fourier transform sequence, wherein
Be imaginary unit, note
So,
Can be designated as
,
If
Mean-square value be
Note
, definition
Complexity is
If
Be the constant sequence, then
If
Be periodic sequence, then
If
Be a random time sequence, obey independent same distribution, and 4 limited rank squares.Then work as
The time,
Convergence with probability 1 in
Work as especially
The time,
Convergence with probability 1 is in 1.
The specific algorithm of maximum lyapunov index (LLE) is as follows: to the given chaos time sequence phase space reconfiguration of at first delaying time, and search for the nearest-neighbor point of each point of given track entropy (promptly embedding vector), promptly
, wherein
Be time series average period, can be by seasonal effect in time series FFT be estimated to obtain.The geometric meaning of maximum lyapunov index is that the index of quantization inceptive closed orbit is dispersed the overall chaos level with estimating system, has in view of the above
, taken the logarithm in both sides:
, this shows,
Equal top this haply and organize collinear slope, so LLE can organize " G-bar " in county to obtain by least square fitting person, promptly
, here
Expression is to all
Ask average.
Relevant dimension
Specific algorithm (see figure 5): establish existing scalar time time series
, utilize time-delay coordinate method structural regime vector
, wherein
,
Be called delay time (delay time),
Be called and embed dimension (Embedding dimension).The key of phase space reconfiguration is exactly to determine at the place
With
, and, the selected time-delay of reconstruct
Be and selected embedding dimension
Be independently.
The GP algorithm of compute associations dimension is as follows: in known time-delay
, embed dimension
The time, correlation integral
The meaning of expression is that phase space middle distance after the reconstruct is less than scale
The some ratio somewhat right to accounting for.Work as scale
Obtain excessive, somewhat right distance can not surpass
, correlation integral
=1, such
Do not react the dynamic characteristic of system;
Obtain too small, the right distance of then nearly all point all greater than
, do not reflect the characteristic of system so scale yet
Certain scope restriction is arranged.Scale
With correlation integral
Between have following relational expression to set up:
, conversion gets:
, generally, we make correlation integral
With scale
Double logarithmic curve, i.e. correlation integral curve chart.One section pairing scale range of near linear scope is as the scale district among the figure, by this straight line of least square fitting, collinear slope is exactly the correlation dimension of being asked.
1.4: the comatose state index merges
According to the individual characteristic parameter sequence of calculating gained in the previous step, calculate its serial correlation (1 to 1,1 represents perfect positive correlation, and-1 represents perfect negative correlation) between in twos respectively,
 |
Approximate entropy | Complexity | LLE | Relevant dimension |
| Approximate entropy | 1.00 |
 |
 |
 |
| Complexity |
 |
1.00 |
 |
|
| LLE |
 |
1.00 |
 |
|
| Relevant dimension |
 |
 |
 |
1.00 |
In this table
Represent the dependency between i and j kind parameter, and
By being normalized to 0 to 100 numeral to the brain death state and dividing from clear-headed, concrete rule is as follows with each characteristic parameter: waking state=100, and brain death state=0, the initial value of all the other intermediatenesses carries out linear fit according to linear function.
Fusion coefficients: the relative coefficient of each characteristic sequence, determine its weights by its dependency size (taking absolute value)
, wherein
, i.e. the weight coefficient of the shared brain state indices of this feature.Initial relative coefficient determined by EEG signals under the waking state, treat that clinical testing data storehouse (seeing third part) is set up after, serve as to instruct to adjust weight coefficient with data base's sample.Then 4 kinds of characteristic parameters are calculated according to weight coefficient
, draw last comatose state index CSI(coma state index), here
Represent 4 kinds of characteristic parameters respectively.
Second portion: clinical examination analysis
2.1: clinical symptoms, sign
Adopt traditional Glasgow marking system to check that the patient opens eyes, reaches in a minute features such as motor reaction, obtains Glasgow stupor index, as one of exponential auxiliary characteristics of comatose state.
2.2: other supplementary instruments detect
In the gatherer process of whole EEG signals, measure simultaneously human body other as basic body index such as pulse, heart beating, breathing, blood pressure, note the sign index value under the different comatose state indexes respectively, as one of exponential clinical indices of comatose state.
2.3: comatose state index and Clinical symptoms synopsis thereof
With the Clinical symptoms inspection of first two steps, as replenishing: the GCS index of patient's correspondence under the promptly different comatose state indexes, corresponding vital sign to different comatose state index clinical signs.See Fig. 6.
Third part: hierarchical data base is set up
3.1: the clinical case sample collection
Collection all ages and classes, different sexes, difference cause patient's case of stupor reason, with identical eeg signal acquisition method (seeing 1.1), note patient's relevant information and EEG signals.
3.2: set up the contrasting data storehouse
According to the EEG signals under the above comatose state, calculate its Feature Fusion coefficient respectively, set up and improve the data base at last, establishment all ages and classes section, different sexes, difference cause stupor reason patient fusion coefficients separately, so that adopt when comatose state Index for Calculation (seeing 1.4).
Claims (2)
1. one kind based on non-linear and the stupor degree evaluation method multiple index of complexity, it is characterized in that this method may further comprise the steps:
(1) gathers the EEG signal;
(2) Signal Pretreatment: adopt the PCA method to carry out the removal of the electric artefact of eye, record EEG and EOG signal when the patient finishes the eye movement and the task of nictation, calculate the main constituent of these signals again, main constituent as the eye movement artefact, from mixed signal, remove this composition, the signal after obtaining proofreading and correct then;
(3) extract signal characteristic: calculate a sub-eigenvalue with per 256 sample points, extract approximate entropy, complexity, lyapunov index and relevant dimension characteristic parameter respectively as EEG signals;
(4) the comatose state index merges: according to the characteristic parameter sequence that step (3) obtains, calculate its serial correlation between in twos respectively; By being normalized to 0 to 100 numeral to the brain death state and dividing from clear-headed, concrete rule is as follows with each characteristic parameter: waking state=100, and brain death state=0, the initial value of all the other intermediatenesses carries out linear fit according to linear function; The relative coefficient of each characteristic sequence is determined its weights by its dependency size (taking absolute value)
, wherein,
, i.e. the weight coefficient of the shared brain state indices of this feature; Initial relative coefficient determined by EEG signals under the waking state, treat that the clinical testing data storehouse is set up after, serve as to instruct to adjust weight coefficient with data base's sample; Then 4 kinds of characteristic parameters are calculated according to weight coefficient
, draw last comatose state index CSI, here
Represent 4 kinds of characteristic parameters respectively.
2. it is characterized in that based on the non-linear and stupor degree evaluation method multiple index of complexity that according to claim 1 is described in the described step (4), described clinical testing data storehouse is set up by following substep:
(A) clinical case sample collection: collection all ages and classes, different sexes, difference cause patient's case of stupor reason, with identical eeg signal acquisition method, note patient's relevant information and EEG signals;
(B) set up the contrasting data storehouse: according to the EEG signals of step (A), calculate its Feature Fusion coefficient respectively, set up and improve the data base at last, establishment all ages and classes section, different sexes, difference cause stupor reason patient fusion coefficients separately.
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| RU2738583C1 (en) * | 2020-05-13 | 2020-12-14 | Федеральное государственное бюджетное образовательное учреждение высшего образования "Рязанский государственный медицинский университет имени академика И.П. Павлова" Министерства здравоохранения Российской Федерации | Diagnostic technique for focal epilepsy based on electroencephalogram analysis |
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