CN106037725B - Bioelectricity brain signal analysis method based on wavelet package transforms - Google Patents
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Abstract
This case is related to a kind of bioelectricity brain signal analysis method based on wavelet package transforms, comprising: pre-processes to collected brain signal, obtains preprocessed signal；For the preprocessed signal, selects the highest wavelet basis function of matching degree to carry out wavelet transformation decomposition, obtain the characteristic signal of band of interest or frequency subband；State demarcation is carried out to the characteristic signal by establishing threshold value T, finally obtains the state encoding of band of interest or frequency subband.This case extracts interested frequency band or frequency subband come more accurate using wavelet package transforms, and carries out condition discrimination, combines different band conditions and portrays patient's states, thus analysis of the guidance to patient's states；This method has state specificity and adaptivity.
Description
Technical field
The present invention relates to a kind of data processing methods of bioelectricity brain signal, in particular to a kind of to be based on wavelet package transforms
Brain field potential signal analysis method.
Background technique
The accurate determination of patient's states is particularly significant for clinical analysis, but many physiological status are not all objective at present
The degree of the standard of evaluation such as dyskinesia, the grade of chronic ache usually rely primarily on doctor's warp to the determination of these states
It tests and the experience of patient itself, there is very big subjectivity.Brain activity is found for the research of human-body biological pcs signal
It is horizontal related with many physiological status, these signals can be applied to the qualitative assessment of patient's states, and guide controlling for patient
It treats.
Contain a large amount of information, but the information of only component frequency band and particular state phase in bioelectricity brain signal
It closes, it is therefore desirable to frequency information be extracted, and obtain the relationship between these information and patient's states.But existing
Relatively simple to the extracting method of these information in technology, the method for substantially similar clean cut sets a power-threshold
Value, is a kind of state more than the threshold value, what is be less than is another state, and this rough extracting method is obviously subsequent shape
State analysis provides higher erroneous judgement information.
Summary of the invention
In view of the deficienciess of the prior art, this case provides a kind of bioelectricity brain signal based on wavelet package transforms point
Analysis method.Wavelet package transforms provide a kind of finer data classification method, it dynamically can effectively extract narrowband letter
Number, and frequency band can be adaptive selected according to demand and then select suitable wavelet function to obtain accurately according to frequecy characteristic
Band information.Combined expression is carried out when multiple frequency bands are related to patient's states finer to carry out patient's states
It portrays.
To achieve the above object, this case is achieved through the following technical solutions:
A kind of bioelectricity brain signal analysis method based on wavelet package transforms comprising:
Step 1) pretreatment:
The initial data of collected brain signal is screened, useful signal is picked out；
The useful signal is carried out differential signal is calculated；
Hz noise and baseline drift are carried out to the differential signal, finally obtain preprocessed signal；
Step 2) feature extraction:
For the preprocessed signal, selects the highest wavelet basis function of matching degree to carry out wavelet package transforms decomposition, obtain
The characteristic signal of band of interest or frequency subband；
Step 3) condition discrimination:
State demarcation is carried out to the characteristic signal by establishing threshold value T, finally obtains band of interest or frequency
The state encoding of band.
Preferably, the bioelectricity brain signal analysis method based on wavelet package transforms, wherein in step 1)
It further include that low-pass filtering is carried out to the differential signal.
Preferably, the bioelectricity brain signal analysis method based on wavelet package transforms, wherein in step 1)
It further include that down-sampled processing is carried out to the differential signal.
Preferably, the bioelectricity brain signal analysis method based on wavelet package transforms, wherein in step 2),
The selection method of the wavelet basis function are as follows:
According to requirement of the characteristic signal of band of interest or frequency subband in terms of symmetry, orthogonality, compact sup-port, slightly
Several wavelet basis functions with similarity degree are selected, calculate separately to obtain if then substituting into wavelet package transforms function respectively
Wavelet packet basis functions corresponding to the smallest entropy are asserted the highest wavelet basis letter of matching degree by the entropy of dry wavelet packet coefficient
Number.
Preferably, the bioelectricity brain signal analysis method based on wavelet package transforms, wherein band of interest
Or the characteristic signal of frequency subband is the small echo using the corresponding band of interest or frequency subband that obtain after wavelet package transforms
Packet coefficient C is characterized.
Preferably, the bioelectricity brain signal analysis method based on wavelet package transforms, wherein in step 3),
The threshold value T is prepared by the following:
Intermediate value is taken to the wavelet packet coefficient C in a period of time earlier than current data, obtains median (| C |)；
If σ=median (| C |)/0.6745；
σ substitution minimaxi function is obtained into calculated result t；
By t multiplied by threshold weights, threshold value T corresponding to current data is obtained.
Preferably, the bioelectricity brain signal analysis method based on wavelet package transforms, wherein in step 3)
In, the method for state demarcation are as follows:
With threshold value T be it is critical, each wavelet packet coefficient C is compared with threshold value T respectively, thus by the feelings of all C >=T
Shape and the situation of all C < T distinguish.
Preferably, the bioelectricity brain signal analysis method based on wavelet package transforms, wherein the state is compiled
Code uses binary coding.
The beneficial effects of the present invention are: this case using wavelet package transforms come more accurate to interested frequency band or frequency
Subband extracts, and carries out condition discrimination, combines different band conditions and portrays patient's states, so that guidance is to trouble
The analysis of person's state；This method has state specificity and adaptivity, and wavelet package transforms is selected more subtly to extract and suffer from
The relevant Bio-computer signal characteristic of person's state, and the selection scheme for solving parameter in wavelet package transforms is provided, effectively
The validity and accuracy of the wavelet package transforms of raising；And this case selects threshold model to differentiate patient's states, to shape
State is encoded, and finer can be divided to morbid state.
Detailed description of the invention
Fig. 1 is the model framework figure that patient's states differentiation is realized based on Bio-computer signal.
Fig. 2 is the pretreatment schematic diagram in patient's states distinguished number model.
Fig. 3 is the feature extraction schematic diagram in patient's states distinguished number model.
Fig. 4 is the schematic diagram that condition discrimination is carried out to the feature of extraction.
Fig. 5 is theta the and alpha band activity schematic diagram extracted using wavelet package transforms.
Fig. 6 is different wavelet basis functions in the wavelet packet coefficient entropy for extracting theta and alpha band activity.
Fig. 7 is the alpha frequency band analog signal generated, and centre frequency 10.5Hz, signal-to-noise ratio is -5dB, and envelope is week
Phase is the trapezoidal wave figure of 4s.
Fig. 8 is the comparison diagram of the result and notional result tested using algorithm model to analog signal.
Fig. 9 is to carry out signal analysis extraction to true chronic ache signal using this case model to obtain theta and alpha frequency
The wavelet packet coefficient of band simultaneously divides each rhythmic activity state.
Figure 10 is to be encoded using the state of theta and alpha rhythmic activity to patient's states.
Specific embodiment
Present invention will be described in further detail below with reference to the accompanying drawings, to enable those skilled in the art referring to specification text
Word can be implemented accordingly.
The bioelectricity brain signal analysis method based on wavelet package transforms of an embodiment is listed in this case comprising:
Step 1) pretreatment:
The initial data of collected brain signal is screened, useful signal is picked out；
Useful signal is carried out differential signal is calculated；
Hz noise and baseline drift are carried out to differential signal, finally obtain preprocessed signal；
Step 2) feature extraction:
For preprocessed signal, selects the highest wavelet basis function of matching degree to carry out wavelet transformation decomposition, obtain interested
The characteristic signal of frequency band or frequency subband；
Step 3) condition discrimination:
State demarcation is carried out to characteristic signal by establishing threshold value T, finally obtains band of interest or frequency subband
State encoding.
It wherein, further include that low-pass filtering is carried out to differential signal in step 1).
It wherein, further include that down-sampled processing is carried out to differential signal in step 1).
Wherein, in step 2), the selection method of wavelet basis function are as follows:
According to requirement of the characteristic signal of band of interest or frequency subband in terms of symmetry, orthogonality, compact sup-port, slightly
Several wavelet basis functions with similarity degree are selected, calculate separately to obtain if then substituting into wavelet package transforms function respectively
The entropy of dry wavelet packet coefficient, is asserted the highest wavelet packet basis letter of matching degree for wavelet basis function corresponding to the smallest entropy
Number.
Wherein, the characteristic signal of band of interest or frequency subband is emerging using the corresponding sense obtained after wavelet package transforms
The wavelet packet coefficient C of interesting frequency band or frequency subband is characterized.
Wherein, in step 3), threshold value T is prepared by the following:
Intermediate value is taken to the wavelet packet coefficient C in a period of time earlier than current data, obtains median (| C |)；
If σ=median (| C |)/0.6745；
σ substitution minimaxi function is obtained into calculated result t；
By t multiplied by threshold weights, threshold value T corresponding to current data is obtained.
Wherein, in step 3), the method for state demarcation are as follows:
With threshold value T be it is critical, each wavelet packet coefficient C is compared with threshold value T respectively, thus by the feelings of all C >=T
Shape and the situation of all C < T distinguish.
Wherein, state encoding preferably uses binary coding.The band of interest of selection or the number of frequency subband will be certainly
It delimits the organizational structure the digit of code, such as when only 1 band of interest or frequency subband, is divided into the shape of two states: greater than be equal to threshold value
State is " 1 ", and the state for being less than threshold value is " 0 "；If there are two core, there are 1 and 2 frequency-of-interest subbands in group respectively, then suffering from
Person's state will have 8 kinds of integrated modes: 000,001,010,011,100,101,110 and 111.This case is not limited to binary system volume
Code, other coding forms can also.
Band of interest and frequency subband determines according to actual conditions, wherein actual conditions refer to: for example: Parkinson's state with
The beta frequency range (15-30Hz) of STN core group is closely related, and research discovery pain status and PAG and VPL core roll into a ball nervous activity for another example
Theta to alpha frequency band or frequency subband it is related etc..This case can be used for any frequency band, and example bands include δ band, α band, β
The frequency subband of band and γ band and these frequency bands.Frequency subband has the band width narrower than frequency band, therefore frequency band can be by more
A frequency subband is defined.
The different frequency bands of bioelectricity brain signal are associated with different conditions.The one of frequency band division is shown in the following table 1
A example:
Table 1, frequency band divide
| Frequency band | Range (Hz) |
| δ frequency band | 1.5-4Hz |
| θ frequency band | 4-8Hz |
| α frequency band | 8-13Hz |
| β frequency band | 13-30Hz |
| γ frequency band | 30-90Hz |
It may include multiple frequency subbands (for example, can be by however, frequency range is not limited only to the range in above table
Multiple frequency subbands composition), each frequency subband has a width more narrower than frequency band, the frequency subband of frequency band can have it is identical or
Different width.
Activity in the special frequency band of bioelectricity brain signal detected may since the state of patient changes and
Change, it is also possible to the reason of patient's states change, therefore the bioelectric in special frequency band and patient's states are close
Correlation can be used to indicate patient's states.
Compared to the state of an individual band signal, the active state of multiple frequency subbands of multiple core groups can be more preferable
Patient's states are divided, such as multiple frequency subbands can divide the different brackets of pain, from level-one to ten grades.It is based on
One or more band of interest of one or more core groups or the wavelet packet coefficient of frequency subband calculate threshold value division state can
To analyze patient's states.
The present embodiment is by taking chronic pain conditions as an example, but in other examples, other than chronic ache, the algorithm model
It can be applied to the analysis of other patient's states, such as, but be not limited to, the judgement of the states such as dyskinesia.
Although the bioelectricity brain signal through this case is mainly by taking human deep field potential signal (LFP) as an example, this case
Provided algorithm can also be used in such as, but be not limited to, and scalp brain is electric (EEG), magneticencephalogram, functional MRI data etc. and patient
The relevant bioelectricity brain signal of state analysis.
This algorithm model is broadly divided into 3 modules, structural schematic diagram preprocessing module, feature extraction mould as shown in Figure 1:
Block and condition discrimination module.
The specific implementation of algorithm model is as follows:
In order to keep result more accurate, Preprocessing is carried out to collected initial data, as shown in Fig. 2, main packet
Include following steps:
1) signal bad to signal quality is rejected, including the use of the signal that receives of signal judgement whether from
Pre-selected cerebral nucleus group.
2) differential signal is calculated from the four-way signal that four contacts of recording electrode obtain；
3) 50Hz notch filter is carried out to signal and removes Hz noise, due to local field potentials master relevant to patient's states
90Hz is concentrated on hereinafter, 90Hz low-pass filtering removal high-frequency signal therefore can be done, 3Hz high-pass filtering is then done and gets rid of base
Line drift, it is 500Hz to reduce calculation amount that last signal, which is downsampled to sample rate, improves calculating speed.
Feature extraction, specific steps characteristic extracting module as shown in Figure 3 are carried out to pretreated data.Utilize small echo
Packet transform decomposition data extracts the signal of band of interest or frequency subband, characterizes these band signals using wavelet packet coefficient
The situation of activity.
Feature extraction is real-time perfoming, and characteristic extracting module carries out very short time window to the signal received and handles, example
As often received, 0.2s signal processing is primary, also can choose other shorter time windows.This algorithm model has real-time,
It is achieved in that each module only handles the latest data of very short time every time, and the processing window length of each module is all the same,
It is confirmed as theta (6-9Hz) frequency band being 0.33s after processing window length is chosen in this example, and alpha (9-12Hz) frequency band is
0.29s.The priori data of condition discrimination module also has real-time simultaneously, and mode is when discrimination model completes a time window
Just data in window are brought into priori data after the differentiation of interior data, and abandon the isometric number in the tail portion in original priori data
According to, therefore the length of prior information had not only been kept, but also be updated simultaneously.
Local field potentials signal is decomposed using wavelet package transforms calculating in window, and extracts characteristic spectra, is obtained
Corresponding wavelet packet coefficient.It first has to carry out parameter selection before doing wavelet package transforms, comprising: determine suitable wavelet basis function
Come Decomposition order, entropy when extracting the specific signal rhythm and pace of moving things, and decomposing, and extract the setting of the parameters such as node.But due to decomposing
The number of plies, entropy, and extract these parameters of node selection be under current state it is well known in the art general, therefore, these ginseng
Known to number is equivalent to, uniquely need selection is exactly wavelet basis function.
The above-mentioned selection method for wavelet basis function in wavelet package transforms is: in conjunction with need extract with patient's states phase
The characteristic of the prosodic feature of pass and the characteristic of wavelet basis function itself, if symmetry, orthogonality and compact sup-port are to wavelet basis function
It carries out coarse sizing and obtains one group of reliable wavelet basis function group.It is small after being compared with these wavelet basis functions extraction feature
The entropy of wave packet coefficient, the more suitable current feature extraction of the smaller corresponding wavelet basis function of entropy, to obtain special for the rhythm and pace of moving things
The Optimum wavelet basic function of sign.Fig. 6 is using the more different wavelet basis functions of multiple groups actual signal as a result, statistics is found
Bior3.7 wavelet basis is extracting alpha frequency band entropy relative to other with good performance, and for theta frequency band rbio3.7
Preferably.
The above-mentioned determination method for doing Decomposition order and extraction node when WAVELET PACKET DECOMPOSITION extracts feature: it needs according to feature section
The frequency range of rule changes the sample rate of original signal, and the required rhythm and pace of moving things is enable completely to extract from WAVELET PACKET DECOMPOSITION tree.Example
If the frequency range of the feature rhythm and pace of moving things is 9-12Hz, then need original signal resampling that sample rate is made to become 384Hz, in this way according to small echo
Packet decomposes law, and 9-12Hz is present in node [6 2], therefore Decomposition order is 6 layers, and extracting node is [6 2].
The parameter medium entropy of above-mentioned wavelet package transforms uses Shannon entropy.
Condition discrimination is carried out to the feature of extraction, method is as shown in figure 4, small echo of the condition discrimination module based on extraction feature
Packet coefficient calculates threshold value using threshold model, is more than or equal to threshold value corresponding states " 1 ", is less than threshold value corresponding states " 0 ".It is this right
The coding of state is using binary coding, and this case also can choose other coding forms.
Above-mentioned threshold model is constructed based on threshold denoising, and theory is the small echo of the signal rhythm and pace of moving things with pattern feature
Packet coefficient is greater than equal to the wavelet packet coefficient under stochastic regime.Its specific steps includes:
1) obtain carrying out estimation σ to noise level, be median (| Cj,k|)/0.6745, wherein Cj,kFor wavelet packet coefficient.
2) select " minimaxi " threshold estimation method estimation threshold value t；
3) threshold value t is obtaining final threshold value T multiplied by threshold weights, and threshold weights are set as 1 in this example.
Further, feature decision module have predictive ability, use regular length priori data calculate threshold value as
Prediction to current data threshold value.
In order to verify the analog signal (Fig. 8) that the validity experiment of this case method generates the 9-12Hz rhythm and pace of moving things, center frequency
Rate is the signal and addition -5dB noise signal of 10.5Hz, and envelope is the trapezoidal wave for being 4s in the period, is calculated using algorithm model
Obtain the wavelet basis function selection bior3.7 of wavelet package transforms, Decomposition order is 6 layers, and extracting node is [6 2], and theoretically
Calculate the time for opening and closing stimulation.Further include in Fig. 8 have the dynamic threshold being calculated using the algorithm model of this case and
State demarcation, and be compared with theory state, algorithm has higher precision and accuracy as the result is shown, and algorithm is correctly opened
Opening rate is 95.14%, and correct closing rate is 98.28%.It should be noted that the signal-to-noise ratio of analog signal for accuracy rate have compared with
Big influence.
In this example, state demarcation, band of interest are carried out using field potential signal of the algorithm to chronic pain patient
Respectively theta (6-9Hz) and alpha frequency band (9-12Hz) (Fig. 9), wherein signal mainly from PVAG and feels thalamic nuclei
Group.It tests and exists simultaneously the four kinds of modes (Figure 10) combined by two kinds of frequency bands: " 00 " in discovery data, " 01 ", " 10 ",
" 11 ", and the percentage of these four modes is related with the state of patient.
Analogue data is with true pain field potential data the experiment proves that algorithm model can effectively realize patient's states
Differentiation, overcome it is previous it is simple use power spectrumanalysis as measurement relevant to patient's states, while also solving standard
The problem of really extracting the rhythm and pace of moving things.
Although the embodiments of the present invention have been disclosed as above, but its is not only in the description and the implementation listed
With it can be fully applied to various fields suitable for the present invention, for those skilled in the art, can be easily
Realize other modification, therefore without departing from the general concept defined in the claims and the equivalent scope, the present invention is simultaneously unlimited
In specific details and legend shown and described herein.
Claims (5)
1. a kind of bioelectricity brain signal analysis method based on wavelet package transforms characterized by comprising
Step 1) pretreatment:
The initial data of collected brain signal is screened, useful signal is picked out；
The useful signal is carried out differential signal is calculated；
Hz noise and baseline drift are carried out to the differential signal, finally obtain preprocessed signal；
Step 2) feature extraction:
For the preprocessed signal, the highest wavelet basis function of matching degree is selected to carry out wavelet package transforms decomposition, the sense of access is emerging
The characteristic signal of interesting frequency band or frequency subband；
Step 3) condition discrimination:
State demarcation is carried out to the characteristic signal by establishing threshold value T, finally obtains band of interest or frequency subband
State encoding；
It wherein, further include that low-pass filtering is carried out to the differential signal in step 1)；
It further include that down-sampled processing is carried out to the differential signal in step 1)；
In step 2), the selection method of the wavelet basis function are as follows:
According to requirement of the characteristic signal of band of interest or frequency subband in terms of symmetry, orthogonality, compact sup-port, roughing goes out
Several have the wavelet basis function of similarity degree with characteristic signal, then substitute into wavelet package transforms function and calculate separately respectively
The entropy of several wavelet packet coefficients is obtained, wavelet packet basis functions corresponding to the smallest entropy are asserted the highest small echo of matching degree
Basic function.
2. the bioelectricity brain signal analysis method based on wavelet package transforms as described in claim 1, which is characterized in that sense is emerging
The characteristic signal of interesting frequency band or frequency subband is to utilize the corresponding band of interest or frequency subband obtained after wavelet package transforms
Wavelet packet coefficient C characterized.
3. the bioelectricity brain signal analysis method based on wavelet package transforms as described in claim 1, which is characterized in that step
3) in, the threshold value T is prepared by the following:
Intermediate value is taken to the wavelet packet coefficient C in a period of time earlier than current data, obtains median (| C |)；
If σ=median (| C |)/0.6745；
σ substitution minimaxi threshold calculations function is obtained into calculated result t；
By t multiplied by threshold weights, threshold value T corresponding to current data is obtained.
4. the bioelectricity brain signal analysis method based on wavelet package transforms as described in claim 1, which is characterized in that in step
It is rapid 3) in, the method for state demarcation are as follows:
Be with threshold value T it is critical, each wavelet packet coefficient C is compared with threshold value T respectively, thus by the situation of all C >=T and
The situation of all C < T distinguishes.
5. the bioelectricity brain signal analysis method based on wavelet package transforms as described in claim 1, which is characterized in that described
State encoding uses binary coding.
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