CN107595247A - A kind of monitoring method and system of the depth of anesthesia based on EEG signals - Google Patents
A kind of monitoring method and system of the depth of anesthesia based on EEG signals Download PDFInfo
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Abstract
The invention discloses a kind of monitoring method and system of the depth of anesthesia based on EEG signals, methods described includes：The EEG signal of patient is gathered, and the EEG signal is pre-processed to obtain noiseless EEG signal；The wave function of the noiseless EEG signal is calculated using improved elimination trend moving average algorithm, and the Cerebral state index is calculated according to the wave function.The present invention is calculated noiseless EEG signals to obtain Cerebral state index by improved elimination trend moving average algorithm, so as to improve the real-time of anesthesia depth monitoring, robustness (or stability) and accuracy, the reliability of detection can also be kept in the case where EEG signals are second-rate.
Description
Technical field
The present invention relates to field of medical technology, the monitoring method of more particularly to a kind of depth of anesthesia based on EEG signals and
System.
Background technology
Anaesthetize and refer to by means of caused whole body or the disappearance locally felt and forgotten memory state the methods of medicine, it
Being smoothed out for operation is may insure, anesthesia is too deep or excessively shallow all patient can be damaged.Therefore, the monitoring of depth of anesthesia is outstanding
To be important.And brain electricity can directly reflect the activity of central nervous system.Therefore brain power technology, which turns into, determines depth of anesthesia
One of best means.Being currently based on the anesthesia depth monitoring method of EEG signals mainly includes frequency-domain analysis, Bispectral index, anesthesia
Trend and entropy index analysis etc..Bispectral index (bispectral index scale, BIS) is that most commonly used anesthesia is deep at present
Monitoring index is spent, it is using Beta ratios, the relative synchronization of Concerning With Fast-slow Waves, outburst inhibiting rate tetra- indexs of BSR and QUAZI, root
The weights of each index are adjusted according to narcosis, BIS index is then obtained by weighted sum.Research finds that BIS is obviously dependent on
The use of arcotic, and it is sensitive to the difference of patient.Further, since larger artifacts be present using average algorithm and removal
The processing procedures such as data segment, the renewal of BIS parameters is caused to postpone.Anesthesia trend (Narcotrend) is united using Kugler multi-parameters
Count and anesthesia brain wave is divided into six big phase of A, B, C, D, E, F, 14 groups.Research shows that anesthesia trend has with Bispectral index
Similar clinical manifestation, the difference between arcotic and individual patient be present.
In recent years, Nonlinear Dynamics started to be widely used in electroencephalogramsignal signal analyzing and anesthesia depth monitoring
In research.Method using entropy Depth of Anesthesia is exactly one kind therein.Approximate entropy be a kind of complexity of metric sequence and
The rule of statistic quantification, the temporal signatures of electroencephalogram are analyzed, be characterized in that there is the preferably anti-interference and energy of anti-noise
Power.But the Complexity Algorithm such as existing approximate entropy due to sequence length needed for calculating is long or it is long the time required to calculating the shortcomings that without
Method realizes monitoring in real time.For example, Chinese patent application 2015100855324 uses the Nonlinear Dynamics based on complexity
EEG signals are handled, calculate the Trellis Complexity, marginal frequency and outburst rejection ratio of EEG signals respectively, and utilizes and determines
Plan tree algorithm is fitted to obtain Cerebral state index.But the narcosis classification that this method needs expert to provide is used as reference,
The accuracy of its monitoring result is influenceed by decision tree training quality.Chinese patent application 2007101248131 proposes one
Anesthesia depth monitoring method of the kind based on ordering entropy, segment processing is carried out to eeg data, and calculates the ordering entropy of each data segment,
According to the size estimation depth of anesthesia of sequence entropy.Chinese patent application 2014800085154 proposes a kind of by using
The method and apparatus that spectral technology measures depth of anesthesia, but it only considered the frequency domain information of EEG signals.
Thus prior art could be improved and improve.
The content of the invention
The technical problem to be solved in the present invention is, in view of the shortcomings of the prior art, there is provided one kind is based on improved elimination
The monitoring method and system of the depth of anesthesia of the EEG signals of trend moving average method, reach and improved anesthesia depth monitoring
The purpose of real-time, robustness and accuracy.
In order to solve the above-mentioned technical problem, the technical solution adopted in the present invention is as follows：
A kind of monitoring method of the depth of anesthesia based on EEG signals, it includes：
The EEG signal of patient is gathered, and the EEG signal is pre-processed to obtain noiseless EEG signal；
Using the wave function of the improved elimination trend moving average algorithm calculating noiseless EEG signal, and according to
The wave function calculates the Cerebral state index.
The monitoring method of the depth of anesthesia based on EEG signals, wherein, the EEG signal of the collection patient, and it is right
The EEG signal is pre-processed is specially to obtain noiseless EEG signal：
The EEG information of patient is gathered, and use the noise jamming of the wavelet entropy threshold method elimination EEG signal to obtain nothing
Disturb EEG signal.
The monitoring method of the depth of anesthesia based on EEG signals, wherein, it is described to be slided using improved elimination trend
Average algorithm calculates the wave function of the noiseless EEG signal, and calculates the depth of anesthesia according to the wave function and refer to
Number is specially：
If the noiseless EEG signal is EEG signals time series, and obtains the equal of the EEG signals time series
Value；
The cumulative sequence of the EEG signals time series is determined according to the average, and uses backward sliding average method
Calculate the trend sequence of a stationary window length；
The elimination trend sequence of the EEG signals time series is calculated according to the trend sequence, and according to the elimination
Trend sequence calculates the undulating value of the noiseless EEG signal of length of window；
The wave function of the noiseless EEG signal of length of window is calculated according to the undulating value, and according to the fluctuation
Function calculates the Cerebral state index.
The monitoring method of the depth of anesthesia based on EEG signals, wherein, the wave function is：
Wherein, EmFor Wavelet Entropy, s is length of window, and L is positive integer；Represent undulating value.
The monitoring method of the depth of anesthesia based on EEG signals, wherein, the Cerebral state index is：
Wherein, it is describedFor gain index, FMDMA(s) it is wave function.
A kind of monitoring system of the depth of anesthesia based on EEG signals, it includes：
Processing module, for gathering the EEG signal of patient, and the EEG signal is pre-processed noiseless to obtain
EEG signal；
Computing module, for calculating the ripple of the noiseless EEG signal using improved elimination trend moving average algorithm
Dynamic function, and the Cerebral state index is calculated according to the wave function.
The monitoring system of the depth of anesthesia based on EEG signals, wherein, the processing module is specifically used for：
The EEG information of patient is gathered, and use the noise jamming of the wavelet entropy threshold method elimination EEG signal to obtain nothing
Disturb EEG signal.
The monitoring system of the depth of anesthesia based on EEG signals, wherein, the computing module specifically includes：
Acquiring unit, for setting the noiseless EEG signal as EEG signals time series, and obtain the EEG signals
The average of time series；
First computing unit, for determining the cumulative sequence of the EEG signals time series according to the average, and adopt
The trend sequence of a stationary window length is calculated with backward sliding average method；
First computing unit, for calculating the elimination trend sequence of the EEG signals time series according to the trend sequence
Row, and according to the undulating value of the elimination trend sequence calculating noiseless EEG signal of length of window；
3rd computing unit, for calculating the fluctuation letter of the noiseless EEG signal of length of window according to the undulating value
Number, and the Cerebral state index is calculated according to the wave function.
The monitoring system of the depth of anesthesia based on EEG signals, wherein, the wave function is：
Wherein, EmFor Wavelet Entropy, s is length of window, and L is positive integer；Represent undulating value.
The monitoring system of the depth of anesthesia based on EEG signals, wherein, the Cerebral state index is：
Wherein, it is describedFor gain index, FMDMA(s) it is wave function.
Beneficial effect：Compared with prior art, the invention provides a kind of monitoring of the depth of anesthesia based on EEG signals
Method and system, methods described include：The EEG signal of patient is gathered, and the EEG signal is pre-processed to obtain without dry
Disturb EEG signal；The wave function of the noiseless EEG signal, and root are calculated using improved elimination trend moving average algorithm
The Cerebral state index is calculated according to the wave function.The present invention is by improved elimination trend moving average algorithm to without dry
EEG signals are disturbed to be calculated to obtain Cerebral state index, it is (or steady so as to improve the real-time of anesthesia depth monitoring, robustness
It is qualitative) and accuracy, the reliability detected can also be kept in the case where EEG signals are second-rate.
Brief description of the drawings
Fig. 1 is the flow chart that the monitoring method of the depth of anesthesia provided by the invention based on EEG signals is preferably implemented.
Fig. 2 illustrates for the flow of step S100 in the monitoring method of the depth of anesthesia provided by the invention based on EEG signals
Figure.
Fig. 3 is the comparison that noise result is removed using wavelet entropy threshold method and SureShink threshold values, Minimax threshold methods
Figure, wherein, (a) is raw EEG signal, and (b) is the result using SureShink threshold method denoisings, and (c) is using Minimax
The result of threshold method denoising, (d) are the result using wavelet entropy threshold method denoising.
Fig. 4 illustrates for the flow of step S200 in the monitoring method of the depth of anesthesia provided by the invention based on EEG signals
Figure.
Fig. 5 is the Cerebral state index F that the present invention monitorsmin(s) the comparison figure of trend and BIS trend.
Fig. 6 is the structure principle chart of the monitoring system of the depth of anesthesia provided by the invention based on EEG signals.
Fig. 7 is that the structure of one embodiment of the monitoring system of the depth of anesthesia provided by the invention based on EEG signals is former
Reason figure.
Embodiment
The present invention provides a kind of monitoring method and system of the depth of anesthesia based on EEG signals, to make the mesh of the present invention
, technical scheme and effect it is clearer, clear and definite, the present invention is described in more detail for the embodiment that develops simultaneously referring to the drawings.
It should be appreciated that specific embodiment described herein is not intended to limit the present invention only to explain the present invention.
In the present invention, using the suffix of such as " module ", " part " or " unit " for representing element only for favourable
In the explanation of the present invention, itself do not have specific meaning.Therefore, " module ", " part " or " unit " can mixedly make
With.
Terminal device can be implemented in a variety of manners.For example, the terminal described in the present invention can include such as moving
Phone, smart phone, notebook computer, digit broadcasting receiver, PDA (personal digital assistant), PAD (tablet personal computer), PMP
The mobile terminal of (portable media player), guider etc. and such as digital TV, desktop computer etc. are consolidated
Determine terminal.However, it will be understood by those skilled in the art that in addition to being used in particular for moving the element of purpose, according to this hair
The construction of bright embodiment can also apply to the terminal of fixed type.
Below in conjunction with the accompanying drawings, by the description to embodiment, the content of the invention is described further.
Fig. 1 is refer to, Fig. 1 is the preferable implementation of the monitoring method of the depth of anesthesia provided by the invention based on EEG signals
The flow chart of example.Methods described includes：
S100, the EEG signal for gathering patient, and the EEG signal is pre-processed to obtain noiseless EEG signal；
S200, the wave function using the improved elimination trend moving average algorithm calculating noiseless EEG signal, and
The Cerebral state index is calculated according to the wave function.
In the present embodiment, the EEG signals EEG of the patient first to collecting pre-processed with eliminate low-frequency noise and
Sharp wave is disturbed to obtain noiseless EEG signal, and the EEG signal is being counted using improved elimination trend moving average algorithm
Calculation obtains Cerebral state index.So cause anesthesia depth monitoring that there is smaller time delay, can faster reflect fiber crops
The situation of change of liquor-saturated depth.Also, by using improved elimination trend moving average algorithm (MDMA), signal fluctuation index model
Enclose bigger.In the case where signal quality is poor, can also accurate measurements to stable Cerebral state index.
Specifically, in the step s 100, the EEG signal is brain electric information.The EEG signal of the collection patient refers to
Be EEG signals using patient.The EEG signals collected are the noisy EEG signals of tool, e.g., noise, point
Wave interference etc..So as to, it is necessary to be pre-processed to the EEG signal to remove interference after patient's EEG signal is collected.
In the present embodiment, the EEG signal is pre-processed using the method for wavelet entropy threshold to obtain noiseless EEG signal.
Exemplary, as shown in Fig. 2,3 and 4, the EEG signal of the collection patient, and the EEG signal is located in advance
Reason is specifically included with obtaining noiseless EEG signal：
S101, the EEG signal using patient, to the EEG signal application wavelet transform to obtain approximation wavelet system
Number and detail wavelet coefficients；
S102, calculate the stationary window length EEG signal wavelet entropy threshold；
S103, using more wavelet entropy thresholds to the detail wavelet coefficients carry out soft-threshold Nonlinear Processing；
S104, wavelet inverse transformation is carried out to the detail wavelet coefficients after processing to estimate low-frequency noise signal；
S105, glitch-free EEG signal obtained according to the low-frequency noise signal and EEG signal.
Specifically, it is described that the EEG signal application wavelet transform is referred to adopting in the step S101
WAVELET PACKET DECOMPOSITION is carried out to the EEG signal with db16 small echos and obtains approximation wavelet coefficients and detail wavelet coefficients, wherein, it is described
Approximation wavelet coefficients and detail wavelet coefficients can be expressed as：
Wherein, Aj(k) it is approximation wavelet coefficients, Dj(k) it is and detail wavelet coefficients that j is the wavelet decomposition number of plies, low pass filtered
Ripple device g and high-pass filter h uses db16 small echos.
In the step S102, the calculation formula of the wavelet entropy threshold can be：
Wherein, Em is according to the Wavelet Entropy of correlated wavelets energy balane, and m represents the length of single treatment EEG signal, G and
Voffset is the empirical value obtained in off-line analysis, is preferably, G=10 and Voffset=8.
Further, the calculating process of the Wavelet Entropy Em can be：
Length (length of window) m of single treatment EEG signal is chosen first, and the energy definition by moment k is：
So, the gross energy in whole window is：
Then, correlated wavelets energy is；
And then the calculation formula of the Wavelet Entropy can be：
In the step S103, using wavelet entropy threshold detail wavelet coefficients are carried out with the tool of soft-threshold Nonlinear Processing
Body processing mode is：
In the step S104, the detail wavelet coefficients after described pair of processingIt is specially pair to carry out wavelet inverse transformation
Detail wavelet coefficients after the processingDiscrete wavelet inverse transformation is carried out, for example, it is inverse to carry out discrete wavelet using db16 small echos
Change brings estimation low-frequency noise signal ei。
It is described that glitch-free EEG letters are obtained according to the low-frequency noise signal and EEG signal in the step S105
Number be specially to subtract low-frequency noise signal using the EEG signal to be eliminated the EEG signal of noise, i.e., noiseless EEG signal.
The calculation formula of the noiseless EEG signal can be：
xi=yi-ei
Wherein, xiFor noiseless EEG signal, yiFor the EEG signal of collection.
Further, it is described that the nothing is calculated using improved elimination trend moving average algorithm in the step S200
The wave function of interference EEG signal refers to regarding the noiseless EEG signal as EEG signals time series xi, i=1,
2 ..., L, depth of anesthesia is calculated using improved elimination trend moving average DMA algorithms to the EEG signals time series
Index.
In the present embodiment, as shown in Fig. 5,6 and 7, the step S200, using improved elimination trend moving average calculate
Method calculates the wave function of the noiseless EEG signal, and specific according to the wave function calculating Cerebral state index
Including：
S201, the noiseless EEG signal is set as EEG signals time series, and obtain the EEG signals time series
Average；
S202, the cumulative sequence for determining according to the average EEG signals time series, and using it is backward slide it is flat
Averaging method calculates the trend sequence of a stationary window length；
S203, the elimination trend sequence according to the trend sequence calculating EEG signals time series, and according to institute
State and eliminate the undulating value that trend sequence calculates the noiseless EEG signal of length of window；
S204, the wave function according to the undulating value calculating noiseless EEG signal of length of window, and according to described
Wave function calculates the Cerebral state index.
Specifically, in the step S201, it is assumed that the noiseless EEG signal is EEG signals time series
xi, i=1,2 ..., L, calculating EEG signals time series x (i) average Xmean, wherein, Computer Corp. of the Xmean can
Think：
Wherein, K represents the moment.
It is described to determine that the cumulative sequence of the EEG signals time series has according to the average in the step S202
Body is：EEG signals time series xiEach single item subtract the average Xmean and obtain a new time series, then to new
Time series carry out cumulative summation and obtain sequence of summing.A fixed length of window s is being chosen, using simple backward slip
The method of average asks for trend sequence.In actual applications, the calculation formula of the cumulative sequence Y (i) can be：
The trend sequenceFormula can be：
In the step S203, the elimination that the EEG signals time series is calculated according to the trend sequence becomes
Gesture sequence is specifically to subtract trend sequence from cumulative sequence Y (i)The sequence C for the trend that is eliminateds(i), the elimination
The sequence C of trends(i) calculation formula can be：
It is s when institutes according to the sequence calculation window length of the elimination trend after the sequence of elimination trend is calculated again
State undulating value corresponding to EEG signalThe calculation formula of the undulating value can be：
Wherein, Ls=[L/s], v=1 ..., 2Ls。
In the step S204, by undulating valueCalculate wave function FMDMA(s), the wave function calculates public
Formula is as follows：
Wherein, Em is the Wavelet Entropy according to correlated wavelets energy balane.
After obtaining the wave function, Cerebral state index F is calculated according to the wave functionmin(s), the anesthesia
The calculation formula of depth factor can be：
Wherein,For gain index, it is preferably
Present invention also offers a kind of monitoring system of the depth of anesthesia based on EEG signals, as shown in fig. 6, its specific bag
Include：
Processing module 101, for gathering the EEG signal of patient, and the EEG signal is pre-processed to obtain without dry
Disturb EEG signal；
Computing module 102, for calculating the noiseless EEG signal using improved elimination trend moving average algorithm
Wave function, and the Cerebral state index is calculated according to the wave function.
The monitoring system of the depth of anesthesia based on EEG signals, wherein, the processing module is specifically used for：
The EEG information of patient is gathered, and use the noise jamming of the wavelet entropy threshold method elimination EEG signal to obtain nothing
Disturb EEG signal.
The monitoring system of the depth of anesthesia based on EEG signals, wherein, the computing module specifically includes：
Acquiring unit, for setting the noiseless EEG signal as EEG signals time series, and obtain the EEG signals
The average of time series；
First computing unit, for determining the cumulative sequence of the EEG signals time series according to the average, and adopt
The trend sequence of a stationary window length is calculated with backward sliding average method；
First computing unit, for calculating the elimination trend sequence of the EEG signals time series according to the trend sequence
Row, and according to the undulating value of the elimination trend sequence calculating noiseless EEG signal of length of window；
3rd computing unit, for calculating the fluctuation letter of the noiseless EEG signal of length of window according to the undulating value
Number, and the Cerebral state index is calculated according to the wave function.
The monitoring system of the depth of anesthesia based on EEG signals, wherein, the wave function is：
Wherein, EmFor Wavelet Entropy, s is length of window, and L is positive integer；Represent undulating value.
The monitoring system of the depth of anesthesia based on EEG signals, wherein, the Cerebral state index is：
Wherein, it is describedFor gain index, FMDMA(s) it is wave function.
In one embodiment of the invention, the monitoring system of the depth of anesthesia based on EEG signals is described to brain
The processing of electric signal and according to EEG signals detect depth of anesthesia index can be realized by processor, that is to say, that such as
Shown in Fig. 7, it can include:
Acquisition module 201, for gathering the brain electric information of patient；
Processor 202, for removing the artefact in EEG signals, and to the EEG signals application after removal artefact and noise
Elimination trend moving average method, obtain indicating the index F of depth of anesthesiamin(s)。
In the present embodiment, the monitoring system of the depth of anesthesia based on EEG signals can also include：
A/D converter 203, data signal is converted into for simulating EEG signals, and the digital data transmission is extremely located
Manage device.
It is worth explanation, the processor calculates the index F of depth of anesthesiamin(s) process has been said in the above-mentioned methods
It is bright, do not repeating here.
The modules of the monitoring system of the above-mentioned depth of anesthesia based on EEG signals are in the above-mentioned methods specifically
It is bright, just no longer state one by one herein.
In embodiment provided by the present invention, it should be understood that disclosed system and method, others can be passed through
Mode is realized.For example, device embodiment described above is only schematical, for example, the division of the module, is only
A kind of division of logic function, can there is an other dividing mode when actually realizing, for example, multiple units or component can combine or
Person is desirably integrated into another system, or some features can be ignored, or does not perform.Another, shown or discussed is mutual
Between coupling or direct-coupling or communication connection can be INDIRECT COUPLING or communication link by some interfaces, device or unit
Connect, can be electrical, mechanical or other forms.
The unit illustrated as separating component can be or may not be physically separate, show as unit
The part shown can be or may not be physical location, you can with positioned at a place, or can also be distributed to multiple
On NE.Some or all of unit therein can be selected to realize the mesh of this embodiment scheme according to the actual needs
's.
In addition, each functional unit in each embodiment of the present invention can be integrated in a processing unit, can also
That unit is individually physically present, can also two or more units it is integrated in a unit.Above-mentioned integrated list
Member can both be realized in the form of hardware, can also be realized in the form of hardware adds SFU software functional unit.
The above-mentioned integrated unit realized in the form of SFU software functional unit, can be stored in one and computer-readable deposit
In storage media.Above-mentioned SFU software functional unit is stored in a storage medium, including some instructions are causing a computer
It is each that equipment (can be personal computer, server, or network equipment etc.) or processor (processor) perform the present invention
The part steps of embodiment methods described.And foregoing storage medium includes：USB flash disk, mobile hard disk, read-only storage (Read-
Only Memory, ROM), random access memory (Random Access Memory, RAM), magnetic disc or CD etc. it is various
Can be with the medium of store program codes.
Finally it should be noted that：The above embodiments are merely illustrative of the technical solutions of the present invention, rather than its limitations；Although
The present invention is described in detail with reference to the foregoing embodiments, it will be understood by those within the art that：It still may be used
To be modified to the technical scheme described in foregoing embodiments, or equivalent substitution is carried out to which part technical characteristic；
And these modification or replace, do not make appropriate technical solution essence depart from various embodiments of the present invention technical scheme spirit and
Scope.
Claims (10)
1. a kind of monitoring method of the depth of anesthesia based on EEG signals, it is characterised in that it includes：
The EEG signal of patient is gathered, and the EEG signal is pre-processed to obtain noiseless EEG signal；
The wave function of the noiseless EEG signal is calculated using improved elimination trend moving average algorithm, and according to described
Wave function calculates the Cerebral state index.
2. the monitoring method of 1 depth of anesthesia based on EEG signals is required as requested, it is characterised in that the collection is suffered from
The EEG signal of person, and the EEG signal is pre-processed and is specially to obtain noiseless EEG signal：
The EEG information of patient is gathered, and uses the noise jamming of the wavelet entropy threshold method elimination EEG signal noiseless to obtain
EEG signal.
3. the monitoring method of the depth of anesthesia based on EEG signals according to claim 1, it is characterised in that described use changes
The elimination trend moving average algorithm entered calculates the wave function of the noiseless EEG signal, and according to the wave function meter
Calculating the Cerebral state index is specially：
If the noiseless EEG signal is EEG signals time series, and obtains the average of the EEG signals time series；
The cumulative sequence of the EEG signals time series is determined according to the average, and is calculated using backward sliding average method
The trend sequence of one stationary window length；
The elimination trend sequence of the EEG signals time series is calculated according to the trend sequence, and according to the elimination trend
Sequence calculates the undulating value of the noiseless EEG signal of length of window；
The wave function of the noiseless EEG signal of length of window is calculated according to the undulating value, and according to the wave function
Calculate the Cerebral state index.
4. according to the monitoring method of the depth of anesthesia based on EEG signals described in claim 1 or 3, it is characterised in that the ripple
Dynamic function is：
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<mi>F</mi>
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<mi>A</mi>
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<mn>2</mn>
</mrow>
</msup>
</mrow>
Wherein, EmFor Wavelet Entropy, s is length of window, and L is positive integer；Represent undulating value.
5. the monitoring method of the depth of anesthesia based on EEG signals according to claim 4, it is characterised in that the anesthesia is deep
Spending index is：
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Wherein, it is describedFor gain index, FMDMA(s) it is wave function.
6. a kind of monitoring system of the depth of anesthesia based on EEG signals, it is characterised in that it includes：
Processing module, for gathering the EEG signal of patient, and the EEG signal is pre-processed to obtain noiseless EEG letters
Number；
Computing module, for calculating the fluctuation letter of the noiseless EEG signal using improved elimination trend moving average algorithm
Number, and the Cerebral state index is calculated according to the wave function.
7. the monitoring system of 6 depth of anesthesia based on EEG signals is required as requested, it is characterised in that the processing mould
Block is specifically used for：
The EEG information of patient is gathered, and uses the noise jamming of the wavelet entropy threshold method elimination EEG signal noiseless to obtain
EEG signal.
8. the monitoring system of the depth of anesthesia based on EEG signals according to claim 6, it is characterised in that the calculating mould
Block specifically includes：
Acquiring unit, for setting the noiseless EEG signal as EEG signals time series, and obtain the EEG signals time
The average of sequence；
First computing unit, for determining the cumulative sequence of the EEG signals time series according to the average, and after use
The trend sequence of a stationary window length is calculated to sliding average method；
First computing unit, for calculating the elimination trend sequence of the EEG signals time series according to the trend sequence,
And the undulating value of the noiseless EEG signal of length of window is calculated according to the elimination trend sequence；
3rd computing unit, for calculating the wave function of the noiseless EEG signal of length of window according to the undulating value, and
The Cerebral state index is calculated according to the wave function.
9. according to the monitoring system of the depth of anesthesia based on EEG signals described in claim 6 or 8, it is characterised in that the ripple
Dynamic function is：
<mrow>
<msub>
<mi>F</mi>
<mrow>
<mi>M</mi>
<mi>D</mi>
<mi>M</mi>
<mi>A</mi>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<mi>s</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<msup>
<mrow>
<mo>&lsqb;</mo>
<mfrac>
<msub>
<mi>E</mi>
<mi>m</mi>
</msub>
<mi>L</mi>
</mfrac>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>s</mi>
<mo>=</mo>
<mn>3</mn>
</mrow>
<mi>L</mi>
</munderover>
<msubsup>
<mi>F</mi>
<mi>M</mi>
<mn>2</mn>
</msubsup>
<mrow>
<mo>(</mo>
<mi>s</mi>
<mo>)</mo>
</mrow>
<mo>&rsqb;</mo>
</mrow>
<mrow>
<mn>1</mn>
<mo>/</mo>
<mn>2</mn>
</mrow>
</msup>
</mrow>
Wherein, EmFor Wavelet Entropy, s is length of window, and L is positive integer；Represent undulating value.
10. the monitoring system of the depth of anesthesia based on EEG signals according to claim 9, it is characterised in that the anesthesia
Depth factor is：
<mrow>
<msub>
<mi>F</mi>
<mi>min</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>s</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<msubsup>
<mi>K</mi>
<mi>min</mi>
<mrow>
<mi>M</mi>
<mi>o</mi>
<mi>d</mi>
<mi>i</mi>
<mi>f</mi>
<mi>y</mi>
</mrow>
</msubsup>
<mi>min</mi>
<mrow>
<mo>(</mo>
<mi>ln</mi>
<mi> </mi>
<msub>
<mi>F</mi>
<mrow>
<mi>M</mi>
<mi>D</mi>
<mi>M</mi>
<mi>A</mi>
</mrow>
</msub>
<mo>)</mo>
</mrow>
</mrow>
Wherein, it is describedFor gain index, FMDMA(s) it is wave function.
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| CN115098832A (en) * | 2022-05-23 | 2022-09-23 | 燕山大学 | Anesthesia depth estimation method based on thalamocortical model |
| CN116172522A (en) * | 2023-05-04 | 2023-05-30 | 江南大学附属医院 | Anesthesia depth monitoring method based on neural network |
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