CN103845052B - Based on the human body faintness prior-warning device gathering EEG signals - Google Patents

Abstract

The present invention proposes a kind of human body faintness method for early warning based on gathering EEG signals. The method comprises: the EEG signals being gathered M passage of the lower manned whizzer of different G value by electroencephalogram EEG Acquisition Instrument, and wherein, M is positive integer; EEG signals in M passage are carried out pre-treatment, to obtain the low frequency eeg data of each passage in M passage; The parameter relevant rate of frequency range in each data section of each EEG signals is obtained according to low frequency eeg dataWhen the relevant rate of parameterWhen meeting early-warning conditions before fainting, carry out human body faintness early warning and remind. The method of the embodiment of the present invention, the state before can being fainted by human body carries out identifying in advance and early warning, is conducive to fullying understand the sign of trainee, instructs human body training to have actual application value.

Description

Based on the human body faintness prior-warning device gathering EEG signals

Technical field

The present invention relates to aviation medicine and biomedical engineering field, particularly relate to a kind of human body faintness method for early warning based on gathering EEG signals.

Background technology

Since high-performance fighter aircraft comes out, high G protection has become aviation medicine great theory urgently to be resolved hurrily and realistic problem. (flight safety in G-LOC (G-inducedlossofconsciousness, G-LOC) serious threat to the loss of consciousness that universal gravity constant causes. For this reason, in flight and training, in order to ensure the safety of pilot, it is necessary to the physiological status of Real-Time Monitoring human body judges and early warning to carry out.

At present, the physical state of pilot is understood by monitoring the electrocardio of pilot, ear arteries and veins signal and EEG signals. Wherein, the irregular pulse in electrocardio is often as the index shut down in training process, and heart rate is the valuable important indicator of tool selecting high G stunt flying and deposit pilot. When irregular pulse in electrocardio, illustrate that the heart of human body has occurred severely subnormal, now need to shut down in order to avoid experimenter causes bigger injury immediately. Ear pulse and head level blood pressure have consistence, and usual ear pulse can be used as the objective index judging+Gz endurance terminal, and wherein ,+Gz represents the universal gravity constant of z-axis positive dirction. Usual ear pulse evens up 1��2 second, namely can be used as the early warning of blackout or the loss of consciousness. Generally thinking that EEG signals are good Testing index now, the acceleration effect produced when simulating high G by various methods such as anoxic, the loss of consciousness, lower body negative pressure at present studies the brain Electrical change feature under high G environment indirectly.

Current Problems existing is, owing to the change of heart rate affects relatively big by individual difference, and movable closely related with behavior and psychology, it is easy to it is subject to ectocine, therefore, it can be used as endurance end point judging to have little significance. What ear arteries and veins reflected is the change of the outer arterial pressure of brainpan, is not the change of arterial pressure in brainpan. In addition, due to the impact by temperature, measured ear arteries and veins signal is inaccurate, and unstable. The research of EEG signals is many at present carrys out brain Electrical change feature when researching human body is in faintness or has closed on faintness based on static eeg data, and taking the high width slow wave of inspectional analysis fulminant as feature, when find naked eyes can " high width slow wave " (such as �� ripple or �� ripple) of identification time, cerebral functional lateralitv may be in serious holddown, then carry out G-LOC early warning and lost meaning. In addition, due to the individual different difference produced, the basis for estimation evaluating brain Electrical change at present is inaccurate, and lacks objectivity.

Summary of the invention

The present invention is intended at least one of solve the problems of the technologies described above.

For this reason, it is an object of the invention to propose a kind of human body faintness method for early warning based on gathering EEG signals. State before human brain faintness can be carried out identifying in advance and early warning by the method, thus avoid and trainee causes bigger injury, is had important practical significance by the high G protection question of solution.

In order to realize above-mentioned purpose, the human body faintness method for early warning based on collection EEG signals of the embodiment of the present invention, comprising: gathered the EEG signals in M passage of the lower manned whizzer of different G value by electroencephalogram EEG Acquisition Instrument, wherein, M is positive integer; EEG signals in described M passage are carried out pre-treatment, to obtain the low frequency eeg data of each passage in described M passage; The parameter relevant rate of frequency range in each data section of EEG signals described in each is obtained according to described low frequency eeg dataWhen the relevant rate of described parameterWhen meeting early-warning conditions before fainting, carry out human body faintness early warning and remind.

The human body faintness method for early warning based on collection EEG signals of the embodiment of the present invention, there is following useful effect: 1, by directly carrying out human experimentation on manned whizzer, brain Electrical change feature under research whizzer+Gz, can fully recognize before fainting according to brain Electrical change feature or the change information of the relevant cerebral functional lateralitv sentenced figure by means of only naked eyes before G-LOC and cannot understand, and according to fainting of causing of the right+Gz of changing conditions of the relevant rate of parameter, early warning and method of discrimination are proposed, for the sign of overall understanding trainee, human body training is instructed to have actual application value, and perform aerial mission takes corresponding safeguard procedures early in training for reminding pilot, solve high G protection question to have important practical significance, 2, by the relevant rate of parameterThe individual different difference produced can be eliminated, can provide when evaluating the brain Electrical change of human body objectively and basis for estimation comparatively accurately.

The aspect that the present invention adds and advantage will part provide in the following description, and part will become obvious from the following description, or be recognized by the practice of the present invention.

Accompanying drawing explanation

The present invention above-mentioned and/or additional aspect and advantage will become from the following description of the accompanying drawings of embodiments obviously with it should be readily understood that wherein,

Fig. 1 is the schema based on the human body faintness method for early warning gathering EEG signals of one embodiment of the invention;

Fig. 2 is the relevant rate of parameter of alpha frequency bandWith the schematic diagram of different G value change; And

Fig. 3 is the relevant rate of parameter of beta frequency bandWith the schematic diagram of different G value change.

Embodiment

Being described below in detail embodiments of the invention, the example of described embodiment is shown in the drawings, and wherein same or similar label represents same or similar element or has element that is identical or similar functions from start to finish. It is exemplary below by the embodiment being described with reference to the drawings, only for explaining the present invention, and limitation of the present invention can not be interpreted as. On the contrary, embodiments of the invention comprise all changes within the scope of the spirit and intension falling into attached claim book, amendment and etc. jljl.

In describing the invention, it is to be understood that term " first ", " the 2nd " etc. are only for describing object, and can not be interpreted as instruction or hint relative importance. In describing the invention, it is necessary to explanation, unless otherwise clearly defined and limited, term " is connected ", " connection " should be interpreted broadly, such as, it is possible to be fixedly connected with, it is also possible to be removably connect, or connect integratedly; Can be mechanically connected, it is also possible to be electrical connection; Can be directly be connected, it is also possible to be indirectly connected by intermediary. For the ordinary skill in the art, it is possible to particular case understands above-mentioned term concrete implication in the present invention. In addition, in describing the invention, unless otherwise explanation, the implication of " multiple " is two or more.

Describe and can be understood in schema or in this any process otherwise described or method, represent and comprise one or more for realizing the module of the code of the performed instruction of the step of specific logical function or process, fragment or part, and the scope of the preferred embodiment of the present invention comprises other realization, wherein can not according to order that is shown or that discuss, comprise according to involved function by the mode while of basic or by contrary order, carrying out n-back test, this should be understood by embodiments of the invention person of ordinary skill in the field.

Below with reference to the accompanying drawings the human body faintness method for early warning based on collection EEG signals of the embodiment of the present invention is described.

At present, it is subject to interference and the measurement difficulty of EEG signals is bigger owing to human body to be carried out EEG signals under the singularity of test requirements document and+Gz environment on manned whizzer. Therefore, indirectly studying the brain Electrical change feature under high G environment to the research of brain Electrical change feature is many by various methods such as anoxic, the loss of consciousness, lower body negative pressure at present, it is mainly taking the high width slow wave of inspectional analysis fulminant as feature. But, when find naked eyes can " the high width slow wave " of identification time, human body cerebral functional lateralitv may be in serious holddown, now again G-LOC is carried out early warning and has lost meaning. If can under different G value, directly gather the EEG signals in manned whizzer, and the EEG signals collected are carried out comprehensive analysis and faints the variation characteristic of EEG signals before front or G-LOC to obtain the lower human body of different G value, then can understand the sign change of trainee more easily, if before pilot or trainee brain Electrical change in training and executing the task meets default faintness, early warning in advance can be carried out, and take corresponding safeguard procedures in time, thus avoid and pilot or experimenter are caused bigger injury. For this reason, the present invention proposes a kind of human body faintness method for early warning based on gathering EEG signals.

Fig. 1 is the schema based on the human body faintness method for early warning gathering EEG signals of one embodiment of the invention.

As shown in Figure 1, should comprise based on the human body faintness method for early warning gathering EEG signals.

S101, by the EEG signals in M passage of manned whizzer under the different G value (i.e. gravity acceleration value) of electroencephalogram EEG Acquisition Instrument collection, wherein, M is positive integer.

Specifically, in the novel three axial novel manned whizzers of high-performance, human trial is carried out. Wherein, the novel three axial novel manned whizzer principal arm length of high-performance are 8m, have 3 axial accelerations. Human body will be produced different impacts by the acceleration change of each axis, and the acceleration in Gz direction is maximum on the impact of human body electroencephalogram's signal, and that is, the universal gravity constant in z-axis direction is maximum on the impact of human body electroencephalogram's signal. Namely the present invention mainly studies+Gz(, the universal gravity constant of z-axis positive dirction) changing conditions of the lower human body EEG signals of effect, that is, main research trainee is in the changing conditions of the EEG signals being subject to the universal gravity constant of z-axis positive dirction and cause. Therefore, when designing the accelerating curve of the novel three axial novel manned whizzers of high-performance, the acceleration angle value (i.e. the universal gravity constant in x-axis and y-axis direction) in Gx and Gy direction is controlled little as much as possible. Such as, can by the Acceleration Control in Gx direction below 1, the Acceleration Control in Gy direction is below 0.5.

Furthermore, being specifically set to of the accelerating curve run each time: when whizzer is static, it is 1G that experimenter experiences acceleration. Novel three axial high-performance novel manned whizzers first arrive baseline with the acceleration rate of increase of 1G/s when starting and continue the several seconds, the maximum G value of the setting that each time is run so that the acceleration rate of increase of 3G/s arrives afterwards, after continuing 10s-15s, fall as 1G taking the acceleration rate of increase of 3G/s again, returning to original state, whizzer stops. Wherein, it is dangerous for directly experimenter being exposed to bigger G value, and the maximum G value therefore every time run, from 2.5G, increases progressively according to the amplitude of 0.5G, until experimenter arrives endurance terminal or occurs shutting down index. Simultaneously, experienced doctor also to the ear arteries and veins of experimenter and electrocardiosignal Real-Time Monitoring and carries out record by the physiological signal register system of manned whizzer, inquire after running that subjects subjective is to the visually-perceptible situation of passenger cabin inside circumference lamp and central authorities' lamp every time, and the expression in conjunction with experimenter, its endurance is made comprehensive descision.

Owing to the EEG signals in dynamic situation are extremely faint, and being easily disturbed, therefore, the difficulty measuring EEG signals in dynamic situation is bigger relative to quiescent conditions. If electrode position is fixing not good, or process produces loosen, then available less in the eeg data collected. In order to the eeg data that can accurately collect in dynamic situation, when being tested by the novel three axial novel manned whizzers of high-performance, portable brain electric registering instrument can be selected to be fixed in passenger cabin and to gather and record EEG signals. When electrode is laid, according to the different sizes of experimenter's head dummy, for they wear the fastening net cap of respective model, each electrode need to be sticked on medical proof fabric and fasten to add, prevent from running loosens. Wherein, brain electricity electrode is placed and is adopted international 10/20 standard, gets 16 path electrodes, power taking pole F_p1, F₃, C₃, P₃, O₁, F_p2, F₄, C₄, P₄, O₂, F₇, T₃, T₅, F₈, T₄, T₆. The reference electrode of all electrodes selects the electrode A at place of hanging down with picking up the ears₁Or A₂, select the metering system of unipolar lead. Specifically, under the effect of different G value, the EEG signals in whizzer Zhong l6 road can be gathered by the portable brain electric registering instrument in the novel three axial novel manned whizzers of high-performance, that is can obtain the EEG signals in the passage of 16 in manned whizzer by 16 path electrodes.

In an embodiment of the present invention, after the EEG signals obtained in 16 passages, first EEG signals enter the AF panel box in passenger cabin through preliminary interference process, enter into the amplification recording box in passenger cabin afterwards, to carry out subsequent disposal in peculiar format record to SD card. Such as, the sample frequency 128Hz of SD card. Wherein, under+Gz effect can be chosen in the position of AF panel box and amplification recording box, EEG signals correct position that is easily collected and record is fixed.

EEG signals in M passage are carried out pre-treatment by S102, to obtain the low frequency eeg data of each passage in M passage.

In an embodiment of the present invention, the EEG signals in M passage are carried out pre-treatment, specifically comprises: by bandpass filter, EEG signals are carried out filtering, and filtered EEG signals can be carried out artifacts Processing for removing.

Specifically, can by being with, logical digital filter carries out pre-treatment by the EEG signals in collect 16 passages, then suitable little ripple base is selected, the WAVELET PACKET DECOMPOSITION number of plies reasonable in design, utilize the method that WAVELET PACKET DECOMPOSITION reconstructs, eliminate the signals such as the flesh electricity in EEG signals, power frequency supply, baseline wander, electrode interference, mainly extract containing alpha(��): 8-13Hz, beta(��): 13-25Hz, delta(��): (0.5-4Hz), theta(��): the low frequency eeg data of (4-8Hz) frequency band. Specifically, containing various artifacts in the EEG signals gathered in dynamic situation, so need to select suitable method that it is carried out artifacts elimination, to improve performance and the effect that data characteristics is extracted. Wavelet filtering is a kind of method in Time-frequency Filter, and it can make signal obtain time domain and the frequency domain resolution of corresponding optimum at different sites, thus signal is spatially completed level discharge rating in a series of different levels. It is very suitable for the analysis of the transient response to non-stationary signal and time-varying characteristics. That is, in low frequency part, there are higher frequency resolving power and lower temporal resolution rate, and in high frequency part, then have higher temporal resolution rate and lower frequency resolving power. And wherein emerging method of wavelet packet, more meticulousr than wavelet decomposition, it can decompose at low frequency and high frequency part simultaneously. Namely frequency band can be divided by many levels, and then the high frequency part do not segmented by many resolution analysis decomposes further, and can adaptively select corresponding frequency band according to the feature of analyzed signal, make it the frequency spectrum with signal to mate, and then the resolving power improved on time-frequency. Wherein, the pseudo-mark of the electricity of the brain in EEG signals mainly contains electromyogram(EMG) EMG(electromyography), electrooculogram EOG(electro-oculogram), body dynamic, rotate, baseline wander and power supply etc. Trainee experiences+Gz when exposing to the open air in passenger cabin, inevitably nervous and do certain antagonism action, and therefore impact by flesh electricity is relatively big, and this frequency range mainly concentrates on the frequency range of 35.8-51Hz; The more difficult removal of EOG, it may be mingled among multiple frequency bands of eeg data, is easy to cause the loss of useful information in elimination process, and therefore experimentally the signal in 0.5-1Hz frequency range can mainly be removed by experience; The power frequency of alternating-current concentrates on about 50Hz; Electrode fixes the low frequency slow wave easily forming 0.8Hz and below 0.2Hz with baseline wander; It is relatively strong, very important that whizzer rotates the interference effect produced, the maximum G value that experimentally can reach, whizzer principal arm radius, and the reduction formula leading to heart acceleration can calculate known whizzer and rotate the interference to signal roughly mainly below 0.5Hz frequency. In addition, power frequency supply, magnetic field, body are dynamic etc. also can produce impact in various degree. Based on considering above, wave filter lower limit gets about 1Hz, and the upper limit gets about 35Hz. According to the feature of sample frequency and Ambulatory EEG signal, selecting daubechies5 little ripple that original signal carries out 6 layers of decomposition, its minimum resolving power can be estimated with following formula, f in formula_sFor sample frequencyBy method of wavelet packet can remove preferably+Gz act under EEG signals in EMG, power frequency supply and other high frequency interference, the slow wave interference of the low-frequency range such as baseline wander, electrode interference is also had obvious effect, and the signal to noise ratio after denoising increases substantially.

S103, obtains the parameter relevant rate of frequency range in each data section of each EEG signals according to low frequency eeg data

In an embodiment of the present invention, low frequency eeg data being analyzed the characteristic parameter obtaining EEG signals, wherein, the characteristic parameter of EEG signals comprises EEG signals characteristic parameter and comprises average amplitudeAverage period energy ratioWith average mid-frequencyThe characteristic parameter of EEG signals is normalized, and obtains the estimated value B of each frequency range of low frequency eeg data according to the characteristic parameter after normalized_z; Estimated value B according to each frequency range_zObtain the relevant rate of parameter of each frequency rangeWherein, z represents each frequency range of EEG signals, and such as z can be ��, �� frequency range or other frequency ranges.

Specifically, after obtaining low frequency eeg data, low frequency eeg data can be carried out periodization process with prefixed time interval, and the eeg data of periodization is carried out Fast Fourier Transform (FFT) FFT(FastFourierTransformation). Such as, can by low frequency eeg data taking 2s as one section, it is divided into continuous print multistage, and the eeg data of each every section, passage is carried out Fast Fourier Transform (FFT), and make the periodogram of each 2s data section, wherein, periodogram is by being formed with the sum of squares of the Fourier's composition in the frequency domain of 0.5Hz length segmentation in 16 passages. And after obtaining periodogram, by each periodogram parameter the signal feature representation of each passage out.

In an embodiment of the present invention, average amplitudeBy following formulae discovery:

$\left({\stackrel{\‾}{A}}_z\left(\mathrm{\μv}\right)\right)=\underset{j=1}{\overset{16}{\mathrm{\Σ}}}{A}_z\left(j\right)/16$

Wherein, A_zJ () is the amplitude of the EEG signals of jth passage z frequency range, j=1,2 ..., 16, wherein, A_zJ () is by following formulae discovery:

$A_z\left(x\right)=6\sqrt{S_z\left(j\right)}$

Wherein, S_zJ () represents the energy of the EEG signals of jth passage z frequency range.

In an embodiment of the present invention, energy average period ratioBy following formulae discovery:

$\left({\stackrel{\‾}{D}}_z(\%)\right)=\underset{j=1}{\overset{16}{\mathrm{\Σ}}}{D}_z\left(j\right)/16$

Wherein, D_zJ () is the periodical energy ratio of the EEG signals of jth passage z frequency range, j=1,2 ..., 16, wherein, D_zJ () is by following formulae discovery:

D_z(j)=S_z(j)/S_T(j)��100

Wherein, S_zJ () represents the energy of the EEG signals of jth passage z frequency range, j=1,2 ..., 16, S_TJ () represents jth passage T(0.5-25Hz) energy of EEG signals of frequency range.

In an embodiment of the present invention, mean center frequencyBy following formulae discovery:

$\left({\stackrel{\‾}{F}}_z\left(\mathrm{Hz}\right)\right)=\underset{j=1}{\overset{16}{\mathrm{\Σ}}}{F}_z\left(j\right)/16$

Wherein, F_zJ () is the mid-frequency of jth passage z frequency range EEG signals, j=1,2 ..., 16, wherein, F_zJ () is by following formulae discovery:

$\left({\stackrel{\‾}{F}}_z\left(\mathrm{Hz}\right)\right)=f_z^C\left(j\right)|P_{\max },P_{\max }=\underset{f_{\mathrm{lower}}\≤f_z\left(x\right)\≤f_{\mathrm{upper}}}{\max }P\left(f_z\left(j\right)\right)$

Represent the mid-frequency of beta maximum energy spectrum in jth passage z frequency range, f_lowerRepresent the lower bound of jth passage z frequency range; f_upRepresent the upper bound of jth passage z frequency range, P (f_z(j)) represent the energy of jth passage in z frequency range.

In an embodiment of the present invention, at the average amplitude of the EEG signals obtaining each channelAverage period energy ratioWith average mid-frequencyAfter, by following formula, the characteristic parameter of EEG signals is normalized:

${\mathrm{\Φ}}_z^Q\left(i\right)=(Q_z\left(i\right)-\min Q_z\left(i\right))/(\max Q_z\left(i\right)-\min Q_z\left(i\right))$

Wherein, i represents the sequence number of data section, Q_zI () representsOrOrminQ_zI () is Q_zMinimum value in (i), maxQ_zI () is Q_zMaximum value in (i);

Specifically, by above-mentioned normalization method formula to the average amplitude of EEG signalsAverage period energy ratioWith average mid-frequencyIt is normalized respectively.

After the characteristic parameter of EEG signals is normalized, by following formulae discovery estimated value B_z:

$B_z\left(i\right)={\mathrm{\Φ}}_z^{\stackrel{\‾}{A}}\left(i\right){\mathrm{\Φ}}_z^{\stackrel{\‾}{D}}\left(i\right){\mathrm{\Φ}}_z^{\stackrel{\‾}{F}}\left(i\right).$

In an embodiment of the present invention, at the estimated value B obtaining each frequency range_zAfter, by the relevant rate of following formulae discovery parameter $R_z^B(\%):$

$R_z^B\left(i\right)=B_z\left(i\right)/B_z\left(\mathrm{std}\right)\×100$

Wherein, i represents the sequence number of data section, B_z(std) when representing that data section is i, estimated value B_zIn standard value. Specifically, when data section is i, the estimated value B of each corresponding frequency range_zIn maximum value be generally defined as the standard value corresponding to these data section i. That is, different data section correspond to different standard values. By the relevant rate of calculating parameterIndividual different difference can be eliminated well, therefore, can provide when evaluating brain Electrical change objectively and basis for estimation more accurately.

S104, when the relevant rate of parameterWhen meeting early-warning conditions before fainting, carry out human body faintness early warning and remind.

In an embodiment of the present invention, found by repetition test, the lower alpha(�� of different G value): 8-13Hz with beta(��): it is obvious that the parameter relevant rate change of 13-25Hz frequency range eeg data is compared, by the relevant rate of analytical parametersWithWhether satisfied front condition of fainting of presetting can realize the front early warning of human body faintness.

Specifically, Fig. 2 and Fig. 3 be experimenter in manned whizzer respectively through the variation diagram of the EEG signals caused by the universal gravity constant of 4G and 5G and ear arteries and veins signal, and the state that in Fig. 2 and Fig. 3, lower and ear arteries and veins is evened up to the ear arteries and veins corresponding to the ear arteries and veins signal of the physiological signal register system record of whizzer manned during G value is marked respectively. The different change state of corresponding ear arteries and veins, parameter is correlated with rateWithShow certain variation characteristic. Can finding when ear arteries and veins is evened up or ear arteries and veins is lower by analyzing, parameter is correlated with rateWithShow obvious variation characteristic. Even up lasting 2s due to ear arteries and veins to be usually considered to human body and be in the state before fainting, the relevant rate of parameter when therefore the corresponding ear arteries and veins of primary study is evened upWithVariation characteristic. Namely when based on the current state of electroencephalogramsignal signal analyzing human body, it is possible to based on the relevant rate of parameterWithThe current state of human body is analyzed, and judges that parameter is correlated with rateWithWhether meet and preset front early-warning conditions of fainting. In an embodiment of the present invention, the frequency range of EEG signals comprises �� frequency range and �� frequency range, and wherein �� frequency range and �� frequency range are respectively 8-13Hz and 13-25Hz, and before fainting, early-warning conditions is: a. �� frequency range rate relevant with the parameter of �� frequency range eeg dataWithAll it is greater than 90%; B., in 2��10s after satisfying condition a, parameter is correlated with rateIt is less than 50%, and the relevant rate of parameterThe lasting section number of state being less than 50% is greater than or equal to 2. When the relevant rate of parameterWithSatisfied default front early-warning conditions of fainting, namely determines that human body is in front state of fainting, carries out faintness early warning prompting, thus can avoid trainee being caused bigger injury.

The human body faintness method for early warning based on collection EEG signals of the embodiment of the present invention, there is following useful effect: 1, by directly carrying out human experimentation on manned whizzer, brain Electrical change feature under research whizzer+Gz, can fully recognize before fainting according to brain Electrical change feature or the change information of the relevant cerebral functional lateralitv sentenced figure by means of only naked eyes before G-LOC and cannot understand, and according to fainting of causing of the right+Gz of changing conditions of the relevant rate of parameter, early warning and method of discrimination are proposed, for the sign of overall understanding trainee, human body training is instructed to have actual application value, and perform aerial mission takes corresponding safeguard procedures early in training for reminding pilot, solve high G protection question to have important practical significance, 2, by the relevant rate of parameterThe individual different difference produced can be eliminated, can provide when evaluating brain Electrical change objectively and basis for estimation comparatively accurately.

It is to be understood that each several part of the present invention can realize with hardware, software, firmware or their combination. In the above-described embodiment, multiple step or method can realize with the software stored in memory and perform by suitable instruction execution system or firmware. Such as, if realized with hardware, the same with in another enforcement mode, can realize with the arbitrary item in following technology well known in the art or their combination: the discrete logic with the logic gates for data signal being realized logic function, there is the application specific integrated circuit of suitable combinational logic gating circuit, programmable gate array (PGA), field-programmable gate array (FPGA) etc.

In the description of this specification sheets, at least one embodiment that the description of reference term " embodiment ", " some embodiments ", " example ", " concrete example " or " some examples " etc. means to be contained in the present invention in conjunction with concrete feature, structure, material or feature that this embodiment or example describe or example. In this manual, the schematic representation of above-mentioned term is not necessarily referred to identical embodiment or example. And, the concrete feature of description, structure, material or feature can combine in an appropriate manner in any one or more embodiment or example.

Although it has been shown and described that embodiments of the invention, it will be understood by those skilled in the art that: these embodiments can be carried out multiple change, amendment, replacement and modification when not departing from principle and the objective of the present invention, the scope of the present invention by claim and etc. jljl limit.

Claims (8)

1. the human body faintness prior-warning device based on collection EEG signals, it is characterised in that, comprising:

First acquisition module, for the EEG signals in M the passage obtaining the lower manned whizzer of different G value from electroencephalogram EEG Acquisition Instrument, wherein, M is positive integer;

Pre-processing module, for carrying out pre-treatment to the EEG signals in described M passage, to obtain the low frequency eeg data of each passage in described M passage;

2nd acquisition module, for described low frequency eeg data is analyzed the characteristic parameter obtaining described EEG signals, wherein, the characteristic parameter of described EEG signals comprises average amplitudeAverage period energy ratioWith average mid-frequencyAnd be normalized by the characteristic parameter of described EEG signals, and obtain the estimated value B of each frequency range of described low frequency eeg data according to the characteristic parameter after normalized_z, and the estimated value B according to each frequency range described_zObtain the relevant rate of parameter of each frequency range

Prompting module, for when the relevant rate of described parameterWhen meeting early-warning conditions before fainting, carry out human body faintness early warning and remind.

2. device as claimed in claim 1, it is characterised in that, described pre-processing module, specifically for:

By bandpass filter, described EEG signals are carried out filtering, and filtered EEG signals are carried out artifacts Processing for removing.

3. device as claimed in claim 1, it is characterised in that, described average amplitudeBy following formulae discovery:

$\left({\stackrel{\‾}{A}}_z\right(\mathrm{\μ}v\left)\right)=\underset{j=1}{\overset{16}{\Σ}}{A}_z\left(j\right)/16$

Wherein, A_zJ () is the amplitude of the EEG signals of jth passage z frequency range, j=1,2 ..., 16, described A_zJ () is by following formulae discovery:

$A_z\left(x\right)=6\sqrt{S_z\left(j\right)}$

Wherein, S_zJ () represents the energy of the EEG signals of jth passage z frequency range.

4. device as claimed in claim 1, it is characterised in that, described average period energy ratioBy following formulae discovery:

$\left({\stackrel{\‾}{D}}_z\right(\%\left)\right)=\underset{j=1}{\overset{16}{\Σ}}{D}_z\left(j\right)/16$

Wherein, D_zJ () is the periodical energy ratio of the EEG signals of jth passage z frequency range, j=1,2 ..., 16, described D_zJ () is by following formulae discovery:

D_z(j)=S_z(j)/S_T(j)��100

Wherein, S_zJ () represents the energy of the EEG signals of jth passage z frequency range, j=1,2 ..., 16, S_TJ () represents the energy of the EEG signals of jth passage T frequency range, wherein, the span of T is 0.5Hz-25Hz.

5. device as claimed in claim 1, it is characterised in that, described mean center frequencyBy following formulae discovery:

$\left({\stackrel{\‾}{F}}_z\right(Hz\left)\right)=\underset{j=1}{\overset{16}{\Σ}}{F}_z\left(j\right)/16$

Wherein, F_zJ () is the mid-frequency of jth passage z frequency range EEG signals, j=1,2 ..., 16, described F_zJ () is by following formulae discovery:

$\left({\stackrel{\‾}{F}}_z\right(Hz\left)\right)=f_z^C\left(j\right)|P_{max},P_{\max }=\underset{f_{lower}\≤f_z\left(x\right)\≤f_{upper}}{max}P(f_z\left(j\right))$

Represent the mid-frequency of beta maximum energy spectrum in jth passage z frequency range, f_lowerRepresent the lower bound of jth passage z frequency range; f_upRepresent the upper bound of jth passage z frequency range, P (f_z(j)) represent the energy of jth passage in z frequency range.

6. device as claimed in claim 1, it is characterised in that, described 2nd acquisition module, specifically for:

By following formula, the characteristic parameter of described EEG signals is normalized:

${\mathrm{\Φ}}_z^Q\left(i\right)=\left(Q_z\right(i)-{\mathrm{minQ}}_z(i\left)\right)/\left({\mathrm{maxQ}}_z\right(i)-{\mathrm{minQ}}_z(i\left)\right)$

Wherein, i represents the sequence number of data section, Q_zI () representsOrOrminQ_zI () is Q_zMinimum value in (i), maxQ_zI () is Q_zMaximum value in (i);

After the characteristic parameter of described EEG signals is normalized, by following formulae discovery estimated value B_z:

$B_z\left(i\right)={\mathrm{\Φ}}_z^{\stackrel{\‾}{A}}\left(i\right){\mathrm{\Φ}}_z^{\stackrel{\‾}{D}}\left(i\right){\mathrm{\Φ}}_z^{\stackrel{\‾}{F}}\left(i\right).$

7. device as claimed in claim 1, it is characterised in that, described 2nd acquisition module, specifically for:

By the relevant rate of following formulae discovery parameter

$R_z^B\left(i\right)=B_z\left(i\right)/B_z\left(std\right)\×100,$

Wherein, i represents the sequence number of data section, B_z(std) when representing that data section is i, estimated value B_zIn standard value.

8. device as claimed in claim 1, it is characterised in that, the frequency range of described EEG signals comprises �� frequency range and �� frequency range, and described �� frequency range and �� frequency range are respectively 8-13Hz and 13-25Hz, and before described faintness, early-warning conditions is:

A. �� frequency range rate relevant with the parameter of �� frequency range eeg dataWithAll it is greater than 90%;

B., in 2��10s after satisfying condition a, described parameter is correlated with rateIt is less than 50%, and the relevant rate of described parameterThe lasting section number of state being less than 50% is greater than or equal to 2.