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

Abstract

The invention proposes a human body fainting early warning method based on collecting electroencephalogram signals. The method includes: collecting the EEG signals of M channels of a centrifuge with different G values by using an EEG acquisition instrument, wherein M is a positive integer; preprocessing the EEG signals in the M channels to obtain The low-frequency EEG data of each channel in the M channels; according to the low-frequency EEG data, the parameter correlation rate of the frequency band of each EEG signal in each data segment is obtained When the parameter correlation rate When the pre-faint warning conditions are met, a human fainting warning reminder will be given. The method of the embodiment of the present invention can carry out early identification and early warning of the state of the human body before fainting, which is beneficial to a comprehensive understanding of the physical signs of the trainee, and has practical application value in guiding human body training.

Description

Human fainting warning device based on collecting EEG signals

technical field

The invention relates to the fields of aeromedicine and biomedical engineering, in particular to a human body fainting early warning method based on collecting electroencephalogram signals.

Background technique

Since the advent of high-performance fighter jets, high-G protection has become a major theoretical and practical problem in aviation medicine. The loss of consciousness caused by the acceleration of gravity (G-LOC (G-induced loss of consciousness, G-LOC) seriously threatens flight safety. Therefore, in flight and training, in order to ensure the safety of pilots, it is necessary to monitor the physiological state of the human body in real time for judgment and early warning.

At present, the pilot's physical state can be understood by monitoring the pilot's electrocardiogram, ear pulse signal and brain signal. Among them, arrhythmia in ECG is often used as an indicator of downtime during training, and heart rate is a valuable and important indicator for selecting high-G special flight and reserve pilots. When there is an arrhythmia in the ECG, it means that the heart of the human body has experienced serious abnormalities. At this time, it is necessary to stop the machine immediately to avoid causing greater harm to the subject. The ear pulse is consistent with the blood pressure at the head level. Usually, the ear pulse can be used as an objective indicator to judge the end point of +Gz endurance, where +Gz represents the gravitational acceleration in the positive direction of the z-axis. Usually the ear pulse is flattened for 1 to 2 seconds, which can be used as an early warning of black vision or loss of consciousness. Now it is generally believed that the EEG signal is a good detection index. At present, various methods such as hypoxia, loss of consciousness, and negative pressure of the lower body can be used to simulate the acceleration effect of high G to indirectly study the EEG changes in the high G environment. feature.

The current problem is that since the change of heart rate is greatly affected by individual differences, and is closely related to behavior and psychological activities, it is easily affected by the outside world. Therefore, it is not meaningful to use it as the end point of endurance. The ear pulse reflects the change of the extracranial arterial pressure, not the change of the intracranial arterial pressure. In addition, due to the influence of temperature, the measured ear pulse signal is not accurate and unstable. At present, the research on EEG signals is mostly based on static EEG data to study the characteristics of EEG changes when the human body is in syncope or close to fainting, and it is characterized by visual analysis of explosive high-amplitude slow waves. When there is a "slow wave" (such as delta wave or theta wave), the state of brain function may have been severely inhibited, and it is meaningless to carry out G-LOC early warning. In addition, due to individual differences, the current judgment basis for evaluating EEG changes is not accurate and lacks objectivity.

Contents of the invention

The present invention aims to solve at least one of the above-mentioned technical problems.

For this reason, the object of the present invention is to propose a human body fainting early warning method based on collecting EEG signals. This method can identify and warn the state of the human brain before fainting in advance, thereby avoiding greater damage to the trainee, and has important practical significance for solving the problem of high G protection.

In order to achieve the above object, the human body fainting early warning method based on collecting EEG signals in the embodiment of the present invention includes: collecting the EEG signals in the M channels of the human centrifuge with different G values through the EEG acquisition instrument, wherein, M is a positive integer; the EEG signals in the M channels are preprocessed to obtain the low-frequency EEG data of each channel in the M channels; each of the EEG data is obtained according to the low-frequency EEG data. The parameter correlation rate of the frequency band of the electrical signal in each data segment When the parameter correlation rate When the pre-faint warning conditions are met, a human fainting warning reminder will be given.

The human body fainting early warning method based on the collection of EEG signals in the embodiment of the present invention has the following beneficial effects: 1. By directly carrying out human experiments on the manned centrifuge, the EEG change characteristics under the centrifuge+Gz are studied, and according to the EEG change Features can fully understand the change information of the brain function state that cannot be understood only by visually judging images before syncope or before G-LOC, and propose early warning and discrimination methods for syncope caused by +Gz according to the changes in the parameter correlation rate. It is of practical application value to fully understand the physical signs of trainees and guide human training, and it has important practical significance to remind pilots to take corresponding protective measures as early as possible during training and flight missions, and to solve the problem of high G protection; 2. Through parameter correlation rate It can eliminate the differences caused by different individuals, and can provide objective and more accurate judgment basis when evaluating the EEG changes of the human body.

Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.

Description of drawings

The above and/or additional aspects and advantages of the present invention will become apparent and easy to understand from the following description of the embodiments in conjunction with the accompanying drawings, wherein,

Fig. 1 is the flow chart of the method for early warning of human fainting based on collecting EEG signals according to an embodiment of the present invention;

Figure 2 is the parameter correlation rate of the alpha frequency band Schematic representation of the variation with different G values; and

Figure 3 is the parameter correlation rate of the beta frequency band Schematic diagram of the variation with different G values.

detailed description

Embodiments of the present invention are described in detail below, examples of which are shown in the drawings, wherein the same or similar reference numerals designate the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the figures are exemplary only for explaining the present invention and should not be construed as limiting the present invention. On the contrary, the embodiments of the present invention include all changes, modifications and equivalents coming within the spirit and scope of the appended claims.

In the description of the present invention, it should be understood that the terms "first", "second" and so on are used for descriptive purposes only, and cannot be interpreted as indicating or implying relative importance. In the description of the present invention, it should be noted that unless otherwise specified and limited, the terms "connected" and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral Ground connection; it can be mechanical connection or electrical connection; it can be direct connection or indirect connection through an intermediary. Those of ordinary skill in the art can understand the specific meanings of the above terms in the present invention in specific situations. In addition, in the description of the present invention, unless otherwise specified, "plurality" means two or more.

Any process or method descriptions described in flowcharts or otherwise herein may be understood as representing a module, segment or portion of code comprising one or more executable instructions for implementing specific logical functions or steps of the process , and the scope of preferred embodiments of the invention includes alternative implementations in which functions may be performed out of the order shown or discussed, including in substantially simultaneous fashion or in reverse order depending on the functions involved, which shall It is understood by those skilled in the art to which the embodiments of the present invention pertain.

A method for early warning of human fainting based on collecting EEG signals according to an embodiment of the present invention will be described below with reference to the accompanying drawings.

At present, due to the particularity of the test requirements on the human body on the manned centrifuge and the EEG signal is easily interfered in the +Gz environment and the measurement of the EEG signal is relatively difficult. Therefore, the current research on the characteristics of EEG changes mostly uses various methods such as hypoxia, loss of consciousness, and lower body negative pressure to indirectly study the characteristics of EEG changes in high-G environments. as a feature. However, when the "high-amplitude slow wave" recognizable to the naked eye is found, the human brain function state may have been severely inhibited, and it is meaningless to warn G-LOC at this time. If it is possible to directly collect the EEG signals in the manned centrifuge at different G values, and conduct a comprehensive analysis of the collected EEG signals to obtain the changes in human EEG signals before fainting or before G-LOC at different G values characteristics, it is easier to understand the changes in the trainee’s physical signs. When the pilot or trainee’s EEG changes meet the preset fainting during the training and execution of the task, an early warning can be given, and corresponding protective measures can be taken in time, so as to avoid In order to cause greater harm to the pilot or the subject. For this reason, the present invention proposes a human body fainting early warning method based on collecting EEG signals.

FIG. 1 is a flow chart of a human fainting early warning method based on collecting EEG signals according to an embodiment of the present invention.

As shown in FIG. 1 , the human fainting early warning method based on collecting EEG signals includes.

S101. Collect the EEG signals in the M channels of the human centrifuge with different G values (ie, the acceleration of gravity) by using the EEG acquisition instrument, where M is a positive integer.

Specifically, human trials were conducted in a novel triaxial high-performance novel manned centrifuge. Among them, the main arm of the new three-axis high-performance new manned centrifuge has a length of 8m and has three-axis acceleration. Acceleration changes in each axis will have different effects on the human body, and the acceleration in the Gz direction has the greatest impact on the human EEG signal. That is to say, the acceleration of gravity in the z-axis direction has the greatest impact on the human EEG signal. The present invention mainly studies the changes of the human body's EEG signal under the action of +Gz (that is, the gravitational acceleration in the positive direction of the z-axis), that is to say, it mainly studies the EEG signals caused by the trainee's gravitational acceleration in the positive direction of the z-axis changes. Therefore, when designing the acceleration curve of the new three-axis high-performance manned centrifuge, the acceleration values in the Gx and Gy directions (that is, the gravitational acceleration in the x-axis and y-axis directions) should be controlled as small as possible. For example, the acceleration in the Gx direction can be controlled below 1, and the acceleration in the Gy direction can be controlled below 0.5.

Furthermore, the specific setting of the acceleration curve for each run is: when the centrifuge is at rest, the subject feels an acceleration of 1G. When the new three-axis high-performance new manned centrifuge starts, it first reaches the baseline with an acceleration rate of 1G/s for a few seconds, and then reaches the maximum G value set for each operation at an acceleration rate of 3G/s, and lasts for a few seconds. After 10s-15s, the acceleration growth rate of 3G/s is reduced to 1G, and the centrifuge stops after returning to the initial state. Among them, it is dangerous to directly expose the subject to a large G value, so the maximum G value of each run starts from 2.5G, and increases in increments of 0.5G until the subject reaches the end of endurance or the shutdown indicator occurs. At the same time, the experienced doctors also monitored and recorded the ear pulse and ECG signals of the subjects in real time through the physiological signal recording system of the manned centrifuge, and asked the subjects to subjectively evaluate the peripheral lights and central lights in the cockpit after each operation. The visual perception of the lamp, combined with the subject's expression, makes a comprehensive judgment on its endurance.

Since the EEG signal under dynamic conditions is extremely weak and susceptible to interference, it is more difficult to measure EEG signals under dynamic conditions than under static conditions. If the electrode position is not fixed well, or if it becomes loose during the process, less of the collected EEG data will be available. In order to accurately collect EEG data under dynamic conditions, a portable EEG recorder can be installed and fixed in the cockpit to collect and record EEG signals when testing with a new type of three-axis high-performance manned centrifuge. When the electrodes are placed, it is necessary to wear corresponding fastening mesh caps according to the different sizes of the heads of the subjects, and attach medical adhesive tape to each electrode to tighten and fix them to prevent loosening during operation. Among them, the placement of EEG electrodes adopts the international 10/20 standard, and 16 electrodes are taken, including electrodes F _p1 , F ₃ , C ₃ , P ₃ , O ₁ , F _p2 , F ₄ , C ₄ , P ₄ , O ₂ , F ₇ , T ₃ , T ₅ , F ₈ , T ₄ , T ₆ . The reference electrode of all electrodes is selected as the electrode A ₁ or A ₂ at the earlobe of the same side, and the measurement method of unipolar lead is selected. Specifically, under the action of different G values, the portable EEG recorder in the new triaxial high-performance manned centrifuge can collect the EEG signals in the l6 channel in the centrifuge, that is to say, through the 16 The EEG signals in the 16 channels in the manned centrifuge can be obtained by only one electrode.

In the embodiment of the present invention, after obtaining the EEG signals in the 16 channels, the EEG signals first enter the interference suppression box in the cockpit for preliminary interference processing, and then enter the amplification recording box in the cockpit, in a unique format Record to SD card for subsequent processing. For example, the sampling frequency of SD card is 128Hz. Among them, the positions of the interference suppression box and the amplification recording box can be selected and fixed at suitable positions where the EEG signals are easily collected and recorded under the action of +Gz.

S102. Perform preprocessing on the EEG signals in the M channels to obtain low-frequency EEG data of each channel in the M channels.

In an embodiment of the present invention, preprocessing the EEG signals in the M channels specifically includes: filtering the EEG signals through a band-pass filter, and performing artifact interference on the filtered EEG signals Eliminate processing.

Specifically, the EEG signals in the 16 channels collected can be preprocessed through a band-pass digital filter, then an appropriate wavelet base can be selected, a reasonable number of wavelet packet decomposition layers can be designed, and the method of wavelet packet decomposition and reconstruction can be used. , to eliminate myoelectricity, power frequency power, baseline drift, electrode interference and other signals in the EEG signal, and mainly extract alpha (α): 8-13Hz, beta (β): 13-25Hz, delta (δ): ( 0.5-4Hz), theta (θ): low-frequency EEG data in the (4-8Hz) frequency band. Specifically, the EEG signals collected under dynamic conditions contain various artifacts, so it is necessary to select an appropriate method to eliminate artifacts to improve the performance and effect of data feature extraction. Wavelet filtering is a method in time-frequency filtering, which can make the signal obtain the corresponding optimal time domain and frequency domain resolution in different parts, so that the signal can be decomposed and reconstructed in a series of different levels of space. It is very suitable for the analysis of transient characteristics and time-varying characteristics of non-stationary signals. That is to say, in the low frequency part, there is a higher frequency resolution and a lower time resolution, while in the high frequency part, there is a higher time resolution and a lower frequency resolution. Among them, the emerging wavelet packet method is more precise than wavelet decomposition, and it can decompose both low frequency and high frequency parts at the same time. That is to say, it can divide the frequency band into multiple levels, and then further decompose the high-frequency part that has not been subdivided by multi-resolution analysis, and can adaptively select the corresponding frequency band according to the characteristics of the analyzed signal, so that it is consistent with the signal The frequency spectrum is matched to improve the time-frequency resolution. Among them, EEG artifacts in EEG signals mainly include EMG (electromyography), EOG (electro-oculogram), body movement, rotation, baseline drift, and power supply. When trainees experience +Gz exposure in the cockpit, they will inevitably be nervous and do certain confrontational actions, so they are greatly affected by myoelectricity, and this frequency band is mainly concentrated in the 35.8-51Hz frequency band; EOG is difficult to remove, it may Mixed in multiple frequency bands of EEG data, it is easy to cause loss of useful information during the elimination process, so according to experimental experience, the signals in the 0.5-1Hz frequency band can be mainly removed; the power frequency of AC power is concentrated around 50Hz; Electrode fixation and baseline drift easily form low-frequency slow waves below 0.8Hz and 0.2Hz; the interference caused by the rotation of the centrifuge is strong and cannot be ignored. According to the maximum G value that can be achieved in the experiment, the radius of the main arm of the centrifuge, and the centripetal acceleration The conversion formula can be calculated, and it can be seen that the interference of the centrifuge rotation on the signal is roughly below the frequency of 0.5Hz. In addition, industrial frequency power supply, magnetic field, body movement, etc. will also have different degrees of influence. Based on the above comprehensive considerations, the lower limit of the filter is about 1Hz, and the upper limit is about 35Hz. According to the sampling frequency and the characteristics of the dynamic EEG signal, the daubechies5 wavelet is selected to decompose the original signal in 6 layers, and its minimum resolution can be estimated by the following formula, where f _s is the sampling frequency The wavelet packet method can better remove EMG, power frequency power, and other high-frequency interference in the EEG signal under the action of +Gz, and it also has a more obvious effect on low-frequency slow-wave interference such as baseline drift and electrode interference. The signal-to-noise ratio after denoising is greatly improved.

S103, obtain the parameter correlation rate of each frequency band of each EEG signal in each data segment according to the low-frequency EEG data

In an embodiment of the present invention, the low-frequency EEG data is analyzed to obtain the characteristic parameters of the EEG signal, wherein the characteristic parameters of the EEG signal include the characteristic parameters of the EEG signal including the average amplitude average cycle energy ratio and average center frequency Perform normalization processing on the characteristic parameters of the EEG signal, and obtain the estimated value B _z of each frequency band of the low-frequency EEG data according to the normalized characteristic parameters; obtain each frequency band according to the estimated value B _z of each frequency band Parametric Correlation Rates for Frequency Bands Wherein, z represents each frequency band of the EEG signal, for example, z may be α, β frequency band or other frequency bands.

Specifically, after acquiring the low-frequency EEG data, the low-frequency EEG data can be periodically processed at preset time intervals, and the periodicized EEG data can be subjected to Fast Fourier Transformation (FFT). For example, the low-frequency EEG data can be divided into continuous multiple segments with 2s as a segment, and fast Fourier transform is performed on each segment of the EEG data of each channel, and a periodogram of each 2s data segment is made, wherein the period The graph consists of the sum of squares of the Fourier components in the frequency domain divided by a length of 0.5 Hz within 16 channels. And after the periodogram is obtained, the signal characteristics of each channel can be expressed through the parameters of each periodogram.

In an embodiment of the present invention, the average amplitude It can be calculated by the following formula:

$<span\; class="notranslate">\; (</span>{\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; A</span>}}}_{\mathrm{<span\; class="notranslate">\; z</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&mu;v</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; 16</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; A</span>}}_{\mathrm{<span\; class="notranslate">\; z</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; 16</span>}$

Among them, A _z (j) is the amplitude of the EEG signal in the z frequency band of the jth channel, j=1, 2, ..., 16, where A _z (j) can be calculated by the following formula:

${\mathrm{<span\; class="notranslate">\; A</span>}}_{\mathrm{<span\; class="notranslate">\; z</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 6</span>}\sqrt{{\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; z</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span>}$

Wherein, S _z (j) represents the energy of the electroencephalogram signal of the z-th frequency band of the j-th channel.

In an embodiment of the present invention, the average cycle energy ratio It can be calculated by the following formula:

$<span\; class="notranslate">\; (</span>{\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; D.</span>}}}_{\mathrm{<span\; class="notranslate">\; z</span>}}<span\; class="notranslate">\; (</span><span\; class="notranslate">\; \%</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; 16</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; D.</span>}}_{\mathrm{<span\; class="notranslate">\; z</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; 16</span>}$

Among them, D _z (j) is the periodic energy ratio of the EEG signal in the z-frequency band of the jth channel, j=1,2,...,16, wherein, D _z (j) can be calculated by the following formula:

D _z (j)=S _z (j)/S _T (j)×100

Among them, S _z (j) represents the energy of the EEG signal of the jth channel z frequency band, j=1,2,...,16, S _T (j) represents the jth channel T (0.5-25Hz) frequency band energy of the EEG signal.

In an embodiment of the present invention, the average center frequency It can be calculated by the following formula:

$<span\; class="notranslate">\; (</span>{\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; f</span>}}}_{\mathrm{<span\; class="notranslate">\; z</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; Hz</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; 16</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; z</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; 16</span>}$

Among them, F _z (j) is the center frequency of the z-band EEG signal of the jth channel, j=1,2,...,16, where, F _z (j) can be calculated by the following formula:

$<span\; class="notranslate">\; (</span>{\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; f</span>}}}_{\mathrm{<span\; class="notranslate">\; z</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; Hz</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; z</span>}}^{\mathrm{<span\; class="notranslate">\; C</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; =</span>\underset{{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; lower</span>}}<span\; class="notranslate">\; \&le;</span>{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; z</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&le;</span>{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; upper</span>}}}{\mathrm{<span\; class="notranslate">\; max</span>}}\mathrm{<span\; class="notranslate">\; P</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; z</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>$

Indicates the center frequency of the maximum energy spectrum in the z-band of the j-th channel, f _lower indicates the lower bound of the z-band of the j-th channel; f _up indicates the upper bound of the z-band of the j-th channel, and P(f _z (j)) indicates the The energy of the j channels in the z-band.

In an embodiment of the present invention, after obtaining the average amplitude of the EEG signal of each channel average cycle energy ratio and average center frequency After that, the characteristic parameters of the EEG signal can be normalized by the following formula:

${\mathrm{<span\; class="notranslate">\; \&Phi;</span>}}_{\mathrm{<span\; class="notranslate">\; z</span>}}^{\mathrm{<span\; class="notranslate">\; Q</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; Q</span>}}_{\mathrm{<span\; class="notranslate">\; z</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; min</span>}{\mathrm{<span\; class="notranslate">\; Q</span>}}_{\mathrm{<span\; class="notranslate">\; z</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; /</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; max</span>}{\mathrm{<span\; class="notranslate">\; Q</span>}}_{\mathrm{<span\; class="notranslate">\; z</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; min</span>}{\mathrm{<span\; class="notranslate">\; Q</span>}}_{\mathrm{<span\; class="notranslate">\; z</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>$

Among them, i represents the serial number of the data segment, Q _z (i) represents or or minQ _z (i) is the minimum value in Q _z (i), and maxQ _z (i) is the maximum value in Q _z (i);

Specifically, the average amplitude of the EEG signal is calculated by the above normalization formula average cycle energy ratio and average center frequency normalized separately.

After normalizing the characteristic parameters of the EEG signal, the estimated value B _z can be calculated by the following formula:

${\mathrm{<span\; class="notranslate">\; B</span>}}_{\mathrm{<span\; class="notranslate">\; z</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; \&Phi;</span>}}_{\mathrm{<span\; class="notranslate">\; z</span>}}^{\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; A</span>}}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; \&Phi;</span>}}_{\mathrm{<span\; class="notranslate">\; z</span>}}^{\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; D.</span>}}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; \&Phi;</span>}}_{\mathrm{<span\; class="notranslate">\; z</span>}}^{\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; f</span>}}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; .</span>$

In the embodiment of the present invention, after obtaining the estimated value B _z of each frequency band, the parameter correlation rate can be calculated by the following formula $R_z^B(\%):$

${\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; z</span>}}^{\mathrm{<span\; class="notranslate">\; B</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; B</span>}}_{\mathrm{<span\; class="notranslate">\; z</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; /</span>{\mathrm{<span\; class="notranslate">\; B</span>}}_{\mathrm{<span\; class="notranslate">\; z</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; std</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&times;</span>\mathrm{<span\; class="notranslate">\; 100</span>}$

Wherein, i represents the serial number of the data segment, and B _z (std) represents the standard value in the estimated value B _z when the data segment is i. Specifically, when the data segment is i, the maximum value among the estimated values B _z of each corresponding frequency band is usually defined as the standard value corresponding to the data segment i. That is to say, different data segments correspond to different standard values. By calculating the parametric correlation ratio Individual differences can be well eliminated, therefore, an objective and relatively accurate judgment basis can be given when evaluating EEG changes.

S104, when the parameter correlation rate When the pre-faint warning conditions are met, a human fainting warning reminder will be given.

In the embodiment of the present invention, it was found through trial and error that the parameter correlation rate changes of alpha (α): 8-13Hz and beta (β): 13-25Hz frequency band EEG data under different G values are relatively obvious. By analyzing the parameters Correlation rate and Whether the preset pre-faint conditions are met can realize the pre-faint early warning of the human body.

Specifically, Fig. 2 and Fig. 3 are graphs of changes in EEG signals and ear pulse signals caused by subjects undergoing gravitational accelerations of 4G and 5G respectively in a manned centrifuge, and in Fig. 2 and Fig. 3 During the G value period, the ear pulse signal recorded by the physiological signal recording system of the manned centrifuge corresponds to the low ear pulse and the flattened ear pulse state, which are marked respectively. Corresponding to the different changing states of the ear pulse, the parameter correlation rate and exhibit certain variability. Through the analysis, it can be found that when the ear pulse is flat or low, the parameter correlation rate and exhibited more pronounced changes. Since the flattening of the ear pulse lasts for 2 seconds, it is usually considered that the human body is already in a pre-syncope state, so the research focuses on the correlation rate of the parameters corresponding to the flattening of the ear pulse and changing characteristics. That is, when analyzing the current state of the human body based on the EEG signal, it can be based on the parameter correlation rate and Analyze the current state of the human body and judge the parameter correlation rate and Whether the preset pre-faint warning conditions are met. In an embodiment of the present invention, the frequency bands of the EEG signal include the α frequency band and the β frequency band, wherein the α frequency band and the β frequency band are 8-13Hz and 13-25Hz respectively, and the warning conditions before fainting are: a. Parameter correlation ratio of electrical data and All greater than 90%; b. Within 2 to 10 seconds after condition a is met, the parameter correlation rate Less than 50%, and the parameter correlation rate Less than 50% of the status durations are greater than or equal to 2. When the parameter correlation rate and Meet the preset pre-faint warning conditions, that is, determine that the human body is in a pre-faint state, and perform a faint warning reminder, so as to avoid greater harm to the trainee.

The human body fainting early warning method based on the collection of EEG signals in the embodiment of the present invention has the following beneficial effects: 1. By directly carrying out human experiments on the manned centrifuge, the EEG change characteristics under the centrifuge+Gz are studied, and according to the EEG change Features can fully understand the change information of the brain function state that cannot be understood only by visually judging images before syncope or before G-LOC, and propose early warning and discrimination methods for syncope caused by +Gz according to the changes in the parameter correlation rate. It is of practical application value to fully understand the physical signs of trainees and guide human training, and it has important practical significance to remind pilots to take corresponding protective measures as early as possible during training and flight missions, and to solve the problem of high G protection; 2. Through parameter correlation rate It can eliminate the differences caused by different individuals, and can provide objective and more accurate judgment basis when evaluating EEG changes.

It should be understood that various parts of the present invention can be realized by hardware, software, firmware or their combination. In the above described embodiments, various steps or methods may be implemented by software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented by any one or combination of the following techniques known in the art: Discrete logic circuits, ASICs with suitable combinational logic gates, Programmable Gate Arrays (PGAs), Field Programmable Gate Arrays (FPGAs), etc.

In the description of this specification, descriptions referring to the terms "one embodiment", "some embodiments", "example", "specific examples", or "some examples" mean that specific features described in connection with the embodiment or example , structure, material or characteristic is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

Although the embodiments of the present invention have been shown and described, those skilled in the art can understand that various changes, modifications, substitutions and modifications can be made to these embodiments without departing from the principle and spirit of the present invention. The scope of the invention is defined by the claims and their equivalents.

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:

$\left({\stackrel{\&OverBar;}{A}}_z\right(\mathrm{\&mu;}v\left)\right)=\underset{j=1}{\overset{16}{\&Sigma;}}{A}_z\left(j\right)/16$

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:

$\left({\stackrel{\&OverBar;}{D}}_z\right(\%\left)\right)=\underset{j=1}{\overset{16}{\&Sigma;}}{D}_z\left(j\right)/16$

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:

$\left({\stackrel{\&OverBar;}{F}}_z\right(Hz\left)\right)=\underset{j=1}{\overset{16}{\&Sigma;}}{F}_z\left(j\right)/16$

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:

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

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:

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

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:

$B_z\left(i\right)={\mathrm{\&Phi;}}_z^{\stackrel{\&OverBar;}{A}}\left(i\right){\mathrm{\&Phi;}}_z^{\stackrel{\&OverBar;}{D}}\left(i\right){\mathrm{\&Phi;}}_z^{\stackrel{\&OverBar;}{F}}\left(i\right).$

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

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

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.