CN115868996A - Brain damage early warning system based on electroencephalogram monitoring and implementation method thereof - Google Patents
Brain damage early warning system based on electroencephalogram monitoring and implementation method thereof Download PDFInfo
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Abstract
The invention discloses a brain damage early warning system based on electroencephalogram monitoring and an implementation method thereof, wherein an acquisition system is used for acquiring electroencephalogram signals of a target object; performing large-scale conversion processing on the electroencephalogram signal through an electroencephalogram amplification analog-to-digital conversion system to obtain electroencephalogram data; decomposing budget processing is carried out on the electroencephalogram data through an electroencephalogram analysis and calculation system, and entropy values of bands of various frequency bands of the electroencephalogram data are obtained; comparing the entropy value according to a preset range through an early warning system, and alarming when the entropy value exceeds the preset range; the power supply supplies power to the acquisition system, the electroencephalogram amplification analog-to-digital conversion system, the electroencephalogram analysis and calculation system and the early warning system. The electroencephalogram signal is analyzed and processed by integrating the acquisition system, the electroencephalogram amplification and conversion system, the electroencephalogram analysis and calculation system and the early warning system, the brain injury can be accurately monitored based on the abnormality of the electroencephalogram signal, and the electroencephalogram signal analysis and calculation system can be widely applied to the technical field of electroencephalogram monitoring.
Description
Technical Field
The invention relates to the technical field of electroencephalogram monitoring, in particular to a brain damage early warning system based on electroencephalogram monitoring and an implementation method thereof.
Background
Heatstroke is a critical illness that is characterized by a body temperature rise of more than 40 ℃ and a series of clinical manifestations, characterized by central nervous system dysfunction, such as delirium, convulsion and even coma, when exposed to a high-heat environment or under strong physical and labor conditions. Severe heat stroke that fails to be intervened and treated early can rapidly progress into multiple organ failure, with a mortality rate as high as 30-50%.
In the injured organs of heatstroke, brain damage is the most common and most serious complication, and even in patients with successful rescue, 20-30% of survivors still leave behind permanent sequelae of middle brain damage. Therefore, early detection of brain damage is of great significance to the occurrence of heatstroke and comprehensive assessment for first aid and prognosis of severe heatstroke. However, currently, there is no objective monitoring means for the occurrence and damage degree of heatstroke brain damage, and how to find the brain damage of a severe heatstroke patient at an early stage and perform corresponding protective measures and early-warning to avoid the occurrence of heatstroke is an urgent problem to be solved.
Disclosure of Invention
In view of this, in order to solve one or more technical problems in the prior art, embodiments of the present invention provide a brain damage warning system based on electroencephalogram monitoring and an implementation method thereof.
In one aspect, an embodiment of the present invention provides a brain damage early warning system based on electroencephalogram monitoring, including:
the acquisition system is used for acquiring an electroencephalogram signal of a target object;
the electroencephalogram amplification analog-to-digital conversion system is used for performing large-scale conversion processing on the electroencephalogram signals to obtain electroencephalogram data;
the electroencephalogram analysis and calculation system is used for carrying out decomposition budget processing on the electroencephalogram data to obtain entropy values of bands of each frequency band of the electroencephalogram data;
the early warning system is used for carrying out comparison processing on the entropy value according to a preset range, and carrying out alarm processing when the entropy value exceeds the preset range;
and the power supply is used for supplying power to the acquisition system, the electroencephalogram amplification analog-to-digital conversion system, the electroencephalogram analysis and calculation system and the early warning system.
Optionally, the collecting system comprises a helmet shell, and a collecting electrode and a circuit fixed inside the helmet shell.
Optionally, the number of the collection motors is 19, and the collection motors include silver/silver chloride dry cylindrical electrodes, system reference electrodes, earlobe reference electrodes, and ground wires.
Optionally, the electroencephalogram amplification analog-to-digital conversion system includes:
the electroencephalogram signal amplification module is used for amplifying the electroencephalogram signals;
the notch filtering module is used for performing notch filtering processing on the electroencephalogram signal;
the analog/digital conversion module is used for performing analog/digital conversion processing on the electroencephalogram signals after the amplification processing and the notch filtering processing to obtain electroencephalogram data;
and the storage module is used for storing the electroencephalogram data.
Optionally, the electroencephalographic analysis computing system includes:
the noise reduction module is used for carrying out noise reduction processing on the electroencephalogram data;
and the characteristic extraction module is used for performing wavelet analysis on the electroencephalogram data subjected to noise reduction processing by using Morlet wavelets as mother waves, performing decomposition calculation through translation or expansion of the mother waves to obtain electroencephalogram data of different frequency band bands, and determining entropy values of the frequency band bands of the electroencephalogram data through an entropy value algorithm.
Optionally, the early warning system comprises:
the automatic analysis module is used for carrying out comparison processing on the entropy value according to a preset range;
and the alarm module is used for carrying out alarm processing according to the result of the comparison processing that the entropy value exceeds the preset range.
Optionally, the power supply adopts a rechargeable lithium battery, and a zero-ohm resistor is arranged in a circuit of the power supply.
On the other hand, the embodiment of the invention provides a method for realizing a brain damage early warning system based on electroencephalogram monitoring, which comprises the following steps:
acquiring an electroencephalogram signal of a target object through an acquisition system;
performing large-scale conversion processing on the electroencephalogram signal through an electroencephalogram amplification analog-to-digital conversion system to obtain electroencephalogram data;
decomposing budget processing is carried out on the electroencephalogram data through an electroencephalogram analysis computing system, and entropy values of bands of each frequency band of the electroencephalogram data are obtained;
comparing the entropy value according to a preset range through an early warning system, and alarming when the entropy value exceeds the preset range;
and the acquisition system, the electroencephalogram amplification analog-to-digital conversion system, the electroencephalogram analysis and calculation system and the early warning system are powered by a power supply.
Optionally, the step of performing amplification conversion processing on the electroencephalogram signal through an electroencephalogram amplification analog-to-digital conversion system to obtain electroencephalogram data includes the following steps;
the electroencephalogram signal is amplified through an electroencephalogram signal amplification module;
performing notch filtering processing on the electroencephalogram signal through a notch filtering module;
performing analog-to-digital conversion processing on the electroencephalogram signal after the amplification processing and the notch filtering processing through an analog-to-digital conversion module to obtain electroencephalogram data;
and storing the electroencephalogram data through a storage module.
Optionally, the step of performing decomposition budget processing on the electroencephalogram data through an electroencephalogram analysis computing system to obtain an entropy value of each band of the electroencephalogram data includes the following steps:
performing noise reduction processing on the electroencephalogram data through a noise reduction module;
wavelet analysis is carried out on the electroencephalogram data after the noise reduction processing by taking Morlet wavelet as a mother wave through a characteristic extraction module, decomposition calculation is carried out through translation or expansion of the mother wave to obtain electroencephalogram data of different frequency band wave bands, and entropy values of all frequency band wave bands of the electroencephalogram data are determined through an entropy value algorithm.
The beneficial effects of the invention are as follows: the method comprises the steps of collecting electroencephalogram signals of a target object through a collection system; performing large-scale conversion processing on the electroencephalogram signal through an electroencephalogram amplification analog-to-digital conversion system to obtain electroencephalogram data; decomposing budget processing is carried out on the electroencephalogram data through an electroencephalogram analysis computing system, and entropy values of bands of each frequency band of the electroencephalogram data are obtained; comparing the entropy value according to a preset range through an early warning system, and alarming when the entropy value exceeds the preset range; and the acquisition system, the electroencephalogram amplification analog-to-digital conversion system, the electroencephalogram analysis and calculation system and the early warning system are powered by a power supply. According to the invention, the electroencephalogram signals are analyzed and processed through the integrated acquisition system, the electroencephalogram amplification and conversion system, the electroencephalogram analysis and calculation system and the early warning system, and the brain injury can be accurately monitored based on the abnormality of the electroencephalogram signals. The invention can realize simple operation and visual monitoring, can early detect severe heatstroke brain injury so as to early warn to take brain protection measures immediately, can continuously monitor the brain injury degree so as to guide effective and active intervention, and can greatly reduce disability rate.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the description below are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a structural block diagram of a brain damage early warning system based on electroencephalogram monitoring according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a brain damage early warning system based on electroencephalogram monitoring according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a plurality of viewing angles of an acquisition system according to an embodiment of the present invention;
fig. 4 is a schematic flow chart of a method for implementing a brain damage warning system based on electroencephalogram monitoring according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and do not limit the invention.
In one aspect, as shown in fig. 1 and fig. 2, an embodiment of the present invention provides a brain damage early warning system based on electroencephalogram monitoring, including: the acquisition system is used for acquiring an electroencephalogram signal of a target object; the electroencephalogram amplification analog-to-digital conversion system is used for performing amplification conversion processing on the electroencephalogram signals to obtain electroencephalogram data; the electroencephalogram analysis and calculation system is used for carrying out decomposition budget processing on the electroencephalogram data to obtain entropy values of bands of each frequency band of the electroencephalogram data; the early warning system is used for carrying out comparison processing on the entropy value according to a preset range, and carrying out alarm processing when the entropy value exceeds the preset range; and the power supply is used for supplying power to the acquisition system, the electroencephalogram amplification analog-to-digital conversion system, the electroencephalogram analysis and calculation system and the early warning system.
The electroencephalogram monitoring system comprises an electroencephalogram amplification analog-to-digital conversion system, an electroencephalogram analysis and calculation system and an early warning system, wherein the acquisition system, the electroencephalogram amplification analog-to-digital conversion system, the electroencephalogram analysis and calculation system and the early warning system are connected in sequence; the power supply is respectively connected with the acquisition system, the electroencephalogram amplification analog-to-digital conversion system, the electroencephalogram analysis and calculation system and the early warning system.
In some embodiments, as shown in fig. 3, the collection system includes a helmet shell and a collection electrode and circuitry fixed inside the helmet shell.
It should be noted that the lines are connected to all the electrodes, and all the motors are connected to the electroencephalogram amplification analog-to-digital conversion system, so as to transmit the electroencephalogram signals collected by the collecting electrodes to the electroencephalogram amplification analog-to-digital conversion system.
In some embodiments, as shown in fig. 3, the number of acquisition motors is 19, including silver/silver chloride dry cylindrical electrodes, system reference electrode, earlobe reference electrode, and ground.
It should be noted that the electrodes FP1, FP2, F7, F3, F4, F8, fz, C3, C4, cz, P3, P4, pz, O1, O2, T3, T4, T5, T6 are defined according to the international 10-20 system. FP1 corresponds to the left frontal polar region, FP2 corresponds to the right frontal polar region, F7 corresponds to the left anterior temporal region, F3 corresponds to the left frontal region, F4 corresponds to the right frontal region, F8 corresponds to the right anterior temporal region, fz corresponds to the frontal midline region, C3 corresponds to the left central region, C4 corresponds to the right central region, cz corresponds to the central midline region, P3 corresponds to the left apical region, P4 corresponds to the right apical region, pz corresponds to the frontal midline region, O1 corresponds to the left occipital region, O2 corresponds to the right occipital region, T3 corresponds to the left temporal region, T4 corresponds to the right temporal region, T5 corresponds to the left posterior temporal region, and T6 corresponds to the right posterior temporal region. The acquisition electrode is a silver/silver chloride dry cylindrical electrode, a system reference electrode (Ref, corresponding to the midpoint of the bilateral frontal polar region), a reference electrode (A1, A2, corresponding to the left and right mastoid regions, respectively), and a ground wire.
Specifically, the acquisition system comprises a multi-lead electroencephalogram monitoring helmet (namely a helmet shell), electroencephalogram acquisition electrodes, system reference electrodes, earlobe reference electrodes and circuits are arranged in the helmet shell, as shown in fig. 3, english letters and numbers in circles are marked as electrode marks, preferably, the electroencephalogram acquisition electrodes are silver/silver chloride dry columnar electrodes, and compared with other materials, the silver/silver chloride dry columnar electrodes have extremely high stability and reversibility, close contact between the electrodes and the scalp is ensured, and low impedance between the electrodes and the skin can be ensured to be less than or equal to 10k omega. The circle in the helmet shell represents a collecting electrode, the number of the electrodes is set to be 19 according to the international 10-20 system electroencephalogram electrode placement standard of the electrode, a system reference electrode (Ref, corresponding to the middle point of a bilateral frontal polar region), a reference electrode (A1, A2, respectively corresponding to a left mastoid region and a right mastoid region) and a ground wire are respectively arranged as independent reference electrodes, electroencephalogram signals collected by the electrodes can be compared and analyzed with the system reference electrode or the earlobe reference electrode, the ground wire can be set to have an anti-interference function, preferably, the multi-conductor electroencephalogram monitoring helmet is designed to be large (58-62 cm), medium (54-58 cm), small (50-54 cm) and 46-50cm for children, the helmet is suitable for users with different headbands and age groups, a professional is not needed, when the other people wear the electrodes and the scalp need to be ensured to be in close contact, hairs are shaved if necessary, conductive paste and frosted cream and skin is not damaged.
In some embodiments, the brain electrical amplification analog-to-digital conversion system comprises: the electroencephalogram signal amplification module is used for amplifying the electroencephalogram signals; the notch filtering module is used for carrying out notch filtering processing on the electroencephalogram signals; the analog/digital conversion module is used for performing analog/digital conversion processing on the electroencephalogram signal after amplification processing and notch filtering processing to obtain electroencephalogram data; and the storage module is used for storing the electroencephalogram data.
It should be noted that the electroencephalogram signal amplification module, the notch filtering module, the analog/digital conversion module and the storage module are connected in sequence; the circuit of the acquisition system is connected with the electroencephalogram signal amplification module, and the storage module is connected with the electroencephalogram analysis and calculation system.
Specifically, as shown in fig. 2, the electroencephalogram amplification module amplifies the electroencephalogram signal collected by the collecting electrode to an analog/digital input voltage range, and the analog/digital conversion module converts the amplified electroencephalogram signal into a digital signal and stores the digital signal in a high-capacity high-speed transmission SD data storage card (i.e., a storage module).
In some embodiments, a brain electrical analysis computing system comprises:
the noise reduction module is used for carrying out noise reduction processing on the electroencephalogram data; and the characteristic extraction module is used for performing wavelet analysis on the electroencephalogram data subjected to noise reduction processing by using Morlet wavelets as mother waves, performing decomposition calculation through translation or expansion of the mother waves to obtain electroencephalogram data of different frequency band bands, and determining entropy values of the frequency band bands of the electroencephalogram data through an entropy value algorithm.
It should be noted that, as shown in fig. 2, an input end of the noise reduction module is connected to the electroencephalogram amplification analog-to-digital conversion system, an input end of the noise reduction module is connected to an input end of the feature extraction module, and an output end of the feature extraction module is connected to the early warning system.
Specifically, a noise reduction algorithm is built in the noise reduction module, and the electroencephalogram signal is subjected to LMS adaptive filtering noise reduction processing through the noise reduction module; the characteristic extraction module is internally provided with a Morlet wavelet analysis algorithm and an entropy algorithm, wavelet transformation is carried out on the denoised electroencephalogram signal through the characteristic extraction module, morlet wavelets are used as mother waves, then electroencephalogram data can be decomposed through translation or expansion of the mother waves, the electroencephalogram signal is decomposed into different frequency band bands based on wavelet analysis, and corresponding entropy parameters are extracted from the different frequency band bands.
In some embodiments, the early warning system comprises: the automatic analysis module is used for carrying out comparison processing on the entropy value according to a preset range; and the alarm module is used for carrying out alarm processing according to the result of the comparison processing that the entropy value exceeds the preset range.
It should be noted that the input end of the automatic analysis module is connected with the electroencephalogram analysis computing system, and the input end of the automatic analysis module is connected with the input end of the alarm module.
Specifically, the automatic analysis module compares the calculated entropy values of different frequency band bands with the entropy value range of normal healthy people, if the parameter exceeds or is lower than the normal range, the alarm module gives an alarm, and people nearby can perform timely and effective brain protection and treatment after receiving alarm feedback.
In some embodiments, the power supply is a rechargeable lithium battery, and a zero-ohm resistor is arranged in a circuit of the power supply.
The working process and principle of the brain damage warning device for electroencephalogram monitoring of the present invention are briefly described below, and the following is an explanation of the technical solution of the present invention and should not be construed as a limitation of the present invention.
The method comprises the steps that a scalp electrode amplifies acquired electroencephalogram signals, interference is eliminated through a signal amplifying and filtering module and then analog-to-digital conversion is carried out, converted electroencephalogram data are subjected to noise reduction through a noise reduction module of an electroencephalogram analysis and calculation system, then, a characteristic extraction module carries out wavelet analysis on an electroencephalogram subjected to noise reduction by taking Morlet wavelets as mother waves, the electroencephalogram data are decomposed through translation or expansion of the mother waves, each section of electroencephalogram is decomposed into different frequency band wave bands, entropy values of the different frequency band wave bands are calculated, and then the electroencephalogram data are compared with a normal value range, if parameters exceed or are lower than a normal range, an alarm module gives an alarm, and other personnel can carry out timely and effective brain protection and treatment after receiving alarm feedback.
In conclusion, the brain damage early warning device based on electroencephalogram monitoring is used for analyzing and developing electroencephalogram signals of severe heatstroke brain damage patients, early warning can be carried out by early discovering brain damage so as to take brain protection measures immediately, the brain damage degree is continuously monitored so as to guide effective and active intervention, and the disability rate can be greatly reduced.
On the other hand, as shown in fig. 4, an embodiment of the present invention provides a method for implementing a brain damage early-warning system based on electroencephalogram monitoring, which is applied to the brain damage early-warning system based on electroencephalogram monitoring according to the embodiment of the present invention, and the method includes:
acquiring an electroencephalogram signal of a target object through an acquisition system;
performing amplification conversion processing on the electroencephalogram signals through an electroencephalogram amplification analog-to-digital conversion system to obtain electroencephalogram data;
decomposing budget processing is carried out on the electroencephalogram data through an electroencephalogram analysis and calculation system, and entropy values of bands of various frequency bands of the electroencephalogram data are obtained;
comparing the entropy value according to a preset range through an early warning system, and alarming when the entropy value exceeds the preset range;
the power supply supplies power to the acquisition system, the electroencephalogram amplification analog-to-digital conversion system, the electroencephalogram analysis and calculation system and the early warning system.
In some embodiments, the step of performing amplification conversion processing on the electroencephalogram signal through an electroencephalogram amplification analog-to-digital conversion system to obtain electroencephalogram data comprises the following steps;
the electroencephalogram signal is amplified through an electroencephalogram signal amplification module;
carrying out notch filtering processing on the electroencephalogram signal through a notch filtering module;
performing analog-to-digital conversion processing on the electroencephalogram signal after amplification processing and notch filtering processing through an analog-to-digital conversion module to obtain electroencephalogram data;
and storing the electroencephalogram data through a storage module.
In some embodiments, the step of performing decomposition budget processing on the electroencephalogram data through an electroencephalogram analysis computing system to obtain entropy values of bands of each frequency band of the electroencephalogram data comprises the following steps:
noise reduction processing is carried out on the electroencephalogram data through a noise reduction module;
wavelet analysis is carried out on the electroencephalogram data after noise reduction processing by taking Morlet wavelets as mother waves through a characteristic extraction module, decomposition calculation is carried out through translation or expansion of the mother waves to obtain electroencephalogram data of different frequency band bands, and entropy values of all frequency band bands of the electroencephalogram data are determined through an entropy value algorithm.
Specifically, a noise reduction algorithm is built in the noise reduction module, the electroencephalogram signal is subjected to LMS adaptive filtering noise reduction processing through the noise reduction module, and the expression of the noise reduction algorithm is as follows:
e(n)＝d(n)-X(n)W(n)
W(n+1)＝W(n)+2ue(n)X(n)
Y(n+1)＝X(n+1)W(n+1)
x (n) represents an input signal vector at time n; w (n) represents the weight coefficient of the adaptive filter at time n; d (n) represents a desired output value; e (n) represents the error between the desired response and the actual output of the filter at the input of X (n). u denotes a step factor as a parameter for controlling stability and convergence speed. Y (n) represents the output of the filter actually obtained after the input X (n) has passed through the filter.
The characteristic extraction module is internally provided with a Morlet wavelet analysis algorithm (comprising a Morlet wavelet function and a wavelet transformation function) and an entropy algorithm, the characteristic extraction module is used for carrying out wavelet transformation on the denoised electroencephalogram signal, the Morlet wavelet is used as a mother wave, and then the electroencephalogram data can be decomposed through translation or expansion of the mother wave:
expression of Morlet wavelet function:
expression of the wavelet transform function:
C i,j representing the wavelet-transformed signal sequence; tau is i Represents the time location, s j Represents a wavelet range, x (t) represents an electroencephalogram signal sequence, t represents a time sequence, and Ψ (t) represents a mother wave function.
Decomposing the EEG into different frequency band bands based on wavelet analysis, and extracting corresponding entropy value parameters from the different frequency band bands by an entropy value algorithm, wherein the expression of the entropy value algorithm is as follows:
P j the power of the electroencephalogram signal after wavelet transformation in the range j is represented, the entropy value spectrum of the electroencephalogram signal after wavelet transformation in all ranges is represented by WE, and the power is a parameter for describing the complexity of an electroencephalogram.
The content of the embodiment of the device of the invention is applicable to the embodiment of the method, the function of the embodiment of the method is the same as that of the embodiment of the device, and the beneficial effect is the same as that of the device.
In alternative embodiments, the functions/acts noted in the block diagrams may occur out of the order noted in the operational illustrations. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved. Furthermore, the embodiments presented and described in the flow charts of the present invention are provided by way of example in order to provide a more thorough understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed and in which sub-operations described as part of larger operations are performed independently.
Furthermore, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise stated to the contrary, one or more of the described functions and/or features may be integrated in a single physical device and/or software module, or one or more functions and/or features may be implemented in a separate physical device or software module. It will also be appreciated that a detailed discussion of the actual implementation of each module is not necessary for an understanding of the present invention. Rather, the actual implementation of the various functional modules in the apparatus disclosed herein will be understood within the ordinary skill of an engineer given the nature, function, and interrelationships of the modules. Accordingly, those skilled in the art can, using ordinary skill, practice the invention as set forth in the claims without undue experimentation. It is also to be understood that the specific concepts disclosed are merely illustrative of and not intended to limit the scope of the invention, which is to be determined from the appended claims along with their full scope of equivalents.
The functions may be stored in a computer-readable storage medium if they are implemented in the form of software functional units and sold or used as separate products. Based on such understanding, the technical solution of the present invention or a part thereof which substantially contributes to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The logic and/or steps represented in the flowcharts or otherwise described herein, such as an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution apparatus, device, or device (e.g., a computer-based apparatus, processor-containing apparatus, or other device that can fetch the instructions from the instruction execution apparatus, device, or device and execute the instructions). For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution apparatus, device, or apparatus.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by suitable instruction execution devices. For example, if implemented in hardware, as in another embodiment, any one or combination of the following technologies, which are well known in the art, may be used: discrete logic currents with logic gate currents for implementing logic functions on data signals, application specific integrated currents with appropriate combinational logic gate currents, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), etc.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.
While the preferred embodiments of the present invention have been illustrated and described, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. A brain damage early warning system based on electroencephalogram monitoring is characterized by comprising:
the acquisition system is used for acquiring an electroencephalogram signal of a target object;
the electroencephalogram amplification analog-to-digital conversion system is used for performing amplification conversion processing on the electroencephalogram signals to obtain electroencephalogram data;
the electroencephalogram analysis and calculation system is used for carrying out decomposition budget processing on the electroencephalogram data to obtain entropy values of bands of each frequency band of the electroencephalogram data;
the early warning system is used for carrying out comparison processing on the entropy value according to a preset range, and carrying out alarm processing when the entropy value exceeds the preset range;
and the power supply is used for supplying power to the acquisition system, the electroencephalogram amplification analog-to-digital conversion system, the electroencephalogram analysis and calculation system and the early warning system.
2. The brain damage early warning system based on electroencephalogram monitoring of claim 1, wherein the acquisition system comprises a helmet shell, and an acquisition electrode and a circuit which are fixed in the helmet shell.
3. The brain damage early warning system based on electroencephalogram monitoring as claimed in claim 2, wherein the number of the collection motors is 19, and the collection motors comprise silver/silver chloride dry cylindrical electrodes, system reference electrodes, earlobe reference electrodes and ground wires.
4. The brain damage early warning system based on electroencephalogram monitoring as claimed in claim 1, wherein the electroencephalogram amplification analog-to-digital conversion system comprises:
the electroencephalogram signal amplification module is used for amplifying the electroencephalogram signals;
the notch filtering module is used for carrying out notch filtering processing on the electroencephalogram signals;
the analog/digital conversion module is used for performing analog/digital conversion processing on the electroencephalogram signal after the amplification processing and the notch filtering processing to obtain electroencephalogram data;
and the storage module is used for storing the electroencephalogram data.
5. The brain damage early warning system based on electroencephalogram monitoring of claim 1, wherein the electroencephalogram analysis computing system comprises:
the noise reduction module is used for carrying out noise reduction processing on the electroencephalogram data;
and the characteristic extraction module is used for performing wavelet analysis on the electroencephalogram data subjected to noise reduction processing by using Morlet wavelets as mother waves, performing decomposition calculation through translation or expansion of the mother waves to obtain electroencephalogram data of different frequency band bands, and determining entropy values of the frequency band bands of the electroencephalogram data through an entropy value algorithm.
6. The brain damage early warning system based on electroencephalogram monitoring of claim 1, wherein the early warning system comprises:
the automatic analysis module is used for carrying out comparison processing on the entropy value according to a preset range;
and the alarm module is used for carrying out alarm processing according to the comparison processing result that the entropy value exceeds the preset range.
7. The brain damage early warning system based on electroencephalogram monitoring as claimed in claim 1, wherein the power supply adopts a rechargeable lithium battery, and a zero-ohm resistor is arranged in a circuit of the power supply.
8. A realization method of a brain damage early warning system based on electroencephalogram monitoring is characterized by comprising the following steps:
acquiring an electroencephalogram signal of a target object through an acquisition system;
performing large-scale conversion processing on the electroencephalogram signal through an electroencephalogram amplification analog-to-digital conversion system to obtain electroencephalogram data;
decomposing budget processing is carried out on the electroencephalogram data through an electroencephalogram analysis computing system, and entropy values of bands of each frequency band of the electroencephalogram data are obtained;
comparing the entropy value according to a preset range through an early warning system, and alarming when the entropy value exceeds the preset range;
and the acquisition system, the electroencephalogram amplification analog-to-digital conversion system, the electroencephalogram analysis and calculation system and the early warning system are powered by a power supply.
9. The method for implementing the brain damage early warning system based on electroencephalogram monitoring as claimed in claim 8, wherein the step of performing amplification conversion processing on the electroencephalogram signal through an electroencephalogram amplification analog-to-digital conversion system to obtain electroencephalogram data comprises the following steps;
the electroencephalogram signal is amplified through an electroencephalogram signal amplification module;
carrying out notch filtering processing on the electroencephalogram signal through a notch filtering module;
performing analog/digital conversion processing on the electroencephalogram signal after the amplification processing and the notch filtering processing through an analog/digital conversion module to obtain electroencephalogram data;
and storing the electroencephalogram data through a storage module.
10. The method for implementing the brain damage early warning system based on electroencephalogram monitoring as claimed in claim 1, wherein the step of performing decomposition budget processing on the electroencephalogram data through an electroencephalogram analysis computing system to obtain the entropy of each band of the electroencephalogram data comprises the following steps:
carrying out noise reduction processing on the electroencephalogram data through a noise reduction module;
wavelet analysis is carried out on the electroencephalogram data after the noise reduction processing by taking Morlet wavelet as a mother wave through a characteristic extraction module, decomposition calculation is carried out through translation or expansion of the mother wave to obtain electroencephalogram data of different frequency band wave bands, and entropy values of all frequency band wave bands of the electroencephalogram data are determined through an entropy value algorithm.
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