阅读说明：本技术 一种麻醉深度监测系统 (Anesthesia depth monitoring system ) 是由 李宏明 冯永春 于 2021-05-21 设计创作，主要内容包括：本发明公开了一种麻醉深度监测系统,包括微处理器、脑电信号采集模块、脑电信号处理模块、数据存储模块、电源管理模块、外接电源模块、内置电池模块、网络接口、USB接口、LCD显示模块和触摸屏模块,本发明涉及软件识别算法、信号处理和滤波技术领域。该麻醉深度监测系统,可实现通过高质量采集脑电信号,在信号传输过程中进行抗干扰处理,并通过基于脑电信号特征的滤波器进行滤波,降低外界干扰对系统的影响,满足脑电信号的高质量需求,从而给微处理系统提供稳定、高质量的脑电信号数据、以及更高的采集精度,减小对于系统计算引起的偏差,可实现通过利用共模电感抑制共模噪声的特点在差分采集电路中加入共模电感。(The invention discloses an anesthesia depth monitoring system which comprises a microprocessor, an electroencephalogram signal acquisition module, an electroencephalogram signal processing module, a data storage module, a power management module, an external power module, a built-in battery module, a network interface, a USB interface, an LCD display module and a touch screen module. The anesthesia depth monitoring system can realize the high-quality acquisition of electroencephalogram signals, performs anti-interference processing in the signal transmission process, performs filtering through a filter based on characteristics of the electroencephalogram signals, reduces the influence of external interference on the system, meets the high-quality requirement of the electroencephalogram signals, provides stable and high-quality electroencephalogram signal data and higher acquisition precision for a micro-processing system, reduces the deviation caused by system calculation, and can realize the addition of a common-mode inductor in a differential acquisition circuit by utilizing the characteristic of common-mode inductor suppression of common-mode noise.)

1. An anesthesia depth monitoring system, characterized in that: comprises a microprocessor (1), an electroencephalogram signal acquisition module (2), an electroencephalogram signal processing module (3), a data storage module (4), a power management module (5), an external power supply module (6), a built-in battery module (7), a network interface (8), a USB interface (9), an LCD display module (10) and a touch screen module (11), wherein the output end of the electroencephalogram signal acquisition module (2) is electrically connected with the input end of the electroencephalogram signal processing module (3), the output end of the electroencephalogram signal processing module (3) is electrically connected with the input end of the microprocessor (1), the data storage module (4), the power management module (5), the external power supply module (6), the built-in battery module (7), the network interface (8) and the USB interface (9) are all in bidirectional connection with the microprocessor (1), and the output end of the microprocessor (1) is electrically connected with the input end of the LCD display module (10), the output end of the touch screen module (11) is electrically connected with the input end of the microprocessor (1);

the electroencephalogram signal acquisition module (2) inputs a differential signal, after primary amplification processing, secondary amplification and 200Hz low-pass filtering are carried out, and then tertiary amplification and 50Hz power frequency notch filtering are carried out to filter out the high-frequency part of the electroencephalogram signal; after three-stage amplification, the amplification factor is 10000 times, and the calculation is carried out according to the following formula:

wherein f is_cRepresenting the filter cut-off frequency, C₁Representing filter capacitances 1, C₂Representing the filter capacitance 2, R₁Representing filter resistances 1, R₂Representing the filter resistance 2.

2. The system of claim 1, wherein: after the system is powered on, the power management module (5) judges that the external power module (6) or the built-in battery module (7) is used for supplying power to the system, the electroencephalogram signal acquisition module (2) starts to work, the electroencephalogram signal acquisition module (2) outputs the acquired electroencephalogram signals to the electroencephalogram signal processing module (3), analog signals are converted into digital signals in the electroencephalogram signal processing module (3), and the digital signals are calculated to obtain relevant parameter data and output to the microprocessor (1).

3. The system of claim 2, wherein: the microprocessor (1) displays the parameters on an LCD display screen through an LCD display module (10), the microprocessor (1) reads and stores the parameter data of the data storage module (4), and the microprocessor (1) responds to the touch operation of a user through a touch screen module (11).

4. The system of claim 1, wherein: the microprocessor (1) carries out network communication through a network interface (8), and the microprocessor (1) accesses connected USB equipment through a USB interface (9).

5. The system of claim 1, wherein: brain wave signals are collected and transmitted by a lead system, enter a preamplifier for amplification treatment, then are sent to a low-pass filter for filtering, then are sent to a secondary amplifier for secondary amplification treatment, after analog signals are secondarily amplified, intervals among signals with different frequencies are expanded, wherein the frequency of a delta wave signal is 0.5-4Hz, the frequency of a theta wave signal is 4-8Hz, the frequency of an alpha wave signal is 8-13Hz, the frequency of a beta wave signal is 13-30Hz, and the frequency of a gamma wave signal is more than 30Hz, finally the signals are sent to a microcontroller for analog-to-digital conversion, and fast Fourier transform and signal processing system operation are carried out in the microcontroller to finally obtain a sedation/consciousness index (IoC1), a pain index (IoC2), an analgesic burst suppression ratio (BS), an electromyographic index (EMG), Signal Quality (SQI) and impedance value parameters of the electrodes.

6. The system of claim 1, wherein: the cut-off frequency is set to be low, on one hand, high-frequency clutter is filtered, on the other hand, noise is suppressed, then the noise is sent to a single chip microcomputer AD for digital-to-analog conversion, the AD range is 0-3.3V, and after the time sampling and the filtering processing of the single chip microcomputer, the noise is input to an algorithm model for calculation.

7. The system of claim 1, wherein: the micro-processor (1) adopts a chip imx6q, the micro-processor (1) integrates a serial port driving interface function, the electroencephalogram signal acquisition module (2) adopts a chip ADS1299, and the electroencephalogram signal processing module (3) adopts a micro-controller based on an ARM Cortex-M3 inner core.

Technical Field

The invention relates to the technical field of software identification algorithm, signal processing and filtering, in particular to an anesthesia depth monitoring system.

Background

In current clinical practice, since clinical anesthesia status is mostly the result of the combined effects of many drugs, including loss of consciousness, forgetfulness, analgesia, muscle relaxation, suppression of physical movement, suppression of cardiovascular and endocrine systems responses to surgical stimuli, effective anesthesia monitoring is crucial, and anesthesia depth assessment is the most subjective and most controversial topic in the field of anesthesia, since ether anesthesia has been used clinically, there are various views on the definition of anesthesia depth, and there are two main findings in summary: first, the unconsciousness induced by general anesthesia medicine; the second is the state of unconsciousness induced by total anesthesia plus the state of suppression of the response of the anesthetic to surgical trauma. The current mainstream view is that: the anesthesia depth is the comprehensive reflection of indexes such as the sedation level, the analgesia level, the stimulation response degree and the like, the anesthesia depth is difficult to comprehensively evaluate by a single parameter, in the surgical anesthesia process, sedation, analgesia and muscle relaxation are the three most basic factors of general anesthesia, the three factors can mutually influence each other, the sedation can enhance the analgesia and the analgesia can also enhance the sedation, both can enhance the muscle relaxation effect, and otherwise, the muscle relaxation can also affect the sedation and analgesia effect to a certain extent. Clinically, comprehensive analysis and judgment can be carried out according to the blood pressure, the heart rate, the respiratory amplitude and rhythm, the muscle relaxation degree and other manifestations of the patient in the operation. The ideal depth of anesthesia should be such that the patient is painless and unconscious during the procedure, hemodynamically stable, well-defined and not known during the procedure. However, since the judgment of the depth of anesthesia is influenced by too many factors, it is important to effectively judge the depth of anesthesia by various means in clinical work.

With the wide application of the electronic computer technology, the monitoring technology of the anesthesia depth has a qualitative leap. The early anesthesia depth monitoring aims to prevent dangers caused by excessive anesthetics, the modern anesthesia depth monitoring aims to effectively prevent potential dangerous hemodynamic changes and awakening in the anesthesia, eliminate intraoperative memory and regulate and control the dosage of the anesthetics, single parameters are adopted in the past, a conceptual anesthesia depth monitor is used, and a clinician pays more attention to the comprehensive condition of a patient at present.

At present, a nonlinear dynamics method is widely applied to the research of electroencephalogram signal analysis and anesthesia depth monitoring, a method for monitoring the anesthesia depth by utilizing entropy is one of the methods, approximate entropy is a rule for measuring the complexity and the statistical quantization of a sequence, time domain characteristics of an electroencephalogram are analyzed, the method is characterized by having better anti-interference and anti-noise capabilities, but the existing complexity algorithms such as the approximate entropy cannot realize real-time monitoring due to the defect that the length of the sequence required by calculation is long or the time required by calculation is long, at present, the nonlinear dynamics method based on the complexity is used for processing the electroencephalogram signal, the grid complexity, the edge frequency and the burst suppression ratio of the electroencephalogram signal are respectively calculated, and a decision tree algorithm is used for fitting to obtain the anesthesia depth index.

The existing anesthesia depth monitoring system has the following two defects:

1) because the electroencephalogram signal is very weak, the characteristics of the electroencephalogram signal are possibly changed due to inaccurate electroencephalogram signal processing, and the acquired electroencephalogram signal is low in acquisition quality and easy to interfere due to interference of the external environment, so that data calculation is influenced.

2) The existing medical equipment generally adopts a differential mode to collect EEG signals, so that the anti-interference capability is improved, and the EEG signals collected by the electrodes are small in amplitude and low in EEG frequency, so that the EEG signals are easily interfered by an electromagnetic environment in the medical equipment, particularly by high-frequency and high-energy signals, and the EEG analysis processing and the application are greatly influenced.

Disclosure of Invention

Technical problem to be solved

Aiming at the defects of the prior art, the invention provides an anesthesia depth monitoring system, which solves the problems that the existing anesthesia depth monitoring system is low in electric signal acquisition quality, easy to interfere and easy to influence data calculation, and meanwhile, because the EEG signal acquired by an electrode is small in amplitude and low in EEG frequency, the EEG signal is easy to interfere by an electromagnetic environment in medical equipment, particularly interference of a high-frequency high-energy signal, and thus great influence is caused on EEG analysis processing and application.

(II) technical scheme

In order to achieve the purpose, the invention is realized by the following technical scheme: the utility model provides an anesthesia depth monitoring system, includes microprocessor, brain electrical signal collection module, brain electrical signal processing module, data storage module, power management module, external power supply module, built-in battery module, network interface, USB interface, LCD display module and touch-sensitive screen module, brain electrical signal collection module's output and brain electrical signal processing module's input electric connection, and brain electrical signal processing module's output and microprocessor's input electric connection, data storage module, power management module, external power supply module, built-in battery module, network interface and USB interface all realize both way junction with microprocessor, and microprocessor's output and LCD display module's input electric connection, touch-sensitive screen module's output and microprocessor's input electric connection.

The electroencephalogram signal acquisition module inputs a differential signal, after primary amplification processing, secondary amplification and 200Hz low-pass filtering are carried out, and then tertiary amplification and 50Hz power frequency notch filtering are carried out to filter out the high-frequency part of the electroencephalogram signal; after three-stage amplification, the amplification factor is 10000 times, and the calculation is carried out according to the following formula:

wherein f is_cRepresenting the filter cut-off frequency, C₁Representing filter capacitances 1, C₂Representing the filter capacitance 2, R₁Representing filter resistances 1, R₂Representing the filter resistance 2.

Preferably, after the system is powered on, the power management module judges whether an external power module or a built-in battery module is used for supplying power to the system, the electroencephalogram signal acquisition module starts to work, the electroencephalogram signal acquisition module outputs acquired electroencephalogram signals to the electroencephalogram signal processing module, analog signals are converted into digital signals in the electroencephalogram signal processing module, and the digital signals are calculated to obtain related parameter data and output to the microprocessor.

Preferably, the microprocessor displays the parameters on the LCD display screen through the LCD display module, and the microprocessor reads and stores the parameter data in the data storage module, and the microprocessor responds to the touch operation of the user through the touch screen module.

Preferably, the microprocessor performs network communication through a network interface, and the microprocessor accesses a connected USB device through a USB interface.

Preferably, brain wave signals are collected and transmitted through a lead system, enter a preamplifier for amplification processing, then are sent to a low-pass filter for filtering, then are sent to a secondary amplifier for secondary amplification processing, after analog signals are secondarily amplified, intervals among signals with different frequencies are expanded, wherein the frequency of a delta wave signal is 0.5-4Hz, the frequency of a theta wave signal is 4-8Hz, the frequency of an alpha wave signal is 8-13Hz, the frequency of a beta wave signal is 13-30Hz, the frequency of a gamma wave signal is more than 30Hz, the signal strength is weak and insufficient for analog-to-digital conversion, the signals need to be sent to a tertiary amplifier for amplification, finally the signals are sent to a microcontroller for analog-to-digital conversion, and fast Fourier transform and signal processing system operation are carried out in the microcontroller, and finally a sedation/consciousness index (IoC1) and a consciousness index (IoC1) are obtained, Analgesia/pain index (IoC2), burst suppression ratio (BS), electromyography index (EMG), Signal Quality (SQI), and impedance value parameters of the electrodes.

Preferably, the cut-off frequency is set to be low, on one hand, high-frequency clutter is filtered, on the other hand, noise is suppressed, then the noise is sent to the single chip microcomputer AD for digital-to-analog conversion, the AD range is 0-3.3V, and after the sampling and filtering processing are carried out on the signal chip microcomputer at regular time, the noise is input to an algorithm model for calculation.

Preferably, the microprocessor adopts a chip imx6q, the electroencephalogram signal acquisition module adopts a chip ADS1299, and the electroencephalogram signal processing module adopts a microcontroller based on an ARM Cortex-M3 inner core.

Preferably, the pins, the package and the functions of the integrated serial port drive chip are respectively compatible with the industrial standard, and even if the integrated serial port drive chip works at a high data rate, the minimum transmitter output voltage of plus or minus 5.0V required by the RS-232 standard can still be maintained.

(III) advantageous effects

The invention provides an anesthesia depth monitoring system. Compared with the prior art, the method has the following beneficial effects:

(1) the anesthesia depth monitoring system can realize the high-quality acquisition of electroencephalogram signals, performs anti-interference processing in the signal transmission process, filters through a filter based on characteristics of the electroencephalogram signals, reduces the influence of external interference on the system, meets the high-quality requirement of the electroencephalogram signals, provides stable and high-quality electroencephalogram signal data and higher acquisition precision for a micro-processing system, and reduces the deviation caused by system calculation.

(2) According to the anesthesia depth monitoring system, the common-mode inductor is added into the differential acquisition circuit by utilizing the characteristic that the common-mode inductor inhibits common-mode noise, and through actual detection, the common-mode inductor and a special circuit principle selected by equipment can well inhibit high-frequency high-energy conduction disturbance.

Drawings

FIG. 1 is a block diagram of a circuit system according to the present invention;

FIG. 2 is a schematic flow chart of EEG signal processing according to the present invention;

FIG. 3 is a functional frame structure diagram of the present invention.

In the figure, 1 microprocessor, 2 electroencephalogram signal acquisition modules, 3 electroencephalogram signal processing modules, 4 data storage modules, 5 power management modules, 6 external power supply modules, 7 built-in battery modules, 8 network interfaces, 9USB interfaces, 10LCD display modules and 11 touch screen modules.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

Referring to fig. 1-3, an embodiment of the present invention provides a technical solution: an anesthesia depth monitoring system comprises a microprocessor 1, an electroencephalogram signal acquisition module 2, an electroencephalogram signal processing module 3, a data storage module 4, a power management module 5, an external power supply module 6, an internal battery module 7, a network interface 8, a USB interface 9, an LCD display module 10 and a touch screen module 11, wherein the output end of the electroencephalogram signal acquisition module 2 is electrically connected with the input end of the electroencephalogram signal processing module 3, the output end of the brain electrical signal processing module 3 is electrically connected with the input end of the microprocessor 1, the data storage module 4, the power management module 5, the external power module 6, the built-in battery module 7, the network interface 8 and the USB interface 9 are all in bidirectional connection with the microprocessor 1, the output end of the microprocessor 1 is electrically connected with the input end of the LCD display module 10, and the output end of the touch screen module 11 is electrically connected with the input end of the microprocessor 1.

In the embodiment of the invention, the electroencephalogram signal acquisition module 2 inputs a differential signal, after primary amplification processing, secondary amplification and 200Hz low-pass filtering are carried out, and then tertiary amplification and 50Hz power frequency notch filtering are carried out to filter out the high-frequency part of the electroencephalogram signal; after three-stage amplification, the amplification factor is 10000 times, and the calculation is carried out according to the following formula:

wherein f is_cRepresenting the filter cut-off frequency, C₁Representing filter capacitances 1, C₂Representing the filter capacitance 2, R₁Representing filter resistances 1, R₂Representing the filter resistance 2.

In the embodiment of the invention, after the system is powered on, the power management module 5 judges that the external power module 6 or the internal battery module 7 is used for supplying power to the system, the electroencephalogram signal acquisition module 2 starts to work, the electroencephalogram signal acquisition module 2 outputs the acquired electroencephalogram signal to the electroencephalogram signal processing module 3, in the EEG signal processing module 3, the analog signal is converted into a digital signal, and the related parameter data is obtained through calculation, and outputs to the microprocessor 1, the microprocessor 1 displays the parameters on the LCD screen through the LCD display module 10, and the microprocessor 1 reads and stores the parameter data from the data storage module 4, the microprocessor 1 responds to the touch operation of the user through the touch screen module 11, the microprocessor 1 performs network communication through the network interface 8, and the microprocessor 1 accesses the connected USB device through the USB interface 9.

In the embodiment of the invention, brain wave signals are collected and transmitted through the lead system and enter the preamplifierThe device is used for amplification processing, then the signals are sent into a low-pass filter for filtering, then the signals are sent into a secondary amplifier for secondary amplification processing, after analog signals are secondarily amplified, intervals among signals with different frequencies are expanded, wherein the frequency of a delta wave signal is 0.5-4Hz, the frequency of a theta wave signal is 4-8Hz, the frequency of an alpha wave signal is 8-13Hz, the frequency of a beta wave signal is 13-30Hz, the frequency of a gamma wave signal is more than 30Hz, the signal strength is weak and is not enough for analog-to-digital conversion, the signals need to be sent into a tertiary amplifier for amplification, finally the signals are sent into a microcontroller for analog-to-digital conversion, and fast Fourier transform and signal processing system operation are carried out in the microcontroller to finally obtain a sedation/consciousness index (IoC1), an analgesia/pain index (IoC2), an explosion suppression ratio (BS), Electromyographic index (EMG), Signal Quality (SQI) and impedance value parameters of electrodes, four different frequency band energy parameters of original electroencephalogram signal frequency spectrum analyzed by a fast Fourier theory operating system, and theta wave energy ratio E_θ＝ln(E_4-8Hz/E_0-47Hz) Energy ratio of alpha wave E_α＝ln(E_8-13Hz/E_0-47Hz) Energy ratio of beta wave E_β＝1n(E_13-30Hz/E_0-47Hz) Delta wave energy ratio E_δ＝ln(E_0.5-4Hz/E_0-47Hz) The Fuzzy model is accessed into four same-frequency-band energy parameters, the analgesia/pain index is preliminarily calculated (IoC2), and the output Fuzzy of the Fuzzy model is corrected by correcting the explosion suppression ratio (BS) of the output system_outputThe formula for the analgesia/pain index (IoC2) is as follows:

analgesia/pain index (IoC2) ═ max (0, 1-BS/30) · Fuzzy_output+min(1，BS/30)·(41-0.41BS)。

In the embodiment of the invention, the cut-off frequency is set to be low, on one hand, high-frequency clutter is filtered, on the other hand, noise is suppressed, then the noise is sent to a singlechip AD for digital-to-analog conversion, the AD range is 0-3.3V, and the noise is input to an algorithm model for calculation after being sampled and filtered by the singlechip at regular time.

In the embodiment of the invention, the microprocessor 1 adopts a chip imx6q, the microprocessor 1 integrates the function of a serial port driving interface, the electroencephalogram signal acquisition module 2 adopts an ADS1299 chip, the electroencephalogram signal processing module 3 adopts a microcontroller based on an ARM Cortex-M3 kernel, pins, packaging and functions of the integrated serial port driving chip are respectively compatible with an industrial standard, and even if the integrated serial port driving chip works at a high data rate, the minimum transmitter output voltage of plus or minus 5.0V required by an RS-232 standard can still be maintained.

In the embodiment of the invention, the collected electroencephalogram signals are input into an electroencephalogram signal processing system through a special electroencephalogram sensor, the electroencephalogram signals are amplified through a preamplifier, then are sent into a low-pass filter for filtering, then are sent into a secondary amplifier for second amplification, after analog signals are amplified for the second time, the intervals among different frequency signals are expanded, in the classification of the electroencephalogram signals in the international electroencephalogram standard, the frequency of delta wave signals is 0.5-4Hz, the frequency of theta wave signals is 4-8Hz, the frequency of alpha wave signals is 8-13Hz, the frequency of beta wave signals is 13-30Hz, the frequency of gamma wave signals is 30-42.5Hz, the signal strength is weak and is not enough for analog-to-digital conversion, the signals need to be sent into a tertiary amplifier for amplification, and finally the signals are sent into a microcontroller for analog-to-digital conversion, and fast Fourier transform and signal processing system operation are carried out in the microcontroller, and finally a sedation/consciousness index (IoC1), an analgesia/pain index (IoC2), a burst suppression ratio (BS), an electromyography index (EMG), a Signal Quality (SQI) and an impedance value parameter of the electrode are obtained.

In conclusion, the invention can realize high-quality electroencephalogram signal acquisition, anti-interference processing in the signal transmission process, filtering by the filter based on electroencephalogram signal characteristics, reducing the influence of external interference on the system, and meeting the high-quality requirement of the electroencephalogram signal, thereby providing stable and high-quality electroencephalogram signal data and higher acquisition precision for the micro-processing system, reducing the deviation caused by system calculation, adding the common-mode inductor in the differential acquisition circuit by utilizing the characteristic of common-mode inductor for inhibiting common-mode noise, and effectively inhibiting the high-frequency and high-energy conduction disturbance by the common-mode inductor selected by the equipment and the special circuit principle through actual detection.

And those not described in detail in this specification are well within the skill of those in the art.

It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.

Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.