CN115990108B - Acupuncture control system based on brain electrical signals, readable storage medium and electronic equipment - Google Patents
Acupuncture control system based on brain electrical signals, readable storage medium and electronic equipment Download PDFInfo
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Abstract
The invention provides an electroencephalogram signal-based acupuncture control system, a readable storage medium and electronic equipment, and belongs to the technical field of data identification and data representation. The control system includes: after any electric needle is applied to the main body to be acupuncture, acquiring an electroencephalogram signal of the main body to be acupuncture; obtaining a pain level of a current electric needle injection position of a main body to be acupuncture according to the obtained electroencephalogram signals and a preset first classifier model, wherein the pain level comprises three stages, one stage is painless, the other stage is pain tolerance, and the other stage is pain intolerance; when the pain level is three-level, adjusting the electric needle parameters so as to reduce the pain level to one level or two levels; when the pain level is secondary and the maintenance time is greater than or equal to the first set time, adjusting the electric needle parameters so as to reduce the pain level to the primary; when the total time of the electric needle application is greater than or equal to the second set time, closing the electric needle; the invention greatly improves the experience of the acupuncture treatment of the acupuncture subject.
Description
Technical Field
The invention relates to the technical field of data identification and data representation, in particular to an electroencephalogram signal-based acupuncture control system, a readable storage medium and electronic equipment.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
The electric acupuncture is a method for treating diseases by using an electric acupuncture instrument to output a certain form of current on the basis of traditional Chinese medicine acupuncture and moxibustion, acting on meridian points of a human body through a milli needle or skin, and using acupuncture to cooperate with electric or warm dual-stimulation to dredge the meridian, so that the treatment effect of the milli needle is improved, and the treatment range of acupuncture is widened.
The electric acupuncture instrument has been developed for about 60 years, and has undergone the development history of direct-current electric acupuncture machine, buzzing electric acupuncture machine, electron tube electric acupuncture instrument, transistor electric acupuncture instrument and pulse electric acupuncture instrument.
The inventor finds that the existing electric needle control mostly comprises presetting electric needle parameters by doctors, directly executing the electric needle parameters set by the doctors after needle application, wherein the electric needle parameters lack automatic feedback control, and especially cannot be adjusted based on the real-time pain change of the acupuncture subject (i.e. patient), and in a general medical institution, one doctor needs to be responsible for acupuncture treatment of a plurality of acupuncture subjects, so that the doctor cannot timely adjust the electric needle parameters according to pain requirements of the acupuncture subjects, and further the acupuncture subjects have poor experience of acupuncture.
Disclosure of Invention
In order to solve the defects in the prior art, the invention provides an electroencephalogram signal-based acupuncture control system, a readable storage medium and electronic equipment, and the electroencephalogram signal of an acupuncture subject is acquired in real time when acupuncture is performed, the pain level of the acupuncture subject is obtained by processing the electroencephalogram signal, and feedback adjustment of electric needle parameters is automatically performed according to the pain level, so that the experience of the acupuncture treatment of the acupuncture subject is greatly improved.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
the first aspect of the invention provides an electroencephalogram signal-based acupuncture control system.
An electroencephalogram signal based acupuncture control system comprising:
an electroencephalogram signal acquisition module configured to: after any electric needle is applied to the main body to be acupuncture, acquiring an electroencephalogram signal of the main body to be acupuncture;
a pain class generation module configured to: obtaining a pain level of a current electric needle injection position of a main body to be acupuncture according to the obtained electroencephalogram signals and a preset first classifier model, wherein the pain level comprises three stages, one stage is painless, the other stage is pain tolerance, and the other stage is pain intolerance;
A first adjustment module configured to: when the pain level is three-level, adjusting the electric needle parameters so as to reduce the pain level to one level or two levels;
a second adjustment module configured to: when the pain level is secondary and the maintenance time is greater than or equal to the first set time, adjusting the electric needle parameters so as to reduce the pain level to the primary;
an electrical needle closing control module configured to: and when the total time of the electric needle application is greater than or equal to the second set time, closing the electric needle.
As an optional implementation manner of the first aspect, the electrical needle parameter includes: current magnitude, waveform, amplitude, bandwidth, frequency, and duration.
As an optional implementation manner of the first aspect, the electric needles are multi-channel electric needles, each channel of electric needles is sequentially applied to the body to be acupuncture, and then current is sequentially applied to each electric needle, so that the pain level of each needle application position is first or second.
As an optional implementation manner of the first aspect, the electric needles are multi-channel electric needles, each channel of electric needles are sequentially applied to the body to be acupuncture, and then current is simultaneously applied to each electric needle, so that the pain level of each needle application position is first or second.
As a further limitation of the first aspect, when at least two electric needles are applied to the body to be acupuncture and energized, and the pain level of each electric needle application site is one or two;
judging the overall pain level of the acupuncture subject according to the acquired electroencephalogram signals and a preset second classifier model, wherein the overall pain level comprises a level A, a level B and a level C, the level A is painless, the level B is tolerable pain, and the level C is intolerable pain;
when the overall pain level is three-level, adjusting parameters of all electric needles so as to reduce the overall pain level to one level or two levels;
when the overall pain level is second level and the maintenance time is greater than or equal to the third set time, the adjustment of each of the electrical needle parameters is performed so that the overall pain level is reduced to first level.
As a further limitation, the first classifier model and the second classifier model are both random forest classification models.
A second aspect of the present invention provides a computer-readable storage medium.
A computer-readable storage medium having stored thereon a program which, when executed by a processor, performs the following process:
after any electric needle is applied to the main body to be acupuncture, acquiring an electroencephalogram signal of the main body to be acupuncture;
Obtaining a pain level of a current electric needle injection position of a main body to be acupuncture according to the obtained electroencephalogram signals and a preset first classifier model, wherein the pain level comprises three stages, one stage is painless, the other stage is pain tolerance, and the other stage is pain intolerance;
when the pain level is three-level, adjusting the electric needle parameters so as to reduce the pain level to one level or two levels;
when the pain level is secondary and the maintenance time is greater than or equal to the first set time, adjusting the electric needle parameters so as to reduce the pain level to the primary;
and when the total time of the electric needle application is greater than or equal to the second set time, closing the electric needle.
As an optional implementation manner of the second aspect, the electric needle is a multi-channel electric needle, when at least two electric needles are applied to the body to be acupuncture and electrified, and the pain level of each electric needle application position is one level or two levels;
judging the overall pain level of the acupuncture subject according to the acquired electroencephalogram signals and a preset second classifier model, wherein the overall pain level comprises a level A, a level B and a level C, the level A is painless, the level B is tolerable pain, and the level C is intolerable pain;
When the overall pain level is three-level, adjusting parameters of all electric needles so as to reduce the overall pain level to one level or two levels;
when the overall pain level is second level and the maintenance time is greater than or equal to the third set time, the adjustment of each of the electrical needle parameters is performed so that the overall pain level is reduced to first level.
A third aspect of the invention provides an electronic device.
An electronic device comprising a memory, a processor, and a program stored on the memory and executable on the processor, wherein the program when executed by the processor performs the following:
after any electric needle is applied to the main body to be acupuncture, acquiring an electroencephalogram signal of the main body to be acupuncture;
obtaining a pain level of a current electric needle injection position of a main body to be acupuncture according to the obtained electroencephalogram signals and a preset first classifier model, wherein the pain level comprises three stages, one stage is painless, the other stage is pain tolerance, and the other stage is pain intolerance;
when the pain level is three-level, adjusting the electric needle parameters so as to reduce the pain level to one level or two levels;
when the pain level is secondary and the maintenance time is greater than or equal to the first set time, adjusting the electric needle parameters so as to reduce the pain level to the primary;
And when the total time of the electric needle application is greater than or equal to the second set time, closing the electric needle.
As an optional implementation manner of the third aspect, the electric needle is a multi-channel electric needle, when at least two electric needles are applied to the body to be acupuncture and electrified, and the pain level of each electric needle application position is one or two levels;
judging the overall pain level of the acupuncture subject according to the acquired electroencephalogram signals and a preset second classifier model, wherein the overall pain level comprises a level A, a level B and a level C, the level A is painless, the level B is tolerable pain, and the level C is intolerable pain;
when the overall pain level is three-level, adjusting parameters of all electric needles so as to reduce the overall pain level to one level or two levels;
when the overall pain level is second level and the maintenance time is greater than or equal to the third set time, the adjustment of each of the electrical needle parameters is performed so that the overall pain level is reduced to first level.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the electroencephalogram signal-based acupuncture control system, the readable storage medium and the electronic equipment, when acupuncture is performed, the electroencephalogram signal of the acupuncture subject is obtained in real time, the pain level of the acupuncture subject is obtained through processing the electroencephalogram signal, feedback adjustment of electric needle parameters is automatically performed according to the pain level, and the experience of the acupuncture subject in acupuncture treatment is greatly improved.
2. According to the electroencephalogram signal-based acupuncture control system, the readable storage medium and the electronic equipment, according to the electroencephalogram signal of the acupuncture subject, not only is pain class division carried out by single needle application performed, but also the overall pain class division during multi-needle application can be carried out, the electric needle parameters can be accurately adjusted based on the pain change of the acupuncture subject, and the acupuncture experience is further improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention.
Fig. 1 is a schematic structural diagram of an electroencephalogram signal-based acupuncture control system according to embodiment 1 of the present invention.
Fig. 2 is a schematic diagram of an electrical stimulation generating circuit according to embodiment 3 of the present invention.
Detailed Description
The invention will be further described with reference to the drawings and examples.
It should be noted that the following detailed description is illustrative and is intended to provide further explanation of the invention. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the present invention. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.
Embodiments of the invention and features of the embodiments may be combined with each other without conflict.
Example 1:
as shown in fig. 1, embodiment 1 of the present invention provides an electroencephalogram signal-based acupuncture control system, including:
an electroencephalogram signal acquisition module configured to: after any electric needle is applied to the main body to be acupuncture, acquiring an electroencephalogram signal of the main body to be acupuncture;
a pain class generation module configured to: obtaining a pain level of a current electric needle injection position of a main body to be acupuncture according to the obtained electroencephalogram signals and a preset first classifier model, wherein the pain level comprises three stages, one stage is painless, the other stage is pain tolerance, and the other stage is pain intolerance;
A first adjustment module configured to: when the pain level is three-level, adjusting the electric needle parameters so as to reduce the pain level to one level or two levels;
a second adjustment module configured to: when the pain level is secondary and the maintenance time is greater than or equal to the first set time, adjusting the electric needle parameters so as to reduce the pain level to the primary;
an electrical needle closing control module configured to: and when the total time of the electric needle application is greater than or equal to the second set time, closing the electric needle.
Specifically, this embodiment takes insomnia as an example, and includes:
(1) Acquisition of brain electrical signals
According to the characteristics of the electroencephalogram signals, the electroencephalogram signals need to be preprocessed before being analyzed, noise removal of the electroencephalogram signals is a first step of analyzing the electroencephalogram signals, the electroencephalogram signals need to be amplified to a range of amplitude values which can meet the conversion of an analog-to-digital converter before being transmitted to a processor for processing, and noise, interference and various artifacts need to be removed in order to acquire ideal electroencephalogram original signals.
In the embodiment, the useful signal is obtained by processing the original signal, so that the preamplifier with high common mode rejection ratio, low noise, low drift and high input impedance is required to be arranged during the acquisition of the electroencephalogram signal;
In order to ensure that the signal can be restored without distortion after sampling, the sampling frequency is at least more than twice the highest frequency of the signal according to the sampling theorem; in order to prevent data loss during display and storage, it should be noted that the time to complete one data storage and display cannot be longer than the sampling interval time of the pre-amplifier; in addition, aiming at the electrode placement position during electroencephalogram acquisition, the embodiment adopts a 10-20 lead electrode placement mode.
The key elements of the scheme adopted by most electroencephalogram products in the market at present are mainly ADS1299 bioelectrical measurement special chips of Texas instruments or ASIC special for electroencephalogram.
(2) Preprocessing of electroencephalogram signals
The electroencephalogram signals belong to non-stationary and time-varying signals, and from the processing process, digital filtering processing is mainly carried out to remove noise in the electroencephalogram signals; waveform detection and feature extraction for detecting the occurrence of specific EEG waves and extracting the fundamental rhythm of brain electricity; classification recognition and processing are used for distinguishing brain waves in different states through obvious characteristic quantities. From the treatment method, there are mainly four types:
the analysis method of the time domain or the frequency domain is particularly characterized by Fourier transformation in the time domain method, and the frequency domain method is represented by power spectrum, and can be used for removing noise or detecting waveforms and the like in the electroencephalogram;
Time-frequency domain analysis methods, such as wavelet transform, can be used for denoising, feature extraction, etc.;
according to the nonlinear dynamics method, by utilizing the chaos of an electroencephalogram system, a proper nonlinear index is found to analyze, so that different electroencephalogram states are represented;
the method of pattern recognition and artificial neural network firstly establishes a model, then researches the model, selects proper parameters and the like, can be used for recognizing various states of electroencephalogram and the like, and is preferably used for electroencephalogram signal processing by adopting a classification model in the embodiment.
Specifically, the embodiment adopts a random forest classification model (RF), including:
RF is a classifier widely used in the fields of data mining, biological information processing, etc. to solve high-dimensional and nonlinear samples, in this embodiment, in order to verify the validity of the feature extraction method and improve the prediction accuracy of the classification model, a Wrapper feature selection algorithm based on RF is adopted;
the method comprises the steps of sorting the importance of the features by using a variable importance measurement mode based on the classification accuracy of the data outside the bag, then removing a feature with the minimum importance score from a feature set each time by adopting a sequence backward selection algorithm (Sequential Backward Selection, SBS), sequentially iterating, calculating the classification accuracy, and finally taking the feature set with the highest classification accuracy as a feature selection result;
In order to evaluate the robustness of the classifier, a cross-validation method is used in each iteration, only one sample is left as a test set at a time, and other samples are regarded as training sets.
In this embodiment, the electroencephalogram data is divided into two parts, the first part is 3 data sets (painless, pain tolerant and pain intolerable) in single needle acupuncture, and the second part is 3 data sets (painless, pain tolerant and pain intolerable) in multi-needle acupuncture.
Classifying the data set of the first portion into a training set and a test set for training and testing the first classifier model based on the self-description of pain by the subject being acupuncture into painless, pain-tolerant and pain-intolerant data from 100 healthy subjects, respectively;
the experimental equipment adopts an 8-channel electroencephalogram signal acquisition instrument, the sampling frequency is 250Hz, a 50Hz trap filter and a 0-48 Hz band-pass filter are provided, the electrode placement positions of the electroencephalogram signal acquisition instrument are placed at the positions of C3, C4, P7, P8, O1, O2, FP1 and FP2 according to the 10-20 lead electrodes, and the reference electrode is fixed on the double earlobes; in the data acquisition process, the test subject is required to be closed and relaxed, the acquisition time is about 3 min, signals obviously belonging to interference noise are manually removed, and finally, each group of electroencephalogram data contains 5000 sampling points in total and is used for subsequent feature extraction;
4 layers of wavelet decomposition is carried out on 8-channel brain electrical signals of each subject by utilizing db4 wavelet, so that an approximation coefficient A4 and detail coefficients D1-D4 are obtained; based on the wavelet coefficient of each layer decomposition, calculating a wavelet statistical characteristic value (maximum value, minimum value, average value and standard deviation), each sub-band energy duty ratio, sample entropy and a phase locking value between every two channel pairs; finally, extracting feature vectors containing 380 elements for each group of electroencephalogram signals;
and performing feature selection and classification prediction on the extracted 380 element feature vectors by adopting an RF-based sequence backward selection (RF-SBS) algorithm to obtain the final pain level (primary, secondary and tertiary) during single needle acupuncture.
For the data set of the second part, a second classifier model is trained by the same method, and is used for obtaining the overall pain level (A level, B level and C level) during multi-needle acupuncture according to the acquired electroencephalogram signals.
In this embodiment, the electrical needle parameters include: current magnitude, waveform, amplitude, bandwidth, frequency, and duration.
The adjusting of the electric needle parameters comprises the following steps: reducing the magnitude of the current; performing conversion of set waveforms, amplitudes and wave widths; the frequency or duration of the reduction can be reduced, only one parameter can be adjusted, and a plurality of parameters can be adjusted simultaneously, so that a person skilled in the art can select an adjustment mode according to specific working conditions, and the details are not repeated here.
In this embodiment, the electric needles are multi-channel electric needles, each channel of electric needles is sequentially applied to the body to be acupuncture, and then current is sequentially applied to each electric needle, so that the pain level of each needle application position is first or second.
Optionally, in other implementations, the electric needles are multi-channel electric needles, each channel of electric needles is sequentially applied to the body to be acupuncture, and then current is simultaneously applied to each electric needle, so that the pain level of each needle application position is first or second.
Alternatively, in other implementations, when at least two electric needles are applied to the body to be acupuncture and energized, and the pain level of each electric needle application site is one or two levels;
judging the overall pain level of the acupuncture subject according to the acquired electroencephalogram signals and a preset second classifier model, wherein the overall pain level comprises a level A, a level B and a level C, the level A is painless, the level B is tolerable pain, and the level C is intolerable pain;
when the overall pain level is three-level, adjusting parameters of all electric needles so as to reduce the overall pain level to one level or two levels;
when the overall pain level is second level and the maintenance time is greater than or equal to the third set time, the adjustment of each of the electrical needle parameters is performed so that the overall pain level is reduced to first level.
In this embodiment, the first set time acquisition method includes:
when a plurality of subjects are subjected to single needle injection, obtaining tolerance time of all subjects to secondary pain, and taking the minimum tolerance time of all subjects as a first set time; or, acquiring tolerance time of all subjects to the secondary pain, sequencing all the tolerance time in a sequence from small to large, removing the minimum 10% of the tolerance time in the sequence from small to large to obtain a residual time sequence, and taking the minimum value of the residual time sequence as a first set time; and obtaining the tolerance time of all subjects to the secondary pain, sequencing all the tolerance time in a sequence from small to large, removing the minimum 10% of the tolerance time in the sequence from small to large, removing the maximum 10% of the tolerance time, and obtaining a residual time sequence, wherein the average value of the residual time sequence is used as the first set time.
In this embodiment, the third set time obtaining manner includes:
when multiple needles are applied to multiple subjects, obtaining tolerance time of all subjects to secondary pain, and taking the minimum tolerance time of all subjects as a third set time; or, acquiring tolerance time of all subjects to the secondary pain, sequencing all the tolerance time in a sequence from small to large, removing the minimum 10% of the tolerance time in the sequence from small to large to obtain a residual time sequence, and taking the minimum value of the residual time sequence as a third set time; and obtaining the tolerance time of all subjects to the secondary pain, sequencing all the tolerance time in a sequence from small to large, removing the minimum 10% of the tolerance time in the sequence from small to large, removing the maximum 10% of the tolerance time, obtaining a residual time sequence, and taking the average value of the residual time sequence as a third set time.
The second setting time is the needle applying time determined by the doctor according to the illness state, and is not described herein.
Example 2:
embodiment 2 of the present invention provides a computer-readable storage medium having stored thereon a program which, when executed by a processor, realizes the following procedure:
after any electric needle is applied to the main body to be acupuncture, acquiring an electroencephalogram signal of the main body to be acupuncture;
obtaining a pain level of a current electric needle injection position of a main body to be acupuncture according to the obtained electroencephalogram signals and a preset first classifier model, wherein the pain level comprises three stages, one stage is painless, the other stage is pain tolerance, and the other stage is pain intolerance;
when the pain level is three-level, adjusting the electric needle parameters so as to reduce the pain level to one level or two levels;
when the pain level is secondary and the maintenance time is greater than or equal to the first set time, adjusting the electric needle parameters so as to reduce the pain level to the primary;
and when the total time of the electric needle application is greater than or equal to the second set time, closing the electric needle.
Specifically, this embodiment takes an example of performing electric acupuncture treatment on an insomnia patient, and includes:
(1) Acquisition of brain electrical signals
According to the characteristics of the electroencephalogram signals, the electroencephalogram signals are required to be preprocessed before being analyzed, noise removal of the electroencephalogram signals is a first step of analyzing the electroencephalogram signals, the electroencephalogram signals are required to be amplified to a range of amplitude values which can meet the conversion of an analog-to-digital converter before being transmitted to a processor for processing, and noise, interference and various artifacts are required to be removed for acquiring ideal electroencephalogram original signals.
In the embodiment, the useful signal is obtained by processing the original signal, so that the preamplifier with high common mode rejection ratio, low noise, low drift and high input impedance is required to be arranged during the acquisition of the electroencephalogram signal;
in order to ensure that the signal can be restored without distortion after sampling, the sampling frequency is at least more than twice the highest frequency of the signal according to the sampling theorem; in order to prevent data loss during display and storage, it should be noted that the time to complete one data storage and display cannot be longer than the sampling interval time of the pre-amplifier; in addition, aiming at the electrode placement position during electroencephalogram acquisition, the embodiment adopts a 10-20 lead electrode placement mode.
The key elements of the scheme adopted by most electroencephalogram products in the market at present are mainly ADS1299 bioelectrical measurement special chips of Texas instruments or ASIC special for electroencephalogram.
(2) Preprocessing of electroencephalogram signals
The electroencephalogram signals belong to non-stationary and time-varying signals, and from the processing process, digital filtering processing is mainly carried out to remove noise in the electroencephalogram signals; waveform detection and feature extraction for detecting the occurrence of specific EEG waves and extracting the fundamental rhythm of brain electricity; classification recognition and processing are used for distinguishing brain waves in different states through obvious characteristic quantities, and the classification is divided into four types from the processing method:
The analysis method of the time domain or the frequency domain is particularly characterized by Fourier transformation in the time domain method, and the frequency domain method is represented by power spectrum, and can be used for removing noise or detecting waveforms and the like in the electroencephalogram;
time-frequency domain analysis methods, such as wavelet transform, can be used for denoising, feature extraction, etc.;
according to the nonlinear dynamics method, by utilizing the chaos of an electroencephalogram system, a proper nonlinear index is found to analyze, so that different electroencephalogram states are represented;
the method of pattern recognition and artificial neural network firstly establishes a model, then researches the model, selects proper parameters and the like, can be used for recognizing various states of electroencephalogram and the like, and is preferably used for electroencephalogram signal processing by adopting a classification model in the embodiment.
Specifically, the embodiment adopts a random forest classification model (RF), including:
RF is a classifier widely used in the fields of data mining, biological information processing, etc. to solve high-dimensional and nonlinear samples, in this embodiment, in order to verify the validity of the feature extraction method and improve the prediction accuracy of the classification model, a Wrapper feature selection algorithm based on RF is adopted;
the method comprises the steps of sorting the importance of the features by using a variable importance measurement mode based on the classification accuracy of the data outside the bag, then removing a feature with the minimum importance score from a feature set each time by adopting a sequence backward selection algorithm (Sequential Backward Selection, SBS), sequentially iterating, calculating the classification accuracy, and finally taking the feature set with the highest classification accuracy as a feature selection result;
In order to evaluate the robustness of the classifier, a cross-validation method was used to leave only one sample at a time as a test set and the other samples as training sets in each iteration, while in order to test the performance of the classifier, we calculated the classification accuracy, sensitivity and specificity, respectively.
In this embodiment, the electroencephalogram data is divided into two parts, the first part is 3 data sets (painless, pain tolerant and pain intolerable) in single needle acupuncture, and the second part is 3 data sets (painless, pain tolerant and pain intolerable) in multi-needle acupuncture.
For the first portion of the data set, the training set and the test set were divided for training and testing of the first classifier model, the painless set, the pain tolerant and the pain intolerant data were respectively from 100 healthy subjects, and the data were classified as painless, pain tolerant and pain intolerant based on the self-description of pain by the subject to be acupuncture;
the experimental equipment adopts an 8-channel electroencephalogram signal acquisition instrument, the sampling frequency is 250Hz, a 50Hz trap filter and a 0-48 Hz band-pass filter are arranged, the electrode placement positions of the electroencephalogram signal acquisition instrument are placed at the positions of C3, C4, P7, P8, O1, O2, FP1 and FP2 according to the 10-20 lead electrodes, and the reference electrode is fixed on the earlobe. In the data acquisition process, the test subject is required to be closed and relaxed, the acquisition time is about 3 min, signals obviously belonging to interference noise are manually removed, and finally, each group of electroencephalogram data contains 5000 sampling points in total and is used for subsequent feature extraction;
4 layers of wavelet decomposition is carried out on 8-channel brain electrical signals of each subject by utilizing db4 wavelet, so that an approximation coefficient A4 and detail coefficients D1-D4 are obtained; based on the wavelet coefficient of each layer decomposition, calculating a wavelet statistical characteristic value (maximum value, minimum value, average value and standard deviation), each sub-band energy duty ratio, sample entropy and a phase locking value between every two channel pairs; finally, extracting feature vectors containing 380 elements for each group of electroencephalogram signals;
and performing feature selection and classification prediction on the extracted 380 element feature vectors by adopting an RF-based sequence backward selection (RF-SBS) algorithm to obtain the final pain level (primary, secondary and tertiary) during single needle acupuncture.
For the data set of the second part, a second classifier model is trained by the same method, and is used for obtaining the overall pain level (A level, B level and C level) during multi-needle acupuncture according to the acquired electroencephalogram signals.
In this embodiment, the electrical needle parameters include: current magnitude, waveform, amplitude, bandwidth, frequency, and duration.
The adjusting of the electric needle parameters comprises the following steps: reducing the magnitude of the current; performing conversion of set waveforms, amplitudes and wave widths; the frequency or duration of the reduction can be reduced, only one parameter can be adjusted, and a plurality of parameters can be adjusted simultaneously, so that a person skilled in the art can select an adjustment mode according to specific working conditions, and the details are not repeated here.
In this embodiment, the electric needles are multi-channel electric needles, each channel of electric needles is sequentially applied to the body to be acupuncture, and then current is sequentially applied to each electric needle, so that the pain level of each needle application position is first or second.
Optionally, in other implementations, the electric needles are multi-channel electric needles, each channel of electric needles is sequentially applied to the body to be acupuncture, and then current is simultaneously applied to each electric needle, so that the pain level of each needle application position is first or second.
Alternatively, in other implementations, when at least two electric needles are applied to the body to be acupuncture and energized, and the pain level of each electric needle application site is one or two levels;
judging the overall pain level of the acupuncture subject according to the acquired electroencephalogram signals and a preset second classifier model, wherein the overall pain level comprises a level A, a level B and a level C, the level A is painless, the level B is tolerable pain, and the level C is intolerable pain;
when the overall pain level is three-level, adjusting parameters of all electric needles so as to reduce the overall pain level to one level or two levels;
when the overall pain level is second level and the maintenance time is greater than or equal to the third set time, the adjustment of each of the electrical needle parameters is performed so that the overall pain level is reduced to first level.
In this embodiment, the first set time acquisition method includes:
when a plurality of subjects are subjected to single needle injection, obtaining tolerance time of all subjects to secondary pain, and taking the minimum tolerance time of all subjects as a first set time; or, acquiring tolerance time of all subjects to the secondary pain, sequencing all the tolerance time in a sequence from small to large, removing 10% of the tolerance time in the sequence from small to large to obtain a residual time sequence, and taking the minimum value of the residual time sequence as a first set time; and obtaining the tolerance time of all subjects to the secondary pain, sequencing all the tolerance time in a sequence from small to large, removing the minimum 10% of the tolerance time in the sequence from small to large, removing the maximum 10% of the tolerance time, and obtaining a residual time sequence, wherein the average value of the residual time sequence is used as the first set time.
In this embodiment, the third set time obtaining manner includes:
when multiple needles are applied to multiple subjects, obtaining tolerance time of all subjects to secondary pain, and taking the minimum tolerance time of all subjects as a third set time; or, acquiring tolerance time of all subjects to the secondary pain, sequencing all the tolerance time in a sequence from small to large, removing 10% of the tolerance time in the sequence from small to large to obtain a residual time sequence, and taking the minimum value of the residual time sequence as a third set time; and obtaining the tolerance time of all subjects to the secondary pain, sequencing all the tolerance time in a sequence from small to large, removing the minimum 10% of the tolerance time in the sequence from small to large, removing the maximum 10% of the tolerance time, obtaining a residual time sequence, and taking the average value of the residual time sequence as a third set time.
The second setting time is the needle applying time determined by the doctor according to the illness state, and is not described herein.
Example 3:
an embodiment 3 of the present invention provides an electronic device, including a memory, a processor, and a program stored in the memory and executable on the processor, wherein the processor implements the following procedure when executing the program:
after any electric needle is applied to the main body to be acupuncture, acquiring an electroencephalogram signal of the main body to be acupuncture;
obtaining a pain level of a current electric needle injection position of a main body to be acupuncture according to the obtained electroencephalogram signals and a preset first classifier model, wherein the pain level comprises three stages, one stage is painless, the other stage is pain tolerance, and the other stage is pain intolerance;
when the pain level is three-level, adjusting the electric needle parameters so as to reduce the pain level to one level or two levels;
when the pain level is secondary and the maintenance time is greater than or equal to the first set time, adjusting the electric needle parameters so as to reduce the pain level to the primary;
and when the total time of the electric needle application is greater than or equal to the second set time, closing the electric needle.
Specifically, this embodiment takes an example of performing electric acupuncture treatment on an insomnia patient, and includes:
(1) Acquisition of brain electrical signals
According to the characteristics of the electroencephalogram signals, the electroencephalogram signals are required to be preprocessed before being analyzed, noise removal of the electroencephalogram signals is a first step of analyzing the electroencephalogram signals, the electroencephalogram signals are required to be amplified to a range of amplitude values which can meet the conversion of an analog-to-digital converter before being transmitted to a processor for processing, and noise, interference and various artifacts are required to be removed for acquiring ideal electroencephalogram original signals.
In the embodiment, the useful signal is obtained by processing the original signal, so that the preamplifier with high common mode rejection ratio, low noise, low drift and high input impedance is required to be arranged during the acquisition of the electroencephalogram signal;
in order to ensure that the signal can be restored without distortion after sampling, the sampling frequency is at least more than twice the highest frequency of the signal according to the sampling theorem; in order to prevent data loss during display and storage, it should be noted that the time to complete one data storage and display cannot be longer than the sampling interval time of the pre-amplifier; in addition, aiming at the electrode placement position during electroencephalogram acquisition, the embodiment adopts a 10-20 lead electrode placement mode.
The key elements of the scheme adopted by most electroencephalogram products in the market at present are mainly ADS1299 bioelectrical measurement special chips of Texas instruments or ASIC special for electroencephalogram.
(2) Preprocessing of electroencephalogram signals
The electroencephalogram signals belong to non-stationary and time-varying signals, and from the processing process, digital filtering processing is mainly carried out to remove noise in the electroencephalogram signals; waveform detection and feature extraction for detecting the occurrence of specific EEG waves and extracting the fundamental rhythm of brain electricity; classification recognition and processing are used for distinguishing brain waves in different states through obvious characteristic quantities, and the classification is divided into four types from the processing method:
the analysis method of the time domain or the frequency domain is particularly characterized by Fourier transformation in the time domain method, and the frequency domain method is represented by power spectrum, and can be used for removing noise or detecting waveforms and the like in the electroencephalogram;
time-frequency domain analysis methods, such as wavelet transform, can be used for denoising, feature extraction, etc.;
according to the nonlinear dynamics method, by utilizing the chaos of an electroencephalogram system, a proper nonlinear index is found to analyze, so that different electroencephalogram states are represented;
the method of pattern recognition and artificial neural network firstly establishes a model, then researches the model, selects proper parameters and the like, can be used for recognizing various states of electroencephalogram and the like, and is preferably used for electroencephalogram signal processing by adopting a classification model in the embodiment.
Specifically, the embodiment adopts a random forest classification model (RF), including:
RF is a classifier widely used in the fields of data mining, biological information processing, etc. to solve high-dimensional and nonlinear samples, in this embodiment, in order to verify the validity of the feature extraction method and improve the prediction accuracy of the classification model, a Wrapper feature selection algorithm based on RF is adopted;
the method comprises the steps of sorting the importance of the features by using a variable importance measurement mode based on the classification accuracy of the data outside the bag, then removing a feature with the minimum importance score from a feature set each time by adopting a sequence backward selection algorithm (Sequential Backward Selection, SBS), sequentially iterating, calculating the classification accuracy, and finally taking the feature set with the highest classification accuracy as a feature selection result;
in order to evaluate the robustness of the classifier, a cross-validation method was used to leave only one sample at a time as a test set and the other samples as training sets in each iteration, while in order to test the performance of the classifier, we calculated the classification accuracy, sensitivity and specificity, respectively.
In this embodiment, the electroencephalogram data is divided into two parts, the first part is 3 data sets (painless, pain tolerant and pain intolerable) in single needle acupuncture, and the second part is 3 data sets (painless, pain tolerant and pain intolerable) in multi-needle acupuncture.
For the first portion of the data set, the training set and the test set were divided for training and testing of the first classifier model, the painless set, the pain tolerant and the pain intolerant data were respectively from 100 healthy subjects, and the data were classified as painless, pain tolerant and pain intolerant based on the self-description of pain by the subject to be acupuncture;
the experimental equipment adopts an 8-channel electroencephalogram signal acquisition instrument, the sampling frequency is 250Hz, a 50Hz trap filter and a 0-48 Hz band-pass filter are arranged, the electrode placement positions of the electroencephalogram signal acquisition instrument are placed at the positions of C3, C4, P7, P8, O1, O2, FP1 and FP2 according to the 10-20 lead electrodes, and the reference electrode is fixed on the earlobe. In the data acquisition process, the test subject is required to be closed and relaxed, the acquisition time is about 3 min, signals obviously belonging to interference noise are manually removed, and finally, each group of electroencephalogram data contains 5000 sampling points in total and is used for subsequent feature extraction;
4 layers of wavelet decomposition is carried out on 8-channel brain electrical signals of each subject by utilizing db4 wavelet, so that an approximation coefficient A4 and detail coefficients D1-D4 are obtained; based on the wavelet coefficient of each layer decomposition, calculating a wavelet statistical characteristic value (maximum value, minimum value, average value and standard deviation), each sub-band energy duty ratio, sample entropy and a phase locking value between every two channel pairs; finally, extracting feature vectors containing 380 elements for each group of electroencephalogram signals;
And performing feature selection and classification prediction on the extracted 380 element feature vectors by adopting an RF-based sequence backward selection (RF-SBS) algorithm to obtain the final pain level (primary, secondary and tertiary) during single needle acupuncture.
For the data set of the second part, a second classifier model is trained by the same method, and is used for obtaining the overall pain level (A level, B level and C level) during multi-needle acupuncture according to the acquired electroencephalogram signals.
In this embodiment, the electrical needle parameters include: current magnitude, waveform, amplitude, bandwidth, frequency, and duration.
The adjusting of the electric needle parameters comprises the following steps: reducing the magnitude of the current; performing conversion of set waveforms, amplitudes and wave widths; the frequency or duration of the reduction can be reduced, only one parameter can be adjusted, and a plurality of parameters can be adjusted simultaneously, so that a person skilled in the art can select an adjustment mode according to specific working conditions, and the details are not repeated here.
In this embodiment, the electric needles are multi-channel electric needles, each channel of electric needles is sequentially applied to the body to be acupuncture, and then current is sequentially applied to each electric needle, so that the pain level of each needle application position is first or second.
Optionally, in other implementations, the electric needles are multi-channel electric needles, each channel of electric needles is sequentially applied to the body to be acupuncture, and then current is simultaneously applied to each electric needle, so that the pain level of each needle application position is first or second.
Alternatively, in other implementations, when at least two electric needles are applied to the body to be acupuncture and energized, and the pain level of each electric needle application site is one or two levels;
judging the overall pain level of the acupuncture subject according to the acquired electroencephalogram signals and a preset second classifier model, wherein the overall pain level comprises a level A, a level B and a level C, the level A is painless, the level B is tolerable pain, and the level C is intolerable pain;
when the overall pain level is three-level, adjusting parameters of all electric needles so as to reduce the overall pain level to one level or two levels;
when the overall pain level is second level and the maintenance time is greater than or equal to the third set time, the adjustment of each of the electrical needle parameters is performed so that the overall pain level is reduced to first level.
In this embodiment, the first set time acquisition method includes:
when a plurality of subjects are subjected to single needle injection, obtaining tolerance time of all subjects to secondary pain, and taking the minimum tolerance time of all subjects as a first set time; or, acquiring tolerance time of all subjects to the secondary pain, sequencing all the tolerance time in a sequence from small to large, removing 10% of the tolerance time in the sequence from small to large to obtain a residual time sequence, and taking the minimum value of the residual time sequence as a first set time; and obtaining the tolerance time of all subjects to the secondary pain, sequencing all the tolerance time in a sequence from small to large, removing the minimum 10% of the tolerance time in the sequence from small to large, removing the maximum 10% of the tolerance time, and obtaining a residual time sequence, wherein the average value of the residual time sequence is used as the first set time.
In this embodiment, the third set time obtaining manner includes:
when multiple needles are applied to multiple subjects, obtaining tolerance time of all subjects to secondary pain, and taking the minimum tolerance time of all subjects as a third set time; or, acquiring tolerance time of all subjects to the secondary pain, sequencing all the tolerance time in a sequence from small to large, removing 10% of the tolerance time in the sequence from small to large to obtain a residual time sequence, and taking the minimum value of the residual time sequence as a third set time; and obtaining the tolerance time of all subjects to the secondary pain, sequencing all the tolerance time in a sequence from small to large, removing the minimum 10% of the tolerance time in the sequence from small to large, removing the maximum 10% of the tolerance time, obtaining a residual time sequence, and taking the average value of the residual time sequence as a third set time.
The second setting time is the needle applying time determined by the doctor according to the illness state, and is not described herein.
In this embodiment, as shown in fig. 2, the generating circuit of the electric needle stimulation includes a BOOST circuit, a pulse discharge control circuit and a constant current source circuit, where the BOOST circuit is mainly responsible for providing a supply voltage and providing a power source of the electric stimulation, and the BOOST mode can be implemented by a standard BOOST circuit or a BOOST transformer, and in this embodiment, a BOOST circuit driven by a MOS transistor is adopted.
In the embodiment, the application processor part selects the positive X2000 series of Beijing, the chip is a dual-core MIPS32 architecture processor, the core frequency of the processor reaches 1.2GHz, and the SDK provides a development kit based on Linux 4.4; the electric needle control board of the system uses a domestic sub-specific force 32-bit MCU to realize 6-channel electric needle discharge control; the electroencephalogram signal acquisition part uses openbci compatible hardware design, the core chip is an ADS1299 bioelectricity acquisition chip of TI, and data transmission is performed by using wireless; in addition, TGAM brain wave sensor module manufactured by Neurosky company can be used for brain wave part.
In the embodiment, the system is prepared to adopt an embedded Linux operating system to carry out overall system design, a QT5.15 embedded version is adopted for programming a GUI interface, a C++ is adopted for programming, and rootfs is prepared to adopt a mipsel architecture-based Debian11 as a root file system, so that subsequent development is facilitated.
In this embodiment, the electroencephalogram data is obtained through bluetooth or 2.4G wireless, and real-time data packet analysis and electroencephalogram analysis and feature extraction are performed.
In this embodiment, the electric pin control bus adopts a design of CAN bus/485 bus communication, and CAN perform communication control through a standard interface under Linux, so that module communication protocol and control on the electric pin module are completed at present, including parameter control, acquisition and monitoring of operation states.
In this embodiment, the system configuration part stores the configuration parameters of the multiple channels, a factory default operation policy, parameters fed back by the brain control part, and the like in the form of JSON configuration files.
In this embodiment, the method is mainly responsible for service logic codes of GUI interface parts, start and stop of an electric needle channel, operation monitoring of the electric needle channel, real-time display of electroencephalogram, feedback control logic, interactive information processing of users, and the like.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, magnetic disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Those skilled in the art will appreciate that implementing all or part of the above-described methods in accordance with the embodiments may be accomplished by way of a computer program stored on a computer readable storage medium, which when executed may comprise the steps of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), or the like.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (9)
1. An acupuncture control system based on brain electrical signals, which is characterized in that:
the method comprises the following steps:
an electroencephalogram signal acquisition module configured to: after any electric needle is applied to the main body to be acupuncture, acquiring an electroencephalogram signal of the main body to be acupuncture;
a pain class generation module configured to: obtaining a pain level of a current electric needle injection position of a main body to be acupuncture according to the obtained electroencephalogram signals and a preset first classifier model, wherein the pain level comprises three stages, one stage is painless, the other stage is pain tolerance, and the other stage is pain intolerance;
a first adjustment module configured to: when the pain level is three-level, adjusting the electric needle parameters so as to reduce the pain level to one level or two levels;
a second adjustment module configured to: when the pain level is secondary and the maintenance time is greater than or equal to the first set time, adjusting the electric needle parameters so as to reduce the pain level to the primary;
An electrical needle closing control module configured to: when the total time of the electric needle application is greater than or equal to the second set time, closing the electric needle;
the electric needles are multichannel electric needles, and when at least two electric needles are applied to the acupuncture main body and electrified, and the pain level of each electric needle application part is one level or two levels;
judging the overall pain level of the acupuncture subject according to the acquired electroencephalogram signals and a preset second classifier model, wherein the overall pain level comprises a level A, a level B and a level C, the level A is painless, the level B is tolerable pain, and the level C is intolerable pain;
when the overall pain level is three-level, adjusting parameters of all electric needles so as to reduce the overall pain level to one level or two levels;
when the overall pain level is second level and the maintenance time is greater than or equal to the third set time, the adjustment of each of the electrical needle parameters is performed so that the overall pain level is reduced to first level.
2. The brain signal based acupuncture control system according to claim 1, wherein:
electrical needle parameters, comprising: current magnitude, waveform, amplitude, bandwidth, frequency, and duration.
3. The brain signal based acupuncture control system according to claim 1, wherein:
The electric needles of all channels are sequentially applied to the body to be acupuncture, and then electric currents are sequentially applied to the electric needles, so that the pain level of each needle application part is primary or secondary.
4. The brain signal based acupuncture control system according to claim 1, wherein:
the electric needles of all channels are sequentially applied to the body to be acupuncture, and then current is simultaneously applied to all the electric needles, so that the pain level of each needle application part is primary or secondary.
5. The brain signal based acupuncture control system according to claim 1, wherein:
the first classifier model and the second classifier model are both random forest classification models.
6. A computer readable storage medium having stored thereon a program which when executed by a processor performs the following process when executed by a system according to any of claims 1-5:
after any electric needle is applied to the main body to be acupuncture, acquiring an electroencephalogram signal of the main body to be acupuncture;
obtaining a pain level of a current electric needle injection position of a main body to be acupuncture according to the obtained electroencephalogram signals and a preset first classifier model, wherein the pain level comprises three stages, one stage is painless, the other stage is pain tolerance, and the other stage is pain intolerance;
When the pain level is three-level, adjusting the electric needle parameters so as to reduce the pain level to one level or two levels;
when the pain level is secondary and the maintenance time is greater than or equal to the first set time, adjusting the electric needle parameters so as to reduce the pain level to the primary;
and when the total time of the electric needle application is greater than or equal to the second set time, closing the electric needle.
7. The computer-readable storage medium of claim 6, wherein:
the electric needles are multichannel electric needles, and when at least two electric needles are applied to the acupuncture main body and electrified, and the pain level of each electric needle application part is one level or two levels;
judging the overall pain level of the acupuncture subject according to the acquired electroencephalogram signals and a preset second classifier model, wherein the overall pain level comprises a level A, a level B and a level C, the level A is painless, the level B is tolerable pain, and the level C is intolerable pain;
when the overall pain level is three-level, adjusting parameters of all electric needles so as to reduce the overall pain level to one level or two levels;
when the overall pain level is second level and the maintenance time is greater than or equal to the third set time, the adjustment of each of the electrical needle parameters is performed so that the overall pain level is reduced to first level.
8. An electronic device comprising a memory, a processor and a program stored on the memory and executable on the processor, wherein the processor performs the following when executing the system of any one of claims 1-5:
after any electric needle is applied to the main body to be acupuncture, acquiring an electroencephalogram signal of the main body to be acupuncture;
obtaining a pain level of a current electric needle injection position of a main body to be acupuncture according to the obtained electroencephalogram signals and a preset first classifier model, wherein the pain level comprises three stages, one stage is painless, the other stage is pain tolerance, and the other stage is pain intolerance;
when the pain level is three-level, adjusting the electric needle parameters so as to reduce the pain level to one level or two levels;
when the pain level is secondary and the maintenance time is greater than or equal to the first set time, adjusting the electric needle parameters so as to reduce the pain level to the primary;
and when the total time of the electric needle application is greater than or equal to the second set time, closing the electric needle.
9. The electronic device of claim 8, wherein:
the electric needles are multichannel electric needles, and when at least two electric needles are applied to the acupuncture main body and electrified, and the pain level of each electric needle application part is one level or two levels;
Judging the overall pain level of the acupuncture subject according to the acquired electroencephalogram signals and a preset second classifier model, wherein the overall pain level comprises a level A, a level B and a level C, the level A is painless, the level B is tolerable pain, and the level C is intolerable pain;
when the overall pain level is three-level, adjusting parameters of all electric needles so as to reduce the overall pain level to one level or two levels;
when the overall pain level is second level and the maintenance time is greater than or equal to the third set time, the adjustment of each of the electrical needle parameters is performed so that the overall pain level is reduced to first level.
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| 穴位电刺激对痛信息处理过程的调制作用之脑电图研究(英文);张蔚婷, 罗非, 亓颖伟, 王颖, 张景渝, Donald J.WOODWORD, 陈昭燃, 韩济生;北京大学学报(医学版)(03);全文 * |
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