CN111493836A - Postoperative acute pain prediction system based on brain-computer interface and deep learning and application - Google Patents

Postoperative acute pain prediction system based on brain-computer interface and deep learning and application Download PDF

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CN111493836A
CN111493836A CN202010481486.0A CN202010481486A CN111493836A CN 111493836 A CN111493836 A CN 111493836A CN 202010481486 A CN202010481486 A CN 202010481486A CN 111493836 A CN111493836 A CN 111493836A
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高忠科
曲志勇
侯林华
马文庆
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Tianjin University
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Abstract

A postoperative acute pain prediction system based on brain-computer interface and deep learning and application thereof comprise: acquiring resting state electroencephalogram signals of a patient before operation for 0-3 days by portable electroencephalogram acquisition equipment; performing data segmentation on the preoperative resting state electroencephalogram signal through a sliding window to obtain a series of resting state electroencephalogram signal segments; a series of resting state electroencephalogram signal segments are correspondingly converted into a series of two-dimensional images by utilizing a preprocessing method for subsequent analysis; constructing a data set, adding labels and dividing a training set and a testing set; and constructing a prediction model based on deep learning, determining a model structure and model parameters to be optimized, and obtaining the prediction model based on deep learning, which can predict postoperative acute pain, through training and testing. The portable electroencephalogram acquisition equipment can conveniently and efficiently acquire electroencephalogram signals; the postoperative pain category is predicted under the condition of no damage to the patient through the analysis of the brain electrical signals of the patient in the preoperative resting state.

Description

Postoperative acute pain prediction system based on brain-computer interface and deep learning and application
Technical Field
The invention relates to prediction of postoperative acute pain. In particular to a postoperative acute pain prediction system based on brain-computer interface and deep learning and application thereof.
Background
Pain is a common condition defined by the international association for pain research as "an unpleasant subjective feeling and emotional experience associated with tissue damage or potential tissue damage". Pain can be classified into acute pain and chronic pain according to the duration and nature of the pain. Acute pain refers to pain that exists for a short period of time, usually after a noxious stimulus, and may progress to chronic pain if not completely controlled in the initial phase. Chronic pain refers to pain which lasts for more than three months due to untimely treatment or improper medication, and long-term chronic pain can cause abnormal changes of the cerebral nervous system and seriously affect the life quality of patients. The scalp electroencephalogram signals are an important way for objectively evaluating pain diseases, research and analysis on the electroencephalogram signals of the patient before operation in a resting state can provide important basis for classification of acute pain and chronic pain, and effective prediction of postoperative acute pain has important guiding significance for a medical operation process.
The brain electrical activity is derived from the free discharge activity of the brain neuron population, and is the general reflection of the electrophysiological activity of the brain nerve cells on the surface of the cerebral cortex or scalp. The electroencephalogram signals contain a large amount of physiological and disease information, and research and analysis on the electroencephalogram signals can not only provide diagnosis basis for certain brain diseases, but also provide effective treatment means for certain brain diseases. People try to effectively extract and analyze electroencephalogram signals through a brain-computer interface (BCI), thereby achieving a certain control purpose. Because the electroencephalogram signal is a non-stationary random signal without ergodicity and the noise influence is great, the analysis and the processing of the electroencephalogram signal are important research contents. The electroencephalogram signal analysis method comprises the traditional methods of Fourier transform, frequency domain analysis, time domain analysis and the like, and then wavelet analysis, matching tracking method, neural network analysis, chaos analysis and the like appear, so that deep learning provides a more efficient analysis method for electroencephalogram analysis, and the development of the electroencephalogram signal analysis method is powerfully promoted.
Deep learning, which is a data-driven algorithm, can automatically learn abstract representation features of original data, and is widely used in a feature extraction process. The convolutional neural network is one of the most effective deep learning algorithms and is mainly applied to the fields of image recognition, face detection, character recognition and the like. The convolutional neural network uses convolutional layers and pooling layers, and simultaneously introduces a mechanism of local receptive field and weight sharing, so that the number of parameters to be trained is greatly reduced, the best convolutional kernels aiming at a specific classification task and the combination mode of the convolutional kernels are automatically learned, and the best feature expression of an input image for the classification task is calculated. In recent years, the use of the convolutional neural network in time sequence analysis is gradually increased, the convolutional neural network is used for fully extracting the characteristics existing in the time sequence, and the analysis of the time sequence, particularly the effective analysis of electroencephalogram signals can be effectively realized.
Disclosure of Invention
The invention aims to solve the technical problem of providing a postoperative acute pain prediction system based on brain-computer interface and deep learning and capable of obtaining a relatively accurate prediction result and application thereof.
The technical scheme adopted by the invention is as follows: a postoperative acute pain prediction system based on brain-computer interface and deep learning comprises the following steps:
1) obtaining resting state electroencephalogram signals of 0-3 days before operation of a patient through portable electroencephalogram acquisition equipment
Figure BDA0002517561920000021
Wherein g denotes the g-th channel, LeIndicating the data length of each signal;
2) for resting state electroencephalogram signals before operation
Figure BDA0002517561920000022
A passage length of leThe sliding window is used for carrying out data segmentation, the sliding step length of the sliding window is s, a series of resting state electroencephalogram signal segments are obtained, and the p-th resting state electroencephalogram signal segment is expressed as
Figure BDA0002517561920000023
3) A series of resting state electroencephalogram signal segments are correspondingly converted into a series of two-dimensional images by utilizing a preprocessing method for subsequent analysis;
4) constructing a data set, adding labels and dividing a training set and a testing set;
5) and constructing a prediction model based on deep learning, determining a model structure and model parameters to be optimized, and obtaining the prediction model based on deep learning, which can predict postoperative acute pain, through training and testing.
Use of a brain-computer interface and deep learning based post-operative acute pain prediction system according to claim 1, comprising:
1) method for acquiring preoperative resting state electroencephalogram signals of patients in actual medical process based on portable electroencephalogram acquisition equipment
Figure BDA0002517561920000024
Uploading, storing and data partitioning;
2) the data signals obtained by segmentation are subjected to the prediction system of the postoperative acute pain based on the brain-computer interface and the deep learning
Figure BDA0002517561920000025
Carrying out pretreatment;
3) inputting the preprocessed two-dimensional image into a prediction model capable of predicting postoperative acute pain and based on deep learning to predict postoperative acute pain category of the patient, and displaying the postoperative acute pain category on a display in real time.
According to the postoperative acute pain prediction system based on the brain-computer interface and the deep learning and the application thereof, the portable electroencephalogram acquisition equipment is low in manufacturing cost, open in interface, small in size, convenient to carry and wear and capable of conveniently and efficiently acquiring electroencephalogram signals; the implicit characteristics in the electroencephalogram signals can be fully extracted by using an intelligent algorithm based on deep learning, and the result of accurately predicting postoperative acute pain can be obtained; the intelligent algorithm based on deep learning can change the model structure by adjusting the number of different layers, so that the method is suitable for the calculation and analysis of data with different data volumes and complexity and has good expansibility; the postoperative pain category is predicted under the condition of no damage to the patient through the analysis of the preoperative resting state electroencephalogram signal of the patient, and the method has important guiding significance for the operation medical treatment.
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FIG. 1 is a flow chart of the post-operative acute pain prediction system and application based on brain-computer interface and deep learning of the present invention;
FIG. 2 is a system block diagram of a portable electroencephalogram acquisition device in the invention;
FIG. 3 is a diagram illustrating the distribution and names of the 32-lead EEGs according to the present invention;
FIG. 4 is a schematic diagram of a method for preprocessing brain electrical signals according to the present invention;
FIG. 5 is a schematic diagram of the deep learning-based prediction model of the present invention.
Detailed Description
The following describes the brain-computer interface and deep learning based postoperative acute pain prediction system and application of the present invention in detail with reference to the accompanying drawings.
As shown in fig. 1, the system for predicting acute postoperative pain based on brain-computer interface and deep learning of the present invention comprises the following steps:
1) obtaining resting state electroencephalogram signals of 0-3 days before operation of a patient through portable electroencephalogram acquisition equipment
Figure BDA0002517561920000031
Wherein g denotes the g-th channel, LeIndicating the data length of each signal;
as shown in fig. 2, the portable electroencephalogram acquisition device comprises: a brain electrode cap and a patch cord 1 thereof which are connected in sequence and used for collecting brain electrical signals, a bioelectric signal collecting module 2 used for amplifying and converting the brain electrical signals, an FPGA processor 3 used for controlling the collection of the brain electrical signals and outputting the brain electrical signals through a USB communication circuit 4, and a system power supply circuit 5 respectively connected with the bioelectric signal collecting module 2 and the FPGA processor 3, wherein,
the brain electrode cap and the brain electrode cap in the patch cord 1 thereof are used for collecting brain electrical signals of different brain areas, are connected with the bioelectric signal collecting module 2 through the patch cord and a DSUB37 interface and are used for collecting and transmitting bioelectric signals;
the bioelectrical signal acquisition module 2 consists of a plurality of bioelectrical signal acquisition chips which are integrated with a high common mode rejection ratio analog input module for receiving the electroencephalogram voltage signals acquired by the electroencephalogram cap, a low-noise programmable gain amplifier for amplifying the electroencephalogram voltage signals and a high-resolution synchronous sampling analog-to-digital converter for converting the analog signals into digital signals;
the FPGA processor 3 is used for adjusting the acquisition mode and parameters of the bioelectricity signal acquisition module 2 and controlling the USB communication circuit 4 to output electroencephalogram signal data;
the USB communication circuit 4 works in an asynchronous FIFO mode, has the highest transmission rate of 8 MB/s, and periodically outputs the acquired electroencephalogram signals in the form of data packets under the control of the FPGA processor 3;
the system power supply circuit 5 has an input voltage of 5V, is powered by the USB interface, and provides working voltages of different chips of the system through the voltage conversion module.
As shown in fig. 3, the portable electroencephalogram acquisition device acquires electroencephalogram signals of twenty-one electrodes of a subject corresponding to FP1, FP2, F3, F7, T7, P7, O1, C3, P3, F4, C4, P4, F8, O2, Fz, Cz, Pz, FT9, FT10, T8 and P8; the electrode distribution of the brain electrode cap conforms to 10/20 international standard leads.
2) For resting state electroencephalogram signals before operation
Figure BDA0002517561920000032
A passage length of leThe sliding window of (2) performs data segmentation and slidingThe sliding step length of the dynamic window is s, a series of resting state electroencephalogram signal segments are obtained, and the p-th resting state electroencephalogram signal segment is expressed as
Figure BDA0002517561920000033
3) A series of resting state electroencephalogram signal segments are correspondingly converted into a series of two-dimensional images by utilizing a preprocessing method for subsequent analysis; as shown in fig. 3, includes:
randomly obtaining a length of L from each resting state electroencephalogram signal segment2Of the signal segment of (a), the value of each time point in the signal segment being s (i), i ═ 1,2, … L2Where i represents the time point and L is the length, the pixel intensity of the converted two-dimensional image is
Figure BDA0002517561920000034
j is 1,2, … N, k is 1,2, … N, round (-) is a rounding function, max (-) is a maximum function, min (-) is a minimum function, the integer pixel value is normalized to the range of 0-255, i.e. the pixel intensity of the gray scale image;
wherein, P is the pixel intensity, S is the signal value, j and k are respectively the abscissa and ordinate corresponding to the pixel point, and N is the maximum coordinate value.
4) Constructing a data set, adding labels and dividing a training set and a testing set;
the data set is a series of preprocessed two-dimensional images, the labels are two kinds of labels of postoperative acute pain and postoperative chronic pain, the postoperative acute pain is to carry out continuous follow-up investigation on a patient after operation, the patient is subjected to pain digital scoring by using a European five-dimensional health scale and a simple pain assessment scale, and the postoperative acute pain is defined if the pain digital scoring on the third day after operation is more than 3; the postoperative chronic pain is obtained by performing follow-up visits on patients for 1,2, 4, 6 and 8 weeks after operation, performing pain numerical scoring on the patients by using a European five-dimensional health scale, a concise pain assessment scale and a pain catastrophe scale, and defining the postoperative chronic pain as the postoperative chronic pain if the pain numerical scoring is greater than or equal to 2 at 8 weeks after operation.
5) And constructing a prediction model based on deep learning, determining a model structure and model parameters to be optimized, and obtaining the prediction model based on deep learning, which can predict postoperative acute pain, through training and testing. Wherein the content of the first and second substances,
(A) as shown in fig. 5, the prediction model based on deep learning includes: n is1A convolution layer, n2Individual pooling layer, n3A full connection layer, n4Batch normalization layer, n5A random loss layer, 1 branch fusion structure; wherein n is1、n2、n3、n4And n5Is set artificially according to the data quantity and the data characteristics, n1、n2And n4Are all positive integers greater than 5, n3And n5All values are positive integers not less than 2;
each convolutional layer extracts valid features from the input of the model by convolution calculation, which is as follows:
Xl＝f(∑Xl-1*wl+bl)
wherein XlAnd Xl-1Feature maps, w, representing the current and previous layers, respectivelylRepresents a weight, blRepresenting bias, f represents an activation function, and a Relu activation function is selected;
each pooling layer is used for enlarging the receptive field, a matrix window is used for scanning on the characteristic diagram, the number of elements in each matrix is reduced through a pooling method, the spatial position relation of the characteristics is kept, and the pooling method is a maximum pooling method, an average pooling method or a spatial pyramid pooling method;
each full connection layer is used for flattening multi-dimensional data and converting the multi-dimensional data into one-dimensional vectors;
each batch normalization layer is used for distributing the neurons output by the previous layer in a standard normal distribution with the mean value of 0 and the variance of 1, and the formula is
Figure BDA0002517561920000041
Wherein, yoDenotes output after batch normalization, BN denotes batch normalization, x0Is a neuron before the batch standardization,
Figure BDA0002517561920000042
is the neuron after the conversion, and the neuron is the neuron,
Figure BDA0002517561920000043
is the mean of the smallest batch, m is the smallest batch,
Figure BDA0002517561920000044
is the variance at the current batch, γ and β are the parameters learned, are a constant;
each random loss layer is used for randomly deleting a part of neurons in the network according to a set quantity so as to reduce the overfitting phenomenon;
the branch fusion structure comprises more than 2 convolutional neural networks, and features extracted by the convolutional neural networks are fused together through vector addition or vector splicing.
(B) The model training process comprises the following steps:
(1) setting an initial learning rate by taking a training set as the input of a prediction model based on deep learning, and optimizing model parameters by using an optimization algorithm for training, wherein the optimization algorithm is Adam or SGD;
(2) taking a test set as the input of a prediction model based on deep learning after training, and adjusting the structure and parameters of the prediction model based on deep learning through a generated accuracy rate change curve and a loss condition, wherein a cross entropy loss function is used for evaluating the difference condition of probability distribution and real distribution obtained by current training;
(3) and (3) repeating the steps (1) to (2) until a deep learning-based prediction model with the accuracy rate of predicting the postoperative acute pain larger than 90% is obtained as the deep learning-based prediction model capable of predicting the postoperative acute pain.
The invention discloses an application of a postoperative acute pain prediction system based on brain-computer interface and deep learning, which comprises the following steps:
1) method for acquiring preoperative resting state electroencephalogram signals of patients in actual medical process based on portable electroencephalogram acquisition equipment
Figure BDA0002517561920000051
Uploading, storing and data partitioning;
2) the data signals obtained by segmentation are subjected to the prediction system of the postoperative acute pain based on the brain-computer interface and the deep learning
Figure BDA0002517561920000052
Carrying out pretreatment;
3) inputting the preprocessed two-dimensional image into a prediction model capable of predicting postoperative acute pain and based on deep learning to predict postoperative acute pain category of the patient, and displaying the postoperative acute pain category on a display in real time.
The branch fusion structure extracts multi-scale features by using convolution kernels with different sizes, wherein a convolution calculation in one branch structure uses a convolution kernel of 3 × 3, a convolution calculation in the other branch structure uses a convolution kernel of 5 × 5, the features extracted by the two branch structures are fused in series, then a series of operations such as convolution, pooling, batch standardization and full connection are carried out again, a Relu activation function is selected for activation after random loss, and finally an output result is obtained through a full connection layer with the dimension of 2 and the activation function of softmax, an initial learning rate of 0.0005 is set in the training process, model parameters are optimized by using an Adam algorithm, a data set comprises 355 training set samples, 40 testing set samples, and the testing accuracy rate of over 94% is obtained through the training of 500 epochs, so that the effectiveness of the branch fusion structure is verified.
The above description of the present invention and the embodiments is not limited thereto, and the description of the embodiments is only one of the implementation manners of the present invention, and any structure or embodiment similar to the technical solution without inventive design is within the protection scope of the present invention without departing from the inventive spirit of the present invention.

Claims (8)

1. A postoperative acute pain prediction system based on brain-computer interface and deep learning is characterized by comprising the following steps:
1) obtaining resting state electroencephalogram signals of 0-3 days before operation of a patient through portable electroencephalogram acquisition equipment
Figure FDA0002517561910000011
Wherein g denotes the g-th channel, LeIndicating the data length of each signal;
2) for resting state electroencephalogram signals before operation
Figure FDA0002517561910000012
A passage length of leThe sliding window is used for carrying out data segmentation, the sliding step length of the sliding window is s, a series of resting state electroencephalogram signal segments are obtained, and the p-th resting state electroencephalogram signal segment is expressed as
Figure FDA0002517561910000013
3) A series of resting state electroencephalogram signal segments are correspondingly converted into a series of two-dimensional images by utilizing a preprocessing method for subsequent analysis;
4) constructing a data set, adding labels and dividing a training set and a testing set;
5) and constructing a prediction model based on deep learning, determining a model structure and model parameters to be optimized, and obtaining the prediction model based on deep learning, which can predict postoperative acute pain, through training and testing.
2. The system for predicting postoperative acute pain based on brain-computer interface and deep learning of claim 1, wherein the portable brain electricity collecting device of step 1) comprises: a brain electrode cap and a patch cord (1) thereof which are connected in sequence and used for collecting brain electrical signals, a bioelectric signal collecting module (2) used for amplifying and converting the brain electrical signals, an FPGA processor (3) used for controlling the collection of the brain electrical signals and outputting the brain electrical signals through a USB communication circuit (4), and a system power supply circuit (5) respectively connected with the bioelectric signal collecting module (2) and the FPGA processor (3), wherein,
the brain electrode cap and the brain electrode cap in the patch cord (1) thereof are used for collecting brain electrical signals of different brain areas, are connected with the bioelectricity signal collecting module (2) through the patch cord and a DSUB37 interface and are used for collecting and transmitting bioelectricity signals;
the bioelectrical signal acquisition module (2) is composed of a plurality of bioelectrical signal acquisition chips which are integrated with a high common mode rejection ratio analog input module for receiving electroencephalogram voltage signals acquired by an electroencephalogram cap, a low-noise programmable gain amplifier for amplifying the electroencephalogram voltage signals and a high-resolution synchronous sampling analog-to-digital converter for converting the analog signals into digital signals;
the FPGA processor (3) is used for adjusting the acquisition mode and parameters of the bioelectricity signal acquisition module (2) and controlling the USB communication circuit (4) to output electroencephalogram signal data;
the USB communication circuit (4) works in an asynchronous FIFO mode, the highest transmission rate is 8 MB/s, and the acquired electroencephalogram signals are periodically output in a data packet mode under the control of the FPGA processor (3);
the input voltage of the system power supply circuit (5) is 5V, the USB interface supplies power, and the working voltage of different chips of the system is provided through the voltage conversion module.
3. The brain-computer interface and deep learning based postoperative acute pain prediction system of claim 2, wherein the portable electroencephalograph acquisition device acquires electroencephalograms of twenty-one electrodes in total of the subject corresponding to FP1, FP2, F3, F7, T7, P7, O1, C3, P3, F4, C4, P4, F8, O2, Fz, Cz, Pz, FT9, FT10, T8, P8 of the electroencephalogram cap; the electrode distribution of the brain electrode cap conforms to 10/20 international standard leads.
4. The brain-computer interface and deep learning based post-operative acute pain prediction system of claim 1, wherein step 3) comprises: from each oneRandomly obtaining L length from each resting state electroencephalogram signal segment2Of the signal segment of (a), the value of each time point in the signal segment being s (i), i ═ 1,2, … L2Where i represents the time point and L is the length, the pixel intensity of the converted two-dimensional image is
Figure FDA0002517561910000021
j is 1,2, … N, k is 1,2, … N, round (-) is a rounding function, max (-) is a maximum function, min (-) is a minimum function, the integer pixel value is normalized to the range of 0-255, i.e. the pixel intensity of the gray scale image;
wherein, P is the pixel intensity, S is the signal value, j and k are respectively the abscissa and ordinate corresponding to the pixel point, and N is the maximum coordinate value.
5. The brain-computer interface and deep learning based postoperative acute pain prediction system according to claim 1, wherein the data set in step 4) is a pre-processed series of two-dimensional images, the labels are two types of labels of postoperative acute pain and postoperative chronic pain, the postoperative acute pain is to be continuously followed and investigated for patients after operation, the patients are scored with a European five-dimensional health scale and a concise pain assessment scale, and the postoperative acute pain is defined if the score of the pain score is greater than 3 on the third day after operation; the postoperative chronic pain is obtained by performing follow-up visits on patients for 1,2, 4, 6 and 8 weeks after operation, performing pain numerical scoring on the patients by using a European five-dimensional health scale, a concise pain assessment scale and a pain catastrophe scale, and defining the postoperative chronic pain as the postoperative chronic pain if the pain numerical scoring is greater than or equal to 2 at 8 weeks after operation.
6. The brain-computer interface and deep learning based prediction system for acute postoperative pain according to claim 1, wherein the deep learning based prediction model in step 5) comprises: n is1A convolution layer, n2Individual pooling layer, n3A full connection layer, n4Batch normalization layer, n5A random lossLayer, 1 branch fusion structure; wherein n is1、n2、n3、n4And n5Is set artificially according to the data quantity and the data characteristics, n1、n2And n4Are all positive integers greater than 5, n3And n5All values are positive integers not less than 2;
each convolutional layer extracts valid features from the input of the model by convolution calculation, which is as follows:
Xl＝f(∑Xl-1*wl+bl)
wherein XlAnd Xl-1Feature maps, w, representing the current and previous layers, respectivelylRepresents a weight, blRepresenting bias, f represents an activation function, and a Relu activation function is selected;
each pooling layer is used for enlarging the receptive field, a matrix window is used for scanning on the characteristic diagram, the number of elements in each matrix is reduced through a pooling method, the spatial position relation of the characteristics is kept, and the pooling method is a maximum pooling method, an average pooling method or a spatial pyramid pooling method;
each full connection layer is used for flattening multi-dimensional data and converting the multi-dimensional data into one-dimensional vectors;
each batch normalization layer is used for distributing the neurons output by the previous layer in a standard normal distribution with the mean value of 0 and the variance of 1, and the formula is
Figure FDA0002517561910000022
Wherein, yoDenotes output after batch normalization, BN denotes batch normalization, x0Is a neuron before the batch standardization,
Figure FDA0002517561910000023
is the neuron after the conversion, and the neuron is the neuron,
Figure FDA0002517561910000024
is the mean of the smallest batch, m is the smallest batch,
Figure FDA0002517561910000031
is the variance at the current batch, γ and β are the parameters learned, are a constant;
each random loss layer is used for randomly deleting a part of neurons in the network according to a set quantity so as to reduce the overfitting phenomenon;
the branch fusion structure comprises more than 2 convolutional neural networks, and features extracted by the convolutional neural networks are fused together through vector addition or vector splicing.
7. The brain-computer interface and deep learning based acute pain prediction system of claim 1, wherein the model training process in step 5) comprises:
(1) setting an initial learning rate by taking a training set as the input of a prediction model based on deep learning, and optimizing model parameters by using an optimization algorithm for training, wherein the optimization algorithm is Adam or SGD;
(2) taking a test set as the input of a prediction model based on deep learning after training, and adjusting the structure and parameters of the prediction model based on deep learning through a generated accuracy rate change curve and a loss condition, wherein a cross entropy loss function is used for evaluating the difference condition of probability distribution and real distribution obtained by current training;
(3) and (3) repeating the steps (1) to (2) until a deep learning-based prediction model with the accuracy rate of predicting the postoperative acute pain larger than 90% is obtained as the deep learning-based prediction model capable of predicting the postoperative acute pain.
8. Use of a brain-computer interface and deep learning based post-operative acute pain prediction system according to claim 1, comprising:
1) method for acquiring preoperative resting state electroencephalogram signals of patients in actual medical process based on portable electroencephalogram acquisition equipment
Figure FDA0002517561910000032
Uploading, storing and data partitioning;
2) the data signals obtained by segmentation are subjected to the prediction system of the postoperative acute pain based on the brain-computer interface and the deep learning
Figure FDA0002517561910000033
Carrying out pretreatment;
3) inputting the preprocessed two-dimensional image into a prediction model capable of predicting postoperative acute pain and based on deep learning to predict postoperative acute pain category of the patient, and displaying the postoperative acute pain category on a display in real time.
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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112263253A (en) * 2020-11-18 2021-01-26 山东大学 Depression recognition system, medium and equipment based on deep learning and electrocardiosignal
CN113925509A (en) * 2021-09-09 2022-01-14 杭州回车电子科技有限公司 Electroencephalogram signal based attention value calculation method and device and electronic device
CN116831595A (en) * 2023-06-21 2023-10-03 中国人民解放军西部战区总医院 Pain grade assessment method

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20110087125A1 (en) * 2009-10-09 2011-04-14 Elvir Causevic System and method for pain monitoring at the point-of-care
CN106503799A (en) * 2016-10-11 2017-03-15 天津大学 Deep learning model and the application in brain status monitoring based on multiple dimensioned network
CN107292887A (en) * 2017-06-20 2017-10-24 电子科技大学 A kind of Segmentation Method of Retinal Blood Vessels based on deep learning adaptive weighting
CN108433722A (en) * 2018-02-28 2018-08-24 天津大学 Portable brain electric collecting device and its application in SSVEP and Mental imagery
CN108446020A (en) * 2018-02-28 2018-08-24 天津大学 Merge Mental imagery idea control method and the application of Visual Graph and deep learning
CN110599413A (en) * 2019-08-15 2019-12-20 江苏大学 Laser spot image denoising method based on deep learning convolution neural network
CN111079837A (en) * 2019-12-16 2020-04-28 桂林电子科技大学 Method for detecting, identifying and classifying two-dimensional gray level images

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20110087125A1 (en) * 2009-10-09 2011-04-14 Elvir Causevic System and method for pain monitoring at the point-of-care
CN106503799A (en) * 2016-10-11 2017-03-15 天津大学 Deep learning model and the application in brain status monitoring based on multiple dimensioned network
CN107292887A (en) * 2017-06-20 2017-10-24 电子科技大学 A kind of Segmentation Method of Retinal Blood Vessels based on deep learning adaptive weighting
CN108433722A (en) * 2018-02-28 2018-08-24 天津大学 Portable brain electric collecting device and its application in SSVEP and Mental imagery
CN108446020A (en) * 2018-02-28 2018-08-24 天津大学 Merge Mental imagery idea control method and the application of Visual Graph and deep learning
CN110599413A (en) * 2019-08-15 2019-12-20 江苏大学 Laser spot image denoising method based on deep learning convolution neural network
CN111079837A (en) * 2019-12-16 2020-04-28 桂林电子科技大学 Method for detecting, identifying and classifying two-dimensional gray level images

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112263253A (en) * 2020-11-18 2021-01-26 山东大学 Depression recognition system, medium and equipment based on deep learning and electrocardiosignal
CN113925509A (en) * 2021-09-09 2022-01-14 杭州回车电子科技有限公司 Electroencephalogram signal based attention value calculation method and device and electronic device
CN113925509B (en) * 2021-09-09 2024-01-23 杭州回车电子科技有限公司 Attention value calculation method and device based on electroencephalogram signals and electronic device
CN116831595A (en) * 2023-06-21 2023-10-03 中国人民解放军西部战区总医院 Pain grade assessment method
CN116831595B (en) * 2023-06-21 2024-04-12 中国人民解放军西部战区总医院 Pain grade assessment method

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