CN116831595B - Pain grade assessment method - Google Patents
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Abstract
The invention discloses a pain level assessment method, which belongs to the technical field of electric digital signal processing, and comprises the steps of starting from brain wave signals, collecting brain wave sequences, carrying out orthogonal decomposition on the brain wave sequences, splitting the brain wave sequences, thus obtaining various signal components, screening out signals with highest pain sensitivity, discarding other insensitive or less sensitive signal components, selecting out effective signals, obtaining a pain target signal, extracting significant characteristic values from the pain target signal, combining with non-significant characteristics in the pain target signal, assessing the pain level based on a pain level assessment model, and improving the accuracy of assessing the pain level.
Description
Technical Field
The invention relates to the technical field of electric digital signal processing, in particular to a pain level assessment method.
Background
The existing pain level evaluation method obtains a target low-frequency coefficient and a target high-frequency coefficient by collecting an electromyographic signal and decomposing the electromyographic signal, and extracts a signal characteristic value from the target low-frequency coefficient, thereby obtaining an objective pain level according to the signal characteristic value. The electromyographic signals are signals transmitted from the nervous system to the body where the body is stimulated, and reflect the muscle activity, not the pain level perceived by the brain, and the pain is perceived by the brain.
Disclosure of Invention
Aiming at the defects in the prior art, the pain level evaluation method provided by the invention solves the problem of low evaluation precision in the existing method for evaluating the pain level by the electromyographic signals.
In order to achieve the aim of the invention, the invention adopts the following technical scheme: a method of pain class assessment comprising the steps of:
s1, sampling brain waves to obtain an electroencephalogram sequence;
s2, carrying out orthogonal decomposition on the electroencephalogram sequence to obtain a plurality of signal components;
s3, screening out the signal with the highest pain sensitivity degree from the plurality of signal components to be used as a pain target signal;
s4, extracting a significant characteristic value from the pain target signal, and constructing a significant characteristic value sequence;
and S5, obtaining the pain level based on the pain level evaluation model according to the significant characteristic value sequence and the pain target signal.
Further, the expression of the signal component in S2 is:
，
wherein,is->Signal components>For the length of the brain electrical sequence, < > a->Is the +.>The data of the plurality of data,as an exponential function based on natural constants, < +.>Is imaginary.
The beneficial effects of the above further scheme are: the invention adopts the exponential factor to decompose the electroencephalogram sequence, so that the electroencephalogram sequence is divided into a plurality of signal components, thereby splitting the signal components and facilitating the screening of the most effective signals.
Further, the step S3 includes the following sub-steps:
s31, calculating a sensitivity value of the signal component to pain;
s32, selecting the signal component with the largest sensitivity value as a pain target signal.
Further, the formula for calculating the sensitivity value in S31 is:
，
wherein,is->Sensitivity value of the individual signal components,/>Is->First of the signal components>Numerical value->Stores the normal brain electrical sequence +.>First of the signal components>Numerical value->Is the number of values in the signal component.
The beneficial effects of the above further scheme are: according to the invention, the difference between the numerical value in each signal component and the numerical value in each signal component of the electroencephalogram sequence under normal conditions is calculated, when the difference is large, the signal component is more sensitive to pain, and the most sensitive signal component is selected, so that on one hand, noise can be removed, and meanwhile, the interference of other insensitive signals can be removed, and the evaluation accuracy is improved.
Further, the significant eigenvalue sequence in S4 isWherein->For the 1 st salient feature value, +.>For the 2 nd salient feature value, +.>For the 3 rd salient feature value, +.>Is the 4 th salient feature value;
the 1 st salient feature valueThe expression of (2) is:
，
wherein,to take the maximum value of the sequence, +.>Is the +.>Numerical value->Is the number of values in the pain target signal;
the 2 nd significant bitSign valueThe expression of (2) is:
，
the 3 rd salient feature valueThe expression of (2) is:
，
the 4 th salient feature valueThe expression of (2) is:
。
the beneficial effects of the above further scheme are: the invention passes through the 1 st significant characteristic valueExpress max, 2 nd salient feature value +.>Express mean, 3 rd salient feature value +.>Expressing the degree of deviation from the average value, expressing the fluctuation distribution of the data, 4 th salient feature value +.>The difference between the values is enhanced by taking the 4 th power of each value, and the overall distribution of the values is further expressed.
Further, the pain level assessment model in S5 includes: the device comprises a plurality of feature extraction networks, a feature fusion layer, a normalization layer, a first hiding layer, a second hiding layer and an output layer;
the input end of the characteristic extraction network is used for inputting the segmented pain target signal; the first input end of the feature fusion layer is used for being connected with the output end of the feature extraction network, and the second input end of the feature fusion layer is used for inputting a significant feature value sequence; the input end of the normalization layer is connected with the output end of the feature fusion layer, and the output end of the normalization layer is connected with the input end of the first hiding layer; the input end of the second hiding layer is connected with the output end of the first hiding layer; the input end of the output layer is connected with the output end of the second hidden layer, and the output end of the output layer is used as the output end of the pain class evaluation model.
Further, the feature extraction network includes: a Maxpool layer, an Avgpool layer, a Concat layer, a first Conv layer, a second Conv layer, a Softmax layer, a multiplier, and a sigmoid layer;
the input end of the first Conv layer is respectively connected with the input end of the Maxpool layer and the input end of the Avgpool layer and is used as the input end of the feature extraction network; the input end of the Concat layer is respectively connected with the output end of the Maxpool layer and the output end of the Avgpool layer, and the output end of the Concat layer is connected with the first input end of the multiplier; the second input end of the multiplier is connected with the output end of the Softmax layer, and the output end of the multiplier is connected with the input end of the second Conv layer; the input end of the Softmax layer is connected with the output end of the first Conv layer; the input end of the sigmoid layer is connected with the output end of the second Conv layer, and the output end of the sigmoid layer is used as the output end of the feature extraction network.
The beneficial effects of the above further scheme are: according to the invention, a first Conv layer is set to extract characteristics, a weight value is calculated through a Softmax layer and is applied to a multiplier, a maximum value characteristic is extracted through a Maxpool layer, an average value characteristic is extracted through an Avgpool layer, a Concat layer splices the maximum value characteristic and the average value characteristic to obtain a spliced signal, and the spliced signal is multiplied by the weight value, so that self-adaptive application attention degree is realized, the expression of characteristics is enhanced or weakened, the characteristics are extracted again through a second Conv layer, the condition of the output characteristics of the second Conv layer is comprehensively considered through a sigmoid layer, and the fine characteristics are extracted.
Further, the expression of the sigmoid layer is:
，
wherein,is a fine feature value->For S-type activation function, +.>Output of the +.>Numerical value->Is the +.>Personal weight(s)>The number of values is output for the second Conv layer.
The beneficial effects of the above further scheme are: in the sigmoid layer, weighting is carried out on all values output by the second Conv layer, so that a fine characteristic value of each target sub-signal is obtained.
Further, the step S5 includes the following sub-steps:
s51, segmenting the pain target signal to obtain a target sub-signal;
s52, converting the target sub-signals into matrixes to obtain matrix signals;
s53, extracting fine characteristic values from the matrix signals by adopting a characteristic extraction network, and constructing a fine characteristic value sequence;
s54, fusing the fine characteristic value sequence and the remarkable characteristic value sequence by adopting a characteristic fusion layer to obtain a fusion signal;
s55, carrying out normalization processing on the fusion signal by adopting a normalization layer to obtain normalized data;
s56, adopting a first hidden layer and a second hidden layer to perform continuous linear transformation on the normalized data twice to obtain the output of the hidden layers;
and S57, processing the output of the hidden layer by adopting the output layer to obtain the pain level.
The beneficial effects of the above further scheme are: according to the invention, the pain target signal is segmented, so that the fine characteristic value of each part is extracted, the fine characteristic value sequence and the obvious characteristic value sequence are fused, the data characteristic is fully expressed, and then the data characteristic is input into the BP neural network for evaluation, so that the evaluation accuracy is improved.
Further, the expression of the fusion signal in S54 is:
，
wherein,for fusion signal, ++>For the 1 st minute characteristic value, +.>Is->Fine feature value->Is->Fine feature value->For the 1 st salient feature value, +.>For the 2 nd salient feature value, +.>For the 3 rd salient feature value, +.>For the 4 th salient feature value, +.>Is a transpose operation.
In summary, the invention has the following beneficial effects: the invention starts from brain wave signals, acquires brain wave sequences, carries out orthogonal decomposition on the brain wave sequences, splits the brain wave sequences, thereby obtaining various signal components, screens out signals with highest pain sensitivity, discards other insensitive or insensitive signal components, picks out effective signals, obtains pain target signals, extracts obvious characteristic values of the pain target signals, combines with non-obvious characteristics in the pain target signals, evaluates pain grades based on a pain grade evaluation model, and improves the accuracy of evaluating the pain grades.
Drawings
FIG. 1 is a flow chart of a method of pain class assessment;
FIG. 2 is a schematic diagram of a pain class assessment model;
fig. 3 is a schematic diagram of the structure of the feature extraction network.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and all the inventions which make use of the inventive concept are protected by the spirit and scope of the present invention as defined and defined in the appended claims to those skilled in the art.
As shown in fig. 1, a pain level assessment method includes the steps of:
s1, sampling brain waves to obtain an electroencephalogram sequence;
s2, carrying out orthogonal decomposition on the electroencephalogram sequence to obtain a plurality of signal components;
the expression of the signal component in S2 is:
，
wherein,is->Signal components>For the length of the brain electrical sequence, < > a->Is the +.>The data of the plurality of data,as an exponential function based on natural constants, < +.>Is imaginary.
The invention adopts the exponential factor to decompose the electroencephalogram sequence, so that the electroencephalogram sequence is divided into a plurality of signal components, thereby splitting the signal components and facilitating the screening of the most effective signals.
S3, screening out the signal with the highest pain sensitivity degree from the plurality of signal components to be used as a pain target signal;
the step S3 comprises the following substeps:
s31, calculating a sensitivity value of the signal component to pain;
the formula for calculating the sensitivity value in S31 is:
，
wherein,is->Sensitivity value of the individual signal components,/>Is->First of the signal components>Numerical value->Stores the normal brain electrical sequence +.>First of the signal components>Numerical value->Is the number of values in the signal component;
s32, selecting the signal component with the largest sensitivity value as a pain target signal.
According to the invention, the difference between the numerical value in each signal component and the numerical value in each signal component of the electroencephalogram sequence under normal conditions is calculated, when the difference is large, the signal component is more sensitive to pain, and the most sensitive signal component is selected, so that on one hand, noise can be removed, and meanwhile, the interference of other insensitive signals can be removed, and the evaluation accuracy is improved.
S4, extracting a significant characteristic value from the pain target signal, and constructing a significant characteristic value sequence;
the significant eigenvalue sequence in S4 isWherein->For the 1 st salient feature value, +.>For the 2 nd salient feature value, +.>For the 3 rd salient feature value, +.>Is the 4 th salient feature value;
the 1 st salient feature valueThe expression of (2) is:
，
wherein,to take the maximum value of the sequence, +.>Is the +.>Numerical value->Is the number of values in the pain target signal;
the 2 nd significant eigenvalueThe expression of (2) is:
，
the 3 rd salient feature valueThe expression of (2) is:
，
the 4 th salient feature valueThe expression of (2) is:
。
the invention passes through the 1 st significant characteristic valueExpress max, 2 nd salient feature value +.>Express mean, 3 rd salient feature value +.>Expressing the degree of deviation from the average value, expressing the fluctuation distribution of the data, 4 th salient feature value +.>The difference between the values is enhanced by taking the 4 th power of each value, and the overall distribution of the values is further expressed.
And S5, obtaining the pain level based on the pain level evaluation model according to the significant characteristic value sequence and the pain target signal.
As shown in fig. 2, the pain class assessment model in S5 includes: the device comprises a plurality of feature extraction networks, a feature fusion layer, a normalization layer, a first hiding layer, a second hiding layer and an output layer;
the input end of the characteristic extraction network is used for inputting the segmented pain target signal; the first input end of the feature fusion layer is used for being connected with the output end of the feature extraction network, and the second input end of the feature fusion layer is used for inputting a significant feature value sequence; the input end of the normalization layer is connected with the output end of the feature fusion layer, and the output end of the normalization layer is connected with the input end of the first hiding layer; the input end of the second hiding layer is connected with the output end of the first hiding layer; the input end of the output layer is connected with the output end of the second hidden layer, and the output end of the output layer is used as the output end of the pain class evaluation model.
As shown in fig. 3, the feature extraction network includes: a Maxpool layer, an Avgpool layer, a Concat layer, a first Conv layer, a second Conv layer, a Softmax layer, a multiplier, and a sigmoid layer;
the input end of the first Conv layer is respectively connected with the input end of the Maxpool layer and the input end of the Avgpool layer and is used as the input end of the feature extraction network; the input end of the Concat layer is respectively connected with the output end of the Maxpool layer and the output end of the Avgpool layer, and the output end of the Concat layer is connected with the first input end of the multiplier; the second input end of the multiplier is connected with the output end of the Softmax layer, and the output end of the multiplier is connected with the input end of the second Conv layer; the input end of the Softmax layer is connected with the output end of the first Conv layer; the input end of the sigmoid layer is connected with the output end of the second Conv layer, and the output end of the sigmoid layer is used as the output end of the feature extraction network.
The Maxpool layer is the largest pooling layer and the Avgpool layer is the average pooling layer.
According to the invention, a first Conv layer is set to extract characteristics, a weight value is calculated through a Softmax layer and is applied to a multiplier, a maximum value characteristic is extracted through a Maxpool layer, an average value characteristic is extracted through an Avgpool layer, a Concat layer splices the maximum value characteristic and the average value characteristic to obtain a spliced signal, and the spliced signal is multiplied by the weight value, so that self-adaptive application attention degree is realized, the expression of characteristics is enhanced or weakened, the characteristics are extracted again through a second Conv layer, the condition of the output characteristics of the second Conv layer is comprehensively considered through a sigmoid layer, and the fine characteristics are extracted.
The expression of the sigmoid layer is as follows:
，
wherein,is a fine feature value->For S-type activation function, +.>Output of the +.>Numerical value->Is the +.>Personal weight(s)>The number of values is output for the second Conv layer.
In the sigmoid layer, weighting is carried out on all values output by the second Conv layer, so that a fine characteristic value of each target sub-signal is obtained.
The step S5 comprises the following substeps:
s51, segmenting the pain target signal to obtain a target sub-signal;
s52, converting the target sub-signals into matrixes to obtain matrix signals;
s53, extracting fine characteristic values from the matrix signals by adopting a characteristic extraction network, and constructing a fine characteristic value sequence;
s54, fusing the fine characteristic value sequence and the remarkable characteristic value sequence by adopting a characteristic fusion layer to obtain a fusion signal;
the expression of the fusion signal in S54 is:
，
wherein,for fusion signal, ++>For the 1 st minute characteristic value, +.>Is->Fine feature value->Is->Fine feature value->For the 1 st salient feature value, +.>For the 2 nd salient feature value, +.>For the 3 rd salient feature value, +.>For the 4 th salient feature value, +.>Is a transpose operation.
S55, carrying out normalization processing on the fusion signal by adopting a normalization layer to obtain normalized data;
s56, adopting a first hidden layer and a second hidden layer to perform continuous linear transformation on the normalized data twice to obtain the output of the hidden layers;
and S57, processing the output of the hidden layer by adopting the output layer to obtain the pain level.
According to the invention, the pain target signal is segmented, so that the fine characteristic value of each part is extracted, the fine characteristic value sequence and the obvious characteristic value sequence are fused, the data characteristic is fully expressed, and then the data characteristic is input into the BP neural network for evaluation, so that the evaluation accuracy is improved.
The invention starts from brain wave signals, acquires brain wave sequences, carries out orthogonal decomposition on the brain wave sequences, splits the brain wave sequences, thereby obtaining various signal components, screens out signals with highest pain sensitivity, discards other insensitive or insensitive signal components, picks out effective signals, obtains pain target signals, extracts obvious characteristic values of the pain target signals, combines with non-obvious characteristics in the pain target signals, evaluates pain grades based on a pain grade evaluation model, and improves the accuracy of evaluating the pain grades.
The above is only a preferred embodiment 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 (8)
1. A method of pain rating assessment, comprising the steps of:
s1, sampling brain waves to obtain an electroencephalogram sequence;
s2, carrying out orthogonal decomposition on the electroencephalogram sequence to obtain a plurality of signal components;
s3, screening out the signal with the highest pain sensitivity degree from the plurality of signal components to be used as a pain target signal;
s4, extracting a significant characteristic value from the pain target signal, and constructing a significant characteristic value sequence;
s5, obtaining a pain grade based on the pain grade evaluation model according to the significant characteristic value sequence and the pain target signal;
the step S5 comprises the following substeps:
s51, segmenting the pain target signal to obtain a target sub-signal;
s52, converting the target sub-signals into matrixes to obtain matrix signals;
s53, extracting fine characteristic values from the matrix signals by adopting a characteristic extraction network, and constructing a fine characteristic value sequence;
s54, fusing the fine characteristic value sequence and the remarkable characteristic value sequence by adopting a characteristic fusion layer to obtain a fusion signal;
s55, carrying out normalization processing on the fusion signal by adopting a normalization layer to obtain normalized data;
s56, adopting a first hidden layer and a second hidden layer to perform continuous linear transformation on the normalized data twice to obtain the output of the hidden layers;
s57, processing the output of the hidden layer by adopting the output layer to obtain a pain level;
the expression of the fusion signal in S54 is:
，
wherein,for fusion signal, ++>For the 1 st minute characteristic value, +.>Is->Fine feature value->Is->Fine feature value->Is 1 stSignificant eigenvalue(s)>For the 2 nd salient feature value, +.>For the 3 rd salient feature value, +.>For the 4 th salient feature value, +.>Is a transpose operation.
2. The pain level assessment method according to claim 1, wherein the expression of the signal component in S2 is:
，
wherein,is->Signal components>For the length of the brain electrical sequence, < > a->Is the +.>Data of->As an exponential function based on natural constants, < +.>Is imaginary.
3. The pain level assessment method according to claim 2, wherein S3 comprises the following substeps:
s31, calculating a sensitivity value of the signal component to pain;
s32, selecting the signal component with the largest sensitivity value as a pain target signal.
4. A pain level assessment method according to claim 3, wherein the formula for calculating the sensitivity value in S31 is:
，
wherein,is->Sensitivity value of the individual signal components,/>Is->First of the signal components>Numerical value->Stores the normal brain electrical sequence +.>First of the signal components>Numerical value->Is the number of values in the signal component.
5. The pain level assessment method according to claim 2, wherein the sequence of salient feature values in S4 isWherein->For the 1 st salient feature value, +.>For the 2 nd salient feature value, +.>For the 3 rd salient feature value, +.>Is the 4 th salient feature value;
the 1 st salient feature valueThe expression of (2) is:
，
wherein,to take the maximum value of the sequence, +.>Is the +.>Numerical value->Is the number of values in the pain target signal;
the 2 nd significant eigenvalueThe expression of (2) is:
，
the 3 rd salient feature valueThe expression of (2) is:
，
the 4 th salient feature valueThe expression of (2) is:
。
6. the pain level assessment method according to claim 1, wherein the pain level assessment model in S5 comprises: the device comprises a plurality of feature extraction networks, a feature fusion layer, a normalization layer, a first hiding layer, a second hiding layer and an output layer;
the input end of the characteristic extraction network is used for inputting the segmented pain target signal; the first input end of the feature fusion layer is used for being connected with the output end of the feature extraction network, and the second input end of the feature fusion layer is used for inputting a significant feature value sequence; the input end of the normalization layer is connected with the output end of the feature fusion layer, and the output end of the normalization layer is connected with the input end of the first hiding layer; the input end of the second hiding layer is connected with the output end of the first hiding layer; the input end of the output layer is connected with the output end of the second hidden layer, and the output end of the output layer is used as the output end of the pain class evaluation model.
7. The pain level assessment method according to claim 6, wherein the feature extraction network comprises: a Maxpool layer, an Avgpool layer, a Concat layer, a first Conv layer, a second Conv layer, a Softmax layer, a multiplier, and a sigmoid layer;
the input end of the first Conv layer is respectively connected with the input end of the Maxpool layer and the input end of the Avgpool layer and is used as the input end of the feature extraction network; the input end of the Concat layer is respectively connected with the output end of the Maxpool layer and the output end of the Avgpool layer, and the output end of the Concat layer is connected with the first input end of the multiplier; the second input end of the multiplier is connected with the output end of the Softmax layer, and the output end of the multiplier is connected with the input end of the second Conv layer; the input end of the Softmax layer is connected with the output end of the first Conv layer; the input end of the sigmoid layer is connected with the output end of the second Conv layer, and the output end of the sigmoid layer is used as the output end of the feature extraction network.
8. The pain level assessment method according to claim 7, wherein the sigmoid layer has the expression:
，
wherein,is a fine feature value->For S-type activation function, +.>Output of the +.>The number of the values to be used in the process,is the +.>Personal weight(s)>The number of values is output for the second Conv layer.
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