CN116250844B - Electrocardiosignal noise reduction optimization method and system based on condition generation countermeasure network - Google Patents
Electrocardiosignal noise reduction optimization method and system based on condition generation countermeasure network Download PDFInfo
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Abstract
The invention discloses an electrocardiosignal noise reduction optimization method and system based on a condition generation countermeasure network, comprising the following steps: acquiring an electrocardiosignal, dividing the electrocardiosignal according to the sample length T, and carrying out maximum and minimum normalization processing on the divided data fragments; inputting the processed data segment into a trained electrocardiosignal noise reduction model to obtain a noise-reduced electrocardiosignal; the electrocardiosignal noise reduction model is constructed based on a deep neural network and a condition generation countermeasure network; and determining the optimal values of modeling parameters such as the sample length, the number of layers of the neural network of the encoder and the decoder and the like according to the fitting function curve of the corresponding modeling parameters and the index on the premise of ensuring the noise reduction performance of the model by calculating the noise reduction performance and the calculation complexity ratio index of the model, so that the noise reduction performance and the calculation cost of the noise reduction model are optimal. The invention provides a noise reduction system with better cost performance by providing a noise reduction performance and calculation complexity ratio index.
Description
Technical Field
The invention relates to the technical field of electrocardiosignal noise reduction, in particular to an electrocardiosignal noise reduction optimization method and system based on a condition generation countermeasure network.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
Electrocardiography is a technology for reflecting the heart activity condition by collecting electrophysiological signals at fixed positions on a body surface, and is widely used for diagnosing heart diseases at present and is one of the gold standards for medical diagnosis of heart diseases. However, the electrocardiographic signal (hereinafter referred to as an electrocardiographic signal) is easily interfered by various noises such as physical activity and circuit noise, and thus the electrocardiographic signal is affected for diagnosing heart diseases.
Traditional electrocardiosignal noise reduction methods include: filters, fourier transforms, wavelet decomposition, etc., are generally directed to a single noise type, and the time-frequency characteristics of the noise should be clearly different from that of the electrocardiograph signal itself. With the development of deep learning, deep learning noise reduction algorithms for various noises are more and more, such as: the method comprises the steps of noise reduction, self-encoder, convolution self-encoder and the like, wherein the method comprises the steps of countermeasure noise reduction based on a fully connected network, generation countermeasure noise reduction based on the convolution self-encoder and the like, and the problems that electrocardiosignals in the algorithm need to be divided according to heart beats before noise reduction, mixed noise cannot be processed and the like are solved to a certain extent.
However, the algorithm also has the defects of insufficient potential mining of model learning data distribution, lack of reasonable model, optimization of a system, high model calculation complexity and difficulty in deployment on remote medical equipment.
Such as:
The prior art discloses a method for reducing electrocardiosignal noise based on generation countermeasure network, wherein a generator network mostly adopts network structures such as full connection, convolution and the like, but modeling is carried out by adopting a Convolution Neural Network (CNN) or a bidirectional long-short-time memory network (BiLSTM), and due to the lack of an optimization method for modeling of a high-efficiency noise reduction network model, the structure of a model established by the prior art is complex, the scale is large, the calculation complexity is high, and the requirement on deployment environment is harsh.
Disclosure of Invention
In order to solve the problems, the invention provides an electrocardiosignal noise reduction optimization method and system based on a condition generation countermeasure network, an electrocardiosignal noise reduction model framework is constructed based on a deep neural network and the condition generation countermeasure network, and simultaneously, an index which takes noise reduction performance and calculation cost into consideration is provided, and systematic optimization design is carried out on the aspects of the internal structure, the learning framework, data segmentation, model complexity and the like of the noise reduction model, so that the noise reduction model has better noise reduction effect while the cost is lower.
In some embodiments, the following technical scheme is adopted:
An electrocardiosignal noise reduction optimization method based on a condition generation countermeasure network comprises the following steps:
acquiring an electrocardiosignal, dividing the electrocardiosignal according to a sample length T, and carrying out maximum and minimum normalization processing on the divided data fragments;
inputting the processed data segment into a trained electrocardiosignal noise reduction model to obtain a noise-reduced electrocardiosignal;
The electrocardiosignal noise reduction model is constructed based on a deep neural network and a condition generation countermeasure network and comprises a generator and a discriminator; for each modeling parameter in the electrocardiosignal noise reduction model, respectively establishing a noise reduction model under different values (usually more than or equal to 3 conditions) of the modeling parameter according to a certain step length to obtain a noise reduction performance and calculation complexity ratio index (The ratio of Signal-to-NoiseRatio to Computational Complexity, SNR-CC) corresponding to the noise reduction model, predicting a corresponding optimal value of each modeling parameter by adopting a fitting function, and determining that the noise reduction performance and calculation cost of the model reach the optimal through verification;
The modeling parameters include: at least one or more of a sample length T, a number N of deep neural network layers of an encoder and a decoder in the generator, and a number L x of neurons per layer, and a number M of deep neural network layers of the arbiter, and a number R x of neurons per layer.
As a further scheme, the generator is composed of a noise reduction self-encoder (DAE) composed of depth neural networks, wherein the DAE comprises an encoder composed of N layers of depth neural networks and a decoder composed of N layers of depth neural networks, and the discriminator in the training process is composed of two classes of depth neural networks composed of M layers of depth neural networks;
The input of the generator is an electrocardiosignal data segment with the length T, and the electrocardiosignal data segment is output as a noise-reduced signal; the discriminator is combined with the generator to perform anti-game learning when the electrocardiosignal noise reduction model is trained.
As a further scheme, the loss function of the generator increases the difference l dist between the noise-reduced electrocardiosignal and the original electrocardiosignal and the maximum local error l max between the noise-reduced electrocardiosignal and the original electrocardiosignal on the basis of the loss function of the condition generation countermeasure network.
As a further scheme, the ratio index of the noise reduction performance and the computational complexity of the electrocardiosignal noise reduction model is specifically as follows: the ratio of the average signal-to-noise ratio of the noise reduced signal during model testing to the time required by the model to process a single data sample.
As a further scheme, the specific procedure for predicting the optimal value of the sample length T using the fitting function is as follows:
Dividing the electrocardiosignal according to the sample length T, and carrying out data normalization to obtain a data set corresponding to the sample length T;
Setting values of a plurality of different sample lengths T, and respectively obtaining a data set corresponding to each sample length T;
fixing other modeling parameters unchanged, and respectively modeling and training the noise reduction model according to the set T value and the corresponding data set;
For the trained electrocardiosignal noise reduction model, respectively calculating the noise reduction performance and the calculation complexity ratio index of the model; and performing curve fitting according to different sample lengths T and corresponding SNR-CC data to obtain a fitting function f (T), deriving the fitted function to obtain f '(T), and calculating to obtain a value T 0 of T when f' (T) =0. And (4) reestablishing a database by adopting the T 0, and training, testing and verifying the noise reduction model. If the average signal-to-noise ratio of the signal after the model noise reduction is not less than the set expected value, the current sample length T 0 is the optimal value of T.
Meanwhile, the modeling parameters can be optimized together with other indexes which possibly influence the deployment of the model, such as: the size of the occupied memory of the model parameters, etc. And combining the modeling parameter optimization process, limiting the occupied memory size of the modeling parameter, obtaining a corresponding piecewise fitting function, and analyzing and predicting the piecewise fitting function to determine the optimal modeling parameter value in the piecewise.
And for the number N of the deep neural network layers of the encoder and the decoder in the generator and the number L x of the deep neural network neurons of each layer in the generator, respectively adopting an optimization method which is the same as the value of the sample length T, fixing modeling parameters which are not to be optimized, and selecting the optimal value of the corresponding modeling parameters to be optimized.
For the number M of layers of the deep neural network in the discriminator and the number R x of neurons of the deep neural network in each layer of the discriminator, respectively adopting an optimization method which is the same as the value of the sample length T, and selecting a corresponding optimal value;
After optimization of the optimized parameters of the deep neural network in the discriminator is completed, the noise reduction model is reconstructed by combining the optimized generator parameters, and the noise reduction effect of the optimized parameters is further verified until the noise reduction performance and the calculation complexity ratio index of the model tend to be stable near the maximum value position.
Selecting different kinds of deep neural networks and condition generation countermeasure networks to construct corresponding electrocardiosignal noise reduction models; and respectively optimizing modeling parameters of the corresponding noise reduction model by adopting the optimization method, comparing the noise reduction performance of the optimized corresponding model with the ratio index of the noise reduction performance and the computational complexity, and selecting the noise reduction model with the optimal performance as a final electrocardiosignal noise reduction model.
In other embodiments, the following technical solutions are adopted:
an electrocardiosignal noise reduction optimization system based on a condition generation countermeasure network, comprising:
the data acquisition module is used for acquiring electrocardiosignals, dividing the electrocardiosignals according to the sample length T, and carrying out maximum and minimum normalization processing on the divided data fragments;
The noise reduction module is used for inputting the processed data fragments into a trained electrocardiosignal noise reduction model to obtain a noise-reduced electrocardiosignal;
The electrocardiosignal noise reduction model is constructed based on a deep neural network and a condition generation countermeasure network and comprises a generator and a discriminator; for each modeling parameter in the electrocardiosignal noise reduction model, selecting different values according to a set step length, respectively establishing a noise reduction model of the modeling parameter under the different values, obtaining a corresponding noise reduction performance and calculation complexity ratio index, and predicting an optimal value of each modeling parameter by adopting a fitting function so as to optimize the noise reduction performance and calculation cost of the model;
The modeling parameters include: at least one or more of a sample length T, a number N of deep neural network layers of an encoder and a decoder in the generator, and a number L x of neurons per layer, and a number M of deep neural network layers of the arbiter, and a number R x of neurons per layer.
In other embodiments, the following technical solutions are adopted:
A terminal device comprising a processor and a memory, the processor for implementing instructions; the memory is for storing a plurality of instructions adapted to be loaded by the processor and to perform the above-described condition-based generation of an electrocardiographic signal noise reduction optimization method against the network.
Compared with the prior art, the invention has the beneficial effects that:
in summary, an index which is lacking at present and can simultaneously give consideration to the computational complexity and the noise reduction performance and an optimization method which is sufficient, reasonable and systematic for modeling a model according to the index are established and proposed, and the problems that modeling time is long, the computational complexity of the noise reduction model is high, and operation cost is high when the existing noise reduction methods are implemented can be solved.
(1) The invention combines the deep neural network with the condition generation countermeasure network to realize the noise reduction optimization of the electrocardiosignal; the deep neural network can learn the distribution of real data in the sample so as to remove noise; the conditional generation countermeasure network is an improved generation countermeasure network, which combines a noisy signal as a condition with an original input signal (a noise-reduced signal or an original signal) as an input of a discriminator, so that a generator and the discriminator can better perform countermeasure game, and the learning direction of the generator is controlled to be closer to the real data distribution in a sample.
(2) The invention provides a noise reduction performance and calculation complexity ratio index which is used for evaluating the noise reduction performance and calculation cost of a model, curve fitting is carried out by utilizing the provided index and corresponding modeling parameter experimental data, a fitting function is obtained and derived, the optimal value of the modeling parameter is further predicted, and the optimal value of each modeling parameter to be optimized is verified through experiments. The optimization method can utilize a small amount of experiments to realize the combination optimization of model parameters, so that the model has the lowest possible model calculation cost on the premise of achieving the same noise reduction performance.
(3) The loss function of the noise reduction model generator is based on the loss function of a conventional CGAN network, and the distance between the noise reduction signal and the pure electrocardiosignal and the maximum local error are also added. The distance between the noise reduction signal and the pure electrocardiosignal reflects the integral difference between the noise reduction signal and the pure electrocardiosignal, and can control the big direction of the data generated by the generator; the maximum local error between the noise reduction signal and the pure electrocardiosignal represents the local difference to be improved between the noise reduction signal and the pure signal, namely the signal detail which needs to be improved when the generator generates data. When the loss function of the generator is added with the two parts, the data generation process of the generator can be controlled in the whole direction and the local detail, and further, a better noise reduction effect is obtained.
Additional features and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
FIG. 1 is a schematic diagram of an electrocardiosignal noise reduction model optimization process in an embodiment of the invention;
FIG. 2 is a schematic diagram of the overall structure of an electrocardiosignal noise reduction model in an embodiment of the invention;
FIG. 3 is a graph showing a fitted function of modeling parameter T and SNR-CC in an embodiment of the present invention;
Fig. 4 is a schematic diagram of an electrocardiosignal noise reduction process based on a condition generation countermeasure network in an embodiment of the invention.
Detailed Description
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.
Example 1
In one or more embodiments, an electrocardiosignal noise reduction optimization method based on a condition generation countermeasure network is disclosed, which specifically comprises the following steps:
(1) Acquiring an electrocardiosignal, dividing the electrocardiosignal according to the sample length T, and carrying out maximum and minimum normalization processing on the divided data fragments;
(2) Inputting the processed data segment into a trained electrocardiosignal noise reduction model to obtain a noise-reduced electrocardiosignal;
The electrocardiosignal noise reduction model is constructed based on a deep neural network and a condition generation countermeasure network; and (3) calculating a ratio index of the noise reduction performance and the computational complexity of the electrocardiosignal noise reduction model, predicting an optimal modeling parameter value through curve fitting, and determining that the noise reduction performance and the computational cost of the model reach the optimal value through verification. The modeling parameters may be one or more of a sample length T, a number of layers N of the deep neural network of the encoder and the decoder, and a number of neurons L x of each layer, a number of layers M of the deep neural network of the arbiter, and a number of neurons R x of each layer.
In the embodiment, with reference to fig. 2, the electrocardiosignal noise reduction model is constructed based on a deep neural network (such as a two-way long-short time memory network, a convolutional neural network, a cyclic neural network and the like) and a condition generation countermeasure network combination; the electrocardiosignal noise reduction model consists of a generator and a discriminator, wherein the generator can be formed by a noise reduction self-encoder (DAE) formed by a depth neural network, the DAE comprises an encoder formed by N layers of depth neural networks and a decoder formed by the N layers of depth neural networks, and the discriminator consists of two types of depth neural networks formed by M layers of depth neural networks.
Noisy signal with length T input to the generatorThe output is the noise reduced signal/>The input of the discriminator is the combination of the noise-carrying signal and the noise-reducing signal or the combination of the noise-carrying signal and the original signal, and the output is true or false.
The training and optimizing process of the electrocardiosignal noise reduction model is described below with reference to fig. 1.
A) And (5) adding noise to the signals.
The cleaner electrocardiosignals are selected from a public or specific electrocardiosignal database to be used as original signals (x n), various common electrocardiosignals are selected from an electrocardiosignal noise database to be used as noise signals (e n), and the sampling frequencies of the original signals and the noise signals are f. According to different input signal-to-noise ratios (Signal to Noise Ratio, SNR), respectively superposing multiple single noises and multiple mixed noises with the original signal to obtain a noisy signal
B) And setting a modeling parameter set to be optimized of the model.
The modeling parameter set C to be optimized of the electrocardiosignal noise reduction model comprises the following components: the generator modeling parameters (sample length T, number of encoder and decoder deep neural network layers N, number of neurons per layer L x), the arbiter modeling parameters (number of arbiter deep neural network layers M, number of neurons per layer R x).
Firstly, the modeling parameters to be optimized of the noise reduction model are preset according to the following rules.
The sample length T determines both the size of the amount of information that the model inputs and the number of start-layer neurons of the generator and the arbiter (i.e., the model start size). Typically, the heart rate is in the range of 50-150 beats/min, i.e. the period of a single beat is 0.4-1.2s, and at a sampling rate f, to ensure that each sample segment contains at least one beat period, it is preferred that T should be selected from a plurality of values in the range of ≡1.2 f.
In order to enable the generator and the arbiter of the noise reduction model to better resist games during training, a better noise reduction effect is achieved. In general, the neural network architecture used by the generator and the arbiter should be similar, and where M should be N or greater. That is, an appropriate N should be selected according to the characteristics of the deep neural network constituting the generator, and then the number of neurons of the corresponding network layer should be set according to the type of the deep neural network selected by selecting M to be a value equal to or greater than N (L x、Rx).
C) Data segmentation and normalization: for noisy signals according to the sample length (T)And the original signal (x n) are divided to obtain electrocardiosignal data segments with corresponding lengths, and the maximum and minimum values of the electrocardiosignal data segments are normalized respectively to form a corresponding data set (D T). In dataset (D T), data representing C% of the total data were used for training and 1-C% of the data were used for testing.
The calculation formula for maximum and minimum normalization of the data is as follows:
Where x n is the value of the nth sample point of the signal to be normalized, x min is the minimum value of the sample points of the signal to be normalized, and x max is the maximum value of the sample points of the signal to be normalized.
D) And (5) model training.
The constructed electrocardiosignal noise reduction model is trained by using the prepared data set (D T) through countermeasure game learning. When the arbiter cannot distinguish whether the input is the original signal or the noise reduction signal, the countermeasure learning is considered to reach Nash equilibrium, and the model converges at this time.
E) Noise reduction model test: and (3) using a trained generator, inputting a data set to test the noisy signal to obtain a noise-reduced signal, and calculating noise reduction performance indexes such as a signal-to-noise ratio (SNR), a root mean square error (Root mean square error, RMSE) and the like of the noise-reduced signal for noise reduction effect evaluation. Then, the parameters such as the parameter quantity, the memory occupied by the parameters, the calculated quantity and the like of the noise reduction model are used for evaluating the calculation consumption (cost) of the model. And the model noise reduction performance indexes and the model calculation cost under different modeling parameter values to be optimized are comprehensively compared, and the modeling parameter values to be optimized are optimized.
In order to facilitate comprehensive comparison of the noise reduction performance and the computation complexity index, the present embodiment provides an index of a noise reduction performance to computation complexity ratio (The ratio of Signal-to-Noise Ratio to Computational Complexity, SNR-CC), and the computation method is as follows:
Where O is the value of SNR-CC, representing the ratio between the noise reduction performance of the model and the computational complexity, the larger the index, the smaller the computational time (computational overhead) consumed when the noise reduction signal reaches the same signal-to-noise ratio. SNR Average is the average signal-to-noise ratio of the noise reduced signal at the time of model testing. t s is the time required for calculating a single sample by the trained noise reduction model (using the generator part thereof), and also reflects the comprehensive influence of indexes such as the parameter quantity, the memory occupied by the parameter, the calculated quantity and the like of the noise reduction model to a certain extent. When SNR Average is greater than or equal to desired value S 0, the greater SNR-CC of the noise reduction model is, the better. Wherein S 0 is the expected value of the noise reduction performance of the optimized model, i.e. the average SNR, and can be obtained by referring to the performance index of other advanced noise reduction algorithms.
When the algorithm model architecture is optimized, curve fitting is firstly carried out according to modeling parameters p and SNR-CC corresponding data to obtain a fitting function f (p), and a fitting function diagram of different values of the sample length T and corresponding SNR-CC values is given in FIG. 3. Deriving the fitting function to obtain f' (p). Then, when f' (p) =0, the value p 0 of p is obtained by calculation. A common third order polynomial fitting function and its derivatives are shown below:
O＝f(p)＝ap3+bp2+cp+d(3)
O′＝f′(p)＝3ap2+2bp+c(4)
Then, modeling is conducted again according to the modeling parameter p 0, training and testing are conducted, the SNR-CC value O 0 corresponding to the SNR value at the moment and the parameter is calculated, and the noise reduction performance and the calculation loss of the model at the moment are verified. That is, when f' (p) =0 and SNR Average≥S0, it is considered as a preferred value of this modeling parameter in the current case of the model.
At this time, if the memory size occupied by the noise reduction model parameters is required, the fitting function f (p) is transformed into a piecewise function according to the specific limitation.
O=f (p) =ap 3+bp2+cp+d(SNRAverage≥S0 and mc+.ltoreq.mc 0) (5)
Wherein MC is the size of the memory occupied by the model parameters, and MC 0 is the maximum value of the memory occupied by the expected model parameters. When SNR Average≥S0 and MC.ltoreq.MC 0, p min, which is the minimum that f' (p) can take, is considered the preferred value for this modeling parameter in the current case of the model.
In addition, a noise reduction model constructed when the modeling parameters to be optimized take the preferred values is adopted, noise reduction processing is carried out on the test data, and then classification performance evaluation is carried out on the original electrocardiosignals before noise reduction, after noise reduction and by utilizing an electrocardiosignal classification algorithm based on the electrocardiosignal characteristics, so that the capability of the noise reduction model for retaining medical value information is evaluated.
F) And optimizing a generator and a discriminator model.
Only the generator part of the trained noise reduction model is used in the noise reduction process, so T, N is two important parameters in the model optimization process. But the value of M must also be optimized because M can affect the performance of the arbiter in response to game play throughout the training process.
Through analysis, the value of T, N, M, the number of neurons of each deep neural network layer of the generator (L x) and the number of neurons of each deep neural network layer of the generator (R x) can greatly influence the noise reduction effect of the model. Therefore, the model modeling parameters need to be optimized.
The general optimization process of the model modeling parameters is as follows: the current non-optimized modeling parameters are fixed, noise reduction models under different values (usually more than or equal to 3 conditions) of the modeling parameters to be optimized are respectively built according to a certain step length, corresponding noise reduction performance and calculation complexity ratio indexes are obtained according to the steps b) -e), the optimal value of the modeling parameters to be optimized is predicted by using a fitting function, a training noise reduction model is built according to the optimal value, and the comprehensive performance of the noise reduction model is tested and verified. The remaining modeling parameter values are then optimized sequentially in order.
Since the model finally implements the noise reduction process by the generator, the generator model should be optimized first. The method specifically adopts the following scheme: the preference of the values of the modeling parameters T is first carried out and, after completion of the preference, the generator model (i.e. the values of N, L x) is sequentially preferred over a range by cycling through the above-described preference steps, in N, L x order, using the corresponding data set (D T).
And then optimizing the value of the modeling parameter (M, R x) to be optimized in the discriminator according to the same method. Since N, M values can affect the performance of the generator and the arbiter, respectively, in an opponent game, there is a certain relationship between the two. Therefore, firstly, the optimal value of N is determined in generator optimization, then, after optimization of the optimized parameters of the deep neural network in the discriminator is completed, a noise reduction model is reconstructed together with the optimized generator parameters, and the noise reduction effect of the optimized parameters is further verified until the noise reduction performance and the calculation complexity ratio index of the model tend to be stable at a high position (near the maximum position).
Finally, the optimized T, N, M, L x、Rx value and the trained noise reduction algorithm optimization model are obtained.
After training and optimizing the electrocardiosignal noise reduction model, when noise reduction is implemented, the noise reduction is implemented through a generator part in the optimized model; the specific process is as follows:
and (3) dividing the actually acquired electrocardiosignal data by a fixed length according to a preferable T value, and then normalizing the maximum and minimum values of the divided data fragments to obtain the noisy data to be processed. And then inputting the noise signals into a generator part of the trained noise reduction algorithm optimization model for calculation, and further obtaining noise-reduced signals.
Example two
The electrocardiograph noise reduction model of the embodiment is constructed by a bidirectional long and short time memory network (bidirectional long short-term memory, biLSTM) and a condition generation countermeasure network CGAN, the model adopts a CGAN learning architecture, and is composed of a generator and a discriminator which are composed of the bidirectional long and short time memory network.
The initial model structure is shown in fig. 2, in which the generator is composed of an encoder composed of 2 layers (N) BiLSTM, a decoder composed of 2 layers (N) BiLSTM, and a noise reduction self-encoder composed of one full-connection layer, and the discriminator is composed of a two-class deep learning network composed of 3 layers (M) BiLSTM, one full-connection layer, and an activation function Sigmoid. The input sample length of the generator and the discriminator is T, the number of neurons of the encoder and the decoder in the generator is L x = {400, 200}, and the number of neurons in the discriminator is R x = {400, 200, 100}. The formula for calculating the activation function Sigmoid is as follows:
The input of the generator is a noise signal, and the output is a noise-reduced signal; the input of the discriminator is the combination of the noise-contained signal and the noise-reduced signal or the combination of the noise-contained signal and the original signal, and the output is true or false.
In this embodiment, the loss function of the electrocardiographic noise reduction model generator is based on the loss function of the conventional CGAN network, and the difference l dist between the noise-reduced signal and the pure electrocardiographic signal and the maximum local error l max are also added.
The loss function of the generator is specifically:
wherein, Is a noisy signal, obeys the noisy signal distribution/> Is the generator input is the noisy signal/>Output of time,/>Is input as/>And/>The output at time, λ 1 and λ 2 are the weight coefficients of l dist and l max, respectively, taking 0.7 and 0.2, respectively.
The calculation formula of the difference l dist between the noise-reduced signal and the pure electrocardiosignal is as follows:
wherein, For the noise reduced signal, x n is the original signal and T is the sample length.
The calculation formula of the maximum local error l max between the noise-reduced signal and the pure electrocardiosignal is as follows:
the overall loss function of the model at this time is:
Wherein x is an original signal and obeys an original signal distribution p data (x); Is a noisy signal, obeys the noisy signal distribution
The model is realized by Pytorch programming, a RMSProp (Root Mean Square Prop root mean square transfer) optimizer is adopted for parameter updating, and the learning rate is set to be 0.0001. Model training and testing was run on a Dell T640 server (NVIDIA GTX 3090 24GB). The model training and optimizing process is the same as that in the first embodiment, and the specific implementation process is as follows:
a) The electrocardiographic signals denoted 103, 105, 111, 116, 122, 205, 213, 219, 223, 230 are selected as raw signals (x n) in the MIT-BIH arrhythmia database. Three common electrocardiographic noises including muscle artifact, electrode motion artifact and baseline drift are selected from the electrocardiographic noise database as noise signals (e n). According to the input SNR being respectively set as 0dB, 1dB, 2dB, 3dB, 4dB and 5dB, three single noise, three mixed noise overlapped by two and three mixed noise overlapped by three noise are respectively overlapped with the original signal to obtain noisy signals with different input SNRs and different types of noise The sampling rate of the electrocardiosignal is 360Hz, so the sample length T is more than or equal to 432 sampling points.
B) According to the optimization strategy, a modeling parameter set C to be optimized is preset. According to the data feature extraction capability of BiLSTM networks, t=512, n=2, m=3, L x = {400, 200} of the generator, and R x = {400, 200, 100} of the arbiter are tentatively set.
C) The noisy signals with different input SNR and noise and the original signals are divided according to corresponding T values (512 at this time), so as to obtain electrocardiosignal data segments with different lengths, and the maximum and minimum values of the electrocardiosignal data segments are normalized respectively, so that a corresponding dataset (D 512) is formed. Data sets 80% of the data were used for training and 20% of the data were used for testing.
D) The electrocardiograph noise reduction model of the embodiment is trained, and different data sets are adopted to train the model through the countermeasure game learning process, so that a generator and a discriminator are continuously optimized. When the arbiter cannot distinguish whether the input is the original signal or the noise reduction signal or the SNR of the generated noise reduction signal is high enough or the RMSE is low enough, the model converges, and the training is finished.
E) At this time, the noise reduction model constructed with the modeling parameter (t=512) is statistically and calculated as follows: snr=28.71 dB, SNR-cc= 97.32dB/ms. The model desired noise reduction performance index S 0 =28 dB is set, at which point the model noise reduction performance SNR > S 0.
F) Generator model optimization: first, taking the preferred example of the value of T, the procedure is as follows: setting a plurality of sample length (T) values (such as 512, 1024, 2048, 4096 and the like), obtaining a data set (D T) of a plurality of sample lengths, fixing other modeling parameters to construct a noise reduction model, and training the noise reduction model by adopting the data set (D T). And then, inputting the trained generator into a corresponding data set (D T) for testing the noisy signal to obtain a noise-reduced signal, calculating a signal-to-noise ratio (SNR), SNR-CC noise reduction performance and a calculation cost index of the noise-reduced signal, performing curve fitting according to a T value and the SNR-CC value, wherein the fitting result is shown in fig. 3, and when f' (T) =0, the fitting result is T 0 =2127, and for convenience in calculation, T 0 =2048 is preferable. After T 0 is adopted for remodelling, various indexes are comprehensively compared, the noise reduction performance SNR of the model is more than S 0, the SNR is more than a good level (SNR= 36.21 dB), the SNR-CC= 109.74dB/ms, and the calculation cost is smaller. And then, adopting an electrocardiosignal characteristic-based electrocardiosignal classification algorithm to compare classification performances of the original electrocardiosignals before noise reduction, after noise reduction and evaluate the capability of the noise reduction model for retaining medical value information.
Repeating the steps, and predicting and optimizing other modeling parameters (N and L x) in the generator by comprehensively comparing the noise reduction performance index, the capability of reserving medical information and the calculation cost of the model, namely calculating and analyzing the SNR-CC, the modeling parameters, the SNR-CC fitting function and the derivative thereof. And sequentially determining the optimized value of N and L x.
And then repeating the steps, and optimizing M and R x in sequence through fixing the value of the modeling parameter to be optimized of the generator, namely optimizing the discriminator model. After determining the preferred value of M, repeating the above steps to perform re-optimization of the N value, so as to determine the validity of N.
The result shows that when the value range of N is {1,2,3,4} and the value range of M is {3,4,5,6}, the average SNR=45.12 dB of the electrocardiosignals after noise reduction is obtained when N=3, M=4, L x = {800, 400, 200}, R x = {800, 400, 200, 100} of the generator and R x = {800, 400, 200, 100} of the generator, and the model SNR-CC= 131.54dB/ms, at the moment, the calculation consumption of the model is lower than that of other algorithms similar to the noise reduction effect, and the balance between the calculation consumption and the noise reduction effect is better, namely, the SNR-CC value of the model can reach high-order stability. In addition, an electrocardiosignal characteristic-based electrocardiosignal classification algorithm (support vector machine) is adopted to evaluate four classification performances of abnormal electrocardiosignals before noise reduction, after noise reduction and the original electrocardiosignals, and the classification accuracy of the signals after noise reduction by adopting the model is close to the classification accuracy (more than or equal to 95%) of the original signals, so that a great amount of medical value information is reserved for the signals after noise reduction.
Referring to fig. 4, for the trained electrocardiograph noise reduction model, electrocardiograph signal data is acquired, the electrocardiograph signal data is segmented according to a fixed length of t=2048, and then maximum and minimum normalization is performed on the segmented data segments to obtain noisy electrocardiograph signal segments to be processed. And then inputting the signals into a generator in the electrocardio noise reduction model with optimized training for noise reduction treatment, and obtaining noise-reduced signals.
Example III
In this embodiment, two noise reduction models are respectively constructed:
① th species: generating a noise reduction model constructed by the countermeasure network based on the convolutional neural network and the conditions;
② th species: and generating a noise reduction model constructed by the countermeasure network based on the bidirectional long-short-term memory network and the conditions.
Firstly, based on the same hardware computing platform or system, the two noise reduction models are respectively optimized by adopting the method provided in the first embodiment, and the optimized noise reduction models composed of different depth neural networks are respectively obtained. And then, comparing the noise reduction performance, SNR-CC and other indexes of the two optimized noise reduction models, and determining the better noise reduction model in the two noise reduction models.
The ① th noise reduction model, which is constructed under the optimal modeling parameters after optimization, is tested to have SNR=41.03 dB and SNR-CC=2.28 dB/ms.
The ② th noise reduction model is a model constructed under the optimal modeling parameters after optimization, and the SNR of the model is tested to be 45.12dB, and the SNR of the model is tested to be-CC= 131.54dB/ms.
Therefore, the noise reduction model constructed based on the bidirectional long-short-time memory network and the condition generation countermeasure network has better noise reduction performance and noise reduction cost performance than the noise reduction model constructed based on the convolutional neural network and the condition generation countermeasure network.
Example IV
In one or more embodiments, an electrocardiosignal noise reduction optimization system based on a condition generation countermeasure network is disclosed, comprising:
The data acquisition module is used for acquiring electrocardiosignals, dividing the electrocardiosignals according to the length T, and carrying out maximum and minimum normalization processing on the divided data fragments;
The noise reduction module is used for inputting the processed data fragments into a trained electrocardiosignal noise reduction model to obtain a noise-reduced electrocardiosignal;
The electrocardiosignal noise reduction model is constructed based on a deep neural network and a condition generation countermeasure network and comprises a generator and a discriminator; for each modeling parameter in the electrocardiosignal noise reduction model, selecting different values according to a set step length, respectively establishing a noise reduction model of the modeling parameter under the different values, obtaining a corresponding noise reduction performance and calculation complexity ratio index, and predicting an optimal value of each modeling parameter by adopting a fitting function so as to optimize the noise reduction performance and calculation cost of the model;
The modeling parameters include: at least one or more of a sample length T, a number N of deep neural network layers of an encoder and a decoder in the generator, and a number L x of neurons per layer, and a number M of deep neural network layers of the arbiter, and a number R x of neurons per layer.
The specific implementation of each module and the first embodiment are described in detail, and will not be described in detail here.
Example five
In one or more embodiments, a terminal device is disclosed, comprising a server comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the condition-based generation of an electrocardiosignal noise reduction optimization method of embodiment one, when the program is executed. For brevity, the description is omitted here.
It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general purpose processors, digital signal processors DSP, application specific integrated circuits ASIC, off-the-shelf programmable gate array FPGA or other programmable logic device, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may include read only memory and random access memory and provide instructions and data to the processor, and a portion of the memory may also include non-volatile random access memory. For example, the memory may also store information of the device type.
In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or by instructions in the form of software.
While the foregoing description of the embodiments of the present invention has been presented in conjunction with the drawings, it should be understood that it is not intended to limit the scope of the invention, but rather, it is intended to cover all modifications or variations within the scope of the invention as defined by the claims of the present invention.
Claims (9)
1. An electrocardiosignal noise reduction optimization method based on a condition generation countermeasure network is characterized by comprising the following steps:
acquiring an electrocardiosignal, dividing the electrocardiosignal according to a sample length T, and carrying out maximum and minimum normalization processing on the divided data fragments;
inputting the processed data segment into a trained electrocardiosignal noise reduction model to obtain a noise-reduced electrocardiosignal;
The electrocardiosignal noise reduction model is constructed based on a deep neural network and a condition generation countermeasure network and comprises a generator and a discriminator; for each modeling parameter in the electrocardiosignal noise reduction model, selecting different values according to a set step length, respectively establishing a noise reduction model of the modeling parameter under different values, and obtaining a corresponding noise reduction performance and calculation complexity ratio index of the noise reduction model, wherein the noise reduction performance and calculation complexity ratio index of the electrocardiosignal noise reduction model is specifically as follows: the average signal-to-noise ratio of the noise-reduced signal in the model test and the ratio of the time required by the model to process a single data sample are used for predicting the optimal value of each modeling parameter by adopting a fitting function so as to optimize the noise reduction performance and the calculation cost of the model;
The modeling parameters include: at least one or more of a sample length T, a number N of deep neural network layers of an encoder and a decoder in the generator, and a number L x of neurons per layer, and a number M of deep neural network layers of the arbiter, and a number R x of neurons per layer.
2. The method for optimizing the noise reduction of an electrocardiograph signal based on a condition generating countermeasure network according to claim 1, wherein the generator is composed of a noise reduction self-encoder composed of a deep neural network, and the noise reduction self-encoder comprises an encoder composed of an N-layer deep neural network and a decoder composed of an N-layer deep neural network; the discriminator consists of two classified deep neural networks consisting of M layers of deep neural networks;
the input of the generator is an electrocardiosignal data segment with the sample length T, and the electrocardiosignal data segment is output as a noise-reduced signal; the discriminator is combined with the generator to perform the countermeasure game learning when the electrocardiosignal noise reduction model is trained.
3. The method for optimizing the noise reduction of an electrocardiograph signal based on a condition generating countermeasure network according to claim 2, wherein the loss function of the generator increases a difference l dist between the electrocardiograph signal after noise reduction and the original electrocardiograph signal and a maximum local error l max between the electrocardiograph signal after noise reduction and the original electrocardiograph signal based on the condition generating countermeasure network.
4. The method for optimizing the noise reduction of an electrocardiographic signal based on a condition generating countermeasure network according to claim 1, wherein the specific process of predicting the optimal value of the sample length T by using a fitting function is as follows:
setting values of a plurality of different sample lengths T, and respectively obtaining a data set corresponding to each sample length T;
fixing other modeling parameters unchanged, and respectively modeling and training the noise reduction model according to the set T value and the corresponding data set;
For the trained electrocardiosignal noise reduction model, respectively calculating the noise reduction performance and the calculation complexity ratio index of the model; constructing a fitting function of the sample length T and the ratio index of the noise reduction performance and the computational complexity;
Deriving the fitting function, and calculating the value of the corresponding sample length T when the derivative of the fitting function is zero; and performing model noise reduction test based on the value of the sample length T, and if the average signal-to-noise ratio of the noise reduced signal is not less than the set expected value, obtaining the value of the sample length T as an optimal value.
5. The method for optimizing noise reduction of electrocardiosignal based on condition generation countermeasure network as claimed in claim 4, wherein the number N of deep neural network layers of encoder and decoder in the generator and the number L x of neurons of each deep neural network layer in the generator are respectively optimized by adopting the same optimization method as the value of the sample length T, and the corresponding optimal value is selected.
6. The method for optimizing electrocardiosignal noise reduction based on a condition generation countermeasure network as claimed in claim 5, wherein the number M of layers of deep neural networks in the discriminator and the number R x of neurons of each layer of deep neural networks in the discriminator are respectively optimized by adopting an optimization method which is the same as the value of the sample length T, and the corresponding optimal value is selected;
After optimization of the optimized parameters of the deep neural network in the discriminator is completed, the noise reduction model is reconstructed by combining the optimized generator parameters, and the noise reduction effect of the optimized parameters is further verified until the noise reduction performance and the calculation complexity ratio index of the model tend to be stable near the maximum value position.
7. The method for optimizing the noise reduction of an electrocardiograph signal based on a condition-generating countermeasure network of claim 1, further comprising:
Selecting different kinds of deep neural networks and condition generation countermeasure networks to construct corresponding electrocardiosignal noise reduction models; the optimization method of claim 1 is adopted to optimize modeling parameters of the corresponding noise reduction model, noise reduction performance and a ratio index of the noise reduction performance to the computational complexity of the optimized corresponding model are compared, and the noise reduction model with the optimal performance is selected to be used as a final electrocardiosignal noise reduction model.
8. An electrocardiosignal noise reduction optimization system based on a condition generation countermeasure network, which is characterized by comprising:
the data acquisition module is used for acquiring electrocardiosignals, dividing the electrocardiosignals according to the sample length T, and carrying out maximum and minimum normalization processing on the divided data fragments;
The noise reduction module is used for inputting the processed data fragments into a trained electrocardiosignal noise reduction model to obtain a noise-reduced electrocardiosignal;
The electrocardiosignal noise reduction model is constructed based on a deep neural network and a condition generation countermeasure network and comprises a generator and a discriminator; for each modeling parameter in the electrocardiosignal noise reduction model, selecting different values according to a set step length, respectively establishing a noise reduction model of the modeling parameter under different values, and obtaining a corresponding noise reduction performance and calculation complexity ratio index of the noise reduction model, wherein the noise reduction performance and calculation complexity ratio index of the electrocardiosignal noise reduction model is specifically as follows: the average signal-to-noise ratio of the noise-reduced signal in the model test and the ratio of the time required by the model to process a single data sample are used for predicting the optimal value of each modeling parameter by adopting a fitting function so as to optimize the noise reduction performance and the calculation cost of the model;
The modeling parameters include: at least one or more of a sample length T, a number N of deep neural network layers of an encoder and a decoder in the generator, and a number L x of neurons per layer, and a number M of deep neural network layers of the arbiter, and a number R x of neurons per layer.
9. A terminal device comprising a processor and a memory, the processor for implementing instructions; the memory for storing a plurality of instructions, wherein the instructions are adapted to be loaded by the processor and to perform the condition-based generation of an electrocardiographic signal noise reduction optimization method of any one of claims 1-7.
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