Electrocardiosignal noise reduction method based on interpretable deep neural network
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A deep neural network and electrocardiographic signal technology, applied in the field of electrocardiographic signal processing, can solve the problems of neural network lack of interpretability and robustness, and achieve the effect of improving interpretability and noise reduction ability
Active Publication Date: 2022-07-22
SHANDONG ARTIFICIAL INTELLIGENCE INST +1
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It not only solves the robustness problem of the traditional model, but also solves the problem of the lack of interpretation of the neural network
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Embodiment 1
[0066] In step a), ten clean signals of 103, 105, 111, 116, 122, 205, 213, 219, 223, and 230 in the MIT-BIH database are selected, and BW and EM in the noise pressure database in the MIT-BIH database are selected. , MA as noise data, by inputting the signal-to-noise ratio formula, the noise data with a signal-to-noise ratio of 1.25dB and 5dB is injected into the clean signal to obtain a noisy signal.
Embodiment 2
[0068] The training set samples divided in step b) are 480 segments, each segment has a length of 512, and the divided test set samples are 120 segments, each segment has a length of 512.
Embodiment 3
[0070] Step d) includes the following steps:
[0071] d-1) The first processing unit is sequentially composed of a convolutional layer with a channel number of 64, a convolution kernel size of 1×3, and a LeakyReLU activation function. After the training set is input to the first processing unit, the output channel number is 64-dimensional. characteristic signal T 1 ;
[0072] d-2) The second processing unit is sequentially composed of a convolutional layer with a channel number of 64, a convolution kernel size of 1×5, and a LeakyReLU activation function. After the training set is input to the second processing unit, the output channel number is 64-dimensional. characteristic signal T 2 ;
[0073] d-3) The third processing unit is sequentially composed of a convolutional layer with 64 channels, a convolution kernel size of 1×7, and a LeakyReLU activation function. After the training set is input to the third processing unit, the output channel number is 64-dimensional. char...
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Abstract
The invention discloses an electrocardiosignal noise reduction method based on an interpretable deep neural network, and the method comprises the steps: building an electrocardiosignal model through a sparse representation algorithm, and converting an optimization problem into optimization of two sub-problems through a semi-secondary splitting algorithm. Then, a neural network is built in the two sub-problems, and an optimal solution is sought through end-to-end training of electrocardiosignal noise data and clean data in the built neural network. By designing the electrocardiosignal noise reduction network, the interpretability of the neural network is improved, and meanwhile, the noise reduction capacity of electrocardiosignals is also improved. An attractive bridge is established between a traditional sparse representation noise reduction algorithm and a deep neural network noise reduction model. In this way, the interpretability of the neural network is improved, and the precision and robustness of a traditional sparse representation noise reduction algorithm are improved.
Description
technical field [0001] The invention relates to the technical field of electrocardiographic signal processing, in particular to an electrocardiographic signal noise reduction method based on an interpretable deep neural network. Background technique [0002] Cardiovascular disease seriously endangers human health. However, cardiovascular diseases are diagnosed by collecting ECG signals of patients, and abnormal conditions of different bands of the ECG signals are expressed as different heart diseases of the patient. In the process of collecting ECG signals, it is inevitable that different noises will be introduced to distort the ECG signals. Therefore, in order to obtain a clean signal, the noise reduction of the ECG signal has become an indispensable step. [0003] At present, there are empirical mode decomposition, wavelet filtering, Bayesian filtering, sparse representation and neural network noise reduction methods that have been studied a lot recently. For the tradit...
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