Training method for noise reduction autoencoder, noise reduction method for ECG signal, and related devices and equipment

A technology of ECG signal and self-encoder, which is applied in the fields of sensors, medical science, diagnosis, etc.

Active Publication Date: 2020-12-15
TSINGHUA UNIV +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The technical problem to be solved by the embodiments of the present invention is to provide a training method for a noise reduction autoencoder, a noise reduction method for ECG signals, and related devices and equipment, so as to avoid the technical problems that the autoencoder is difficult to learn and predicts waveform distortion

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  • Training method for noise reduction autoencoder, noise reduction method for ECG signal, and related devices and equipment
  • Training method for noise reduction autoencoder, noise reduction method for ECG signal, and related devices and equipment
  • Training method for noise reduction autoencoder, noise reduction method for ECG signal, and related devices and equipment

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Embodiment 1

[0197] Figure 7A It is a schematic flowchart of a training method for a noise reduction autoencoder provided in Embodiment 1 of the present invention, Figure 7B It is a schematic explanatory diagram of a training method for a noise reduction autoencoder provided by Embodiment 1 of the present invention. Specifically, this method can be image 3 The training device 120 is shown performing. Optionally, steps S702-S706 in this method may also be pre-executed by other functional modules before the training device 120, that is, first preprocess the data of the original samples received or obtained from the database 130 to obtain training samples , and then the training device executes S708 and S710 through the training samples to train the noise reduction autoencoder. Optionally, the method can be processed by a CPU, or can be processed by a CPU and a processor suitable for neural network calculation (such as Figure 6 The shown neural network processor 30) jointly processes ...

Embodiment 2

[0285] Figure 7D It is a schematic flowchart of another training method for a noise reduction autoencoder provided in Embodiment 2 of the present invention. Specifically, this method can be image 3 The training device 120 is shown performing. Optionally, steps S712-S716 in this method may also be pre-executed by other functional modules before the training device 120, that is, first preprocess the data of the original samples received or obtained from the database 130 to obtain training samples , and then the training device executes S718 and S720 through the training samples to train the noise reduction autoencoder. Optionally, the method can be processed by a CPU, or can be processed by a CPU and a processor suitable for neural network calculation (such as Figure 6 The shown neural network processor 30) jointly processes as Figure 6 The shown neural network processor 30 is not limited in this application. The method may include some or all of the following steps:

...

Embodiment 3

[0306] Such as Figure 8A It is a schematic flow chart of an ECG signal noise reduction method provided in Embodiment 2 of the present invention, Figure 8B It is a schematic explanatory diagram of an ECG signal denoising method provided in Embodiment 2 of the present invention. The method uses the target denoising autoencoder trained in Embodiment 1 to implement denoising of the ECG signal to be denoised. Specifically, this method can be image 3 Executed by the execution device 110 shown, the ECG signal to be denoised in this method can be as follows image 3 For the input data given by the user equipment 140 shown, the preprocessing module 113 in the execution device 110 can be used to execute S802-S804 in the method 800. The signal superposition module 114 in the execution device 110 is used to execute In S808 of the method 800, the calculation module 111 in the executing device 110 may be used to execute the S806. Optionally, the method 800 may be processed by a CPU, o...

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Abstract

A denoising autoencoder training method, an electrocardiography signal denoising method, and related apparatuses and devices, which use artificial intelligence to perform electrocardiography signal denoising, and may be applied to fields such as smart electrocardiogram detection. A reference electrocardiography signal to be denoised comprising significant features such as R peak positions and R-R distances of an electrocardiography signal to be denoised is extracted from said electrocardiography signal, and denoising is performed on a remaining electrocardiography signal to be denoised by means of a target denoising autoencoder after said reference electrocardiography signal has been removed from said electrocardiography signal, to prevent the target denoising autoencoder from performing denoising processing on the significant features in said electrocardiography signal, so that said reference electrocardiography signal and the denoised remaining electrocardiography signal are superimposed to obtain a denoised electrocardiography signal, wherein the R peak positions in said electrocardiography signal are better maintained, and distortion of the denoised electrocardiography signal is reduced.

Description

technical field [0001] The present invention relates to the technical field of artificial intelligence, in particular to a training method for a noise-reduction autoencoder, a method for reducing the noise of an electrocardiographic signal, and related devices and equipment. Background technique [0002] With the development of artificial intelligence technology, it is gradually becoming possible to help doctors with electrocardiogram diagnosis through artificial intelligence technology. The quality of the ECG signal directly affects the accuracy of ECG signal diagnosis. The acquisition of ECG signals is usually obtained through electrodes attached to the surface of the skin. Since the electrocardiographic signal on the skin is relatively weak and easily interfered by noise, the collected electrocardiographic signal has a lot of noise, which reduces the accuracy and reliability of electrocardiographic diagnosis. In particular, the ECG collected by the wearable ECG device w...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): A61B5/0402A61B5/0452A61B5/00
CPCA61B5/681A61B5/7203A61B5/7235A61B5/7253A61B5/7267A61B5/316A61B5/318A61B5/349
Inventor 王贵锦黄勇锋丁子建张宇
Owner TSINGHUA UNIV
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