Electrocardiosignal noise reduction method based on adversarial generative network

Abstract

According to the electrocardiosignal noise reduction method based on the generative adversarial network, the conditional generative adversarial network is used, so that the directionality of generateddata and the matching performance of a signal after noise reduction and a noisy signal are ensured, and the generalization ability of a model is improved. In the training process, uncertain noise mixed data is used for training, so that the model can perform noise reduction on various noise mixed signals without pre-judgment, and the complexity of the method is simplified. Design of an improved loss function is realized, the resistance in the original loss function of the CGAN is reserved, the root-mean-square error and the noise-to-signal power ratio are increased, the increase of the root-mean-square error enables the model to capture the local features of the signal and maintain the useful medical features of the signal, and the increase of the noise-to-signal power ratio enables the model to capture the global features of the signal, so that the training process is stable.

Description

technical field [0001] The invention relates to the technical field of ECG signal noise reduction, in particular to an ECG signal noise reduction method based on an adversarial generation network. Background technique [0002] Electrocardiography is a technology that uses an electrocardiograph to record the electrical activity changes generated by each cardiac cycle of the heart from the body surface, and is an important signal indicator to measure the health of the heart. In the actual application process, the collected ECG signal often contains a lot of noise, which reduces the accuracy of the ECG signal. Therefore, noise reduction of the ECG signal becomes an important signal processing task. [0003] Usually, when performing signal noise reduction, different methods are used for different types of noise, such as using a median filter to remove noise from baseline drift. If a segment of ECG signal contains multiple noises, it is necessary to predict and combine related m...

AI Technical Summary

Problems solved by technology

In practical applications, the type of noise contained in the signal is usually uncertain. If the signal is not pre-judged and combined with multiple methods to reduce noise, the ECG signal will lose some details and affect the quality of the ECG signal; complexity, limiting the use of methods

Images