A data preprocessing method for ECG signal classification based on deep learning model

Abstract

A data preprocessing method for classifying ECG signals based on a deep learning model, including the following steps: a. Obtain ECG signals marked by experts as training samples, including normal ECG signals and abnormal ECG signals. The measurement time of ECG signals is arbitrary, assuming The longest measurement duration is t seconds, and the sampling frequency is fs; b. Perform noise reduction processing on the original training samples, and use wavelet transform to remove baseline drift; c. Divide the training samples into a training set and a test set, and perform data amplification on the training set; d. Input the training set into the deep learning model for training, and use the test set to optimize the model parameters; e. After the original ECG signal is preprocessed in b and c, take t×fs data points as samples and input it into the model to obtain the ECG signal classification results. The present invention can increase the number of samples, achieve sample balance at the same time, make the model easier to train, and help improve the classification ability and robustness of the model. Sample balance makes the model easier to train, and helps improve the classification ability and robustness of the model. sex.

Description

technical field [0001] The invention relates to a data preprocessing method for classifying electrocardiographic signals based on a deep learning model. It can make the training of deep learning model easier and help to improve the robustness and classification ability of the model. Background technique [0002] ECG signal analysis is an important means for doctors to diagnose heart disease. Traditional analysis methods usually classify signals by extracting ECG signal features. In recent years, with the rise of deep neural network technology, research on ECG signal classification using deep learning methods has also increased. [0003] Most of the existing deep learning models are data-driven, and researchers need to provide a large number of training samples as the support of the model. However, it is currently difficult to obtain high-quality ECG signal classification samples. On the one hand, since the vast majority of ECG signals collected from clinics are normal si...

AI Technical Summary

Problems solved by technology

[0004] In order to overcome the lack of unbalanced samples and the scarcity of the total sample size in the prior art, which makes it difficult to train the deep learning model, the present invention proposes a data preprocessing method for classifying ECG signals based on the deep learning model

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