Convolutional neural network-extremely randomized trees (CNN-ET) model-based electrocardiogram monitoring method

Abstract

The invention discloses a CNN-ET model-based electrocardiogram monitoring method. The electrocardiogram monitoring method comprises the following steps: S1, collecting ECG signals of a subject to construct an original data set, and acquiring training data and test data; S2, performing normalization processing on the training data and the test data to obtain a training sample and a test sample; S3, constructing a CNN-ET hybrid model; S4, inputting the training sample into the CNN-ET hybrid model for training to obtain a trained CNN-ET hybrid model; and S5, inputting the test sample into the trained CNN-ET hybrid model to carry out classification and identification of the ECG signals. The CNN-ET model organically combines a convolutional neural network and extremely randomized trees, and integrates feature extraction of the ECG signals and a classification task of the ECG signals, so that the relevance and classification precision of the whole classification model are improved, and the classification and identification of the ECG signals can be effectively and accurately implemented.

Description

technical field [0001] The invention relates to the technical field of electrocardiogram intelligent recognition, in particular to an electrocardiogram monitoring method based on a CNN-ET model. Background technique [0002] Electrogram is a technology that uses an electrocardiograph to record images of changes in electrical activity generated by each cardiac cycle of the heart from the body surface. It is an important indicator for measuring human health and one of the main tools for detecting cardiovascular diseases. Real-time and accurate classification and identification of various types of ECG can effectively prevent and identify various arrhythmias, ventricular and atrial hypertrophy, myocardial infarction, abnormal heart rate, myocardial ischemia, heart failure and other diseases. In the traditional medical diagnosis process, it is time-consuming and labor-intensive to classify and identify various types of electrocardiograms based on the experience of doctors, and it...

AI Technical Summary

Problems solved by technology

[0006] The present invention mainly solves the technical problem that the accuracy of the classification model in the existing ECG monitoring method is low, and the classification and recognition of the ECG signal cannot be effectively and accurately realized; it provides a CNN-ET model-based ECG monitoring method, the method In the CNN-ET model, the convolutional neural network (CNN) and extreme random number (ET) are organically combined, and the feature extraction of ECG signals and the classification tasks of ECG signals are integrated, which improves the relevance and classification of the entire classification model. Accuracy, and can effectively and accurately realize the classification and identification of ECG signals

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