Convolutional neural network-extremely randomized trees (CNN-ET) model-based electrocardiogram monitoring method
A technology of electrical monitoring and modeling, applied in diagnostic recording/measurement, medical science, sensors, etc., can solve problems such as inability to effectively and accurately realize ECG signal classification and recognition, and low accuracy of classification models
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[0047] Embodiment: A kind of ECG monitoring method based on CNN-ET model of this embodiment, such as figure 1 shown, including the following steps:
[0048] S1. Collect the ECG signal of the subject to construct the original data set, and obtain training data and test data. The ECG signal of the subject is as follows figure 2 shown;
[0049]S2, normalize the training data and the test data to obtain a training sample and a test sample;
[0050] S3. Build a CNN-EF hybrid model;
[0051] S4, the training sample is input into the CNN-ET hybrid model for training, and the trained CNN-ET hybrid model is obtained;
[0052] S5. Input the test sample into the trained CNN-ET hybrid model to classify and identify the ECG signal.
[0053] The CNN-ET model organically combines convolutional neural network (CNN) and extreme random number (ET), integrates the feature extraction of ECG signal and the classification task of ECG signal, and improves the relevance and classification accura...
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