Electrocardiogram anomaly recognition method and system based on combination of AlexNet and transfer learning

A technology of transfer learning and abnormal identification, applied in the field of electrocardiogram abnormal identification method and system, to achieve the effect of reducing the amount of parameters and reducing over-fitting

Active Publication Date: 2020-12-01
QILU UNIV OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0023] The technical task of the present invention is to provide an abnormal electrocardiogram identification method and system based on the combination of AlexNet and transfer learning, to solve how to combine AlexNet deep convolutional neural network and transfer lear

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  • Electrocardiogram anomaly recognition method and system based on combination of AlexNet and transfer learning
  • Electrocardiogram anomaly recognition method and system based on combination of AlexNet and transfer learning
  • Electrocardiogram anomaly recognition method and system based on combination of AlexNet and transfer learning

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Embodiment 1

[0108] as attached figure 1 As shown, the ECG abnormal recognition method based on AlexNet and migration learning of the present invention is combined, and the method is specifically as follows:

[0109] S1. Preprocessing: Convert each ECG signal in the data set into an ECG image, and cut out abnormal ECGs in different directions to achieve the purpose of data enhancement; the details are as follows:

[0110] S101. Image conversion: converting each ECG signal in the data set into an ECG image with a size of 227x227;

[0111] S102. Construct heartbeat samples: use wavelet transform to process high-frequency signals of power frequency signal interference and myoelectric signal interference; IIR zero-phase digital filter corrects baseline drift, and realizes QRS wave detection by software discrimination and variable threshold; After the QRS wave is detected, the ECG is segmented around the QRS wave peak, and a total of 256 samples are taken from 90 samples before the QRS wave pe...

Embodiment 2

[0142] The electrocardiogram abnormal recognition system based on the combination of AlexNet and transfer learning of the present invention, the system includes,

[0143] The preprocessing unit is used to convert each electrocardiogram signal in the data set into an electrocardiogram image, and clip abnormal types of electrocardiograms in different directions to achieve the purpose of data enhancement; the preprocessing unit includes,

[0144] The image conversion module is used to convert each electrocardiogram signal in the data set into an electrocardiogram image whose size is 227x227;

[0145] Heart beat sample building module, used to segment ECG around the QRS peak, taking 90 samples before the QRS peak and 165 samples after the peak, a total of 256 samples to form a heart beat sample;

[0146] The ECG amplitude influence module is used to normalize the heartbeat sample on the basis of dividing the heartbeat sample, and eliminate the influence of different amplitudes in ...

Embodiment 3

[0169] The embodiment of the present invention also provides a computer-readable storage medium, in which a plurality of instructions are stored, and the instructions are loaded by the processor, so that the processor executes the abnormal electrocardiogram based on the combination of AlexNet and migration learning in any embodiment of the present invention recognition methods. Specifically, a system or device equipped with a storage medium may be provided, on which a software program code for realizing the functions of any of the above embodiments is stored, and the computer (or CPU or MPU of the system or device) ) to read and execute the program code stored in the storage medium.

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Abstract

The invention discloses an electrocardiogram anomaly recognition method and system based on combination of AlexNet and transfer learning, and belongs to the technical field of feature extraction classification prediction. The technical problem to be solved by the invention is how to accurately and efficiently complete electrocardiogram anomaly recognition in combination with an AlexNet deep convolutional neural network and transfer learning, and how to get rid of dependence on sample data capacity and automatically learn features of data samples. According to the technical scheme, the method specifically comprises the following steps: preprocessing: converting each electrocardiogram signal in a data set into an electrocardiogram image, and clipping an electrocardiogram of an abnormal typein different directions; feature extraction: putting the data-enhanced image into a pre-trained model for training, taking the pre-trained electrocardiogram image as the input of an AlexNet model, automatically extracting features, and carrying out transfer learning by using the AlexNet model pre-trained by an ImageNet data set; and classification prediction: putting high features obtained by a pre-trained AlexNet deep convolutional neural network model into a support vector machine for electrocardiogram classification.

Description

technical field [0001] The invention relates to the technical field of feature extraction, classification and prediction, in particular to an electrocardiogram abnormality identification method and system based on the combination of AlexNet and transfer learning. Background technique [0002] Electrocardiography (ECG or EKG) is a technique that uses an electrocardiogram machine to record the electrical activity changes of the heart every cardiac cycle from the body surface. If there are ST segment or T wave changes in the patient's ECG, special attention should be paid to whether they are continuous changes or dynamic (transient) changes. If they persist, most of them are not caused by myocardial ischemia or coronary heart disease; if ST segment (T wave) changes If it is associated with chest pain, it is most likely unstable angina or myocardial infarction. [0003] Common abnormal ECG diagnoses are as follows: [0004] ①Atrial hypertrophy: divided into left and right atri...

Claims

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Application Information

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IPC IPC(8): A61B5/0452A61B5/0472A61B5/366
CPCA61B5/7225
Inventor 董爱美周晶
Owner QILU UNIV OF TECH
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