Screening method of tachycardia ECG based on deep feature fusion network

A tachycardia, fusion network technology, applied in medical science, sensors, diagnostic recording/measurement, etc., can solve problems such as low efficiency, and achieve the effect of high accuracy and high accuracy

Active Publication Date: 2019-10-15
ZHEJIANG UNIV OF TECH
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AI Technical Summary

Problems solved by technology

[0004] In order to overcome the inefficiency of existing ECG methods for identifying tachycardia

Method used

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  • Screening method of tachycardia ECG based on deep feature fusion network
  • Screening method of tachycardia ECG based on deep feature fusion network
  • Screening method of tachycardia ECG based on deep feature fusion network

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example

[0050] Example: In this case, the ECG used contains two types, including tachycardia and non-tachycardia. There are 849 samples in the total data set, including 438 samples of tachycardia and 411 samples of non-tachycardia, and these 849 cases are all 12-lead ECG images. The training process adopts a 7-fold cross-validation method. For each fold, 727 ECG cases are selected as the training set and 122 ECG cases are used as the test set. The number of ECG samples in the training set and the test set is close to 1:1. The following describes the electrocardiogram preprocessing and reconstruction, network construction and network training and testing process in detail.

[0051] Step 1 ECG preprocessing and reconstruction process:

[0052] Step 1.1 Remove the QRS wave from the original ECG through a one-dimensional median filter with a pixel length of 5, then remove the T wave and P wave from the processed ECG through a one-dimensional median filter with a pixel length of 15, and c...

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Abstract

A screening method of tachycardia ECG based on deep feature fusion network includes the following steps: step 1, data processing: preprocessing an original ECG image, removing baseline drift and powerline interference in the ECG; step 2, data reconstruction: carrying out frame selection separation of 12 leads in the preprocessed ECG image, and reorganizing and reconstructing a data set; step 3, model construction: constructing deep neural network; step 4, model training: inputting the processed and reconstructed data to the network, carrying out parameter adjustment, and training a model; andstep 5, model outputting: screening the tachycardia ECG by the trained model. The method can screen whether the situation is tachycardia according to the ECG.

Description

technical field [0001] The present invention relates to the fields of medical image analysis and machine learning, in particular to a screening for tachycardia applied to twelve-lead electrocardiograms, and belongs to the field of medical image analysis based on deep learning. Background technique [0002] Tachycardia refers to a heart rate exceeding 100 beats per minute, which is a relatively common clinical disease. In medicine, it can be divided into two types: physiological and pathological. Physiological tachycardia is generally related to physical activity, alcohol consumption, etc., which often does not require treatment. Pathological tachycardia is generally caused by anemia, heart disease, etc. Once it exceeds 140 beats per minute, it will cause harm or even sudden death. This requires early treatment, so how to accurately screen out tachycardia based on the electrocardiogram is very important. [0003] During routine ECG examination, 4 limb lead electrodes and V ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/0402
CPCA61B5/7203A61B5/7225A61B5/7267A61B5/318
Inventor 郝鹏翼高翔叶涛涛童清霞吴福理吴健
Owner ZHEJIANG UNIV OF TECH
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