Feature fusion transfer learning arrhythmia classification system based on 2D heart beat

A feature fusion and arrhythmia technology, applied in the field of heart rate duration classification system, can solve problems such as limited information, difficulty in further improving classification accuracy, and failure to fully reflect cardiac physiological changes, so as to improve classification accuracy, reduce cross-term interference, and ensure The effect of time-frequency focusing

Pending Publication Date: 2021-02-05
SHANDONG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Sayantan G et al. use deep belief network and active learning to classify ECG beats, but this method only uses one-dimensional Gauss-Bernoulli deep belief network to learn the feature representation of ECG
Shi et al. proposed a novel multi-input deep learning network and used it for atrial fibrillation detection, however, this method based on active and transfer learning is still based on one-dimensional ECG data
In fact, since the collected ECG data is in one-dimensional form, the above-mentioned 1-D CNN method has the advantages of fast running speed and simple structure in the classification

Method used

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  • Feature fusion transfer learning arrhythmia classification system based on 2D heart beat
  • Feature fusion transfer learning arrhythmia classification system based on 2D heart beat
  • Feature fusion transfer learning arrhythmia classification system based on 2D heart beat

Examples

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Example Embodiment

[0039]Example one

[0040]This embodiment provides a feature fusion transfer learning arrhythmia classification system based on 2D heart beats;

[0041]Feature fusion transfer learning arrhythmia classification system based on 2D heart beats, including:

[0042]The obtaining module is configured to: obtain the target ECG signal;

[0043]A reconstruction module, which is configured to: reconstruct a target ECG signal from one dimension to a two-dimensional ECG signal;

[0044]The feature extraction module is configured to: extract the first feature and the second feature from the two-dimensional ECG signal respectively;

[0045]The feature fusion module is configured to perform fusion processing on the first feature and the second feature;

[0046]The classification module is configured to: input the fused features into the trained classifier, and output the arrhythmia classification result corresponding to the current target ECG signal.

[0047]As one or more embodiments, after the obtaining module and bef...

Example Embodiment

[0144]Example two

[0145]This embodiment also provides an electronic device, including: one or more processors, one or more memories, and one or more computer programs; wherein, the processor is connected to the memory, and the above one or more computer programs are It is stored in a memory, and when the electronic device is running, the processor executes one or more computer programs stored in the memory, so that the electronic device completes the functions of the system described in the first embodiment.

Example Embodiment

[0146]Example three

[0147]This embodiment also provides a computer-readable storage medium for storing computer instructions that, when executed by a processor, complete the functions of the system described in the first embodiment.

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Abstract

The invention discloses a feature fusion transfer learning arrhythmia classification system based on 2D heart beat, and the system comprises: an obtaining module which is configured to obtain a targetelectrocardiosignal; the reconstruction module that is configured to reconstruct the target electrocardiosignal from one dimension to a two-dimensional electrocardiosignal; the feature extraction module that is configured to respectively extract a first feature and a second feature from the two-dimensional electrocardiosignal; the feature fusion module that is configured to perform fusion processing on the first feature and the second feature; and the classification module that is configured to input the fused features into the trained classifier and output an arrhythmia classification resultcorresponding to the current target electrocardiosignal.

Description

technical field [0001] The present application relates to the technical field of heart rate duration classification system, in particular to a 2D heartbeat-based feature fusion transfer learning arrhythmia classification system. Background technique [0002] The statements in this section merely mention the background art related to this application, and do not necessarily constitute the prior art. [0003] There are two main types of traditional arrhythmia classification methods, one is feature extraction combined with machine learning methods, and the other is deep learning methods without feature extraction. [0004] Machine learning method based on feature extraction: Traditional machine learning algorithms need to manually extract features including time domain, frequency domain, etc. based on prior experience, and then train an appropriate machine learning classifier. Therefore, the advantages and disadvantages of the extracted features affect the final classification...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/04
CPCG06N3/045G06F2218/04G06F2218/08G06F2218/12G06F18/253G06F18/214
Inventor 张亚涛张锋李向宇鲍喆
Owner SHANDONG UNIV
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