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Deep convolutional neural network based fine grain electrocardiogram signal classification method fusing with online decisions

A technique of electrocardiogram and deep convolution, applied in the field of signal classification, to achieve the effect of high computational efficiency

Inactive Publication Date: 2018-10-30
HANGZHOU DIANZI UNIV
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  • Abstract
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Problems solved by technology

On the other hand, they still have problems with fine-grained classification, as it requires more discriminative features and classifiers with better modeling capabilities

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  • Deep convolutional neural network based fine grain electrocardiogram signal classification method fusing with online decisions
  • Deep convolutional neural network based fine grain electrocardiogram signal classification method fusing with online decisions
  • Deep convolutional neural network based fine grain electrocardiogram signal classification method fusing with online decisions

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Embodiment Construction

[0035] The present invention will be further described below in conjunction with accompanying drawing.

[0036] Such as figure 1 As shown, the present invention proposes fine-grained ECG signal classification based on deep convolutional neural network and online decision fusion, including the following steps:

[0037] Step (1) Acquisition and processing of ECG signal waveform:

[0038] Step (1-1) Data set acquisition: SKX-2000 ECG signal simulator was used for ECG waveform generation. The simulator is capable of simulating ECG waveforms with various amplitudes and frequencies of different symptoms, including but not limited to more than 20 types of ECG waveforms such as normal, coarse atrial fibrillation, fine atrial fibrillation, and atrial flutter. For the above 20 types of ECG waveform signals, a certain amount of waveform signals under different parameters corresponding to the categories are respectively collected. Mathematically expressed as:

[0039] X={(x i (t),y ...

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Abstract

The invention discloses a deep convolutional neural network (DCNN) based fine grain electrocardiogram classification method fusing with online decisions. Unlike a conventional method of using manual features or learning features from an original signal domain, the DCNN based method provided by the invention learns the features and classifications from a time frequency domain in an end-to-end manner. Firstly, electrocardiogram waveform signals are transformed into the time frequency domain by using short time Fourier transform; secondly, concrete DCNN network models are trained by training samples of a specific length; and finally, an online fusion method is provided to integrate previous and present decisions from the different models into a more accurate decision. Experimental results of20 kinds of ECG data sets show the effectiveness of the provided method.

Description

technical field [0001] The invention belongs to the field of signal classification, and in particular relates to a fine-grained ECG signal classification method based on deep convolutional neural network and online decision-making fusion. Background technique [0002] The electrocardiogram (ECG) records the depolarization and repolarization process of the heart's electrical activity during the cardiac cycle and is widely used to monitor or diagnose a patient's heart condition. Often requiring patients to go to the hospital for a diagnosis by a trained and experienced cardiologist is expensive and inconvenient. Therefore, automated monitoring and diagnosis systems are highly desirable in clinics, community medical centers and home health care programs. Although great progress has been made in the filtering, detection, and classification of ECG in the past few decades, efficient and accurate classification of ECG signals is still a challenge due to the noise and the different...

Claims

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Application Information

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IPC IPC(8): A61B5/0402G06K9/46G06K9/62G06N3/04
CPCA61B5/7257A61B5/7264A61B5/318G06V10/462G06N3/045G06F18/214G06F18/24
Inventor 张敬田婧徐晓滨文成林
Owner HANGZHOU DIANZI UNIV
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