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Realization method of atrial fibrillation detection based on deep convolutional neural network

A neural network and deep convolution technology, applied in the field of atrial fibrillation detection, can solve the problem of not finding the successful application of deep convolutional neural network, and achieve the effect of improving efficiency and accuracy

Active Publication Date: 2017-09-26
HARBIN INST OF TECH AT WEIHAI
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AI Technical Summary

Problems solved by technology

However, due to the particularity of ECG signals, there has been no report on the successful application of deep convolutional neural networks in the field of atrial fibrillation detection.

Method used

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  • Realization method of atrial fibrillation detection based on deep convolutional neural network
  • Realization method of atrial fibrillation detection based on deep convolutional neural network
  • Realization method of atrial fibrillation detection based on deep convolutional neural network

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specific Embodiment approach 1

[0023] Specific Embodiment 1: This embodiment provides an implementation method of atrial fibrillation detection based on a deep convolutional neural network, in order to make the original one-dimensional ECG signal suitable for processing a convolutional neural network structure in the form of two-dimensional data , it is necessary to properly transform the one-dimensional ECG signal to meet the requirements of the deep convolutional neural network for the input signal. In addition, the realization method of atrial fibrillation detection based on the deep convolutional neural network provided in this embodiment, the deep convolutional neural network is constructed based on Caffe, one of the most popular deep learning frameworks at present. The specific implementation steps are as follows:

[0024] (1) Use ECG acquisition equipment to collect continuous ECG data of a single lead of the patient, and the doctor will mark each beat of the ECG data whether there is atrial fibrilla...

specific Embodiment approach 2

[0044] Specific embodiment two: This embodiment provides a method for detecting atrial fibrillation based on static wavelet transform combined with deep convolutional neural network, such as figure 2 As shown, the specific steps are as follows:

[0045] Step (1): read in the records in the MIT-BIH atrial fibrillation database.

[0046] Step (2): Carry out data segmentation for these ECG records. The duration of each data segment is 5 seconds. Since the sampling frequency of the MIT-BIH atrial fibrillation database is 250Hz, each 5-second data segment contains 1250 Sampling point. Set the sample category label for each 5-second data segment according to the annotation file of the MIT-BIH atrial fibrillation database. The basis for setting the label is: if the proportion of the number of atrial fibrillation beats in each 5-second data segment to the entire data segment is ≥ 50%, the data segment is marked as an atrial fibrillation segment, otherwise it is a non-atrial fibrill...

specific Embodiment approach 3

[0058] Specific Embodiment Three: This embodiment provides a method for detecting atrial fibrillation based on short-time Fourier transform combined with a deep convolutional neural network, such as image 3 As shown, the specific steps are as follows:

[0059] Step (1): read in the records in the MIT-BIH atrial fibrillation database.

[0060] Step (2): Carry out data segmentation for these ECG records. The duration of each data segment is 5 seconds. Since the sampling frequency of the MIT-BIH atrial fibrillation database is 250Hz, each 5-second data segment contains 1250 Sampling point. Set the sample category label for each 5-second data segment according to the annotation file of the MIT-BIH atrial fibrillation database. The basis for setting the label is: if the proportion of the number of atrial fibrillation beats in each 5-second data segment to the entire data segment is ≥ 50%, the data segment is marked as an atrial fibrillation segment, otherwise it is a non-atrial ...

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Abstract

The invention discloses a realization method of atrial fibrillation based on a deep convolutional neural network. By the method, single-lead ECG data can be changed into a two-dimensional form through signal conversion to enable the same to be suitable for the deep convolutional neural network for processing two-dimensional data, so that automatic detection on atrial fibrillation is realized finally by automatically learning features and classifying the same by a machine. When the method is used for atrial fibrillation detection, P wave or R-R interval does not need to be detected, and artificial designing of features is not needed also, so that efficiency and accuracy in atrial fibrillation detection are improved greatly, wherein an atrial fibrillation detection method based on stationary wavelet transform combined with the deep convolutional neural network is 98.63% in accuracy, 98.79% in sensitivity and 97.87% in specificity, and an atrial fibrillation detection method based on short-time Fourier transform combined with the deep convolutional neural network is 98.29% in accuracy, 98.34% in sensitivity and 98.24% in specificity.

Description

technical field [0001] The invention belongs to the technical field of atrial fibrillation detection, and relates to an implementation method of atrial fibrillation signal detection in electrocardiographic signal identification, in particular to an implementation method of atrial fibrillation signal detection in electrocardiographic signals based on a machine learning algorithm. Background technique [0002] With the development of artificial intelligence technology, the detection of atrial fibrillation signals can no longer rely on the experience and judgment of professional doctors, and the use of machine learning algorithms in artificial intelligence technology allows machines to detect atrial fibrillation signals, which improves the efficiency of atrial fibrillation detection . Most of the traditional detection of atrial fibrillation signals based on machine learning algorithms needs to detect the P wave or R-R interval in the ECG signal first, and then use machine learn...

Claims

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Application Information

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IPC IPC(8): G06F19/00G06K9/00G06N3/08
CPCG06N3/08G06F2218/06
Inventor 夏勇乌兰娜仁王宽全张恒贵
Owner HARBIN INST OF TECH AT WEIHAI
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