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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Problems solved by technology

However, due to the particularity of ECG signals, there has been no report on the successful a

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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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Example Embodiment

[0023] Specific implementation mode 1: This embodiment provides a method for implementing atrial fibrillation detection based on a deep convolutional neural network, in order to make the original one-dimensional ECG signal applicable to the convolutional neural network structure in the form of processing two-dimensional data , The one-dimensional ECG signal needs to be appropriately transformed to meet the input signal requirements of the deep convolutional neural network. In addition, in the implementation method of atrial fibrillation detection based on a deep convolutional neural network provided by this embodiment, the deep convolutional neural network is constructed based on Caffe, one of the most popular deep learning frameworks. The specific implementation steps are as follows:

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

Example Embodiment

[0044] Embodiment 2: 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 the records in the MIT-BIH atrial fibrillation database.

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

[00...

Example Embodiment

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

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

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

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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

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Claims

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Application Information

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Owner HARBIN INST OF TECH AT WEIHAI
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