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Detection system of arrhythmias based on convolutional neural network

A convolutional neural network, arrhythmia technology, applied in the field of neural networks, can solve the problem of inaccurate detection results, and achieve the effect of improving accuracy and high reliability prediction

Inactive Publication Date: 2018-05-15
北京医拍智能科技有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to provide a detection system for arrhythmia based on a convolutional neural network, which solves the technical problem of inaccurate detection results of the arrhythmia detection system in the prior art

Method used

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  • Detection system of arrhythmias based on convolutional neural network
  • Detection system of arrhythmias based on convolutional neural network
  • Detection system of arrhythmias based on convolutional neural network

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Embodiment 1

[0044] figure 1 It is a schematic diagram of an arrhythmia detection system based on a convolutional neural network according to an embodiment of the present invention, such as figure 1 As shown, the embodiment of the present invention provides a detection system for arrhythmia based on a convolutional neural network, including a segmentation module 10 and a detection module 20, wherein,

[0045] The segmentation module 10 is used to segment the acquired K-lead ECG data of the first patient in chronological order, and obtain multiple K-lead ECG data segments, each of which has the same length as the K-lead ECG data segments , K is a positive integer;

[0046] The detection module 20 is used to input the plurality of K-lead ECG data segments into the trained convolutional neural network model in order of time to obtain the type of arrhythmia of the first patient.

[0047] Further, it also includes:

[0048] The training module is used to obtain the training sample set, the t...

Embodiment 2

[0081] Figure 5 A schematic structural diagram of an electronic device for arrhythmia detection provided by an embodiment of the present invention, such as Figure 5 As shown, the device includes: a processor (processor) 801, a memory (memory) 802 and a bus 803;

[0082] Wherein, the processor 801 and the memory 802 complete mutual communication through the bus 803;

[0083] The processor 801 is used to call the program instructions in the memory 802 to perform the following steps:

[0084] The acquired K-lead ECG data of the first patient is segmented and processed in chronological order, and multiple K-lead ECG data segments are obtained, the length of each K-lead ECG data segment is equal, and K is a positive integer;

[0085] The plurality of K-lead ECG data segments are respectively input into the trained convolutional neural network model according to the sequence of time to obtain the type of arrhythmia of the first patient.

Embodiment 3

[0087] An embodiment of the present invention discloses a computer program product, the computer program product includes a computer program stored on a non-transitory computer-readable storage medium, the computer program includes program instructions, and when the program instructions are executed by a computer, The computer is capable of performing the following steps:

[0088] The acquired K-lead ECG data of the first patient is segmented and processed in chronological order, and multiple K-lead ECG data segments are obtained, the length of each K-lead ECG data segment is equal, and K is a positive integer;

[0089] The plurality of K-lead ECG data segments are respectively input into the trained convolutional neural network model according to the sequence of time to obtain the type of arrhythmia of the first patient.

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Abstract

The invention provides a detection system of arrhythmias based on a convolutional neural network. The system comprises a segmentation module and a detection module; the segmentation module is used forsegmenting the obtained K-lead electrocardiogram data of a first patient in chronological order, multiple K-lead electrocardiogram data segments are obtained, the length of each K-lead electrocardiogram data segment is equal, and K is a positive integer; the detection module is used for inputting the multiple K-lead electrocardiogram data segments into a trained convolutional neural network modelin chronological order, and a type of arrhythmia in the first patient is obtained. According to the provided detection system of arrhythmias based on the convolutional neural network, by combining the convolutional neural network with the electrocardiogram data, feature extraction and classification of arrhythmia are integrated into one step, multiple leads and timing sequence information of electrocardiogram are fully excavated, a high reliable prediction of a case can be made, and thus the accuracy of arrhythmia detection is improved.

Description

technical field [0001] The invention relates to the technical field of neural networks, in particular to a detection system for arrhythmia based on a convolutional neural network. Background technique [0002] With the rapid development and wide application of computer technology, computer-aided diagnosis plays an increasingly important role in human health. [0003] In the prior art, the method for detecting arrhythmia through a computer-aided diagnosis system is as follows: first, according to the sample user data, use the Support Vector Machine (Support Vector Machine, SVM) algorithm to train and learn the statistical model; ECG data within a sampling period, analyze and extract multiple characteristic data in the ECG data, calculate the average value and variance of each characteristic data within the sampling period; combine the average value, variance and multiple characteristic data, A first multidimensional vector corresponding to the patient is obtained; the first ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/0402
CPCA61B5/7264A61B2576/023A61B5/318
Inventor 祖春山杨琼吴诗展
Owner 北京医拍智能科技有限公司
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