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Arrhythmia detection system based on deep neural network

A deep neural network and arrhythmia technology, applied in the field of neural networks, can solve the problems of inaccurate detection results, and achieve the effect of improving accuracy

Inactive Publication Date: 2018-05-15
HANGZHOU ZHUOJIAN INFORMATION TECH CO LTD
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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 deep 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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  • Arrhythmia detection system based on deep neural network
  • Arrhythmia detection system based on deep neural network
  • Arrhythmia detection system based on deep neural network

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Experimental program
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Embodiment 1

[0042] figure 1 It is a schematic diagram of an arrhythmia detection system based on a deep neural network according to an embodiment of the present invention, such as figure 1 As shown, the embodiment of the present invention provides a deep neural network-based arrhythmia detection system, including a segmentation module 10 and a detection module 20, wherein,

[0043] The segmentation module 10 is used to segment the acquired K-lead ECG data of the patient to be detected 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;

[0044] The detection module 20 is used to input the plurality of K-lead ECG data segments into the trained deep neural network model in order of time to obtain the type of arrhythmia of the patient to be detected.

[0045] Further, it also includes:

[0046] The training module is used to obtain the training sample set, the training sample set...

Embodiment 2

[0076] image 3 A schematic structural diagram of an electronic device for arrhythmia detection provided by an embodiment of the present invention, such as image 3 As shown, the device includes: a processor (processor) 801, a memory (memory) 802 and a bus 803;

[0077] Wherein, the processor 801 and the memory 802 complete mutual communication through the bus 803;

[0078] The processor 801 is used to call the program instructions in the memory 802 to perform the following steps:

[0079] The obtained K-lead ECG data of the patient to be detected 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;

[0080] The plurality of K-lead ECG data segments are respectively input into the trained deep neural network model according to the order of time to obtain the type of arrhythmia of the patient to be detected.

Embodiment 3

[0082] 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:

[0083] The obtained K-lead ECG data of the patient to be detected 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;

[0084] The plurality of K-lead ECG data segments are respectively input into the trained deep neural network model according to the order of time to obtain the type of arrhythmia of the patient to be detected.

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Abstract

The invention provides an arrhythmia detection system based on a deep neural network, including a segmentation module and a detection module. The segmentation module is used for segmenting acquired Klead electrocardiogram data of a to-be-detected patient in chronological order to obtain multiple K lead electrocardiogram data segments, wherein the K lead electrocardiogram data segments are equal in length, and K is a positive integer. The detection module is used for inputting the multiple K lead electrocardiogram data segments to a trained deep neural network model in sequence according to the time to obtain the type of arrhythmia of the to-be-detected patient. According to the arrhythmia detection system based on a deep neural network provided by the invention, the case is predicted withhigh reliability by combining the deep neural network with the electrocardiogram data and using the clinical experience knowledge of artificial diagnosis of arrhythmia as prior knowledge, and therefore, 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 cardiac arrhythmia based on a deep 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 multidimensional vector corresponding to the patient is obtained; the multidimensio...

Claims

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Application Information

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IPC IPC(8): G16H50/20G06K9/62G06N3/04
CPCG06N3/045G06F18/24G06F18/214
Inventor 杨琼吴诗展
Owner HANGZHOU ZHUOJIAN INFORMATION TECH CO LTD
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