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A ventricular ectopic beat detection method based on 1d convolutional neural network

A convolutional neural network and ectopic beating technology, applied in the field of medical testing, can solve problems such as error-prone, time-consuming and labor-intensive processes, and achieve the effect of good detection accuracy and strong robustness

Active Publication Date: 2019-09-24
UNIV OF SCI & TECH BEIJING
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Problems solved by technology

Therefore, abnormal detection of ECG signals requires long-term ECG data. However, doctors use this large amount of data to judge heart diseases such as ventricular premature contraction, which is time-consuming, laborious and error-prone.

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  • A ventricular ectopic beat detection method based on 1d convolutional neural network
  • A ventricular ectopic beat detection method based on 1d convolutional neural network
  • A ventricular ectopic beat detection method based on 1d convolutional neural network

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Embodiment Construction

[0032] Specific embodiments of the present invention will be described below in conjunction with the accompanying drawings, so that those skilled in the art can better understand the present invention. It should be noted that in the following description, when detailed descriptions of known functions and designs may dilute the main content of the present invention, these descriptions will be omitted here.

[0033] figure 1 It is a flow chart of a specific embodiment of the method for obtaining medical index data of heart sound signals in the present invention.

[0034] In this example, if figure 1 Described, the ventricular ectopic beat detection method based on 1D (Dimension) convolutional neural network of the present invention comprises the following steps:

[0035] 1. ECG signal preprocessing

[0036] In the present embodiment, the electrocardiogram data used for training 1D (Dimension) convolutional neural network is derived from the MIT-BIH arrhythmia database (MIT-...

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Abstract

The invention discloses a ventricular ectopic beat detection method based on a 1D (Dimension) convolutional neural network. As the convolutional neural network is generally applied to the aspect of two-dimensional image processing and an electrocardiosignal belongs to a one-dimensional time sequence, the invention designs a one-dimensional convolutional neural network used for detecting ventricular ectopic beats specific to the characteristics of the electrocardiosignal; meanwhile, the invention gives specific partition parameters on the electrocardiosignal specific to the characteristics of the electrocardiosignal, and a series of single cardiac beat data obtained by partitioning a cardiac arrhythmias database is input a specially designed one-dimensional convolutional neural network to be trained. Compared with the past method, the ventricular ectopic beat detection method disclosed by the invention has stronger robustness and higher detection precision.

Description

technical field [0001] The invention belongs to the technical field of medical testing, in particular, more specifically, relates to a ventricular ectopic beat detection method based on a 1D (Dimension) convolutional neural network. Background technique [0002] In recent years, the fast-paced lifestyle has made people's life pressure increase year by year, and their health has attracted more attention. Heart disease has always been a serious threat to human health. With the development of social economy, people's lifestyle has undergone profound changes. Especially with the aging population and the acceleration of urbanization, the prevalence of cardiovascular diseases in China is on the rise, leading to a continuous increase in the number of cardiovascular diseases. The prevention and treatment of heart disease has always been a research hotspot at home and abroad. [0003] The electrocardiographic signal plays an important role in the research of clinical medicine. It c...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): A61B5/0245A61B5/0452
CPCA61B5/02405A61B5/0245A61B5/7267A61B5/35
Inventor 刘健宋爽程绍龙
Owner UNIV OF SCI & TECH BEIJING
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