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Electrocardiogram classification method based on deep learning model

A technology of deep learning and classification methods, applied in neural learning methods, biological neural network models, medical science, etc., can solve the problems of high missed diagnosis rate, unbalanced supply of doctors, misdiagnosis, etc. cost saving effect

Inactive Publication Date: 2018-02-02
成都蓝景信息技术有限公司
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Problems solved by technology

[0007] The present invention provides an electrocardiogram classification method based on a deep learning model; the main technical problem to be solved is to distinguish arrhythmia through electrocardiogram data, provide assistance and reference for doctors, and solve serious imbalances in doctor supply in some areas, misdiagnosis, High missed diagnosis rate and other issues

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  • Electrocardiogram classification method based on deep learning model

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Embodiment Construction

[0024] The following will be combined with Figure 1-Figure 5 The present invention is described in detail, and the technical solutions in the embodiments of the present invention are clearly and completely described. Apparently, the described embodiments are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0025] The present invention provides a kind of electrocardiogram classification method based on deep learning model here by improving; Realize as follows, see for example figure 1 ;

[0026] Step 1, data acquisition: collect a large amount of ECG data, including normal, noise and raw data of dozens of arrhythmias;

[0027] Step 2, data processing: We use a single-channel electrocardiogram with a period of 30 seconds to digitize the origina...

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Abstract

The invention discloses an electrocardiogram classification method based on a deep learning model, and the method is characterized in that the method comprises the steps: data obtaining, data processing, model construction, algorithm optimization, and model training. A technical problem to be solved in the invention is to carry out the discrimination of arrhythmia through electrocardiogram data, to provide assistance and reference for a doctor, and to solve problems that doctors are not sufficient in some places and the wrong diagnosis and diagnosis leakage rate are higher. According to the embodiment of the invention, the invention has the following beneficial effects that the method employs the deep learning method, and achieves the discrimination of arrhythmia in the electrocardiogram information through the building of a large-scale convolution neural network. Compared with the conventional model, the method saves the cost, and is higher in accuracy.

Description

technical field [0001] The present invention relates to the related fields of electrocardiogram classification, in particular to an electrocardiogram classification method based on a deep learning model. Background technique [0002] As we all know, ECG waveform data collection and ECG classification results are important auxiliary means and reference information for doctors to diagnose heart disease. Usually, ECG waveform data collection and classification are carried out in hospitals or physical examination centers, which have disadvantages such as inconvenient detection and low detection frequency. Moreover, the electrocardiogram classification information cannot be provided to doctors in time for real-time diagnosis, so it is difficult to effectively prevent and treat heart disease in time. In recent years, with the popularity of the Internet and mobile smart phones, it has become possible to launch portable ECG monitors and family personal ECG monitors. Such monitors c...

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Application Information

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IPC IPC(8): G06N3/08G06N3/04A61B5/0402A61B5/04A61B5/00
CPCG06N3/08A61B5/7264A61B5/316A61B5/318G06N3/048G06N3/045
Inventor 蔡源涛赵二超
Owner 成都蓝景信息技术有限公司
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