Electrocardiography comprehensive sorting method based on deep learning algorithm

A technology of deep learning and classification methods, applied in medical science, sensors, diagnostic recording/measurement, etc., can solve problems such as easy misclassification, low clinical reliability and accuracy, and failure to meet classification needs, so as to improve accuracy , the effect of improving the accuracy rate

Active Publication Date: 2016-12-14
杨一平 +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] The present invention aims at the traditional electrocardiogram measurement classification method existing in the prior art, which is prone to misclassification when the single waveform measurement feature is

Method used

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  • Electrocardiography comprehensive sorting method based on deep learning algorithm

Examples

Experimental program
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Example Embodiment

[0049] Example 1

[0050] A comprehensive classification method of ECG based on deep learning algorithm, the flow chart is as follows figure 1 As shown, it specifically includes the following steps:

[0051] a. (1) Obtain single-lead ECG waveform data and ECG additional information, and intercept the data with a length of 10 seconds according to the single-lead ECG waveform data as the original ECG waveform data, in which the single-lead ECG waveform data and the ECG additional The information can be obtained through the results of the physical examination, or through existing databases such as the ECG Waveform Database (CSE), or through other means. The additional information on the ECG includes gender, height, chest circumference, weight, fat percentage, and race .

[0052] (2) According to needs, the original ECG waveform data obtained in step (1) can be denoised. The denoising includes the following steps:

[0053] i1. Use a high-pass filter to remove baseline wandering noise;

...

Example Embodiment

[0073] Example 2

[0074] An ECG comprehensive classification method based on deep learning algorithm, the flow chart is as follows figure 1 As shown, it specifically includes the following steps:

[0075] a. (1) Obtain single-lead ECG waveform data and ECG additional information, and intercept the data with a length of 8 seconds according to the single-lead ECG waveform data as the original ECG waveform data, including single-lead ECG waveform data and ECG additional The information can be obtained through the results of the physical examination, or through existing databases such as ECG Waveform Database (CSE), or through other means. The additional information on the ECG includes gender, height, chest circumference, weight, fat percentage, and race .

[0076] (2) According to needs, the original ECG waveform data obtained in step (1) can be denoised. The denoising includes the following steps:

[0077] i1. Use a high-pass filter to remove baseline wandering noise;

[0078] i2. Con...

Example Embodiment

[0097] Example 3

[0098] An ECG comprehensive classification method based on deep learning algorithm, the flow chart is as follows figure 1 As shown, it specifically includes the following steps:

[0099] a. (1) Acquire single-lead ECG waveform data and ECG additional information, and intercept the data with a length of 16 seconds according to the single-lead ECG waveform data as the original ECG waveform data, including single-lead ECG waveform data and ECG additional The information can be obtained through the results of the physical examination, or through existing databases such as ECG Waveform Database (CSE), or through other means. The additional information on the ECG includes gender, height, chest circumference, weight, fat percentage, and race .

[0100] (2) According to needs, the original ECG waveform data obtained in step (1) can be denoised. The denoising includes the following steps:

[0101] i1. Use a high-pass filter to remove baseline wandering noise;

[0102] i2. C...

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Abstract

The invention discloses an electrocardiography comprehensive sorting method based on deep learning algorithm. The method comprises the following steps of acquiring original electrocardiography waveform data and electrocardiography additional information as well as electrocardiography rhythm information, representative PQRST waveform data, conducting waveform classification to the related information via trained first deep learning algorithm to achieve a first sorting result, conducting trained second deep learning algorithm to related information to achieve P wave, QRS wave and T wave data and calculating representative PQRST wave featured data and inputting the above into a traditional electrocardiography computer for automatic sorting algorithm to achieve a second sorting result, and adding weight to adjust the sorting results and designating a sorting result having the maximum grade value as a final sorting result. Characteristics of electrocardiography classification are rationally combined; the deep learning method is trained via the above steps and waveform classification is conducted via the deep learning method, so accuracy of sorting result of the electrocardiography explanation can be improved.

Description

technical field [0001] The invention relates to an electrocardiogram classification method, in particular to an electrocardiogram comprehensive classification method based on a deep learning algorithm. Background technique [0002] ECG waveform data acquisition and ECG classification results are important auxiliary means and reference information for doctors to diagnose heart disease. Usually, ECG waveform data acquisition and classification are carried out in hospitals or physical examination centers, which have disadvantages such as inconvenient detection and low detection frequency, and cannot It is difficult to effectively prevent and timely treat heart disease if the ECG classification information is provided to doctors for real-time diagnosis. 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. The monitors currently on the market are based on the traditi...

Claims

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Application Information

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IPC IPC(8): A61B5/00A61B5/0402
CPCA61B5/7267A61B5/318
Inventor 杨一平朱欣
Owner 杨一平
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