Electrocardiogram classification 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 the problems of misclassification, low clinical reliability and accuracy, etc., to improve accuracy, improve understanding, improve Effects of Accuracy and Robustness

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 in the prior art, which is prone to misclassification when the single waveform measurement feature is not obvious, its clinica...

Method used

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  • Electrocardiogram classification method based on deep learning algorithm

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0037] An electrocardiogram classification method based on deep learning algorithm, its flow chart is as follows figure 1 As shown, it specifically includes the following steps:

[0038]a. (1) Obtain single-lead ECG waveform data and ECG additional information, and intercept the data with a length of 10 seconds as the original ECG waveform data according to the single-lead ECG waveform data, in which the single-lead ECG waveform data and ECG additional information The information can be obtained through existing databases such as the ECG waveform database (CSE), or through other means, and the additional information of the ECG includes gender, height, bust, weight, fat percentage, and race.

[0039] (2) as required, the original electrocardiogram waveform data obtained in step (1) can be denoised, and the denoised process includes the following steps:

[0040] a11. Use a high-pass filter to remove baseline drift noise;

[0041] a12. Confirm whether the noise is too high base...

Embodiment 2

[0051] An electrocardiogram classification method based on deep learning algorithm, its flow chart is as follows figure 1 As shown, it specifically includes the following steps:

[0052] a. (1) Obtain single-lead ECG waveform data and ECG additional information, and intercept the data with a length of 8 seconds as the original ECG waveform data according to the single-lead ECG waveform data, in which the single-lead ECG waveform data and ECG additional information The information can be obtained through existing databases such as the ECG waveform database (CSE), or through other means, and the additional information of the ECG includes gender, height, bust, weight, fat percentage, and race.

[0053] (2) as required, the original electrocardiogram waveform data obtained in step (1) can be denoised, and the denoised process includes the following steps:

[0054] a11. Use a high-pass filter to remove baseline drift noise;

[0055] a12. Confirm whether the noise is too high base...

Embodiment 3

[0065] An electrocardiogram classification method based on deep learning algorithm, its flow chart is as follows figure 1 As shown, it specifically includes the following steps:

[0066] a. (1) Obtain single-lead ECG waveform data and ECG additional information, and intercept the data with a length of 16 seconds as the original ECG waveform data according to the single-lead ECG waveform data, in which the single-lead ECG waveform data and ECG additional information The information can be obtained through existing databases such as the ECG waveform database (CSE), or through other means, and the additional information of the ECG includes gender, height, bust, weight, fat percentage, and race.

[0067] (2) as required, the original electrocardiogram waveform data obtained in step (1) can be denoised, and the denoised process includes the following steps:

[0068] a11. Use a high-pass filter to remove baseline drift noise;

[0069] a12. Confirm whether the noise is too high bas...

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Abstract

The invention discloses an electrocardiogram classification method based on the deep learning algorithm. The method includes the following steps that firstly, original electrocardiogram waveform data and electrocardiogram additional information with the measuring time of 8 s or above are acquired, and electrocardiogram rhythm information and reprehensive PQRST waveform data are acquired according to the original electrocardiogram waveform data; secondly, the neural network of the deep learning algorithm is trained, the electrocardiogram rhythm information, the reprehensive PQRST waveform data and the electrocardiogram additional information are arranged into one-dimensional data, then waveform classification is carried out through the trained deep learning algorithm, and an electrocardiogram classification result is obtained. According to the method, the deep learning method is introduced into the electrocardiogram classification field, the deep learning method is trained through the steps in reasonable combination with the electrocardiogram classification characteristics, waveform classification is carried out through the deep learning method, and the quality of electrocardiogram classification auxiliary information provided for a doctor can be greatly improved.

Description

technical field [0001] The invention relates to an electrocardiogram classification method, in particular to an electrocardiogram 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. Such monitors currently on the market, because their classification algorit...

Claims

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Application Information

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IPC IPC(8): A61B5/0402A61B5/00
CPCA61B5/7203A61B5/7225A61B5/725A61B5/7264A61B5/7271A61B5/316
Inventor 杨一平朱欣
Owner 杨一平
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