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Ventricular fibrillation recognition method based on machine learning technique

A technology of machine learning and recognition methods, applied in the medical field, can solve problems such as QRS detection difficulties, and achieve the effect of improving performance

Inactive Publication Date: 2019-06-07
苏州哈特数据科技有限公司
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AI Technical Summary

Problems solved by technology

[0003] Due to the difficulty of QRS detection when ventricular fibrillation occurs, the existing technology often excludes the heart rate characteristics when detecting ventricular fibrillation, which leads to the existing technology sometimes judging the ECG that can be clearly judged as non-ventricular fibrillation by the heart rate as ventricular fibrillation

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  • Ventricular fibrillation recognition method based on machine learning technique
  • Ventricular fibrillation recognition method based on machine learning technique
  • Ventricular fibrillation recognition method based on machine learning technique

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Embodiment Construction

[0029] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention.

[0030] Example.

[0031] A method for recognizing ventricular fibrillation based on machine learning technology, comprising the following steps:

[0032] (1) Preprocess the ECG signal to filter out noise such as baseline drift and power frequency interference, and resample the denoised ECG signal to a fixed sampling rate;

[0033] (2) Carry out band-pass filter analysis on the ECG signal after denoising and resampling, and calculate feature 1. The center frequency of the band-pass filter is 14.6 Hz, which is realized by using an integer coefficient digital filter; the specific method is as follows: assume a band-pass filter The output of FS is FS, and for each secon...

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Abstract

The invention discloses a ventricular fibrillation recognition method based on a machine learning technique. The ventricular fibrillation recognition method comprises the following steps of treating an original electrocardiosignal so as to extract ventricular fibrillation characteristics; through a marked electrocardio database, training a logistic regression model which is used for judging whether each ECG signal is ventricular fibrillation; and through the trained model, acquiring the probability that each ECG signal is the ventricular fibrillation. According to the ventricular fibrillationrecognition method disclosed by the invention, the most effective combination is selected from various characteristics provided in the prior art, so that the properties of a ventricular fibrillation detection algorithm can be effectively improved. Besides, a heart rate characteristic is applied to ventricular fibrillation detection. In order to solve the problems that QRS detection is difficult and heart rate is possibly quite inaccurate when the ventricular fibrillation occurs, according to the method disclosed by the invention, the quality information of the signal is used, so that the modelcan accurately judge the condition when heart rate information needs to be neglected, and the condition when obvious non-ventricular fibrillation electrocardiogram can be eliminated according to theheart rate.

Description

technical field [0001] The invention relates to the field of medical technology, in particular to a method for identifying ventricular fibrillation based on machine learning technology. Background technique [0002] In the prior art, compared with the rule-based ventricular fibrillation detection method, the method based on machine learning technology has been proved to have better performance and better robustness. The key step of the ventricular fibrillation detection algorithm based on machine learning technology is the selection of characteristic parameters. In order to extract features, various analysis methods based on frequency domain or time domain are applied in the prior art, such as complexity analysis, ventricular fibrillation filter analysis, spectrum analysis, time delay algorithm, bandpass filter analysis, covariance analysis, Kurtosis, etc. [0003] Due to the difficulty in QRS detection when ventricular fibrillation occurs, heart rate characteristics are of...

Claims

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Application Information

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IPC IPC(8): A61B5/046A61B5/0472A61B5/361A61B5/366
Inventor 夏鹤年张雷刚时海西周星何红刘伍毕光涛陈元凤纪迎兵朱健
Owner 苏州哈特数据科技有限公司
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