Electrocardio feature extraction method, system and device based on wavelet transform and medium

A technology of wavelet transform and extraction method, applied in medical science, sensor, diagnostic recording/measurement, etc., can solve the problems affecting eigenwave, false detection and missed detection of eigenwave, etc., to reduce the false detection rate and missed detection rate. Effect

Pending Publication Date: 2020-11-13
GUANGDONG PROV MEDICAL INSTR INST
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Problems solved by technology

These two methods are easy to cause false detection and missed detection of characteristic waves,

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  • Electrocardio feature extraction method, system and device based on wavelet transform and medium
  • Electrocardio feature extraction method, system and device based on wavelet transform and medium
  • Electrocardio feature extraction method, system and device based on wavelet transform and medium

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[0064] The present invention will be further explained and illustrated below in conjunction with the accompanying drawings and specific embodiments of the description. The step numbers in the embodiments of the present invention are set only for the convenience of elaboration, and the sequence between the steps is not limited, and the execution sequence of the steps in the embodiments can be performed according to the understanding of those skilled in the art Adaptive adjustment.

[0065] In view of the problems existing in the prior art, an embodiment of the present invention provides an ECG feature extraction method based on wavelet transform, refer to figure 1 , the method of the present invention comprises the following steps:

[0066] S1. Perform wavelet filtering processing on the collected ECG data to determine a wavelet basis function;

[0067] Step S1 of the present invention includes:

[0068] S11. Select a segment of lead waveform from the collected ECG data;

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Abstract

The invention discloses an electrocardio feature extraction method, system and device based on wavelet transform and a medium. The method comprises the steps of: carrying out the wavelet filtering ofcollected electrocardio data, and determining a wavelet basis function; determining an oscillogram of the R wave position value according to the wavelet basis function and the electrocardio data; extracting r waves by setting a threshold value; enhancing Q-wave energy and S-wave energy in the oscillogram according to the wavelet basis function; taking the R wave position as a base point, and extracting a Q wave and an S wave through a set first finite region window; enhancing P-wave energy and T-wave energy in the oscillogram according to the wavelet basis function; taking the Q wave positionas a base point, and extracting a P wave through a set second finite region window; and taking the S wave position as a base point, and extracting the T wave through a set third finite region window.Key features in the electrocardiogram data can be accurately extracted, the false detection rate and the missing detection rate of feature waves are reduced, and the method can be widely applied to the technical field of electrocardiogram data processing.

Description

technical field [0001] The invention relates to the technical field of electrocardiographic data processing, in particular to a method, system, device and medium for extracting electrocardiographic features based on wavelet transform. Background technique [0002] Electrocardiogram (ECG) analysis is one of the most commonly used examinations in the prevention of heart disease, which can help doctors diagnose arrhythmia, myocardial ischemia, myocardial infarction and other cardiovascular diseases. There are many types of arrhythmia electrocardiograms and there are many differences. There are also large differences between the electrocardiograms of patients with the same arrhythmia. Accurate diagnosis requires rich theoretical knowledge and clinical experience; Monitoring generates a large amount of data, and manual identification of different arrhythmia ECGs will consume a lot of manpower and medical resources. And often there may be misjudgment in fatigue recognition. This...

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Application Information

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IPC IPC(8): A61B5/0452
Inventor 李桂香许为康谭仲威徐飞陈军黄德群唐元梁赵琛吴凯
Owner GUANGDONG PROV MEDICAL INSTR INST
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