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Heart rate variability feature classification method based on generalized scale wavelet entropy

A technology of heart rate variability and generalized scale, applied in the field of ECG signal processing, can solve problems such as heart rate variability signal uncertainty, achieve the effect of avoiding the lack of chaotic features and improving accuracy

Inactive Publication Date: 2016-03-23
BEIJING INSTITUTE OF TECHNOLOGYGY
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Problems solved by technology

[0007] In view of the deficiencies of the existing algorithms and the uncertainty of the heart rate variability signal, the purpose of the present invention is to solve the problem of effectively extracting the useful features of the heart rate variability signal while reducing the influence of noise as much as possible

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  • Heart rate variability feature classification method based on generalized scale wavelet entropy
  • Heart rate variability feature classification method based on generalized scale wavelet entropy
  • Heart rate variability feature classification method based on generalized scale wavelet entropy

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Embodiment Construction

[0035] The present invention will be described in detail below, and the technical problems and beneficial effects solved by the technical solutions of the present invention are also described. It should be pointed out that the described examples are only intended to facilitate the understanding of the present invention, and do not have any limiting effect on it. .

[0036] Taking the classification of paroxysmal atrial fibrillation ECG signals and non-paroxysmal atrial fibrillation ECG signals as an example, the specific implementation manner of the present invention will be described in conjunction with the accompanying drawings. Algorithm flow chart see figure 1 .

[0037] Step S1: collect ECG signal and carry out preprocessing, obtain HRV sequence: this step includes:

[0038]S1-1: Acquisition or extraction of multiple ECG signals that are longer than 5 minutes. In this example, we use 50 cases of data from the MIT-BIH standard database, each case is 30 minutes, and the s...

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Abstract

The invention discloses a heart rate variability feature classification method based on generalized scale wavelet entropy, and belongs to the field of electrocardiosignal processing. The method comprises the steps that after an original electrocardiosignal to be processed is preprocessed through interference and baseline drift removal, R wave positioning is performed, and an HRV sequence is obtained by calculating the interval between every two R waves; discrete wavelet transform is performed on the HRV sequence to obtain discrete wavelet coefficients, and then alpha-order generalized wavelet entropy of all layers of the wavelet coefficients is calculated by selecting appropriate alpha values as needed; scales with the statistical difference are screened out to serve as feature layers according to the obtained entropy values, and the alpha-order generalized wavelet entropy values of the feature layers are utilized to construct feature vectors to perform classification identifying on the electrocardiosignal.

Description

technical field [0001] The invention proposes a heart rate variability analysis method, combined with a suitable classifier, which can effectively complete the identification and classification of different types of electrocardiographic signals, and belongs to the field of electrocardiographic signal processing. Background technique [0002] Heart rate variability refers to small differences between successive heartbeats, which arise from the modulation of sinus node automaticity by the autonomic nervous system. The existing heart rate variability analysis is mainly based on nonlinear parameter analysis such as linear parameter analysis and complexity analysis in time domain and transform domain. As a non-invasive method for assessing vagus nerve tone, Heart Rate Variability (HRV) analysis is considered to be an effective means to reflect the function of this type of autonomic nervous system. Using HRV to automatically detect heartbeat has high specificity and sensitivity. ...

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Application Information

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IPC IPC(8): A61B5/0402A61B5/024
CPCA61B5/02405A61B5/7203A61B5/7264A61B5/7275A61B5/316A61B5/318
Inventor 辛怡陈煜王振宇母远慧赵一璋
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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