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A heart rate variability feature classification method based on multi-scale Renyi entropy

A heart rate variability and feature classification technology, applied in instruments, character and pattern recognition, computer parts, etc., can solve problems such as heart rate variability signal uncertainty, and achieve the effect of avoiding the lack of chaotic features

Inactive Publication Date: 2016-02-10
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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  • A heart rate variability feature classification method based on multi-scale Renyi entropy
  • A heart rate variability feature classification method based on multi-scale Renyi entropy
  • A heart rate variability feature classification method based on multi-scale Renyi entropy

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Embodiment Construction

[0034] 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. .

[0035] Taking the classification of paroxysmal atrial fibrillation and normal ECG signals, and the classification of paroxysmal atrial fibrillation and remote paroxysmal atrial fibrillation as an example, the specific embodiments of the present invention will be described with reference to the accompanying drawings. Algorithm flow chart see figure 1 .

[0036] Step S1: collect ECG signal and carry out preprocessing, obtain HRV sequence: this step includes:

[0037] 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 stan...

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Abstract

The invention provides a heart rate variability feature classification method based on multi-scale Renyi entropy and belongs to the field of electrocardiosignal processing. R wave positioning is performed after pretreatment such as interference and baseline drift removal is performed on to-be-processed original electrocardiosignals, and the interval of adjacent R waves is calculated to obtain an HRV sequence; discrete wavelet coefficients are obtained through discrete wavelet conversion of the HRV sequence; a proper q value is selected according to requirements to calculate the Renyi entropy of the wavelet coefficient of each layer; feature vectors are constructed by using the calculated Renyi entropy values of all scales for classification and identification of the electrocardiosignals.

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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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/2411G06F18/214
Inventor 辛怡母远慧赵一璋
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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