Heart rate variability nonlinear characteristic-based automatic diagnosis method for congestive heart failure

A heart rate variability and automatic diagnosis technology, applied in the field of filling heart failure, which can solve the problems of complex implementation and long diagnosis time.

Inactive Publication Date: 2012-09-19
NANJING UNIV +1
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Problems solved by technology

However, the implementation of the Boston standard is relatively complicated, requiring various tests, e

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  • Heart rate variability nonlinear characteristic-based automatic diagnosis method for congestive heart failure
  • Heart rate variability nonlinear characteristic-based automatic diagnosis method for congestive heart failure
  • Heart rate variability nonlinear characteristic-based automatic diagnosis method for congestive heart failure

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Embodiment Construction

[0037] In order to better understand the technical content of the present invention, specific embodiments are given and described below in conjunction with the accompanying drawings.

[0038] figure 1 It is the principle block diagram of the automatic diagnosis method of congestive heart failure (Congestive Heart Failure, CHF) based on the non-linear characteristic of heart rate variability (HRV) of the present invention.

[0039] The CHF automatic diagnosis method based on the nonlinear characteristics of HRV, the steps include:

[0040] 1) For the collected surface electrocardiogram (ECG) signal, extract its beat-to-beat interval (RR interval) to form an HRV sequence {RR i :1≤i≤N}, and extract three sensitive feature parameters (including linear and nonlinear) for the sequence;

[0041]2) The three sensitive characteristic parameters obtained in step 1 are used as the input of artificial neural network, and the result of CHF automatic diagnosis is obtained through a certai...

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Abstract

The invention provides a heart rate variability (HRV) nonlinear characteristic-based automatic diagnosis method for congestive heart failure (CHF), comprising the steps of: (1) extracting heartbeat intervals from a collected body surface electrocardiogram signal to form a heart rate variability sequence, and extracting three sensitive characteristic parameters (including linear and nonlinear parameters) of the sequence; and (2) preprocessing the three sensitive characteristic parameters obtained in step (1), inputting to an artificial neural network, and obtaining an automatic diagnosis result of the congestive heart failure through a determined artificial neural network model. Automatic diagnosis of the HRV is realized by using the HRV noninvasively collected from a body surface, and an examinee is not strictly limited in state (the examinee is not required to lie flatly or sit quietly); the method can be applied to data collected in daily life only if collection time reaches 4h; and actual data tests show that the diagnosis effect is excellent.

Description

technical field [0001] The present invention relates to an automatic diagnosis method for congestive heart failure (CHF) based on the non-linear characteristics of heart rate variability (HRV). Background technique [0002] Filling heart failure (Congestive Heart Failure, CHF) is almost the main complication and final return of various cardiovascular diseases. At present, there are two main methods for the clinical diagnosis of CHF - Framingham and Boston criteria. Framingham criteria are mainly based on epidemiological survey results, and there is no detection of cardiovascular dynamic parameters. Using this criteria, some heart failure will be missed. Therefore, only using Framingham criteria to diagnose CHF is unreliable. The Boston standard integrates medical history, physical signs and chest X-ray examination, and is based on hemodynamic testing, so this standard is more reliable than the Framingham standard. However, the implementation of the Boston standard is more ...

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Application Information

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IPC IPC(8): A61B5/024A61B5/0452G06N3/02
Inventor 侯凤贞黄晓林宁新宝何正大庄建军陈颖
Owner NANJING UNIV
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