Eureka AIR delivers breakthrough ideas for toughest innovation challenges, trusted by R&D personnel around the world.

A method for identifying abnormal heart sounds based on subband energy envelope autocorrelation features

A technology of autocorrelation features and identification methods, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve problems such as dependence, inability to adapt to noisy environments, inability to handle variable-length input signals, etc., to improve robustness The effect of avoiding heart sound segmentation process and improving the recognition performance

Inactive Publication Date: 2017-06-30
HARBIN NORMAL UNIVERSITY
View PDF7 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0008] In order to solve the above-mentioned problems in heart sound recognition that rely on heart sound segmentation, cannot process variable-length input signals, and cannot adapt to feature extraction and classification recognition in real noise environments, an anomaly based on sub-band energy envelope autocorrelation features is provided. Heart Sound Recognition Method

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • A method for identifying abnormal heart sounds based on subband energy envelope autocorrelation features
  • A method for identifying abnormal heart sounds based on subband energy envelope autocorrelation features
  • A method for identifying abnormal heart sounds based on subband energy envelope autocorrelation features

Examples

Experimental program
Comparison scheme
Effect test

specific Embodiment approach 1

[0041] Specific embodiment one: the abnormal heart sound recognition method based on the sub-band energy envelope autocorrelation feature described in this embodiment is realized by the following steps:

[0042] Step 1. Heart sound preprocessing

[0043] The input heart sound signal is energy-standardized, then down-sampled to 2 kHz, and a 6-order Butterworth filter (20-900 Hz) filter is used to band-pass filter the down-sampled heart sound signal to filter out the cut-off Other sounds and noises other than frequencies; Note: Assuming that the sampling frequency of the input heart sound signal is higher than 2kHz, the sampling frequency of the output signal after preprocessing is approximately 2kHz;

[0044] Step 2. Extract the average Shannon energy envelope

[0045] (1) The preprocessed heart sound signal with a sampling frequency of 2kHz is decomposed into 4 levels using the 4th-order Daubechies wavelet to obtain: an approximate coefficient sequence a 4 and four detail co...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention provides an abnormal heart sound recognition method based on sub-band energy envelope autocorrelation characteristics and relates to an abnormal heart sound recognition method. The abnormal heart sound recognition method is used for solving the problems in heart sound recognition that heart sound segmentation is depended, longer signals cannot be processed, and adaption to the characteristic extraction and classified recognition in a real noise environment cannot be realized. The method comprises the steps: carrying out energy standardization on input heart sound signals, then, carrying out down-sampling, and carrying out band-pass filtering; respectively computing an autocorrelation sequence of approximate sub-band energy envelope signals and an autocorrelation sequence of detailed sub-band energy envelope signals, and respectively intercepting first M values, which serve as approximate sub-band energy envelope autocorrelation characteristics and approximate sub-band energy envelope autocorrelation characteristics of the input heart sound signals, from the two sequences; respectively constructing two RM-space-to-Re-space scattering mappings, which act on the energy envelope autocorrelation characteristics, according to the energy envelope autocorrelation characteristics; carrying out dimensionality reduction, and then, merging to obtain the energy envelope autocorrelation characteristics of the heart sound signals; carrying out characteristic extraction on test data, and classifying the data by imputing the test data in a classification model. According to the method, a heart sound segmentation process is avoided, and the robustness in a noise environment is improved.

Description

technical field [0001] The invention relates to a method for identifying abnormal heart sounds, and relates to the fields of biological signal identification technology and intelligent information processing. Background technique [0002] According to the statistics of the World Health Organization, cardiovascular disease has become the number one killer threatening human life and health today. About 17 million people died of cardiovascular diseases in 2004, accounting for 29% of all diseases, of which 7.2 million people died of heart disease, and the number and proportion of diseases and deaths caused by heart disease are still increasing year by year. In my country, according to the results published in the "China Cardiovascular Disease Report 2012": the prevalence of cardiovascular disease in my country is on the rise. It is estimated that the number of patients with cardiovascular disease is 290 million, that is, 2 out of every 10 adults. Suffering from cardiovascular di...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Patents(China)
IPC IPC(8): A61B7/04G06F19/00
Inventor 邓世文韩纪庆唐黎明郑铁然郑贵滨张文杰
Owner HARBIN NORMAL UNIVERSITY
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Eureka Blog
Learn More
PatSnap group products