Research on Recognition of CHD Heart Sound Using MFCC and LPCC

Congenital heart disease (CHD) is a disease that seriously harms the children and family. It needs to be diagnosed and treated in time. The initial diagnosis way of CHD is cardiac auscultation in which the rich experience and expertise are needed. A kind of recognition algorism was proposed to help...

Full description

Saved in:
Bibliographic Details
Published inJournal of physics. Conference series Vol. 1169; no. 1; pp. 12011 - 12017
Main Authors Lili, ZHU, Jiahua, PAN, Jihong, SHI, Hongbo, YANG, Weilian, WANG
Format Journal Article
LanguageEnglish
Published Bristol IOP Publishing 01.02.2019
Subjects
Acoustics
Back propagation networks
Diagnosis
Feature extraction
Heart diseases
Recognition
Online AccessGet full text

Cover

More Information
Summary:Congenital heart disease (CHD) is a disease that seriously harms the children and family. It needs to be diagnosed and treated in time. The initial diagnosis way of CHD is cardiac auscultation in which the rich experience and expertise are needed. A kind of recognition algorism was proposed to help the initial diagnosis and screening of CHD in this work. The heart sounds were analyzed and the feature extraction by using MFCC and LPCC methods in which the frame length was 2048. The BP neural network was selected as classifier. Results show that the specificity and sensitivity of recognition ratios for CHD are 93.02% and 88.89% by using MFCC, and 86.96% and 86.96% by using LPCC respectively. The features extracted by using MFCC method are better than one by using LPCC.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1169/1/012011