TY - JOUR
T1 - Detection of heart murmurs using wavelet analysis and artificial neural networks
AU - Andrisevic, Nicholas
AU - Ejaz, Khaled
AU - Rios-Gutierrez, Fernando
AU - Alba-Flores, Rocio
AU - Nordehn, Glenn
AU - Burns, Stanley G
PY - 2005/11/1
Y1 - 2005/11/1
N2 - This paper presents the algorithm and technical aspects of an intelligent diagnostic system for the detection of heart murmurs. The purpose of this research is to address the lack of effectively accurate cardiac auscultation present at the primary care physician office by development of an algorithm capable of operating within the hectic environment of the primary care office. The proposed algorithm consists of three main stages. First; denoising of input data (digital recordings of heart sounds), via Wavelet Packet Analysis. Second; input vector preparation through the use of Principal Component Analysis and block processing. Third; classification of the heart sound using an Artificial Neural Network. Initial testing revealed the intelligent diagnostic system can differentiate between normal healthy heart sounds and abnormal heart sounds (e.g., murmurs), with a specificity of 70.5% and a sensitivity of 64.7%.
AB - This paper presents the algorithm and technical aspects of an intelligent diagnostic system for the detection of heart murmurs. The purpose of this research is to address the lack of effectively accurate cardiac auscultation present at the primary care physician office by development of an algorithm capable of operating within the hectic environment of the primary care office. The proposed algorithm consists of three main stages. First; denoising of input data (digital recordings of heart sounds), via Wavelet Packet Analysis. Second; input vector preparation through the use of Principal Component Analysis and block processing. Third; classification of the heart sound using an Artificial Neural Network. Initial testing revealed the intelligent diagnostic system can differentiate between normal healthy heart sounds and abnormal heart sounds (e.g., murmurs), with a specificity of 70.5% and a sensitivity of 64.7%.
KW - Artificial Neural Networks
KW - Cardiovascular
KW - Digital Image Processing
KW - Heart Murmur
KW - Medical Diagnostic Device
KW - Wavelet Packet Analysis
UR - http://www.scopus.com/inward/record.url?scp=27444437211&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=27444437211&partnerID=8YFLogxK
U2 - 10.1115/1.2049327
DO - 10.1115/1.2049327
M3 - Article
C2 - 16438225
AN - SCOPUS:27444437211
SN - 0148-0731
VL - 127
SP - 899
EP - 904
JO - Journal of biomechanical engineering
JF - Journal of biomechanical engineering
IS - 6
ER -