Detection of heart murmurs using wavelet analysis and artificial neural networks.
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%.
Andrisevic
Nicholas
N
Department of Electrical and Computer Engineering, University of Minnesota, Duluth, MN 55812, USA. nandrise@d.umn.edu
Ejaz
Khaled
K
Rios-Gutierrez
Fernando
F
Alba-Flores
Rocio
R
Nordehn
Glenn
G
Burns
Stanley
S
D000465:Algorithms; D001185:Artificial Intelligence; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006326:Heart Auscultation / Q000379:methods*; D006337:Heart Murmurs / Q000175:diagnosis*; D006801:Humans; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013018:Sound Spectrography / Q000379:methods*