Fernando Rios-Gutierrez

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%.

Classification of Four Types of Common Murmurs using Wavelets and a Learning Vector Quantization Network

In this work we present the development of a system that can be used for the study, detection and classification of human heart sounds using digital signal processing and artificial intelligence techniques. The design and implementation of such system is broken down into two processes: digital signal processing part and artificial intelligence part. The ultimate goal of the project is to develop an intelligent system that can be used for the detection and classification of various types of human heart murmurs.