Detection and classification of cardiac murmurs using segmentation techniques and artificial neural networks

In this paper we present the implementation of a diagnostic system based on Artificial Neural Networks (ANN) that can be used in the detection and classification of heart murmurs. Segmentation and alignment algorithms serve as important pre-processing steps before heart sounds are applied to the ANN structure. The system enables users to create a classifier that can be trained to detect virtually any desired target set of heart sounds. The output of the system is the classification of the sound as either normal or a type of heart murmur. The ultimate goal of this research is to implement a heart sounds diagnostic system that can be used to help physicians in the auscultation of patients and to reduce the number of unnecessary echocardiograms- those that are ordered for healthy patients. Testing has been conducted using both simulated and recorded patient heart sounds as input. Three sets of results for the tested system are included herein, corresponding to three different target sets of simulated heart sounds. The system is able to classify with up to 85 ± 7.4% accuracy and 95 ± 6.8% sensitivity. For each target set, the accuracy rate of the ANN system is compared to the accuracy rate of a group of 2^nd year medical students who were asked to classify heart sounds from the same group of heart sounds classified by the ANN system. System test results are also explored using recorded patient heart sounds.