Neural network and conventional classifiers to distinguish between first and second heart sounds

A technique to distinguish between the first and second heart sounds without the need for a reference ECG is described. The choice of features for presentation to classifiers is discussed and several types of classifier are introduced. Comparative results for each of the classification techniques are given for data sets obtained from both normal and pathological cases. A misclassification rate of 5.76% is obtained using a neural network classifier whereas conventional classifiers are shown to give a relatively poor performance.