The application of an artificial neural network to Doppler ultrasound waveforms for the classification of arterial disease.

In this study we have investigated the application of an Artificial Neural Net classifier to the diagnosis of vascular disease using Doppler ultrasound blood-velocity/time waveforms. A multi-layer perceptron network was trained with waveforms from control subjects and from patients with arterial disease. The diseased cases were confirmed by angiography and allocated to three groups according to the location of the stenosis: proximal or distal to the site of measurement or multi-segmental. We compared network classification results with a Bayesian classifier following a Principal Component Analysis of the waveforms. Versions of both classifiers were trained to discriminate two classes (normal v. abnormal) and four classes. In both cases the neural networks gave superior discrimination to the Bayesian classifier. While the four-class network was unable to provide useful discrimination among the stenosis sites, discrimination between abnormal classes was obtained which is comparable to that achieved by a human expert observer.