The interpretation of continuous wave ultrasonic Doppler blood velocity signals viewed as a problem in pattern recognition

Compensatory fuzzy neural networks-based intelligent detection of abnormal neonatal cerebral Doppler ultrasound waveforms

Compensatory fuzzy neural networks (CFNN) without normalization, which can be trained with a backpropagation learning algorithm, is proposed as a pattern recognition technique for intelligent detection of Doppler ultrasound waveforms of abnormal neonatal cerebral hemodynamics. Doppler ultrasound signals were recorded from the anterior cerebral arteries of 40 normal full-term babies and 14 mature babies with intracranial pathology. The features of normal and abnormal groups as inputs to pattern recognition algorithms were extracted from the maximum velocity waveforms by using principal component analysis. The proposed technique is compared with the CFNN with normalization and other pattern recognition techniques applied to Doppler ultrasound signals from various arteries. The results show that the proposed method is superior to the others, and can be a powerful technique to be used in analyzing Doppler ultrasound signals from various arteries.

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.