Adaptive AR and Neurofuzzy Approaches: Access to Cerebral Particle Signatures

In recent years, a relationship has been suggested between the occurrence of cerebral embolism and stroke. Ultrasound has therefore become essential in the detection of emboli when monitoring cerebral vascular disorders and forms part of ultrasound brain-imaging techniques. Such detection is based on investigating the middle cerebral artery using a TransCranial Doppler (TCD) system, and analyzing the Doppler signal of the embolism. Most of the emboli detected in practical experiments are large emboli because their signatures are easy to recognize in the TCD signal. However, detection of small emboli remains a challenge. Various approaches have been proposed to solve the problem, ranging from the exclusive use of expert human knowledge to automated collection of signal parameters. Many studies have recently been performed using time-frequency distributions and classical parameter modeling for automatic detection of emboli. It has been shown that autoregressive (AR) modeling associated with an abrupt change detection technique is one of the best methods for detection of microemboli. One alternative to this is a technique based on taking expert knowledge into account. This paper aims to unite these two approaches using AR modeling and expert knowledge through a neurofuzzy approach. The originality of this approach lies in combining these two techniques and then proposing a parameter referred to as score ranging from 0 to 1. Unlike classical techniques, this score is not only a measure of confidence of detection but also a tool enabling the final detection of the presence or absence of microemboli to be performed by the practitioner. Finally, this paper provides performance evaluation and comparison with an automated technique, i.e., AR modeling used in vitro