Microemboli classification using non-linear kernel support vector machines and RF signals

Abstract EN

Man's intelligent behavior is due in part to his ability to select, classify, and abstract significant information reaching him from his environment by way of his senses.

This function, pattern recognition, has become a major focus of research by scientists working in the field of artificial intelligence.

Due to its clinical importance, several classification methods have been studied for microemboli detection and characterization.

In the human body ; emboli can produce severe damage like stroke or heart attack thus the importance of an automatic classification system.

In this paper, we propose a new approach to detect and classify microemboli using support vector machine and the backscatter Radio-Frequency (RF) signal. This short communication demonstrates the opportunity to classify emboli based on a RF signals and support vector machine; the classification rates reached 96.42 %..