A comprehensive review on cerebral emboli detection algorithms

The increasing availability of biomedical data has attracted the interest of many researchers to understand and perform analysis on extracted patterns from data. Stroke is considered as one of the main causes of deaths worldwide. A considerable amount of work has been performed related to the cause of stroke and other physiological effects. Cerebral emboli is considered as one of the main sources of stroke. Algorithms from one of the traditional subjects called signal processing have been used in cerebral emboli detection and lot of researchers have performed emboli detection and classification using Fourier transform based algorithms and different filtering approaches. In this paper, we discuss the physics of Doppler ultrasound and perform review of cerebral emboli detection algorithms and some animal models used in understanding the behaviour, size, and composition of emboli development. Ranging from Fourier transform, wavelet transform based emboli detection to neural network architectures trained with Doppler signal spectra, we performed comprehensive review of signal processing based cerebral emboli detection works and provide some basic understanding of related terms in emboli detection. With natural arterial structural relation between humans and some animals, we highlight some of the animal models used for understanding the nature of emboli development process.