Detection of cerebral micro emboli in Doppler Signal using nonlinear features

An embolus is a blood clot, a fat globule or gas bubbles that may be freely circulating in bloodstream can stop the blood flow and lead to ischemia. In real time assessment of blood flow by Trans Cranial Doppler (TCD) method, travelling solid or gaseous micro emboli in the bloodstream by passing across the assessment area, causes a short time signal with high intensity. While TCD recording including movement of the probe, coughing, sneezing, and head rotation generate high intensity artifacts that make it difficult to make differentiate from embolus. Time consuming and also human mistakes in differentiating emboli from artifact are the main motivations of design of the automatic detection systems. Implementing such systems is nowadays faced with two main challengeous problems: extracting suitable features and designing the proper classifier. In this research, we studied two issues together. In feature extraction part, wavelet coeffiecient, wavelet entropy, fractal dimention and Besov property of signal is extracted, and using by statistical methods we introduced the feature with highest separability rate. In classifier part, a novel method based on hidden markov models for detecting emboli from artifact is proposed, and the results is compared with the results of Adaptive Neuro Fuzzy Inference System classifier. In total, using wavelet coefficients and hidden markov model, we achieved an accuracy rate of 95.3% and specificity of 92.7%.