Detecting Doppler ultrasound embolic signals using the wavelet-based feature extraction algorithm

The non-invasive detection of circulating emboli with the Doppler ultrasound technique is of active significance in clinical applications.In order to eliminate drawbacks of artifacts brought by the movement of probes or patients and detect emboli accurately,relevant feature parameters are extracted from two angles of the wavelet transform of Doppler signals.The singularity of the signal waveform is analyzed based on its wavelet scalogram;then transverse and longitudinal parameters are extracted to represent the scalogram characteristics.A novel method is proposed based on the adaptive wavelet packet basis,from which several parameters such as the energy,the scale,etc.are extracted to represent the optimized signal approximation features.With all feature parameters in two aspects.a classification system is established for Doppler Ultrasound embolic signals by solving the generalized Fisher discriminant plane.From experiments on 300 simulated and 298 clinical Doppler ultrasound signals of cerebral arteries,it is shown that the proposed system can achieve the emboli detection rates of 99.0%and 98.5%for the training set and the testing set respectively.Therefore this method makes an improvement of emboli detection compared to traditional methods and has the possibility to be applied in the automatic detection of clinical emboli.