Temporal and spectral analysis of internal carotid artery Doppler signal for normal and abnormal flow detection.

Detection of carotid artery stenosis is presently highly dependent on ultrasound imaging systems. This work presents a method that can detect the normal and abnormal blood flow in the carotid structure independent of Doppler angle by analysing the time and spectral domain representation of Doppler signal. In the proposed approach, time and spectral domain based features are extracted from the Doppler signals of internal carotid arteries. Further, these features are used in supervised machine learning approach to identify the presence of abnormal blood flow. The proposed method is evaluated on 100 subjects (200 signals) with equal number of normal and abnormal flow profiles. Experimental results show that the maximum classification accuracies of 79.3% and 82.9% are observed with k-nearest neighbours and support vector machine classifiers, respectively.