Degree of Stenosis Quantification From Phonoangiography Signal Analysis for Diagnosing Carotid Artery Disease

Carotid artery stenosis (CAS) is one of the most serious forms of stenosis, which can potentially lead to brain stroke if left untreated. Timely detection of CAS is therefore indispensable so that specialists can formulate better treatment strategies for their patients to avoid fatal neurological deficits. The primary objective of this research is to build an effective experimental protocol mimicking the human cardiovascular system to accurately detect phonoangiography (PAG) signal. Second, we aim to develop a powerful approach for quantifying the degree of stenosis (DOS) from PAG signal analysis. In the present study, we simulate the pulsatile fluid flow through 3D-model of a stenosed vessel. It generates flow sounds, which we capture using acoustic sensor. By varying the severity of stenosis, we detect a range of PAG signals associated with each well-defined DOS. Signal processing methods are then applied to pave the way for DOS quantification. For this purpose, time- and frequency-based methods are first involved to assess the DOS in the PAG signal in terms of global aspect. Thereafter, we leverage the Poincaré plot and wavelet packet (WP) decomposition to bring out the local features that are most relevant to quantify the severity of stenosis. Experimental results demonstrate the effectiveness of these localization methods in distinguishing between mild and severe stenoses. In conclusion, the study confirms that our proposed noninvasive system is very promising and can provide an easy-to-access and affordable solution for detecting and quantifying CAS disease in clinical settings.