Cardiovascular Health Classification Using Arterial Dispersion Ultrasound Vibrometry (ADUV)

Abstract

Arterial stiffness plays a fundamental role in cardiovascular health. To assess arterial health noninvasively, we use arterial dispersion ultrasound vibrometry (ADUV): an acoustic radiation force (ARF) causes propagating waves in the arterial wall that are measured with ultrasound at multiple stages during the cardiovascular cycle. A long-standing problem in this field is developing a fast, reliable map from propagating waves to arterial health. Therefore, we propose an end-to-end classification framework: from ADUV data to arterial health. Our main framework contributions are the treatment of high-dimensional signals and the selection of interpretable features. In particular, we highlight the role of systolic and diastolic cardiovascular stages in classifying arterial health using ADUV signals.Our findings, limited by dataset size, focus on interpretability of features and finding separation between the Healthy Subjects (HS) and Unhealthy Subjects (US). Our feature selection strategies included ranking by the training weights in Linear Support Vector Machines (LSVM), mutual information, and global search. The main goal was to select the best nf features from the set of wave speeds obtained over the cardiac cycle at various set frequencies. We then accessed performance using boostrapping accuracy. The LSVM Weights strategy (Holdout Accuracy = 82.1% ± ̆7.7% for a Systolic Feature Set) performed better than mutual information feature selection (Holdout Accuracy = 72.5% ± 9.6% for a Systolic Feature Set). However, both methods were surpassed by global search (Holdout Accuracy = 87.3% ± 7.8% for a Systolic Feature Set). Additionally, the best systolic, high cardiovascular pressure, feature sets (Holdout Accuracy = 87.3% ± 7.8% for Global Search) had higher mean holdout accuracy than the diastolic, low pressure, feature sets (Holdout Accuracy = 80.1% ± 8.5% for Global Search) for both LSVM weight feature selection and the exhaustive and global searches. The best set of features found (Holdout Accuracy = 90.4% ± 5.8%) was a combined diastolic and systolic set, but the single diastolic feature is at a higher pressure compared to other diastolic features. Therefore, separation exists between the HS and US groups, demonstrating that an end-to-end framework is feasible for this new class of arterial health biomarkers. Lastly, the interpretable framework identified that separation is particularly evident for high-pressure features when geometric features are excluded. Adding geometric features, specifically diameter and thickness, to the classifier input resulted in similar performance for the best feature set.