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Related Experiment Video

Updated: May 28, 2026

Measuring the Carotid to Femoral Pulse Wave Velocity (Cf-PWV) to Evaluate Arterial Stiffness

Measuring the Carotid to Femoral Pulse Wave Velocity (Cf-PWV) to Evaluate Arterial Stiffness

Published on: May 3, 2018

Automated Detection of Carotid Artery Stenosis Using a Sensitive Accelerometer Wearable Sensor and Interpretable

Houriyeh Majditehran¹, Brian Sang¹, Nia Desai¹

¹School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.

|May 26, 2026

Summary

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This summary is machine-generated.

A new wearable sensor detects carotid artery disease using subtle vibrations, offering a non-invasive screening tool. This technology aids in early detection of stroke risk factors like stenosis.

Area of Science:

Biomedical Engineering
Medical Diagnostics
Wearable Technology

Background:

Carotid artery disease significantly elevates ischemic stroke risk.
Current screening methods are limited by accessibility and scalability.
There is a need for non-invasive, user-friendly diagnostic tools for early detection.

Purpose of the Study:

To develop and validate a wearable MEMS accelerometer patch for non-invasive carotid artery disease screening.
To identify and extract interpretable biomarkers from mechano-acoustic vibrations.
To assess the performance of a machine learning model for classifying carotid artery pathology.

Main Methods:

Utilized a wearable MEMS accelerometer patch to capture carotid blood flow vibrations.
Applied Continuous Wavelet Transform (CWT) for time-frequency analysis.

Keywords:

MEMS accelerometer carotid artery disease explainable machine learning feature extraction seismic patch stenosis wearable biosensor

Related Experiment Videos

Last Updated: May 28, 2026

Measuring the Carotid to Femoral Pulse Wave Velocity (Cf-PWV) to Evaluate Arterial Stiffness

Measuring the Carotid to Femoral Pulse Wave Velocity (Cf-PWV) to Evaluate Arterial Stiffness

Published on: May 3, 2018

Extracted spectral and scalogram-derived features and selected six non-redundant biomarkers.

Employed SHapley Additive exPlanations (SHAP) for model interpretability.

Validated the approach in a carotid flow phantom and a clinical study of 74 patients.

Main Results:

Achieved an Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.97 and Area Under the Precision-Recall Curve (AUPR) of 0.947.
Demonstrated high diagnostic performance with 81.7% sensitivity and 93.6% specificity.
Achieved 89.8% accuracy, 85.4% precision, and an F1 score of 83.5%.

Conclusions:

Wearable seismic sensing combined with interpretable machine learning shows significant potential for carotid artery disease screening.
The developed approach enables fast screening and longitudinal monitoring of carotid arteries.
This technology can overcome barriers to frequent monitoring and scalable deployment of diagnostic tools.