MFCC-based Classification of Carotid Artery Doppler Audio Signals Using LSTM Network

The carotid artery assessment is essential for detecting stenosis and vascular abnormalities. Traditional Doppler ultrasound, while effective, requires specialized equipment and trained operators, limiting its accessibility in primary care. This study investigates Doppler audio signal analysis as a non-invasive, cost-effective alternative for assessing carotid artery hemodynamics. Using advanced signal processing techniques like mel-frequency cepstral coefficients (MFCCs) and deep learning models such as Long Short-Term Memory (LSTM) networks, we analyze Doppler audio signals from the common carotid artery (CCA) in 216 individuals. Our findings reveal significant age-related variations in blood flow dynamics and distinct signal patterns, highlighting the potential of Doppler audio analysis for early vascular screening. The changes in MFCCs indicate their usefulness in identifying hemodynamic alterations associated with aging and disease, supporting their role in non-invasive carotid artery health assessment. We also evaluate the deep learning framework, utilizing RNNs to capture long-term dependencies in the signals and providing a comprehensive comparison of network configurations and performance relative to state-of-the-art algorithms.