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

Vidya Gopal T V; Inayathullah Ghori; Avinash Eranki; Renu John

doi:10.54254/2755-2721/2025.18285

Research Article

Open access

Published on 19 December 2024

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V,V.G.T.;Ghori,I.;Eranki,A.;John,R. (2024). MFCC-based Classification of Carotid Artery Doppler Audio Signals Using LSTM Network. Applied and Computational Engineering,100,36-42.

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

Vidya Gopal T V ¹, Inayathullah Ghori ², Avinash Eranki ³, Renu John *^,4,

¹ Department of Biomedical Engineering, Indian Institute of Technology, Hyderabad, India
² Kamineni Hospital, King Koti, Hyderabad, India
³ Department of Biomedical Engineering, Indian Institute of Technology, Hyderabad, India
⁴ Department of Biomedical Engineering, Indian Institute of Technology, Hyderabad, India

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/2025.18285

Abstract

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.

Keywords

Common Carotid Artery, Doppler Ultrasound Audio, Recurrent Neural Network, MFCC Features, Age related variations

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References

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Cite this article

V,V.G.T.;Ghori,I.;Eranki,A.;John,R. (2024). MFCC-based Classification of Carotid Artery Doppler Audio Signals Using LSTM Network. Applied and Computational Engineering,100,36-42.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 5th International Conference on Signal Processing and Machine Learning

Conference website: https://2025.confspml.org/

ISBN：978-1-83558-689-1(Print) / 978-1-83558-690-7(Online)

Conference date: 8 February 2025

Editor：Stavros Shiaeles

Series: Applied and Computational Engineering

Volume number: Vol.100

ISSN：2755-2721(Print) / 2755-273X(Online)

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