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Purpose <p>This study aims to enhance carotid artery health assessment by addressing the limitations of current diagnostic methods for detecting conditions like stenosis, which elevate stroke risk. Traditional Doppler ultrasound, while effective, depends on specialized equipment and trained personnel, restricting its use in primary care and resource-limited settings. To improve accessibility and efficiency, this research explores whether audio-based diagnostic methods, supported by machine learning algorithms, can provide an accurate and reliable alternative for evaluating carotid artery blood flow characteristics and the degree of stenosis.</p> Methods <p>This study investigated the potential of Doppler audio signal analysis as a non-invasive and cost-effective alternative for assessing carotid artery hemodynamics. By employing advanced signal processing techniques including short-time Fourier transform (STFT) and mel-frequency cepstral coefficients (MFCCs), we characterized Doppler audio signals from the common carotid artery (CCA), and internal carotid artery (ICA) from a cohort of 216 individuals.</p> Results <p>The extracted features, correlated with individual resistivity index (RI), and pulsatility index (PI) values demonstrated their utility in identifying hemodynamic alterations associated with aging and disease states highlighting their potential for noninvasive assessment of carotid artery health.</p> Conclusion <p>The analysis revealed significant age-related variations in blood flow dynamics and distinct signal patterns, indicating the potential of Doppler audio analysis for early screening of vascular changes.</p>

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Analysis of Doppler Audio Signals from the Carotid Artery

  • 期刊论文

  • 发表日期：

    2025年3月2日

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  • T. V. Vidya Gopal,
  • Inayathullah Ghori,
  • Avinash Eranki,
  • Annie Qurratulain Hasan,
  • Renu John

摘要

Purpose

This study aims to enhance carotid artery health assessment by addressing the limitations of current diagnostic methods for detecting conditions like stenosis, which elevate stroke risk. Traditional Doppler ultrasound, while effective, depends on specialized equipment and trained personnel, restricting its use in primary care and resource-limited settings. To improve accessibility and efficiency, this research explores whether audio-based diagnostic methods, supported by machine learning algorithms, can provide an accurate and reliable alternative for evaluating carotid artery blood flow characteristics and the degree of stenosis.

Methods

This study investigated the potential of Doppler audio signal analysis as a non-invasive and cost-effective alternative for assessing carotid artery hemodynamics. By employing advanced signal processing techniques including short-time Fourier transform (STFT) and mel-frequency cepstral coefficients (MFCCs), we characterized Doppler audio signals from the common carotid artery (CCA), and internal carotid artery (ICA) from a cohort of 216 individuals.

Results

The extracted features, correlated with individual resistivity index (RI), and pulsatility index (PI) values demonstrated their utility in identifying hemodynamic alterations associated with aging and disease states highlighting their potential for noninvasive assessment of carotid artery health.

Conclusion

The analysis revealed significant age-related variations in blood flow dynamics and distinct signal patterns, indicating the potential of Doppler audio analysis for early screening of vascular changes.