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VASCULAR STENOSIS DETECTION USING TEMPORAL-SPECTRAL DIFFERENCES IN CORRELATED ACOUSTIC MEASUREMENTS.

B Panda1,2, S Mandal2, S J A Majerus3

  • 1Department of Biomedical Engineering, Case Western Reserve University.

... IEEE Signal Processing in Medicine and Biology Symposium (SPMB). IEEE Signal Processing in Medicine and Biology Symposium
|April 19, 2021
PubMed
Summary

Detecting central venous stenosis in hemodialysis patients is challenging. New microphone arrays and signal processing can identify turbulent blood flow, aiding in noninvasive diagnosis of stenosis.

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Area of Science:

  • Biomedical Engineering
  • Medical Imaging
  • Acoustics

Background:

  • Central venous stenosis is frequently undiagnosed in hemodialysis patients due to imaging limitations.
  • Noninvasive, point-of-care detection methods are needed for early diagnosis.

Purpose of the Study:

  • To develop and validate a novel method for detecting central venous stenosis using acoustic signals.
  • To assess the efficacy of flexible microphone arrays and advanced signal processing in identifying turbulent blood flow.

Main Methods:

  • Flexible microphone arrays were used to capture time-correlated blood flow sounds.
  • A signal processing approach involving continuous wavelet transform and spectral feature correlation was developed.
  • The system was tested on pulsatile flow phantoms simulating various degrees of stenosis (10-85%).

Main Results:

  • An inversion in the timing of systolic spectral content was observed proximal and distal to hemodynamically significant stenoses.
  • Specific time shifts (+22 ms for <50% stenosis, -20 to -38 ms for >50% stenosis) were measured.
  • Calculated blood velocity increases (142-155 cm/s) in stenotic phantoms align with ultrasound measurements.

Conclusions:

  • Flexible microphone arrays offer a promising noninvasive approach for detecting central venous stenosis.
  • The developed signal processing technique effectively identifies turbulent flow indicative of stenosis.
  • This technology could improve the diagnosis of central venous stenosis in hemodialysis patients.