The use of the wavelet transform to describe embolic signals

Nizamettin Aydin*, Soundrie Padayachee, Hugh S. Markus

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

70 Citations (Scopus)

Abstract

A number of methods to detect cerebral emboli and differentiate them from artefacts using Doppler ultrasound have been described in the literature. In most, Fourier transform-based (FT) spectral analysis has been used. The FT is not ideally suited to analysis of short-duration embolic signals due to an inherent trade-off between temporal and frequency resolution. An alternative approach that might be expected to describe embolic signals well is the wavelet transform. Wavelets are ideally suited for the analysis of sudden short-duration signal changes. Therefore, we have implemented a wavelet-based analysis and compared the results of this with a conventional FFT-based analysis. The temporal resolution, as measured by the half-width maximum, was significantly better for the continuous wavelet transform (CWT), mean (SD) 8.40 (8.82) ms, compared with the 128-point FFT, 12.92 (9.70) ms, and 64-point FFT, 10.80 (5.69) ms. Time localization of the CWT for the embolic signal was also significantly better than the FFT. The wavelet transform appears well suited to the analysis of embolic signals offering superior time resolution and time localization to the FFT.

Original languageEnglish
Pages (from-to)953-958
Number of pages6
JournalUltrasound in Medicine and Biology
Volume25
Issue number6
DOIs
Publication statusPublished - Jul 1999
Externally publishedYes

Funding

This work was supported by a British Heart Foundation project grant (PG96176). We are grateful to Marisa Cullinane for assistance in analyzing data, Kamran Modaresi for advice on computing aspects and William J. Williams for useful comments on the manuscript.

FundersFunder number
British Heart FoundationPG96176

    Keywords

    • Cerebral embolism
    • Fast Fourier transform
    • Time localization
    • Ultrasonics
    • Wavelet transform

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