Embolic Doppler ultrasound signal detection using discrete wavelet transform

Nizamettin Aydin*, Farokh Marvasti, Hugh S. Markus

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

64 Citations (Scopus)


Asymptomatic circulating emboli can be detected by Doppler ultrasound. Embolic Doppler ultrasound signals are short duration transient like signals. The wavelet transform is an ideal method for analysis and detection of such signals by optimizing time-frequency resolution. We propose a detection system based on the discrete wavelet transform (DWT) and study some parameters, which might be useful for describing embolic signals (ES). We used a fast DWT algorithm based on the Daubechies eighth-order wavelet filters with eight scales. In order to evaluate feasibility of the DWT of ES, two independent data sets, each comprising of short segments containing an ES (N = 100), artifact (N = 100) or Doppler speckle (DS) (N = 100), were used. After applying the DWT to the data, several parameters were evaluated. The threshold values used for both data sets were optimized using the first data set. While the DWT coefficients resulting from artifacts dominantly appear at the higher scales (five, six, seven, and eight), the DWT coefficients at the lower scales (one, two, three, and four) are mainly dominated by ES and DS. The DWT is able to filter out most of the artifacts inherently during the transform process. For the first data set, 98 out of 100 ES were detected as ES. For the second data set, 95 out of 100 ES were detected as ES when the same threshold values were used. The algorithm was also tested with a third data set comprising 202 normal ES; 198 signals were detected as ES.

Original languageEnglish
Pages (from-to)182-190
Number of pages9
JournalIEEE Transactions on Information Technology in Biomedicine
Issue number2
Publication statusPublished - Jun 2004
Externally publishedYes


Manuscript received June 19, 2002; revised March 28, 2003. This work was supported by the British Heart Foundation under Project Grant PG99064. N. Aydin is with the School of Engineering and Electronic, The University of Edinburgh, Edinburgh EH9 3JL, U.K. (e-mail: naydin@ee.ed.ac.uk.). F. Marvasti is with the Sharif University of Technology and Iran Telecommunications Research Center, Tehran, Iran. H. S. Markus is with Clinical Neuroscience, St. George’s Hospital Medical School, London SW17 0RE, U.K. Digital Object Identifier 10.1109/TITB.2004.828882 Fig. 1. Examples of normalized ES seen in vivo and corresponding TF distributions. For clarity, forward and reverse flow components are scaled by 1 and 1, respectively.

FundersFunder number
British Heart FoundationPG99064


    • Cerebral emboli
    • Detection
    • Discrete wavelet transform (DWT)
    • Fuzzy logic
    • Ultrasound
    • Wavelet


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