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Discrimination between emboli and artifacts for outpatient transcranial Doppler ultrasound data.

This paper addresses the detection of emboli in transcranial Doppler ultrasound data acquired from an original portable device. The challenge is the removal of several artifacts (motion and voice) intrinsically related to long-duration (up to 1 h 40 mn per patient) outpatient signals monitoring from...

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Published in:Medical & Biological Engineering & Computing Vol. 55; no. 10; pp. 1787 - 1798
Main Authors: Guépié, Blaise, Sciolla, Bruno, Millioz, Fabien, Almar, Marilys, Delachartre, Philippe, Guépié, Blaise Kévin
Format: Journal Article
Published: Springer Nature Oct2017
Online Access:View this record in EBSCOhost
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      dt: Oct2017
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      pub: Springer Nature
      place: New York, New York
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        atl: Discrimination between emboli and artifacts for outpatient transcranial Doppler ultrasound data.
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          Guépié, Blaise
          Sciolla, Bruno
          Millioz, Fabien
          Almar, Marilys
          Delachartre, Philippe
          Guépié, Blaise Kévin
        affil: Univ Lyon, INSA-Lyon, Universit Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm,CREATIS UMR 5220, U1206 , 69621 Lyon France
      sug:
        subj:
          Embolism Pathology
          Artifacts
          Ultrasonography, Doppler, Transcranial Methods
          Outpatients
          Algorithms
          Cerebrovascular Circulation Physiology
      ab: This paper addresses the detection of emboli in transcranial Doppler ultrasound data acquired from an original portable device. The challenge is the removal of several artifacts (motion and voice) intrinsically related to long-duration (up to 1 h 40 mn per patient) outpatient signals monitoring from this device, as well as high intensities due to the stochastic nature of blood flow. This paper proposes an adapted removal procedure. This firstly consists of reducing the background noise and detecting the blood flow in the time-frequency domain using a likelihood method for contour detection. Then, a hierarchical extraction of features from magnitude and bounding boxes is achieved for the discrimination of emboli and artifacts. After processing of the long-duration outpatient signals, the number of artifacts predicted as emboli is considerably reduced (by 92% for some parameter values) between the first and the last step of our algorithm.
      pubtype: Academic Journal
      doctype: Journal Article
      ougenre: Article
    language: English
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