Automatic detection of emboli in the TCD RF signal using principal component analysis.
The transcranial Doppler (TCD) radio-frequency (RF) signal can provide additional information on events recorded during ultrasonic monitoring. Embolic signals appear as uniform and predictable shapes within the RF signal, enabling pattern recognition and image processing techniques to be used for their automated detection. This paper uses principal component analysis (PCA) to characterise the typical variation in embolic signal shape, within the RF signal, using training sets of in vitro and in vivo data. PCA techniques are then utilised to discriminate between previously unseen embolic and artifact signals. Although the results of this study show that the algorithms described in this paper do not yet have the accuracy required for their use in a clinical setting, it does demonstrate that this novel technique has the potential to be developed further.
Cowe
Joanne
J
Department of Medical Physics, University Hospitals of Leicester NHS Trust, Leicester, UK.
Evans
David H
DH
D000465:Algorithms; D016477:Artifacts; D016894:Endarterectomy, Carotid; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D020766:Intracranial Embolism / Q000000981:diagnostic imaging*; D025341:Principal Component Analysis / Q000379:methods*; D012372:ROC Curve; D012815:Signal Processing, Computer-Assisted; D018615:Ultrasonography, Doppler, Color / Q000379:methods; D017585:Ultrasonography, Doppler, Transcranial / Q000379:methods*