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Conf Proc IEEE Eng Med Biol Soc. 2012;2012:956-9. doi: 10.1109/EMBC.2012.6346091.

Automated drill-stop by SVM classified audible signals.

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1
Institute for Signal Processing, University of Luebeck, 23562 Luebeck, Germany.

Abstract

Neuroscience research often requires direct access to brain tissue in animal models which clearly requires opening of the protective cranium. Minimizing animal numbers requests only well-experienced surgeons, since clumsy performance may lead to premature death of the animal. To minimise those traumatic outcomes, an algorithmic approach for closed-loop control of our Spherical Assistant for Stereotaxic Surgery (SASSU) was designed. Controlling the surgical robot's micro-drill unit by audio pattern recognition proved to be a simple and reliable way to automatically stop the automated drill feed. Sound analysis based on the anatomical morphology of a rat skull was used to train a Support Vector Machine (SVM) classification of the time-frequency representations of the drill sound. Fully automated high throughput animal surgeries are the goal of this approach.

PMID:
23366052
DOI:
10.1109/EMBC.2012.6346091
[Indexed for MEDLINE]
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