Automated drill-stop by SVM classified audible signals

B. M. Pohl, J. O. Jungmann, O. Christ, U. G. Hofmann

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.
OriginalspracheEnglisch
Titel2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Seitenumfang4
Herausgeber (Verlag)IEEE
Erscheinungsdatum01.08.2012
Seiten956-959
Aufsatznummer6346091
ISBN (Print)978-1-4244-4119-8
ISBN (elektronisch)978-1-4577-1787-1
DOIs
PublikationsstatusVeröffentlicht - 01.08.2012
Veranstaltung34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society - San Diego, USA / Vereinigte Staaten
Dauer: 28.08.201201.09.2012
Konferenznummer: 94236

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