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.
Originalsprache | Englisch |
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Titel | 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society |
Seitenumfang | 4 |
Herausgeber (Verlag) | IEEE |
Erscheinungsdatum | 01.08.2012 |
Seiten | 956-959 |
Aufsatznummer | 6346091 |
ISBN (Print) | 978-1-4244-4119-8 |
ISBN (elektronisch) | 978-1-4577-1787-1 |
DOIs | |
Publikationsstatus | Veröffentlicht - 01.08.2012 |
Veranstaltung | 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society - San Diego, USA / Vereinigte Staaten Dauer: 28.08.2012 → 01.09.2012 Konferenznummer: 94236 |