Online Monitoring of Vital Signs & Anesthesia during Neural Recordings
Recording neural data in anaesthetized animals allows for the understanding of intact, functioning brain circuitry. However, extended delivery of anaesthesia must be monitored to ensure the well-ˇbeing and brain-ˇstate level of the subject. Several vital signs, such as the subject’s breathing rate, heart rate, blood oxygentation level, and EEG data, are crucial measures for estimating health and depth of anesthesia. While commercial systems provide mechanisms for monitoring individual vital signs, they do not provide a consolidated platform for evaluating all input streams in a single source. Your task is to configure preexisting monitoring devices to consolidate data output into a single source. Once all data streams are synced, there exists the possibility to establish machine learning techniques in order to determine irregular vital signs and predict needs for alteration of anaesthesia levels. Additionally, configuration of specialized monitors for measuring the change in vital signs over extended experimental periods would be of value.
The project will require you to work towards configuration of a physiological monitoring system and development of an online analysis pipeline. Previous experience in LabView, Matlab or another programming language would be helpful. Knowledge of machine learning techniques would also be useful. You would have the opportunity to work in an established neuroscience laboratory encompassing a wide range of specialties spanning experimental, computational, and engineering fields.
Gwendolyn English, englishg (at) ethz.ch
Dr. Wolfger von der Behrens, wolfger (at) ini.ethz.ch