| Start Date: | 11/28/2016 | Start Time: | 10:00 AM |
| End Date: | 11/28/2016 | End Time: | 12:00 PM |
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Event Description
BIOMED PhD Research Proposal
Title:
Investigation of Hemodynamic Changes During General Anesthesia via Functional Near Infrared Spectroscopy (fNIRS)
Speaker:
Gabriela Hernandez-Meza, PhD Candidate, School of Biomedical Engineering, Science and Health Systems
Advisor:
Kurtulus Izzetoglu, PhD, Associate Research Professor, School of Biomedical Engineering, Science and Health Systems
Abstract:
Anesthesiologists, physiologists and medical device professionals have long been working to design systems that can monitor depth of anesthesia better than routine physiological parameters such as blood pressure (BP), heart rate (HR), respiration rate (RR), peripheral oxygen saturation (SpO2) and end tidal CO2 (EtCO2). A variety of devices have been designed to monitor depth of anesthesia. However, none of the systems have gained widespread use because the signal related to depth of anesthesia is often evidenced after the anesthesiologist has already noted that a change has occurred based on routinely measured physiological parameters. Despite advances in medical imaging technology that significantly help basic science, there remains an unmet clinical need for a practical, inexpensive tool for the reliable and objective assessment of the effects of general anesthetics during surgery. Advanced functional imaging modalities such as fMRI and PET scans deliver superior spatial information, which comes at a high equipment and maintenance cost. Therefore, they are not readily accessible for routine clinical use. Compared to them, fNIRS is non-invasive, safe, portable, and affordable and has good temporal resolution with a short set up time which makes it more suitable for clinical pain applications.
Due to the ease of fNIR measurements on the frontal region, in this thesis we explore the feasibility of using this signal detect the transition from maintenance to emergence during general anesthesia. The primary purpose of this study is to examine the effect of volatile anesthetics, specifically sevoflurane, on hemodynamic parameters measured by functional near infrared spectroscopy (fNIRS), the heart rate, end-tidal carbon dioxide concentration and peripheral oxygen saturation, investigate features of these signals that contain information related to anesthetized state and propose a method for the automatic classification of maintenance and emergence during general anesthesia with sevoflurane using machine learning algorithms. |
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Location:
Monell Chemical Center, 1st Floor Conference Room, located at 3508 Market Street. |
Audience:
Undergraduate StudentsGraduate StudentsFacultyStaff |
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