GROUP 25: Amy Mirro, Caroline Farrington, Jeff Daniels Client: Dr. Dosenbach Sleep Detection and Alert System for fMRI.

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Presentation transcript:

GROUP 25: Amy Mirro, Caroline Farrington, Jeff Daniels Client: Dr. Dosenbach Sleep Detection and Alert System for fMRI

Background fMRI

Background Resting State  Functional Connectivity

Background

Scope A near real time, fMRI compatible method to determine if a patient is sleeping and alert the technician

Specs Acts in near real time MR compatible Hardware must fit in head coil(165mm; 60.4mm) Non-invasive recording technique Accurate Displays on scan room PC Does not obstruct necessary data

Near Real Time Sleep Detection Monitoring eye movement

Near Real Time Sleep Detection Infrared sensor for eyes and breath Camera for head motion

Near Real Time Sleep Detection Eye tracking system Compares the current pixel values of the eyes to the first image acquired

Real Time Tracking in Imaging Head Motion Methods exist to: 1. Alert/ Inform Patients 2. Alter image acquisition protocols

Sleep Detection in Imaging Support Vector Machine  Uses fMRI data to determine if an individual was sleeping during a scan  Applied retrospectively

Adding on to Current Solutions There are methods for detecting sleep not created for imaging applications There is real- time tracking in imaging One group has created an SVM that can recognize sleep after a scan is over Can we put this all together?

Final Project Create the first near real time sleep detection system for use in imaging Improve the quality of images acquired  Prevent wasting of time and money  Ensure the accuracy of resting state fMRI studies

Schedule/ Division of Work

References Clark, James, et al. “Sleep Detection and Driver Alert Patent” Patent 5,689, Nov Karl, Van Orden, et al. “Eye Activity Monitor.” Patent 6,346, Feb 2002 Shin, Duk, Hiroyuki Sakai, and Yuji Uchiyama. "Slow eye movement detection can prevent sleep ‐ related accidents effectively in a simulated driving task." Journal of sleep research 20.3 (2011): Tagliazucchi, Enzo, and Helmut Laufs. "Decoding wakefulness levels from typical fMRI resting-state data reveals reliable drifts between wakefulness and sleep." Neuron 82.3 (2014): Thesen, Stefan, et al. "Prospective acquisition correction for head motion with image ‐ based tracking for real ‐ time fMRI." Magnetic Resonance in Medicine 44.3 (2000): Thulborn, Keith R. "Visual feedback to stabilize head position for fMRI." Magnetic resonance in medicine 41.5 (1999):

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