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Abstract #3913

Spectral Entropy: A Useful Engineered Feature for Classification of fMRI Data Quality?

Christopher O'Grady1,2,3, Steve Patterson4, Alessandro Guida2, James Rioux2, Antonina Omisade5, Javeria Hashmi3,6,7, and Steven Beyea6,8,9

1Department of Research, Nova Scotia Health Authority, Halifax, NS, Canada, 2Biomedical Translational Imaging Centre, Halifax, NS, Canada, 3Brain Networks & Neurophysiology Lab, Halifax, NS, Canada, 4Nova Scotia Department of Health and Wellness, Halifax, NS, Canada, 5Acquired Brain Injury, Nova Scotia Health Authority, Halifax, NS, Canada, 6Department of Physics and Atmospheric Science, Dalhousie University, Halifax, NS, Canada, 7Department of Medical Neuroscience, Dalhousie University, Halifax, NS, Canada, 8School of Biomedical Engineering, Dalhousie University, Halifax, NS, Canada, 9Department of Diagnostic Radiology, Dalhousie University, Halifax, NS, Canada

This work evaluates the use of regularized spectral entropy (rSpecEn) as a potentially useful engineered feature for the classification of fMRI scan quality in real time. rSpecEn relies on the frequency sparsity of task-based fMRI signals, and a data-driven regularization method is employed to counteract unavoidable noise. Comparison to known measures of quality are used, as well as the use of fMRI scans with intentionally degraded quality. Sensitivity to fMRI data quality measures and an exploration of high accuracy scan quality classification are presented in this feasibility study.

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