Decoding Biomarkers of Parkinson’s Disease Using Convolutional Regression Networks

Full Metadata

Overview

Year:: 2020
Contributor:: Lee, Shane (Author); Liu, David (Author); Zheng, Bryan (Author); Asaad, Wael (Author); Brown University. Alpert Medical School. Scholarly Concentration Program. Non-Scholarly Concentrator (research program)
Subject:: Neural networks (Computer science); Parkinson's disease; Computational neuroscience
Language:: English
DOI: https://doi.org/10.26300/0236-vc19

Files

- View MODS File
- Download MODS File
ViewMODS
- View PDF File
- Download PDF File
ViewPDF

Description

Abstract:: The applications of convolutional neural networks (CNN) are nearly endless. As a popular method for image classification, they have demonstrated the ability to categorize Parkinson’s symptomology using neurophysiologic spectral data as inputs. Regression networks, whose outputs are continuous versus categorical or binary, can potentially be a precise tool for identifying neural biomarkers of disease. Insights into such features may advance the implementation of closed-loop deep brain stimulation (DBS).
Notes:: Scholarly concentration: Non-Scholarly Concentrator

Content

Skip item content viewer

Access Conditions

Citation

Lee, Shane, Liu, David, Zheng, Bryan, et al., "Decoding Biomarkers of Parkinson’s Disease Using Convolutional Regression Networks" (2020). Warren Alpert Medical School Academic Symposium. Brown Digital Repository. Brown University Library. https://doi.org/10.26300/0236-vc19

Relations

Collection:

Warren Alpert Medical School Academic Symposium

The Warren Alpert Medical School Academic Symposium is an annual event at Warren Alpert Medical School of Brown University that provides Year II medical students a venue to present their summer research in a poster format. Participation in the Symposium …
...