Anomaly Detection of Moderate Traumatic Brain Injury Using Auto-Regularized Multi-Instance One-Class SVM
Detection and quantification of functional deficits due to moderate traumatic brain injury (mTBI) is crucial for clinical decision-making and timely commencement of functional therapy. In this work, we explore magnetoencephalography (MEG) based functional connectivity features i.e. magnitude squared...
Saved in:
| Main Authors: | Rasheed, W., Tang, T.B. |
|---|---|
| Format: | Article |
| Institution: | Universiti Teknologi Petronas |
| Record Id / ISBN-0: | utp-eprints.23120 / |
| Published: |
Institute of Electrical and Electronics Engineers Inc.
2020
|
| Online Access: |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078348215&doi=10.1109%2fTNSRE.2019.2948798&partnerID=40&md5=d4a3bb30c0f85fc80fda5d5dd0cd2a50 http://eprints.utp.edu.my/23120/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
ANOMALY DETECTION USING MULTI-INSTANCE ONE-CLASS SVM FOR
MODERATE TRAUMATIC BRAIN INJURY CASES
by: RASHEED, WAQAS
Published: (2019) -
Moderate traumatic brain injury identification for MEG data using PU (Positive and Unseen) learning
by: Rasheed, W., et al.
Published: (2018) -
SVM for network anomaly detection using ACO feature subset
by: Mehmood, T., et al.
Published: (2016) -
Equine injury and therapy /
by: BROMILEY, Mary W.
Published: (1987) -
Towards multilevel mental stress assessment using SVM with ECOC: an EEG approach
by: Al-shargie, F., et al.
Published: (2018)