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...

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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/
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spelling utp-eprints.231202021-08-19T05:36:05Z Anomaly Detection of Moderate Traumatic Brain Injury Using Auto-Regularized Multi-Instance One-Class SVM Rasheed, W. Tang, T.B. 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 coherence (MSC) and phase lag index (PLI) to quantify synchronized brain activity patterns as a means to detect functional deficits. We propose a multi-instance one-class support vector machine (SVM) model generated from a healthy control population. Any dispersion from the decision boundary of the model would be identified as an anomaly instance of mTBI case (Glasgow Coma Scale, GCS score between 9 and 13). The decision boundary was optimized by considering the closest anomaly (GCS =13) from the negative class as a support vector. Validated against magnetic resonance imaging (MRI) data, the proposed model at high beta band yielded an accuracy of 94.19 and a sensitivity of 90.00, when tested with our mTBI dataset. The results support the suggestion of multi-instance one-class SVM for the detection of mTBI. © 2001-2011 IEEE. Institute of Electrical and Electronics Engineers Inc. 2020 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078348215&doi=10.1109%2fTNSRE.2019.2948798&partnerID=40&md5=d4a3bb30c0f85fc80fda5d5dd0cd2a50 Rasheed, W. and Tang, T.B. (2020) Anomaly Detection of Moderate Traumatic Brain Injury Using Auto-Regularized Multi-Instance One-Class SVM. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 28 (1). pp. 83-93. http://eprints.utp.edu.my/23120/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description 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 coherence (MSC) and phase lag index (PLI) to quantify synchronized brain activity patterns as a means to detect functional deficits. We propose a multi-instance one-class support vector machine (SVM) model generated from a healthy control population. Any dispersion from the decision boundary of the model would be identified as an anomaly instance of mTBI case (Glasgow Coma Scale, GCS score between 9 and 13). The decision boundary was optimized by considering the closest anomaly (GCS =13) from the negative class as a support vector. Validated against magnetic resonance imaging (MRI) data, the proposed model at high beta band yielded an accuracy of 94.19 and a sensitivity of 90.00, when tested with our mTBI dataset. The results support the suggestion of multi-instance one-class SVM for the detection of mTBI. © 2001-2011 IEEE.
format Article
author Rasheed, W.
Tang, T.B.
spellingShingle Rasheed, W.
Tang, T.B.
Anomaly Detection of Moderate Traumatic Brain Injury Using Auto-Regularized Multi-Instance One-Class SVM
author_sort Rasheed, W.
title Anomaly Detection of Moderate Traumatic Brain Injury Using Auto-Regularized Multi-Instance One-Class SVM
title_short Anomaly Detection of Moderate Traumatic Brain Injury Using Auto-Regularized Multi-Instance One-Class SVM
title_full Anomaly Detection of Moderate Traumatic Brain Injury Using Auto-Regularized Multi-Instance One-Class SVM
title_fullStr Anomaly Detection of Moderate Traumatic Brain Injury Using Auto-Regularized Multi-Instance One-Class SVM
title_full_unstemmed Anomaly Detection of Moderate Traumatic Brain Injury Using Auto-Regularized Multi-Instance One-Class SVM
title_sort anomaly detection of moderate traumatic brain injury using auto-regularized multi-instance one-class svm
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2020
url 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/
_version_ 1741196624097443840
score 11.62408

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