Anomaly Detection of Moderate Traumatic Brain Injury Using Auto-Regularized Multi-Instance One-Class SVM

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.

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Abstract

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.

Item Type: Article
Impact Factor: cited By 4
Uncontrolled Keywords: Brain; Brain mapping; Decision making; Diagnosis; Learning systems; Magnetic resonance imaging; Magnetoencephalography; Support vector machines, Brain activity patterns; Clinical decision making; Detection and quantifications; Magnitude squared coherences; Neural imaging; Neurorehabilitation; One-class support vector machine; Traumatic Brain Injuries, Anomaly detection, adult; amnesia; Article; clinical article; consciousness; controlled study; diagnostic test accuracy study; female; Glasgow coma scale; histogram; human; machine learning; magnetoencephalography; magnetometry; male; mathematical model; nerve cell network; nuclear magnetic resonance imaging; one class support vector machine; outlier detection; performance; sensitivity and specificity; traumatic brain injury; adolescent; algorithm; automation; beta rhythm; brain; diagnostic imaging; factual database; nerve tract; pathophysiology; support vector machine; traumatic brain injury; young adult, Adolescent; Adult; Algorithms; Automation; Beta Rhythm; Brain; Brain Injuries, Traumatic; Databases, Factual; Female; Glasgow Coma Scale; Humans; Magnetic Resonance Imaging; Magnetoencephalography; Male; Neural Pathways; Sensitivity and Specificity; Support Vector Machine; Young Adult
Depositing User: Ms Sharifah Fahimah Saiyed Yeop
Date Deposited: 19 Aug 2021 05:36
Last Modified: 19 Aug 2021 05:36
URI: http://scholars.utp.edu.my/id/eprint/23120

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