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A Novel Dissimilarity of Activity Biomarker and Functional Connectivity Analysis for the Epilepsy Diagnosis

1
Department of Computer Engineering, Khwaja Fareed University of Engineering & Information Technology (KFUEIT), Rahim Yar Khan 64200, Pakistan
2
Department of Electrical Engineering, Khwaja Fareed University of Engineering & Information Technology (KFUEIT), Rahim Yar Khan 64200, Pakistan
3
Department of Electrical and Electronic Engineering, University of Jeddah, Asfan Road, Jeddah 23891, Saudi Arabia
4
Department of Electrical Engineering, University of Central Punjab, Lahore 54000, Pakistan
5
Department of Medicine, and Institute of Genomics and Systems Biology, Knapp Center for Biomedical Discovery, University of Chicago, Chicago, IL 60637, USA
*
Author to whom correspondence should be addressed.
Symmetry 2019, 11(8), 979; https://doi.org/10.3390/sym11080979
Received: 21 June 2019 / Revised: 18 July 2019 / Accepted: 24 July 2019 / Published: 2 August 2019
(This article belongs to the Special Issue Symmetry and Asymmetry in Computational Biology and Bioinformatics)
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Abstract

Epilepsy is a central nervous system disorder that results in asymmetries of brain regional activation and connectivity patterns. The detection of these abnormalities is oftentimes challenging and requires identification of robust bio-markers that are representative of disease activity. Functional Magnetic Resonance Imaging (fMRI) is one of the several methods that can be used to detect such bio-markers. fMRI has a high spatial resolution which makes it a suitable candidate for designing computational methods for computer-aided biomarker discovery. In this paper, we present a computational framework for analyzing fMRI data consisting of 100 epileptic and 80 healthy patients, with an overall goal to produce a novel bio-marker that is predictive of epilepsy. The proposed method is primarily based on Dissimilarity of Activity (DoA) analysis. We demonstrate that the bio-marker presented in this study can be used to capture asymmetries in activities by detecting any abnormalities in Blood Oxygenated Level Dependent (BOLD) signal. In order to represent all asymmetries (of connectivity and activation patterns), we used functional connectivity analysis (FCA) in conjunction with DoA to find underlying connectivity patterns of the regions. Subsequently, these biomarkers were used to train a Support Vector Machine (SVM) classifier that was able to distinguish between healthy and epileptic patients with 87.8% accuracy. These results demonstrate the applicability of computer-aided methods in complex disease diagnosis by simply utilizing the existing data. With the advent of all modern sensing and imaging techniques, the use of intelligent algorithms and advanced computational methods are increasingly becoming the future of computer-aided diagnosis. View Full-Text
Keywords: epilepsy; functional magnetic resonance imaging; dissimilarity of activity; functional connectivity analysis; BOLD signal; support vector machine epilepsy; functional magnetic resonance imaging; dissimilarity of activity; functional connectivity analysis; BOLD signal; support vector machine
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Basit, A.; Ali Khan, S.; Tariq Toor, W.; Maroof, N.; Saadi, M.; Ali Khan, A. A Novel Dissimilarity of Activity Biomarker and Functional Connectivity Analysis for the Epilepsy Diagnosis. Symmetry 2019, 11, 979.

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