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Investigation of EEG-Based Graph-Theoretic Analysis for Automatic Diagnosis of Alcohol Use Disorder

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Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions (ICANN 2019)

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Abstract

Abnormal functional connectivity (FC) has been commonly observed during alcohol use disorder (AUD). In this work, FC analysis has been performed by incorporating EEG-based graph-theoretic analysis and a machine learning (ML) framework. Brain FC was quantified with synchronization likelihood (SL). Undirected graphs for each channel pair were constructed involving the SL measures. Furthermore, the graph-based features such as minimum spanning tree, distances between nodes, and maximum flow between the graph nodes were computed, termed as EEG data matrix. The matrix was used as input data to the ML framework to classify the study participants. The ML framework was validated with data acquired from 30 AUD patients and an age-matched group of 30 healthy controls. In this study, the classifiers such as SVM (accuracy = 98.7%), Naïve Bayes (accuracy = 88.6%), and logistic regression (accuracy = 89%) have shown promising discrimination results. The method was compared with two existing methods that also involve resting-state EEG data. The first method reported a classification accuracy of 91.7% while utilizing the time-based features such as Approximate Entropy (ApEn), Largest Lyapunov Exponent (LLE), Sample Entropy (SampEn), and four other Higher Order Spectra (HOS) features [1]. The second method reported 95.8% accuracy involving wavelet-based signal energy [2]. Since the study has utilized a small sample size, the generalization could not be possible. The FC-based graph-theoretic analysis in combination with ML methods could be used as an endophenotype for screening AUD patients.

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Acknowledgment

This publication was supported by the project LO1506 of the Czech Ministry of Education, Youth and Sports under the program NPU I. The research work was supported by the HiCoE grant for CISIR, Ministry of Education, Malaysia.

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Authors and Affiliations

  1. NTIS - New Technologies for the Information Society, Faculty of Applied Sciences, University of West Bohemia, Univerzitní 8, 306 14, Pilsen, Czech Republic

    Wajid Mumtaz, Lukáš Vařeka & Roman Mouček

  2. Center for Intelligent Signal and Imaging Research (CISIR), Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, 32610, Seri Iskandar, Perak, Malaysia

    Wajid Mumtaz

  3. Department of Electrical Engineering, School of Electrical Engineering and Computer Science, National University of Sciences and Technology, H-12, Islamabad, Pakistan

    Wajid Mumtaz

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  1. Wajid Mumtaz
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  2. Lukáš Vařeka
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Correspondence to Wajid Mumtaz .

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Editors and Affiliations

  1. Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany

    Igor V. Tetko

  2. Institute of Computer Science, Czech Academy of Sciences, Prague 8, Czech Republic

    Věra Kůrková

  3. Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany

    Pavel Karpov

  4. Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany

    Fabian Theis

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Mumtaz, W., Vařeka, L., Mouček, R. (2019). Investigation of EEG-Based Graph-Theoretic Analysis for Automatic Diagnosis of Alcohol Use Disorder. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions. ICANN 2019. Lecture Notes in Computer Science(), vol 11731. Springer, Cham. https://doi.org/10.1007/978-3-030-30493-5_23

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  • DOI: https://doi.org/10.1007/978-3-030-30493-5_23

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  • Online ISBN: 978-3-030-30493-5

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