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Licensed Unlicensed Requires Authentication Published by De Gruyter August 1, 2017

Quantifying the dynamics of electroencephalographic (EEG) signals to distinguish alcoholic and non-alcoholic subjects using an MSE based K-d tree algorithm

  • Lal Hussain ORCID logo EMAIL logo , Wajid Aziz , Sharjil Saeed , Saeed Arif Shah , Malik Sajjad A. Nadeem , Imtiaz Ahmed Awan , Ali Abbas , Abdul Majid and Syed Zaki Hassan Kazmi

Abstract

In this paper, we have employed K-d tree algorithmic based multiscale entropy analysis (MSE) to distinguish alcoholic subjects from non-alcoholic ones. Traditional MSE techniques have been used in many applications to quantify the dynamics of physiological time series at multiple temporal scales. However, this algorithm requires O(N2), i.e. exponential time and space complexity which is inefficient for long-term correlations and online application purposes. In the current study, we have employed a recently developed K-d tree approach to compute the entropy at multiple temporal scales. The probability function in the entropy term was converted into an orthogonal range. This study aims to quantify the dynamics of the electroencephalogram (EEG) signals to distinguish the alcoholic subjects from control subjects, by inspecting various coarse grained sequences formed at different time scales, using traditional MSE and comparing the results with fast MSE (fMSE). The performance was also measured in terms of specificity, sensitivity, total accuracy and receiver operating characteristics (ROC). Our findings show that fMSE, with a K-d tree algorithmic approach, improves the reliability of the entropy estimation in comparison with the traditional MSE. Moreover, this new technique is more promising to characterize the physiological changes having an affect at multiple time scales.


Corresponding author: Dr. Lal Hussain, Assistant Director, Quality Enhancement Cell, The University of Azad Jammu and Kashmir, City Campus, Muzaffarabad, 13100, Azad Kashmir, Pakistan, E-mail:

  1. Author Statement

  2. Research funding: Authors state no funding involved.

  3. Conflict of interest: Authors state no conflict of interest.

  4. Informed consent: Informed consent is not applicable.

  5. Ethical approval: The conducted research is not related to either human or animals use.

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Received: 2017-03-31
Accepted: 2017-06-21
Published Online: 2017-08-01
Published in Print: 2018-07-26

©2018 Walter de Gruyter GmbH, Berlin/Boston

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