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

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. Ho...

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Vydáno v:Biomedizinische Technik Ročník 63; číslo 4; s. 481
Hlavní autoři: Hussain, Lal, Aziz, Wajid, Saeed, Sharjil, Shah, Saeed Arif, Nadeem, Malik Sajjad A, Awan, Imtiaz Ahmed, Abbas, Ali, Majid, Abdul, Kazmi, Syed Zaki Hassan
Médium: Journal Article
Jazyk:angličtina
Vydáno: Germany 01.08.2018
Témata:
Algorithms
Electroencephalography - methods
Entropy
Reproducibility of Results
Sensitivity and Specificity
fast multiscale sample entropy (fMSE)
complexity analysis
electroencephalogram (EEG)
multiscale sample entropy (MSE)
ISSN:1862-278X, 1862-278X
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Shrnutí: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.
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ISSN:1862-278X
1862-278X
DOI:10.1515/bmt-2017-0041