In this paper, we have employed K-d tree algorithmic based Multiscale entropy analysis (MSE) to distinguish the alcoholic subjects from the non-alcoholic. Traditional MSE technique 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 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. The performance was also measured in term of specificity, sensitivity, total accuracy and receiver operating characteristics (ROC). Our findings show that fast MSE, with 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 affecting at multiple time scales.
