Classification of Depth of Coma Using Complexity Measures and Nonlinear Features of Electroencephalogram Signals.

In recent years, some electrophysiological analysis methods of consciousness have been proposed. Mostof these studies are based on visual interpretation or statistical analysis, and there is hardly any workclassifying the level of consciousness in a deep coma. In this study, we perform an analysis of electroencephalography complexity measures by quantifying features efficiency in differentiating patients indifferent consciousness levels. Several measures of complexity have been proposed to quantify the complexity of signals. Our aim is to lay the foundation of a system that will objectively define the level ofconsciousness by performing a complexity analysis of Electroencephalogram (EEG) signals. Therefore,a nonlinear analysis of EEG signals obtained with a recording scheme proposed by us from 39 patientswith Glasgow Coma Scale (GCS) between 3 and 8 was performed. Various entropy values (approximateentropy, permutation entropy, etc.) obtained from different algorithms, Hjorth parameters, Lempel–Zivcomplexity and Kolmogorov complexity values were extracted from the signals as features. The featureswere analyzed statistically and the success of features in classifying different levels of consciousness wasmeasured by various classifiers. Consequently, levels of consciousness in deep coma (GCS between 3 and8) were classified with an accuracy of 90.3%. To the authors’ best knowledge, this is the first demonstration of the discriminative nonlinear features extracted from tactile and auditory stimuli EEG signals indistinguishing different GCSs of comatose patients.Keywords: Complexity measures; Glasgow coma scale; level of consciousness; Electroencephalogram;EEG; entropy; Lempel–Ziv complexity; classification