Akademik Veri Yönetim Sistemi
International journal of neural systems, cilt.32, sa.5, ss.2250018, 2022 (SCI-Expanded)
In recent years, some electrophysiological analysis methods of consciousness have been proposed. Most
of these studies are based on visual interpretation or statistical analysis, and there is hardly any work
classifying 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 in
different 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 of
consciousness 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 patients
with Glasgow Coma Scale (GCS) between 3 and 8 was performed. Various entropy values (approximate
entropy, permutation entropy, etc.) obtained from different algorithms, Hjorth parameters, Lempel–Ziv
complexity and Kolmogorov complexity values were extracted from the signals as features. The features
were analyzed statistically and the success of features in classifying different levels of consciousness was
measured by various classifiers. Consequently, levels of consciousness in deep coma (GCS between 3 and
8) 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 in
distinguishing different GCSs of comatose patients.
Keywords: Complexity measures; Glasgow coma scale; level of consciousness; Electroencephalogram;
EEG; entropy; Lempel–Ziv complexity; classification