EEG entropies as estimators for the diagnosis of encephalopathy
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Brain consists of a network of millions of neurons and the neural activities of the brain are clearly pictured in its signal, electroencephalogram (EEG). Many pathological conditions of brain can be studied in detail by inspecting the EEG signal in detail rather than just visual inspection. Non linear analysis has proved to be an efficient method for exploring the subtle information embedded in EEG. Approximate entropy and sample entropy are utilized in this study for comparing EEGs of patients with a neurological disease called encephalopathy, with normal EEGs. Both entropies were found to be significantly less (p < 0.01; independent sample t test) for encephalopathy group than normal healthy controls. Support vector machine, multilayer perceptron and random forest classifiers have been employed for identifying disease groups based on the EEG entropies and their performance were evaluated. Random forest classifier gave the maximum accuracy of 90% while multilayer perceptron and SVM classifier gave an accuracy of 87% and 84% respectively. The optimum performance was obtained by combining both approximate entropies and sample entropies as features to the classifiers, than using individual set of features. Thus, this work emphasizes that entropies of EEG are good bio-markers for the diagnosis of encephalopathy and that non linear analysis techniques should be employed for analyzing EEG signals.
KeywordsEEG Encephalopathy Approximate entropy Sample entropy Random forest classifier Multi-layer perceptron classifier Support vector machine classifier
Authors would like to thank Dr. Thomas Iype, Professor and Head of Neurology, Government Medical College, Thiruvananthapuram and Dr. Ajith Cherian, Associate Professor, SCTIMST, Thiruvananthapuram for their careful verification of EEG recordings and validation of data collected for this work, effective discussions about various neurological diseases and for giving us information about various methods of EEG acquisition. Authors would like to thank the authorities of Government Medical College, Thiruvananthapuram for giving permission for EEG data collection needed for this study.
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