Multiple entropies performance measure for detection and localization of multi-channel epileptic EEG

• Published in: Biomedical signal processing and control Vol. 38; pp. 158 - 167
• Main Authors: Tibdewal, Manish N., Dey, Himanshu R., Mahadevappa, Manjunatha, Ray, AjoyKumar, Malokar, Monika
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.09.2017
• Subjects: Approximate entropy; Detection; Epilepsy; Fuzzy entropy; Localization; Multi-channel EEG; Renyi entropy; Sample entropy; Shannon entropy; Epilepsy; Shannon entropy; Approximate entropy; Sample entropy; Fuzzy entropy; Detection; Localization; Multi-channel EEG; Renyi entropy
• ISSN: 1746-8094; 1746-8108
• DOI: 10.1016/j.bspc.2017.05.002

•Statistical EEG signal modeling is done based on variance and multiple entropies for multiple channels of Epileptic/Non-epileptic EEG for ‘epilepsy’ detection and its localization.•Fuzzy Entropy discriminates between epileptic EEG and non-epileptic EEG more efficiently with a minimum p-value (0.001) compared to other entropy estimators.•The Fuzzy entropy gives better stability and consistency with highest discriminating ability compared to other entropy estimators.•Results for detection of epilepsy and affected region for localization through FuzzyEn are cross-validated with Neuro-physician remark. The Electroencephalogram (EEG) signal is a time series depictive signal that contains the useful knowledge about the state of the brain. It has high temporal resolution for detection of chronic brain disorders such as epilepsy/seizure, dementia, sleep apnea, schizophrenia, etc. In this work, EEG is a prime concern for seizure/epilepsy detection and localization. Entropy estimator is a good solution to this problem. Here, the time series complexity analysis of brain signal is carried using five different entropy estimators: Shannon Entropy, Renyi Entropy, Approximate Entropy, Sample Entropy, and Fuzzy Entropy. The average entropy values of EEG signal is significantly found lower for epileptic data sets compared to non-epileptic EEG. Experimental results evaluated for discriminating ability of each entropy measure demonstrated that among all entropies, Fuzzy Entropy discriminates between epileptic EEG and non-epileptic EEG more efficiently with a minimum p-value (0.001) compared to other four entropy estimators. Fuzzy Entropy defines the similarity between two vectors fuzzily on the basis of exponential function. Unlike to Approximate and Sample Entropy, the Fuzzy Entropy is free from parameter limitations and offers efficient results even for the small tolerance (r<0.008) which is found to be more stable. The time required for computation of all entropies for 16s EEG time series of all channels is also estimated and compared. The Fuzzy entropy gives better stability and consistency with highest discriminating ability compared to other entropy estimators. Eventually, results for detection and localization of epilepsy for affected channel and region through the variance and FuzzyEn are cross-validated by expert Neuro-physician.