Multiple entropies performance measure for detection and localization of multi-channel epileptic EEG
•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...
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Published in | Biomedical signal processing and control Vol. 38; pp. 158 - 167 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
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01.09.2017
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Abstract | •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. |
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AbstractList | •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. |
Author | Mahadevappa, Manjunatha Ray, AjoyKumar Malokar, Monika Tibdewal, Manish N. Dey, Himanshu R. |
Author_xml | – sequence: 1 givenname: Manish N. surname: Tibdewal fullname: Tibdewal, Manish N. email: mntibdewal@gmail.com, mntibdewal@ssgmce.ac.in, mntibdewal@smst.iitkgp.ernet.in organization: School of Medical Science and Technology, Indian Institute of Technology, Kharagpur 721 302, W.B., India – sequence: 2 givenname: Himanshu R. surname: Dey fullname: Dey, Himanshu R. email: himanshu.dey09@gmail.com organization: Department of Electronics & Telecommunication Engineering, Shri Sant Gajanan Maharaj College of Engineering, Shegaon 444203, Maharashtra, India – sequence: 3 givenname: Manjunatha surname: Mahadevappa fullname: Mahadevappa, Manjunatha email: mmaha2@smst.iitkgp.ernet.in organization: School of Medical Science and Technology, Indian Institute of Technology, Kharagpur 721 302, W.B., India – sequence: 4 givenname: AjoyKumar surname: Ray fullname: Ray, AjoyKumar email: ajoy_ray2004@yahoo.com organization: Dept. of Electronics and Electrical Communication Engineering, Indian Institute of Technology, KHARAGPUR 721302 W.B., India – sequence: 5 givenname: Monika surname: Malokar fullname: Malokar, Monika email: mkanphade@yahoo.com organization: Malokar Hospital, Akola 444 001, Maharashtra, India |
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Keywords | Epilepsy Shannon entropy Approximate entropy Sample entropy Fuzzy entropy Detection Localization Multi-channel EEG Renyi entropy |
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SubjectTerms | Approximate entropy Detection Epilepsy Fuzzy entropy Localization Multi-channel EEG Renyi entropy Sample entropy Shannon entropy |
Title | Multiple entropies performance measure for detection and localization of multi-channel epileptic EEG |
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