Application of Entropy Measures on Intrinsic Mode Functions for the Automated Identification of Focal Electroencephalogram Signals
The brain is a complex structure made up of interconnected neurons, and its electrical activities can be evaluated using electroencephalogram (EEG) signals. The characteristics of the brain area affected by partial epilepsy can be studied using focal and non-focal EEG signals. In this work, a method...
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| Format: | Article |
| Language: | English |
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MDPI AG
2015-02-01
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| Series: | Entropy |
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| Online Access: | http://www.mdpi.com/1099-4300/17/2/669 |
| _version_ | 1828115083904942080 |
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| author | Rajeev Sharma Ram Bilas Pachori U. Rajendra Acharya |
| author_facet | Rajeev Sharma Ram Bilas Pachori U. Rajendra Acharya |
| author_sort | Rajeev Sharma |
| collection | DOAJ |
| description | The brain is a complex structure made up of interconnected neurons, and its electrical activities can be evaluated using electroencephalogram (EEG) signals. The characteristics of the brain area affected by partial epilepsy can be studied using focal and non-focal EEG signals. In this work, a method for the classification of focal and non-focal EEG signals is presented using entropy measures. These entropy measures can be useful in assessing the nonlinear interrelation and complexity of focal and non-focal EEG signals. These EEG signals are first decomposed using the empirical mode decomposition (EMD) method to extract intrinsic mode functions (IMFs). The entropy features, namely, average Shannon entropy (ShEnAvg), average Renyi’s entropy (RenEnAvg ), average approximate entropy (ApEnAvg), average sample entropy (SpEnAvg) and average phase entropies (S1Avg and S2Avg), are computed from different IMFs of focal and non-focal EEG signals. These entropies are used as the input feature set for the least squares support vector machine (LS-SVM) classifier to classify into focal and non-focal EEG signals. Experimental results show that our proposed method is able to differentiate the focal and non-focal EEG signals with an average classification accuracy of 87% correct. |
| first_indexed | 2024-04-11T12:35:14Z |
| format | Article |
| id | doaj.art-23ef336527da4add926214f3f94aa9ab |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-11T12:35:14Z |
| publishDate | 2015-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-23ef336527da4add926214f3f94aa9ab2022-12-22T04:23:38ZengMDPI AGEntropy1099-43002015-02-0117266969110.3390/e17020669e17020669Application of Entropy Measures on Intrinsic Mode Functions for the Automated Identification of Focal Electroencephalogram SignalsRajeev Sharma0Ram Bilas Pachori1U. Rajendra Acharya2Discipline of Electrical Engineering, Indian Institute of Technology Indore, Indore 452017, IndiaDiscipline of Electrical Engineering, Indian Institute of Technology Indore, Indore 452017, IndiaDepartment of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, SingaporeThe brain is a complex structure made up of interconnected neurons, and its electrical activities can be evaluated using electroencephalogram (EEG) signals. The characteristics of the brain area affected by partial epilepsy can be studied using focal and non-focal EEG signals. In this work, a method for the classification of focal and non-focal EEG signals is presented using entropy measures. These entropy measures can be useful in assessing the nonlinear interrelation and complexity of focal and non-focal EEG signals. These EEG signals are first decomposed using the empirical mode decomposition (EMD) method to extract intrinsic mode functions (IMFs). The entropy features, namely, average Shannon entropy (ShEnAvg), average Renyi’s entropy (RenEnAvg ), average approximate entropy (ApEnAvg), average sample entropy (SpEnAvg) and average phase entropies (S1Avg and S2Avg), are computed from different IMFs of focal and non-focal EEG signals. These entropies are used as the input feature set for the least squares support vector machine (LS-SVM) classifier to classify into focal and non-focal EEG signals. Experimental results show that our proposed method is able to differentiate the focal and non-focal EEG signals with an average classification accuracy of 87% correct.http://www.mdpi.com/1099-4300/17/2/669electroencephalogramepilepsyentropyfeature extractionclassifier |
| spellingShingle | Rajeev Sharma Ram Bilas Pachori U. Rajendra Acharya Application of Entropy Measures on Intrinsic Mode Functions for the Automated Identification of Focal Electroencephalogram Signals Entropy electroencephalogram epilepsy entropy feature extraction classifier |
| title | Application of Entropy Measures on Intrinsic Mode Functions for the Automated Identification of Focal Electroencephalogram Signals |
| title_full | Application of Entropy Measures on Intrinsic Mode Functions for the Automated Identification of Focal Electroencephalogram Signals |
| title_fullStr | Application of Entropy Measures on Intrinsic Mode Functions for the Automated Identification of Focal Electroencephalogram Signals |
| title_full_unstemmed | Application of Entropy Measures on Intrinsic Mode Functions for the Automated Identification of Focal Electroencephalogram Signals |
| title_short | Application of Entropy Measures on Intrinsic Mode Functions for the Automated Identification of Focal Electroencephalogram Signals |
| title_sort | application of entropy measures on intrinsic mode functions for the automated identification of focal electroencephalogram signals |
| topic | electroencephalogram epilepsy entropy feature extraction classifier |
| url | http://www.mdpi.com/1099-4300/17/2/669 |
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