{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,2]],"date-time":"2026-08-02T04:44:38Z","timestamp":1785645878756,"version":"3.56.0"},"reference-count":75,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2015,2,3]],"date-time":"2015-02-03T00:00:00Z","timestamp":1422921600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>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\u2019s 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.<\/jats:p>","DOI":"10.3390\/e17020669","type":"journal-article","created":{"date-parts":[[2015,2,3]],"date-time":"2015-02-03T09:20:19Z","timestamp":1422955219000},"page":"669-691","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":279,"title":["Application of Entropy Measures on Intrinsic Mode Functions for the Automated Identification of Focal Electroencephalogram Signals"],"prefix":"10.3390","volume":"17","author":[{"given":"Rajeev","family":"Sharma","sequence":"first","affiliation":[{"name":"Discipline of Electrical Engineering, Indian Institute of Technology Indore, Indore 452017, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ram","family":"Pachori","sequence":"additional","affiliation":[{"name":"Discipline of Electrical Engineering, Indian Institute of Technology Indore, Indore 452017, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2689-8552","authenticated-orcid":false,"given":"U.","family":"Acharya","sequence":"additional","affiliation":[{"name":"Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2015,2,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1016\/j.neulet.2008.08.008","article-title":"A spatio-temporal wavelet-chaos methodology for EEG-based diagnosis of Alzheimer\u2019s disease","volume":"444","author":"Adeli","year":"2008","journal-title":"Neurosci. 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