{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,20]],"date-time":"2025-12-20T21:54:09Z","timestamp":1766267649747},"reference-count":105,"publisher":"Bentham Science Publishers Ltd.","issue":"1","license":[{"start":{"date-parts":[[2021,6,7]],"date-time":"2021-06-07T00:00:00Z","timestamp":1623024000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"content-domain":{"domain":["openbiomedicalengineeringjournal.com","benthamopen.com"],"crossmark-restriction":true},"short-container-title":["TOBEJ"],"published-print":{"date-parts":[[2021,6,7]]},"abstract":"<jats:sec>\n          <jats:title>Background:<\/jats:title>\n        <jats:p>Because about 30% of epileptic patients suffer from refractory epilepsy, an efficient automatic seizure prediction tool is in great demand to improve their life quality.<\/jats:p>\n    <\/jats:sec>\n        <jats:sec>\n          <jats:title>Methods:<\/jats:title>\n        <jats:p>In this work, time-domain discriminating preictal and interictal features were efficiently extracted from the intracranial electroencephalogram of twelve patients, i.e., six with temporal and six with frontal lobe epilepsy. The performance of three types of feature selection methods was compared using Matthews\u2019s correlation coefficient (MCC).<\/jats:p>\n    <\/jats:sec>\n    <jats:sec>\n          <jats:title>Results:<\/jats:title>\n        <jats:p>Kruskal Wallis, a non-parametric approach, was found to perform better than the other approaches due to a simple and less resource consuming strategy as well as maintaining the highest MCC score. The impact of dividing the electroencephalogram signals into various sub-bands was investigated as well. The highest performance of Kruskal Wallis may suggest considering the importance of univariate features like complexity and interquartile ratio (IQR), along with autoregressive (AR) model parameters and the maximum (MAX) cross-correlation to efficiently predict epileptic seizures.<\/jats:p>\n    <\/jats:sec>\n    <jats:sec>\n          <jats:title>Conclusion:<\/jats:title>\n        <jats:p>The proposed approach has the potential to be implemented on a low power device by considering a few simple time domain characteristics for a specific sub-band. It should be noted that, as there is not a great deal of literature on frontal lobe epilepsy, the results of this work can be considered promising.<\/jats:p>\n    <\/jats:sec>","DOI":"10.2174\/1874120702115010001","type":"journal-article","created":{"date-parts":[[2021,6,7]],"date-time":"2021-06-07T11:36:30Z","timestamp":1623065790000},"page":"1-15","update-policy":"http:\/\/dx.doi.org\/10.2174\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Feature Selection Techniques for the Analysis of Discriminative Features in Temporal and Frontal Lobe Epilepsy: A Comparative Study"],"prefix":"10.2174","volume":"15","author":[{"given":"Behrooz","family":"Abbaszadeh","sequence":"first","affiliation":[]},{"given":"Cesar Alexandre Domingues","family":"Teixeira","sequence":"additional","affiliation":[]},{"given":"Mustapha C.E.","family":"Yagoub","sequence":"additional","affiliation":[]}],"member":"965","reference":[{"key":"ref1","doi-asserted-by":"publisher","unstructured":"Ehrens D, Assaf F, Cowan NJ, Sarma SV, Schiller Y. \n          Ultra Broad Band Neural Activity Portends Seizure Onset in a Rat Model of Epilepsy \n          Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS  \n          2018, ; \n          2276-9.","DOI":"10.1109\/EMBC.2018.8512769"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"612","DOI":"10.1016\/S0140-6736(19)30360-5","volume":"393","year":"2019","unstructured":"The Lancet. \n        \tFrom wonder and fear: make epilepsy a global health priority \n          The Lancet  \n          2019; \n          393\n          (10172)\n          : 612.","journal-title":"The Lancet"},{"key":"ref3","doi-asserted-by":"publisher","unstructured":"Abbaszadeh B, Fard RS, Yagoub MCE. \n          Application of Global Coherence Measure to Characterize Coordinated Neural Activity during Frontal and Temporal Lobe Epilepsy \n          Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS  \n          2020, ; \n          3699-702.","DOI":"10.1109\/EMBC44109.2020.9176486"},{"key":"ref4","unstructured":"Hussein R, Ahmed MO, Ward R, Wang ZJ, Kuhlmann L, Guo Y. \n          Human Intracranial EEG Quantitative Analysis and Automatic Feature Learning for Epileptic Seizure Prediction \n          2021.\n          http:\/\/arxiv.org\/abs\/1904.03603"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.103671","volume":"119","author":"Yang S.","year":"2020","unstructured":"Yang S, Li B, Zhang Y, et al. \n          Selection of features for patient-independent detection of seizure events using scalp EEG signals. \n          Comput Biol Med  \n          2020; \n          119\n          103671","journal-title":"Comput. 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