{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T22:08:12Z","timestamp":1773180492612,"version":"3.50.1"},"reference-count":28,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2018,1,23]],"date-time":"2018-01-23T00:00:00Z","timestamp":1516665600000},"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 use of a deep neural network scheme is proposed to help clinicians solve a difficult diagnosis problem in neurology. The proposed multilayer architecture includes a feature engineering step (from time-frequency transformation), a double compressing stage trained by unsupervised learning, and a classification stage trained by supervised learning. After fine-tuning, the deep network is able to discriminate well the class of patients from controls with around 90% sensitivity and specificity. This deep model gives better classification performance than some other standard discriminative learning algorithms. As in clinical problems there is a need for explaining decisions, an effort has been carried out to qualitatively justify the classification results. The main novelty of this paper is indeed to give an entropic interpretation of how the deep scheme works and reach the final decision.<\/jats:p>","DOI":"10.3390\/e20020043","type":"journal-article","created":{"date-parts":[[2018,1,23]],"date-time":"2018-01-23T13:06:51Z","timestamp":1516712811000},"page":"43","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["Information Theoretic-Based Interpretation of a Deep Neural Network Approach in Diagnosing Psychogenic Non-Epileptic Seizures"],"prefix":"10.3390","volume":"20","author":[{"given":"Sara","family":"Gasparini","sequence":"first","affiliation":[{"name":"Department of Medical and Surgical Sciences, Magna Gr\u00e6cia University, 88100 Catanzaro, Italy"},{"name":"Regional Epilepsy Centre, Bianchi-Melacrino-Morelli Hospital, 89124 Reggio Calabria, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4197-3661","authenticated-orcid":false,"given":"Maurizio","family":"Campolo","sequence":"additional","affiliation":[{"name":"Dipartimento di Ingegneria Civile, dell\u2019Energia, dell\u2019Ambiente e dei Materiali, DICEAM Department, University Mediterranea of Reggio Calabria, 89124 Reggio Calabria, Italy"}]},{"given":"Cosimo","family":"Ieracitano","sequence":"additional","affiliation":[{"name":"Dipartimento di Ingegneria Civile, dell\u2019Energia, dell\u2019Ambiente e dei Materiali, DICEAM Department, University Mediterranea of Reggio Calabria, 89124 Reggio Calabria, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4962-3500","authenticated-orcid":false,"given":"Nadia","family":"Mammone","sequence":"additional","affiliation":[{"name":"Istituto di Ricovero e Cura a Carattere Scientifico, IRCCS Centro Neurolesi Bonino-Pulejo, 98124 Messina, Italy"}]},{"given":"Edoardo","family":"Ferlazzo","sequence":"additional","affiliation":[{"name":"Department of Medical and Surgical Sciences, Magna Gr\u00e6cia University, 88100 Catanzaro, Italy"},{"name":"Regional Epilepsy Centre, Bianchi-Melacrino-Morelli Hospital, 89124 Reggio Calabria, Italy"}]},{"given":"Chiara","family":"Sueri","sequence":"additional","affiliation":[{"name":"Regional Epilepsy Centre, Bianchi-Melacrino-Morelli Hospital, 89124 Reggio Calabria, Italy"}]},{"given":"Giovanbattista","family":"Tripodi","sequence":"additional","affiliation":[{"name":"Regional Epilepsy Centre, Bianchi-Melacrino-Morelli Hospital, 89124 Reggio Calabria, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4574-2951","authenticated-orcid":false,"given":"Umberto","family":"Aguglia","sequence":"additional","affiliation":[{"name":"Department of Medical and Surgical Sciences, Magna Gr\u00e6cia University, 88100 Catanzaro, Italy"},{"name":"Regional Epilepsy Centre, Bianchi-Melacrino-Morelli Hospital, 89124 Reggio Calabria, Italy"}]},{"given":"Francesco","family":"Morabito","sequence":"additional","affiliation":[{"name":"Dipartimento di Ingegneria Civile, dell\u2019Energia, dell\u2019Ambiente e dei Materiali, DICEAM Department, University Mediterranea of Reggio Calabria, 89124 Reggio Calabria, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2018,1,23]]},"reference":[{"key":"ref_1","unstructured":"Ban, G.-Y., Karoui, N.E., and Lim, A.E.B. (2016). Machine learning and portfolio optimization. Manag. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Sirignano, J., Sadhwani, A., and Giesecke, K. (2018, January 19). Deep Learning for Mortgage Risk. Available online: https:\/\/ssrn.com\/abstract=2799443.","DOI":"10.2139\/ssrn.2799443"},{"key":"ref_3","first-page":"656","article-title":"Convolutional recursive deep learning for 3D object classification","volume":"1","author":"Socher","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1059","DOI":"10.1002\/rob.20169","article-title":"Improving robot navigation through self-supervised online learning","volume":"23","author":"Sofman","year":"2006","journal-title":"J. Field Robot."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","article-title":"Deep learning in neural networks: An overview","volume":"61","author":"Schmidhuber","year":"2015","journal-title":"Neural Netw."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"036015","DOI":"10.1088\/1741-2560\/8\/3\/036015","article-title":"Modeling electroencephalography waveforms with semi-supervised deep belief nets: Fast classification and anomaly measurement","volume":"8","author":"Wulsin","year":"2011","journal-title":"J. Neural Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1927","DOI":"10.1016\/j.clinph.2009.09.002","article-title":"Classification of patterns of EEG synchronization for seizure prediction","volume":"120","author":"Mirowski","year":"2009","journal-title":"Clin. Neurophysiol."},{"key":"ref_9","unstructured":"Zhao, Y., and He, L. (2014). Deep learning in the EEG diagnosis of Alzheimer\u2019s disease. Asian Conference on Computer Vision, Springer."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1650039","DOI":"10.1142\/S0129065716500398","article-title":"Deep learning representation from electroencephalography of Early-Stage Creutzfeldt-Jakob disease and features for differentiation from rapidly progressive dementia","volume":"27","author":"Morabito","year":"2017","journal-title":"Int. J. Neural Syst."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Morabito, F.C., Campolo, M., Ieracitano, C., Ebadi, J.M., Bonanno, L., Bramanti, A., Desalvo, S., Mammone, N., and Bramanti, P. (2016, January 7\u20139). Deep convolutional neural networks for classification of mild cognitive impaired and Alzheimer\u2019s disease patients from scalp EEG recordings. Proceedings of the 2016 IEEE 2nd International Forum on Research and Technologies for Society and Industry Leveraging a Better Tomorrow (RTSI), Bologna, Italy.","DOI":"10.1109\/RTSI.2016.7740576"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1016\/j.seizure.2009.06.006","article-title":"Psychogenic non-epileptic seizures\u2014Definition, etiology, treatment and prognostic issues: A critical review","volume":"18","author":"Bodde","year":"2009","journal-title":"Seizure"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1212\/01.WNL.0000113755.11398.90","article-title":"Failure to recognize psychogenic nonepileptic seizures may cause death","volume":"62","author":"Reuber","year":"2004","journal-title":"Neurology"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1620","DOI":"10.1212\/01.wnl.0000224953.94807.be","article-title":"Avoiding the costs of unrecognized psychological nonepileptic seizures","volume":"66","author":"LaFrance","year":"2006","journal-title":"Neurology"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2005","DOI":"10.1111\/epi.12356","article-title":"Minimum requirements for the diagnosis of psychogenic nonepileptic seizures: A staged approach","volume":"54","author":"LaFrance","year":"2013","journal-title":"Epilepsia"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1038\/nrneurol.2011.24","article-title":"Differentiating between nonepileptic and epileptic seizures","volume":"7","author":"Devinsky","year":"2011","journal-title":"Nat. Rev. Neurol."},{"key":"ref_17","unstructured":"Bengio, Y., Goodfellow, I.J., and Courville, A. (2018, January 19). Deep Learning. Available online: https:\/\/icdm2016.eurecat.org\/wp-content\/uploads\/2016\/05\/ICDM-Barcelona-13Dec2016-YoshuaBengio.pdf."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Bengio, Y., Lamblin, P., Popovici, D., and Larochelle, H. (2006, January 4\u20137). Greedy layer-wise training of deep networks. Proceedings of the 19th International Conference on Neural Information Processing Systems, Vancouver, BC, Canada.","DOI":"10.7551\/mitpress\/7503.003.0024"},{"key":"ref_19","first-page":"625","article-title":"Why does unsupervised pre-training help deep learning?","volume":"11","author":"Erhan","year":"2010","journal-title":"J. Mach. Learn. Res."},{"key":"ref_20","first-page":"1","article-title":"Exploring strategies for training deep neural networks","volume":"10","author":"Larochelle","year":"2009","journal-title":"J. Mach. Learn. Res."},{"key":"ref_21","unstructured":"Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R.R. (2012). Improving Neural Networks by Preventing Co-Adaptation of Feature Detectors. arXiv, Available online: https:\/\/arxiv.org\/abs\/1207.0580."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"45","DOI":"10.4103\/0301-4738.37595","article-title":"Understanding and using sensitivity, specificity and predictive values","volume":"56","author":"Parikh","year":"2008","journal-title":"Indian J. Ophthalmol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1023\/A:1009715923555","article-title":"A tutorial on support vector machines for pattern recognition","volume":"2","author":"Burges","year":"1998","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_24","unstructured":"McLachlan, G. (2004). Discriminant Analysis and Statistical Pattern Recognition, John Wiley & Sons."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1136\/jnnp-2011-300776","article-title":"Functional connectivity of dissociation in patients with psychogenic non-epileptic seizures","volume":"83","author":"Bodde","year":"2012","journal-title":"J. Neurol Neurosurg. Psychiatr."},{"key":"ref_26","first-page":"3371","article-title":"Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion","volume":"11","author":"Vincent","year":"2010","journal-title":"J. Mach. Learn. Res."},{"key":"ref_27","unstructured":"Shwartz-Ziv, R., and Tishby, N. (2017). Opening the Black Box of Deep Neural Networks via Information. arXiv, Available online: https:\/\/arxiv.org\/abs\/1703.00810."},{"key":"ref_28","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/20\/2\/43\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T14:52:16Z","timestamp":1760194336000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/20\/2\/43"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,1,23]]},"references-count":28,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2018,2]]}},"alternative-id":["e20020043"],"URL":"https:\/\/doi.org\/10.3390\/e20020043","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,1,23]]}}}