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Entropy 2018, 20(2), 43; https://doi.org/10.3390/e20020043

Information Theoretic-Based Interpretation of a Deep Neural Network Approach in Diagnosing Psychogenic Non-Epileptic Seizures

1
Department of Medical and Surgical Sciences, Magna Græcia University, 88100 Catanzaro, Italy
2
Regional Epilepsy Centre, Bianchi-Melacrino-Morelli Hospital, 89124 Reggio Calabria, Italy
3
Dipartimento di Ingegneria Civile, dell’Energia, dell’Ambiente e dei Materiali, DICEAM Department, University Mediterranea of Reggio Calabria, 89124 Reggio Calabria, Italy
4
Istituto di Ricovero e Cura a Carattere Scientifico, IRCCS Centro Neurolesi Bonino-Pulejo, 98124 Messina, Italy
*
Author to whom correspondence should be addressed.
Received: 28 November 2017 / Revised: 12 January 2018 / Accepted: 19 January 2018 / Published: 23 January 2018
(This article belongs to the Special Issue Information Theory Applied to Physiological Signals)
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Abstract

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. View Full-Text
Keywords: psychogenic non-epileptic seizures; deep learning; stacked autoencoders; information theory; entropy psychogenic non-epileptic seizures; deep learning; stacked autoencoders; information theory; entropy
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Gasparini, S.; Campolo, M.; Ieracitano, C.; Mammone, N.; Ferlazzo, E.; Sueri, C.; Tripodi, G.G.; Aguglia, U.; Morabito, F.C. Information Theoretic-Based Interpretation of a Deep Neural Network Approach in Diagnosing Psychogenic Non-Epileptic Seizures. Entropy 2018, 20, 43.

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