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Plausibility of a Neural Network Classifier-Based Neuroprosthesis for Depression Detection via Laughter Records

The present work explores the diagnostic performance for depression of neural network classifiers analyzing the sound structures of laughter as registered from clinical patients and healthy controls. The main methodological novelty of this work is that simple sound variables of laughter are used as...

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Autores principales: Navarro, Jorge, Fernández Rosell, Mercedes, Castellanos, Angel, del Moral, Raquel, Lahoz-Beltra, Rafael, Marijuán, Pedro C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6437104/
https://www.ncbi.nlm.nih.gov/pubmed/30949025
http://dx.doi.org/10.3389/fnins.2019.00267
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author Navarro, Jorge
Fernández Rosell, Mercedes
Castellanos, Angel
del Moral, Raquel
Lahoz-Beltra, Rafael
Marijuán, Pedro C.
author_facet Navarro, Jorge
Fernández Rosell, Mercedes
Castellanos, Angel
del Moral, Raquel
Lahoz-Beltra, Rafael
Marijuán, Pedro C.
author_sort Navarro, Jorge
collection PubMed
description The present work explores the diagnostic performance for depression of neural network classifiers analyzing the sound structures of laughter as registered from clinical patients and healthy controls. The main methodological novelty of this work is that simple sound variables of laughter are used as inputs, instead of electrophysiological signals or local field potentials (LFPs) or spoken language utterances, which are the usual protocols up-to-date. In the present study, involving 934 laughs from 30 patients and 20 controls, four different neural networks models were tested for sensitivity analysis, and were additionally trained for depression detection. Some elementary sound variables were extracted from the records: timing, fundamental frequency mean, first three formants, average power, and the Shannon-Wiener entropy. In the results obtained, two of the neural networks show a diagnostic discrimination capability of 93.02 and 91.15% respectively, while the third and fourth ones have an 87.96 and 82.40% percentage of success. Remarkably, entropy turns out to be a fundamental variable to distinguish between patients and controls, and this is a significant factor which becomes essential to understand the deep neurocognitive relationships between laughter and depression. In biomedical terms, our neural network classifier-based neuroprosthesis opens up the possibility of applying the same methodology to other mental-health and neuropsychiatric pathologies. Indeed, exploring the application of laughter in the early detection and prognosis of Alzheimer and Parkinson would represent an enticing possibility, both from the biomedical and the computational points of view.
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spelling pubmed-64371042019-04-04 Plausibility of a Neural Network Classifier-Based Neuroprosthesis for Depression Detection via Laughter Records Navarro, Jorge Fernández Rosell, Mercedes Castellanos, Angel del Moral, Raquel Lahoz-Beltra, Rafael Marijuán, Pedro C. Front Neurosci Neuroscience The present work explores the diagnostic performance for depression of neural network classifiers analyzing the sound structures of laughter as registered from clinical patients and healthy controls. The main methodological novelty of this work is that simple sound variables of laughter are used as inputs, instead of electrophysiological signals or local field potentials (LFPs) or spoken language utterances, which are the usual protocols up-to-date. In the present study, involving 934 laughs from 30 patients and 20 controls, four different neural networks models were tested for sensitivity analysis, and were additionally trained for depression detection. Some elementary sound variables were extracted from the records: timing, fundamental frequency mean, first three formants, average power, and the Shannon-Wiener entropy. In the results obtained, two of the neural networks show a diagnostic discrimination capability of 93.02 and 91.15% respectively, while the third and fourth ones have an 87.96 and 82.40% percentage of success. Remarkably, entropy turns out to be a fundamental variable to distinguish between patients and controls, and this is a significant factor which becomes essential to understand the deep neurocognitive relationships between laughter and depression. In biomedical terms, our neural network classifier-based neuroprosthesis opens up the possibility of applying the same methodology to other mental-health and neuropsychiatric pathologies. Indeed, exploring the application of laughter in the early detection and prognosis of Alzheimer and Parkinson would represent an enticing possibility, both from the biomedical and the computational points of view. Frontiers Media S.A. 2019-03-21 /pmc/articles/PMC6437104/ /pubmed/30949025 http://dx.doi.org/10.3389/fnins.2019.00267 Text en Copyright © 2019 Navarro, Fernández Rosell, Castellanos, del Moral, Lahoz-Beltra and Marijuán. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Navarro, Jorge
Fernández Rosell, Mercedes
Castellanos, Angel
del Moral, Raquel
Lahoz-Beltra, Rafael
Marijuán, Pedro C.
Plausibility of a Neural Network Classifier-Based Neuroprosthesis for Depression Detection via Laughter Records
title Plausibility of a Neural Network Classifier-Based Neuroprosthesis for Depression Detection via Laughter Records
title_full Plausibility of a Neural Network Classifier-Based Neuroprosthesis for Depression Detection via Laughter Records
title_fullStr Plausibility of a Neural Network Classifier-Based Neuroprosthesis for Depression Detection via Laughter Records
title_full_unstemmed Plausibility of a Neural Network Classifier-Based Neuroprosthesis for Depression Detection via Laughter Records
title_short Plausibility of a Neural Network Classifier-Based Neuroprosthesis for Depression Detection via Laughter Records
title_sort plausibility of a neural network classifier-based neuroprosthesis for depression detection via laughter records
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6437104/
https://www.ncbi.nlm.nih.gov/pubmed/30949025
http://dx.doi.org/10.3389/fnins.2019.00267
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