Cargando…

Neural Network Based Mental Depression Identification and Sentiments Classification Technique From Speech Signals: A COVID-19 Focused Pandemic Study

COVID-19 (SARS-CoV-2) was declared as a global pandemic by the World Health Organization (WHO) in February 2020. This led to previously unforeseen measures that aimed to curb its spread, such as the lockdown of cities, districts, and international travel. Various researchers and institutions have fo...

Descripción completa

Detalles Bibliográficos
Autores principales: Ahmed, Syed Thouheed, Singh, Dollar Konjengbam, Basha, Syed Muzamil, Abouel Nasr, Emad, Kamrani, Ali K., Aboudaif, Mohamed K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8685216/
https://www.ncbi.nlm.nih.gov/pubmed/34938711
http://dx.doi.org/10.3389/fpubh.2021.781827
_version_ 1784617785603653632
author Ahmed, Syed Thouheed
Singh, Dollar Konjengbam
Basha, Syed Muzamil
Abouel Nasr, Emad
Kamrani, Ali K.
Aboudaif, Mohamed K.
author_facet Ahmed, Syed Thouheed
Singh, Dollar Konjengbam
Basha, Syed Muzamil
Abouel Nasr, Emad
Kamrani, Ali K.
Aboudaif, Mohamed K.
author_sort Ahmed, Syed Thouheed
collection PubMed
description COVID-19 (SARS-CoV-2) was declared as a global pandemic by the World Health Organization (WHO) in February 2020. This led to previously unforeseen measures that aimed to curb its spread, such as the lockdown of cities, districts, and international travel. Various researchers and institutions have focused on multidimensional opportunities and solutions in encountering the COVID-19 pandemic. This study focuses on mental health and sentiment validations caused by the global lockdowns across the countries, resulting in a mental disability among individuals. This paper discusses a technique for identifying the mental state of an individual by sentiment analysis of feelings such as anxiety, depression, and loneliness caused by isolation and pauses to the normal chains of operations in daily life. The research uses a Neural Network (NN) to resolve and extract patterns and validate threshold trained datasets for decision making. This technique was used to validate 2,173 global speech samples, and the resulting accuracy of mental state and sentiments are identified with 93.5% accuracy in classifying the behavioral patterns of patients suffering from COVID-19 and pandemic-influenced depression.
format Online
Article
Text
id pubmed-8685216
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-86852162021-12-21 Neural Network Based Mental Depression Identification and Sentiments Classification Technique From Speech Signals: A COVID-19 Focused Pandemic Study Ahmed, Syed Thouheed Singh, Dollar Konjengbam Basha, Syed Muzamil Abouel Nasr, Emad Kamrani, Ali K. Aboudaif, Mohamed K. Front Public Health Public Health COVID-19 (SARS-CoV-2) was declared as a global pandemic by the World Health Organization (WHO) in February 2020. This led to previously unforeseen measures that aimed to curb its spread, such as the lockdown of cities, districts, and international travel. Various researchers and institutions have focused on multidimensional opportunities and solutions in encountering the COVID-19 pandemic. This study focuses on mental health and sentiment validations caused by the global lockdowns across the countries, resulting in a mental disability among individuals. This paper discusses a technique for identifying the mental state of an individual by sentiment analysis of feelings such as anxiety, depression, and loneliness caused by isolation and pauses to the normal chains of operations in daily life. The research uses a Neural Network (NN) to resolve and extract patterns and validate threshold trained datasets for decision making. This technique was used to validate 2,173 global speech samples, and the resulting accuracy of mental state and sentiments are identified with 93.5% accuracy in classifying the behavioral patterns of patients suffering from COVID-19 and pandemic-influenced depression. Frontiers Media S.A. 2021-12-06 /pmc/articles/PMC8685216/ /pubmed/34938711 http://dx.doi.org/10.3389/fpubh.2021.781827 Text en Copyright © 2021 Ahmed, Singh, Basha, Abouel Nasr, Kamrani and Aboudaif. https://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 Public Health
Ahmed, Syed Thouheed
Singh, Dollar Konjengbam
Basha, Syed Muzamil
Abouel Nasr, Emad
Kamrani, Ali K.
Aboudaif, Mohamed K.
Neural Network Based Mental Depression Identification and Sentiments Classification Technique From Speech Signals: A COVID-19 Focused Pandemic Study
title Neural Network Based Mental Depression Identification and Sentiments Classification Technique From Speech Signals: A COVID-19 Focused Pandemic Study
title_full Neural Network Based Mental Depression Identification and Sentiments Classification Technique From Speech Signals: A COVID-19 Focused Pandemic Study
title_fullStr Neural Network Based Mental Depression Identification and Sentiments Classification Technique From Speech Signals: A COVID-19 Focused Pandemic Study
title_full_unstemmed Neural Network Based Mental Depression Identification and Sentiments Classification Technique From Speech Signals: A COVID-19 Focused Pandemic Study
title_short Neural Network Based Mental Depression Identification and Sentiments Classification Technique From Speech Signals: A COVID-19 Focused Pandemic Study
title_sort neural network based mental depression identification and sentiments classification technique from speech signals: a covid-19 focused pandemic study
topic Public Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8685216/
https://www.ncbi.nlm.nih.gov/pubmed/34938711
http://dx.doi.org/10.3389/fpubh.2021.781827
work_keys_str_mv AT ahmedsyedthouheed neuralnetworkbasedmentaldepressionidentificationandsentimentsclassificationtechniquefromspeechsignalsacovid19focusedpandemicstudy
AT singhdollarkonjengbam neuralnetworkbasedmentaldepressionidentificationandsentimentsclassificationtechniquefromspeechsignalsacovid19focusedpandemicstudy
AT bashasyedmuzamil neuralnetworkbasedmentaldepressionidentificationandsentimentsclassificationtechniquefromspeechsignalsacovid19focusedpandemicstudy
AT abouelnasremad neuralnetworkbasedmentaldepressionidentificationandsentimentsclassificationtechniquefromspeechsignalsacovid19focusedpandemicstudy
AT kamranialik neuralnetworkbasedmentaldepressionidentificationandsentimentsclassificationtechniquefromspeechsignalsacovid19focusedpandemicstudy
AT aboudaifmohamedk neuralnetworkbasedmentaldepressionidentificationandsentimentsclassificationtechniquefromspeechsignalsacovid19focusedpandemicstudy