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Classification of Depressive and Schizophrenic Episodes Using Night-Time Motor Activity Signal

Major depressive disorder (MDD) is the most recurrent mental illness globally, affecting approximately 5% of adults. Furthermore, according to the National Institute of Mental Health (NIMH) of the U.S., calculating an actual schizophrenia prevalence rate is challenging because of this illness’s unde...

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Autores principales: Rodríguez-Ruiz, Julieta G., Galván-Tejada, Carlos E., Luna-García, Huizilopoztli, Gamboa-Rosales, Hamurabi, Celaya-Padilla, José M., Arceo-Olague, José G., Galván Tejada, Jorge I.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9318635/
https://www.ncbi.nlm.nih.gov/pubmed/35885784
http://dx.doi.org/10.3390/healthcare10071256
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author Rodríguez-Ruiz, Julieta G.
Galván-Tejada, Carlos E.
Luna-García, Huizilopoztli
Gamboa-Rosales, Hamurabi
Celaya-Padilla, José M.
Arceo-Olague, José G.
Galván Tejada, Jorge I.
author_facet Rodríguez-Ruiz, Julieta G.
Galván-Tejada, Carlos E.
Luna-García, Huizilopoztli
Gamboa-Rosales, Hamurabi
Celaya-Padilla, José M.
Arceo-Olague, José G.
Galván Tejada, Jorge I.
author_sort Rodríguez-Ruiz, Julieta G.
collection PubMed
description Major depressive disorder (MDD) is the most recurrent mental illness globally, affecting approximately 5% of adults. Furthermore, according to the National Institute of Mental Health (NIMH) of the U.S., calculating an actual schizophrenia prevalence rate is challenging because of this illness’s underdiagnosis. Still, most current global metrics hover between 0.33% and 0.75%. Machine-learning scientists use data from diverse sources to analyze, classify, or predict to improve the psychiatric attention, diagnosis, and treatment of MDD, schizophrenia, and other psychiatric conditions. Motor activity data are gaining popularity in mental illness diagnosis assistance because they are a cost-effective and noninvasive method. In the knowledge discovery in databases (KDD) framework, a model to classify depressive and schizophrenic patients from healthy controls is constructed using accelerometer data. Taking advantage of the multiple sleep disorders caused by mental disorders, the main objective is to increase the model’s accuracy by employing only data from night-time activity. To compare the classification between the stages of the day and improve the accuracy of the classification, the total activity signal was cut into hourly time lapses and then grouped into subdatasets depending on the phases of the day: morning (06:00–11:59), afternoon (12:00–17:59), evening (18:00–23:59), and night (00:00–05:59). Random forest classifier (RFC) is the algorithm proposed for multiclass classification, and it uses accuracy, recall, precision, the Matthews correlation coefficient, and F1 score to measure its efficiency. The best model was night-featured data and RFC, with 98% accuracy for the classification of three classes. The effectiveness of this experiment leads to less monitoring time for patients, reducing stress and anxiety, producing more efficient models, using wearables, and increasing the amount of data.
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spelling pubmed-93186352022-07-27 Classification of Depressive and Schizophrenic Episodes Using Night-Time Motor Activity Signal Rodríguez-Ruiz, Julieta G. Galván-Tejada, Carlos E. Luna-García, Huizilopoztli Gamboa-Rosales, Hamurabi Celaya-Padilla, José M. Arceo-Olague, José G. Galván Tejada, Jorge I. Healthcare (Basel) Article Major depressive disorder (MDD) is the most recurrent mental illness globally, affecting approximately 5% of adults. Furthermore, according to the National Institute of Mental Health (NIMH) of the U.S., calculating an actual schizophrenia prevalence rate is challenging because of this illness’s underdiagnosis. Still, most current global metrics hover between 0.33% and 0.75%. Machine-learning scientists use data from diverse sources to analyze, classify, or predict to improve the psychiatric attention, diagnosis, and treatment of MDD, schizophrenia, and other psychiatric conditions. Motor activity data are gaining popularity in mental illness diagnosis assistance because they are a cost-effective and noninvasive method. In the knowledge discovery in databases (KDD) framework, a model to classify depressive and schizophrenic patients from healthy controls is constructed using accelerometer data. Taking advantage of the multiple sleep disorders caused by mental disorders, the main objective is to increase the model’s accuracy by employing only data from night-time activity. To compare the classification between the stages of the day and improve the accuracy of the classification, the total activity signal was cut into hourly time lapses and then grouped into subdatasets depending on the phases of the day: morning (06:00–11:59), afternoon (12:00–17:59), evening (18:00–23:59), and night (00:00–05:59). Random forest classifier (RFC) is the algorithm proposed for multiclass classification, and it uses accuracy, recall, precision, the Matthews correlation coefficient, and F1 score to measure its efficiency. The best model was night-featured data and RFC, with 98% accuracy for the classification of three classes. The effectiveness of this experiment leads to less monitoring time for patients, reducing stress and anxiety, producing more efficient models, using wearables, and increasing the amount of data. MDPI 2022-07-05 /pmc/articles/PMC9318635/ /pubmed/35885784 http://dx.doi.org/10.3390/healthcare10071256 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Rodríguez-Ruiz, Julieta G.
Galván-Tejada, Carlos E.
Luna-García, Huizilopoztli
Gamboa-Rosales, Hamurabi
Celaya-Padilla, José M.
Arceo-Olague, José G.
Galván Tejada, Jorge I.
Classification of Depressive and Schizophrenic Episodes Using Night-Time Motor Activity Signal
title Classification of Depressive and Schizophrenic Episodes Using Night-Time Motor Activity Signal
title_full Classification of Depressive and Schizophrenic Episodes Using Night-Time Motor Activity Signal
title_fullStr Classification of Depressive and Schizophrenic Episodes Using Night-Time Motor Activity Signal
title_full_unstemmed Classification of Depressive and Schizophrenic Episodes Using Night-Time Motor Activity Signal
title_short Classification of Depressive and Schizophrenic Episodes Using Night-Time Motor Activity Signal
title_sort classification of depressive and schizophrenic episodes using night-time motor activity signal
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9318635/
https://www.ncbi.nlm.nih.gov/pubmed/35885784
http://dx.doi.org/10.3390/healthcare10071256
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