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The Actigraphy-Based Identification of Premorbid Latent Liability of Schizophrenia and Bipolar Disorder

(1) Background and Goal: Several studies have investigated the association of sleep, diurnal patterns, and circadian rhythms with the presence and with the risk states of mental illnesses such as schizophrenia and bipolar disorder. The goal of our study was to examine actigraphic measures to identif...

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Autores principales: Nagy, Ádám, Dombi, József, Fülep, Martin Patrik, Rudics, Emese, Hompoth, Emőke Adrienn, Szabó, Zoltán, Dér, András, Búzás, András, Viharos, Zsolt János, Hoang, Anh Tuan, Maczák, Bálint, Vadai, Gergely, Gingl, Zoltán, László, Szandra, Bilicki, Vilmos, Szendi, István
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9863012/
https://www.ncbi.nlm.nih.gov/pubmed/36679755
http://dx.doi.org/10.3390/s23020958
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author Nagy, Ádám
Dombi, József
Fülep, Martin Patrik
Rudics, Emese
Hompoth, Emőke Adrienn
Szabó, Zoltán
Dér, András
Búzás, András
Viharos, Zsolt János
Hoang, Anh Tuan
Maczák, Bálint
Vadai, Gergely
Gingl, Zoltán
László, Szandra
Bilicki, Vilmos
Szendi, István
author_facet Nagy, Ádám
Dombi, József
Fülep, Martin Patrik
Rudics, Emese
Hompoth, Emőke Adrienn
Szabó, Zoltán
Dér, András
Búzás, András
Viharos, Zsolt János
Hoang, Anh Tuan
Maczák, Bálint
Vadai, Gergely
Gingl, Zoltán
László, Szandra
Bilicki, Vilmos
Szendi, István
author_sort Nagy, Ádám
collection PubMed
description (1) Background and Goal: Several studies have investigated the association of sleep, diurnal patterns, and circadian rhythms with the presence and with the risk states of mental illnesses such as schizophrenia and bipolar disorder. The goal of our study was to examine actigraphic measures to identify features that can be extracted from them so that a machine learning model can detect premorbid latent liabilities for schizotypy and bipolarity. (2) Methods: Our team developed a small wrist-worn measurement device that collects and identifies actigraphic data based on an accelerometer. The sensors were used by carefully selected healthy participants who were divided into three groups: Control Group (C), Cyclothymia Factor Group (CFG), and Positive Schizotypy Factor Group (PSF). From the data they collected, our team performed data cleaning operations and then used the extracted metrics to generate the feature combinations deemed most effective, along with three machine learning algorithms for categorization. (3) Results: By conducting the training, we were able to identify a set of mildly correlated traits and their order of importance based on the Shapley value that had the greatest impact on the detection of bipolarity and schizotypy according to the logistic regression, Light Gradient Boost, and Random Forest algorithms. (4) Conclusions: These results were successfully compared to the results of other researchers; we had a similar differentiation in features used by others, and successfully developed new ones that might be a good complement for further research. In the future, identifying these traits may help us identify people at risk from mental disorders early in a cost-effective, automated way.
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spelling pubmed-98630122023-01-22 The Actigraphy-Based Identification of Premorbid Latent Liability of Schizophrenia and Bipolar Disorder Nagy, Ádám Dombi, József Fülep, Martin Patrik Rudics, Emese Hompoth, Emőke Adrienn Szabó, Zoltán Dér, András Búzás, András Viharos, Zsolt János Hoang, Anh Tuan Maczák, Bálint Vadai, Gergely Gingl, Zoltán László, Szandra Bilicki, Vilmos Szendi, István Sensors (Basel) Article (1) Background and Goal: Several studies have investigated the association of sleep, diurnal patterns, and circadian rhythms with the presence and with the risk states of mental illnesses such as schizophrenia and bipolar disorder. The goal of our study was to examine actigraphic measures to identify features that can be extracted from them so that a machine learning model can detect premorbid latent liabilities for schizotypy and bipolarity. (2) Methods: Our team developed a small wrist-worn measurement device that collects and identifies actigraphic data based on an accelerometer. The sensors were used by carefully selected healthy participants who were divided into three groups: Control Group (C), Cyclothymia Factor Group (CFG), and Positive Schizotypy Factor Group (PSF). From the data they collected, our team performed data cleaning operations and then used the extracted metrics to generate the feature combinations deemed most effective, along with three machine learning algorithms for categorization. (3) Results: By conducting the training, we were able to identify a set of mildly correlated traits and their order of importance based on the Shapley value that had the greatest impact on the detection of bipolarity and schizotypy according to the logistic regression, Light Gradient Boost, and Random Forest algorithms. (4) Conclusions: These results were successfully compared to the results of other researchers; we had a similar differentiation in features used by others, and successfully developed new ones that might be a good complement for further research. In the future, identifying these traits may help us identify people at risk from mental disorders early in a cost-effective, automated way. MDPI 2023-01-14 /pmc/articles/PMC9863012/ /pubmed/36679755 http://dx.doi.org/10.3390/s23020958 Text en © 2023 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
Nagy, Ádám
Dombi, József
Fülep, Martin Patrik
Rudics, Emese
Hompoth, Emőke Adrienn
Szabó, Zoltán
Dér, András
Búzás, András
Viharos, Zsolt János
Hoang, Anh Tuan
Maczák, Bálint
Vadai, Gergely
Gingl, Zoltán
László, Szandra
Bilicki, Vilmos
Szendi, István
The Actigraphy-Based Identification of Premorbid Latent Liability of Schizophrenia and Bipolar Disorder
title The Actigraphy-Based Identification of Premorbid Latent Liability of Schizophrenia and Bipolar Disorder
title_full The Actigraphy-Based Identification of Premorbid Latent Liability of Schizophrenia and Bipolar Disorder
title_fullStr The Actigraphy-Based Identification of Premorbid Latent Liability of Schizophrenia and Bipolar Disorder
title_full_unstemmed The Actigraphy-Based Identification of Premorbid Latent Liability of Schizophrenia and Bipolar Disorder
title_short The Actigraphy-Based Identification of Premorbid Latent Liability of Schizophrenia and Bipolar Disorder
title_sort actigraphy-based identification of premorbid latent liability of schizophrenia and bipolar disorder
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9863012/
https://www.ncbi.nlm.nih.gov/pubmed/36679755
http://dx.doi.org/10.3390/s23020958
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