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Learning Using Concave and Convex Kernels: Applications in Predicting Quality of Sleep and Level of Fatigue in Fibromyalgia

Fibromyalgia is a medical condition characterized by widespread muscle pain and tenderness and is often accompanied by fatigue and alteration in sleep, mood, and memory. Poor sleep quality and fatigue, as prominent characteristics of fibromyalgia, have a direct impact on patient behavior and quality...

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Autores principales: Sabeti, Elyas, Gryak, Jonathan, Derksen, Harm, Biwer, Craig, Ansari, Sardar, Isenstein, Howard, Kratz, Anna, Najarian, Kayvan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7514931/
https://www.ncbi.nlm.nih.gov/pubmed/33267156
http://dx.doi.org/10.3390/e21050442
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author Sabeti, Elyas
Gryak, Jonathan
Derksen, Harm
Biwer, Craig
Ansari, Sardar
Isenstein, Howard
Kratz, Anna
Najarian, Kayvan
author_facet Sabeti, Elyas
Gryak, Jonathan
Derksen, Harm
Biwer, Craig
Ansari, Sardar
Isenstein, Howard
Kratz, Anna
Najarian, Kayvan
author_sort Sabeti, Elyas
collection PubMed
description Fibromyalgia is a medical condition characterized by widespread muscle pain and tenderness and is often accompanied by fatigue and alteration in sleep, mood, and memory. Poor sleep quality and fatigue, as prominent characteristics of fibromyalgia, have a direct impact on patient behavior and quality of life. As such, the detection of extreme cases of sleep quality and fatigue level is a prerequisite for any intervention that can improve sleep quality and reduce fatigue level for people with fibromyalgia and enhance their daytime functionality. In this study, we propose a new supervised machine learning method called Learning Using Concave and Convex Kernels (LUCCK). This method employs similarity functions whose convexity or concavity can be configured so as to determine a model for each feature separately, and then uses this information to reweight the importance of each feature proportionally during classification. The data used for this study was collected from patients with fibromyalgia and consisted of blood volume pulse (BVP), 3-axis accelerometer, temperature, and electrodermal activity (EDA), recorded by an Empatica E4 wristband over the courses of several days, as well as a self-reported survey. Experiments on this dataset demonstrate that the proposed machine learning method outperforms conventional machine learning approaches in detecting extreme cases of poor sleep and fatigue in people with fibromyalgia.
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spelling pubmed-75149312020-11-09 Learning Using Concave and Convex Kernels: Applications in Predicting Quality of Sleep and Level of Fatigue in Fibromyalgia Sabeti, Elyas Gryak, Jonathan Derksen, Harm Biwer, Craig Ansari, Sardar Isenstein, Howard Kratz, Anna Najarian, Kayvan Entropy (Basel) Article Fibromyalgia is a medical condition characterized by widespread muscle pain and tenderness and is often accompanied by fatigue and alteration in sleep, mood, and memory. Poor sleep quality and fatigue, as prominent characteristics of fibromyalgia, have a direct impact on patient behavior and quality of life. As such, the detection of extreme cases of sleep quality and fatigue level is a prerequisite for any intervention that can improve sleep quality and reduce fatigue level for people with fibromyalgia and enhance their daytime functionality. In this study, we propose a new supervised machine learning method called Learning Using Concave and Convex Kernels (LUCCK). This method employs similarity functions whose convexity or concavity can be configured so as to determine a model for each feature separately, and then uses this information to reweight the importance of each feature proportionally during classification. The data used for this study was collected from patients with fibromyalgia and consisted of blood volume pulse (BVP), 3-axis accelerometer, temperature, and electrodermal activity (EDA), recorded by an Empatica E4 wristband over the courses of several days, as well as a self-reported survey. Experiments on this dataset demonstrate that the proposed machine learning method outperforms conventional machine learning approaches in detecting extreme cases of poor sleep and fatigue in people with fibromyalgia. MDPI 2019-04-28 /pmc/articles/PMC7514931/ /pubmed/33267156 http://dx.doi.org/10.3390/e21050442 Text en © 2019 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Sabeti, Elyas
Gryak, Jonathan
Derksen, Harm
Biwer, Craig
Ansari, Sardar
Isenstein, Howard
Kratz, Anna
Najarian, Kayvan
Learning Using Concave and Convex Kernels: Applications in Predicting Quality of Sleep and Level of Fatigue in Fibromyalgia
title Learning Using Concave and Convex Kernels: Applications in Predicting Quality of Sleep and Level of Fatigue in Fibromyalgia
title_full Learning Using Concave and Convex Kernels: Applications in Predicting Quality of Sleep and Level of Fatigue in Fibromyalgia
title_fullStr Learning Using Concave and Convex Kernels: Applications in Predicting Quality of Sleep and Level of Fatigue in Fibromyalgia
title_full_unstemmed Learning Using Concave and Convex Kernels: Applications in Predicting Quality of Sleep and Level of Fatigue in Fibromyalgia
title_short Learning Using Concave and Convex Kernels: Applications in Predicting Quality of Sleep and Level of Fatigue in Fibromyalgia
title_sort learning using concave and convex kernels: applications in predicting quality of sleep and level of fatigue in fibromyalgia
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7514931/
https://www.ncbi.nlm.nih.gov/pubmed/33267156
http://dx.doi.org/10.3390/e21050442
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