Cargando…
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...
Autores principales: | , , , , , , , |
---|---|
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 |
_version_ | 1783586701270056960 |
---|---|
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. |
format | Online Article Text |
id | pubmed-7514931 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT sabetielyas learningusingconcaveandconvexkernelsapplicationsinpredictingqualityofsleepandleveloffatigueinfibromyalgia AT gryakjonathan learningusingconcaveandconvexkernelsapplicationsinpredictingqualityofsleepandleveloffatigueinfibromyalgia AT derksenharm learningusingconcaveandconvexkernelsapplicationsinpredictingqualityofsleepandleveloffatigueinfibromyalgia AT biwercraig learningusingconcaveandconvexkernelsapplicationsinpredictingqualityofsleepandleveloffatigueinfibromyalgia AT ansarisardar learningusingconcaveandconvexkernelsapplicationsinpredictingqualityofsleepandleveloffatigueinfibromyalgia AT isensteinhoward learningusingconcaveandconvexkernelsapplicationsinpredictingqualityofsleepandleveloffatigueinfibromyalgia AT kratzanna learningusingconcaveandconvexkernelsapplicationsinpredictingqualityofsleepandleveloffatigueinfibromyalgia AT najariankayvan learningusingconcaveandconvexkernelsapplicationsinpredictingqualityofsleepandleveloffatigueinfibromyalgia |