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...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-04-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/21/5/442 |
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| author | Elyas Sabeti Jonathan Gryak Harm Derksen Craig Biwer Sardar Ansari Howard Isenstein Anna Kratz Kayvan Najarian |
| author_facet | Elyas Sabeti Jonathan Gryak Harm Derksen Craig Biwer Sardar Ansari Howard Isenstein Anna Kratz Kayvan Najarian |
| author_sort | Elyas Sabeti |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-10T08:02:47Z |
| format | Article |
| id | doaj.art-23e7d11b7f944e1abd9739ba18d3bc8b |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-12-10T08:02:47Z |
| publishDate | 2019-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-23e7d11b7f944e1abd9739ba18d3bc8b2022-12-22T01:56:45ZengMDPI AGEntropy1099-43002019-04-0121544210.3390/e21050442e21050442Learning Using Concave and Convex Kernels: Applications in Predicting Quality of Sleep and Level of Fatigue in FibromyalgiaElyas Sabeti0Jonathan Gryak1Harm Derksen2Craig Biwer3Sardar Ansari4Howard Isenstein5Anna Kratz6Kayvan Najarian7Department of Computational Medicine and Bioinformatics, University of Michigan, 2800 Plymouth Rd, NCRC, Ann Arbor, MI 48109-2800, USADepartment of Computational Medicine and Bioinformatics, University of Michigan, 2800 Plymouth Rd, NCRC, Ann Arbor, MI 48109-2800, USADepartment of Mathematics, University of Michigan, 2800 Plymouth Rd, Bldg. 18-163, Ann Arbor, MI 48109-2800, USADepartment of Computational Medicine and Bioinformatics, University of Michigan, 2800 Plymouth Rd, NCRC, Ann Arbor, MI 48109-2800, USADepartment of Computational Medicine and Bioinformatics, University of Michigan, 2800 Plymouth Rd, NCRC, Ann Arbor, MI 48109-2800, USADigidence, LLC 7315 Wisconsin Ave., Bethesda, MD 20814-3202, USADepartment of Physical Medicine & Rehabilitation, University of Michigan, 2800 Plymouth Rd, NCRC B14 #D034, Ann Arbor, MI 48109-2800, USADepartment of Computational Medicine and Bioinformatics, University of Michigan, 2800 Plymouth Rd, NCRC, Ann Arbor, MI 48109-2800, USAFibromyalgia 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.https://www.mdpi.com/1099-4300/21/5/442fibromyalgiaLearning Using Concave and Convex KernelsEmpatica E4self-reported survey |
| spellingShingle | Elyas Sabeti Jonathan Gryak Harm Derksen Craig Biwer Sardar Ansari Howard Isenstein Anna Kratz Kayvan Najarian Learning Using Concave and Convex Kernels: Applications in Predicting Quality of Sleep and Level of Fatigue in Fibromyalgia Entropy fibromyalgia Learning Using Concave and Convex Kernels Empatica E4 self-reported survey |
| 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 | fibromyalgia Learning Using Concave and Convex Kernels Empatica E4 self-reported survey |
| url | https://www.mdpi.com/1099-4300/21/5/442 |
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