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Learning Using Concave and Convex Kernels: Applications in Predicting Quality of Sleep and Level of Fatigue in Fibromyalgia
- Source :
- Entropy, Volume 21, Issue 5, Entropy, Vol 21, Iss 5, p 442 (2019)
- Publication Year :
- 2019
- Publisher :
- MDPI AG, 2019.
-
Abstract
- 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.
- Subjects :
- medicine.medical_specialty
Fibromyalgia
Computer science
media_common.quotation_subject
Quality of sleep
General Physics and Astronomy
lcsh:Astrophysics
Accelerometer
Article
03 medical and health sciences
0302 clinical medicine
Quality of life (healthcare)
Physical medicine and rehabilitation
lcsh:QB460-466
medicine
Quality (business)
030212 general & internal medicine
Learning Using Concave and Convex Kernels
lcsh:Science
Self-Reported Survey
media_common
medicine.disease
lcsh:QC1-999
Mood
Feature (computer vision)
lcsh:Q
Sleep (system call)
Empatica E4
lcsh:Physics
030217 neurology & neurosurgery
Subjects
Details
- ISSN :
- 10994300
- Volume :
- 21
- Database :
- OpenAIRE
- Journal :
- Entropy
- Accession number :
- edsair.doi.dedup.....c14351675d82b234724beae9b6fef270