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

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.

The Association of Body Mass Index and Body Composition with Pain, Disease Activity, Fatigue, Sleep and Anxiety in Women with Fibromyalgia

“…Similarly, women with FMS showed poorer sleep quality and more fatigue as compared with controls, supporting the finding that self-reported sleep quality and fatigue are associated with behavioral indicators of sleep quality in FMS women [30]. Considering the relevance of sleep problems in FMS, a recent study has proposed a machine learning method for detecting extreme cases of poor sleep and fatigue in FMS patients [31].…”

Machine Learning for Stress Detection from Electrodermal Activity: A Scoping Review

“…It also considers the possibility of each class and the probability of the data belonging to each class. There are many classifiers based on DA, being the most common for the detection of stress in EDA: linear discriminant analysis (LDA) [20,53]; quadratic discriminant analysis (QDA) [13,17,44,68,71] and Gaussian discriminant analysis (GDA) [65].…”

“…The most frequently used criterion is information gain, This implies that the entropy reduction caused by dataset division is maximised in every split. Within this type of classifier the most used are tree medium, regression tree[13,18,27,55,64,68] and other ensemble methods like random forest and bagged tree[18,75].• Naive Bayes is defined as a type of probabilistic classifier that aims to process and categorise data.The operation of this classifier is simple. It is essentially a technique for assigning probability theory to classify data.…”

Statistical Machine Learning for Human Behaviour Analysis

“…We solicited submissions on the following topics: information theory-based pattern classification, biometric recognition, multimodal human analysis, low resolution human activity analysis, face analysis, abnormal behaviour analysis, unsupervised human analysis scenarios, 3D/4D human pose and shape estimation, human analysis in virtual/augmented reality, affective computing, social signal processing, personality computing, activity recognition, human tracking in the wild, and application of information-theoretic concepts for human behaviour analysis. In the end, 15 papers were accepted for this special issue [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 ]. These papers, that are reviewed in this editorial, analyse human behaviour from the aforementioned perspectives, defining in most of the cases the state of the art in their corresponding field.…”

“…Finally, two databases have been introduced in this special issue, one for biometric recognition [ 1 ] and one for detecting sleeping issues and fatigue [ 8 ], the later containing a database of patients suffering from Fibromyalgia, which is a situation resulting in muscle pain and tenderness, accompanied by few other signs including sleep, memory, and mood disorders. It uses similarity functions with configurable convexity or concavity to build a classifier on this collected database in order to predict extreme cases of sleeping issues and fatigue.…”