Objective Pain Stimulation Intensity and Pain Sensation Assessment Using Machine Learning Classification and Regression Based on Electrodermal Activity

2021 ◽  
Author(s):  
Hugo F. Posada-Quintero ◽  
Youngsun Kong ◽  
Ki H. Chon
Keyword(s):  
Machine Learning ◽  
Electrodermal Activity ◽  
Adult Population ◽  
Geometric Mean ◽  
Unmet Need ◽  
Pain Sensation ◽  
Machine Learning Classification ◽  
Stimulation Intensity ◽  
Pain Stimulation ◽  
Classification And Regression

An objective measure of pain remains an unmet need of people with chronic pain, estimated to be 1/3 of the adult population in the US. The current gold standard to quantify pain is highly subjective, based upon self-reporting with numerical or visual analog scales (VAS). This subjectivity complicates pain management and exacerbates the epidemic of opioid abuse. We have tested classification and regression machine learning models to objectively estimate pain sensation in healthy subjects using electrodermal activity (EDA). Twenty-three volunteers underwent pain stimulation using thermal grills. Three different "pain stimulation intensities" were induced for each subject, who reported the "pain sensation" right after each stimulus using a VAS (0-10). EDA data were collected throughout the experiment. For machine learning, we computed validated features of EDA based on time-domain decomposition, spectral analysis, and differential features. Models for estimation of pain stimulation intensity and pain sensation achieved maximum macro-averaged geometric mean scores of 69.7% and 69.2%, respectively, when three classes were considered ("No," "Low," and "High"). Regression of levels of stimulation intensity and pain sensation achieved R2 values of 0.357 and 0.47, respectively. Overall, the high variance and inconsistency of VAS scores led to lower performance of pain sensation classification, but regression was better for pain sensation than stimulation intensity. Our results provide that three levels of pain can be quantified with good accuracy, and physiological evidence that sympathetic responses recorded by EDA are more correlated to the applied stimuli's intensity than to the pain sensation reported by the subject.

scholarly journals Machine Learning Classification and Regression Approaches for Optical Network Traffic Prediction

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1578
Author(s):  
Daniel Szostak ◽  
Adam Włodarczyk ◽  
Krzysztof Walkowiak
Keyword(s):  
Machine Learning ◽  
Optical Networks ◽  
Network Traffic ◽  
Optical Network ◽  
Optimization Methods ◽  
Supervised Machine Learning ◽  
Traffic Prediction ◽  
Machine Learning Classification ◽  
Classification And Regression ◽  
Network Technologies

Rapid growth of network traffic causes the need for the development of new network technologies. Artificial intelligence provides suitable tools to improve currently used network optimization methods. In this paper, we propose a procedure for network traffic prediction. Based on optical networks’ (and other network technologies) characteristics, we focus on the prediction of fixed bitrate levels called traffic levels. We develop and evaluate two approaches based on different supervised machine learning (ML) methods—classification and regression. We examine four different ML models with various selected features. The tested datasets are based on real traffic patterns provided by the Seattle Internet Exchange Point (SIX). Obtained results are analyzed using a new quality metric, which allows researchers to find the best forecasting algorithm in terms of network resources usage and operational costs. Our research shows that regression provides better results than classification in case of all analyzed datasets. Additionally, the final choice of the most appropriate ML algorithm and model should depend on the network operator expectations.

scholarly journals Machine Learning models for the Identification of Cognitive Tasks using Autonomic Reactions from Heart Rate Variability and Electrodermal Activity

2019 ◽  
Vol 9 (4) ◽  
pp. 45 ◽  
Author(s):  
Hugo F. Posada-Quintero ◽  
Jeffrey B. Bolkhovsky
Keyword(s):  
Machine Learning ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Electrodermal Activity ◽  
Memory Task ◽  
Autonomic Response ◽  
Support Vector ◽  
Learning Models ◽  
Machine Learning Classification ◽  
Machine Learning Models

Indices of heart rate variability (HRV) and electrodermal activity (EDA), in conjunction with machine learning models, were used to identify one of three tasks a subject is performing based on autonomic response elicited by the specific task. Using non-invasive measures to identify the task performed by a subject can help to provide individual monitoring and guidance to avoid the consequences of reduced performance due to fatigue or other stressors. In the present study, sixteen subjects were enrolled to undergo three tasks: The psychomotor vigilance task (PVT), an auditory working memory task (the n-back paradigm), and a visual search (ship search, SS). Electrocardiogram (ECG) (for HRV analysis) and EDA data were collected during the tests. For task-classification, we tested four machine learning classification tools: k-nearest neighbor classifier (KNN), support vector machines (SVM), decision trees, and discriminant analysis (DA). Leave-one-subject-out cross-validation was used to evaluate the performance of the constructed models to prevent overfitting. The most accurate models were the KNN (66%), linear SVM (62%), and linear DA (62%). The results of this study showed that it is possible to identify the task a subject is performing based on the subject’s autonomic reactions (from HRV and EDA). This information can be used to monitor individuals within a larger group to assist in reducing errors caused by uncoordinated or poor performance by allowing for automated tracking of and communication between individuals.

Intelligent Techniques Analysis for Glycosylation Site Prediction

2021 ◽  
Vol 15 ◽  
Author(s):  
Alhassan Alkuhlani ◽  
Walaa Gad ◽  
Mohamed Roushdy ◽  
Abdel-Badeeh M. Salem
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Cell Interaction ◽  
Glycosylation Site ◽  
Machine Learning Classification ◽  
Site Prediction ◽  
Glycosylation Sites ◽  
Wide Range ◽  
Feature Extraction And Selection ◽  
Computational Intelligent

Background: Glycosylation is one of the most common post-translation modifications (PTMs) in organism cells. It plays important roles in several biological processes including cell-cell interaction, protein folding, antigen’s recognition, and immune response. In addition, glycosylation is associated with many human diseases such as cancer, diabetes and coronaviruses. The experimental techniques for identifying glycosylation sites are time-consuming, extensive laboratory work, and expensive. Therefore, computational intelligence techniques are becoming very important for glycosylation site prediction. Objective: This paper is a theoretical discussion of the technical aspects of the biotechnological (e.g., using artificial intelligence and machine learning) to digital bioinformatics research and intelligent biocomputing. The computational intelligent techniques have shown efficient results for predicting N-linked, O-linked and C-linked glycosylation sites. In the last two decades, many studies have been conducted for glycosylation site prediction using these techniques. In this paper, we analyze and compare a wide range of intelligent techniques of these studies from multiple aspects. The current challenges and difficulties facing the software developers and knowledge engineers for predicting glycosylation sites are also included. Method: The comparison between these different studies is introduced including many criteria such as databases, feature extraction and selection, machine learning classification methods, evaluation measures and the performance results. Results and conclusions: Many challenges and problems are presented. Consequently, more efforts are needed to get more accurate prediction models for the three basic types of glycosylation sites.

Multiclass machine learning classification of functional brain images for Parkinson's disease stage prediction

2020 ◽  
Vol 13 (5) ◽  
pp. 508-523 ◽  
Author(s):  
Guan‐Hua Huang ◽  
Chih‐Hsuan Lin ◽  
Yu‐Ren Cai ◽  
Tai‐Been Chen ◽  
Shih‐Yen Hsu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Disease Stage ◽  
Brain Images ◽  
Functional Brain ◽  
Machine Learning Classification

scholarly journals Automatic Identification of Upper Extremity Rehabilitation Exercise Type and Dose Using Body-Worn Sensors and Machine Learning: A Pilot Study

2021 ◽  
pp. 158-166
Author(s):  
Noah Balestra ◽  
Gaurav Sharma ◽  
Linda M. Riek ◽  
Ania Busza
Keyword(s):  
Machine Learning ◽  
Upper Extremity ◽  
Sensor Data ◽  
Inpatient Setting ◽  
Accelerometer Data ◽  
Data Set ◽  
Machine Learning Classification ◽  
Exercise Type ◽  
Exercise Dose ◽  
Rehabilitation Exercises

<b><i>Background:</i></b> Prior studies suggest that participation in rehabilitation exercises improves motor function poststroke; however, studies on optimal exercise dose and timing have been limited by the technical challenge of quantifying exercise activities over multiple days. <b><i>Objectives:</i></b> The objectives of this study were to assess the feasibility of using body-worn sensors to track rehabilitation exercises in the inpatient setting and investigate which recording parameters and data analysis strategies are sufficient for accurately identifying and counting exercise repetitions. <b><i>Methods:</i></b> MC10 BioStampRC® sensors were used to measure accelerometer and gyroscope data from upper extremities of healthy controls (<i>n</i> = 13) and individuals with upper extremity weakness due to recent stroke (<i>n</i> = 13) while the subjects performed 3 preselected arm exercises. Sensor data were then labeled by exercise type and this labeled data set was used to train a machine learning classification algorithm for identifying exercise type. The machine learning algorithm and a peak-finding algorithm were used to count exercise repetitions in non-labeled data sets. <b><i>Results:</i></b> We achieved a repetition counting accuracy of 95.6% overall, and 95.0% in patients with upper extremity weakness due to stroke when using both accelerometer and gyroscope data. Accuracy was decreased when using fewer sensors or using accelerometer data alone. <b><i>Conclusions:</i></b> Our exploratory study suggests that body-worn sensor systems are technically feasible, well tolerated in subjects with recent stroke, and may ultimately be useful for developing a system to measure total exercise “dose” in poststroke patients during clinical rehabilitation or clinical trials.

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