Pain Intensity Recognition Rates via Biopotential Feature Patterns with Support Vector Machines

Gruss, Sascha; Treister, Roi; Werner, Philipp; Traue, Harald C.; Crawcour, Stephen; Andrade, Adriano O.; Walter, Steffen

doi:10.1371/journal.pone.0140330

Cited by 112 publications

(119 citation statements)

References 31 publications

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“…Thirdly, the development of high-density surface EMG systems has introduced the concept of a surface EMG image and thus dramatically increased the volume of data [14,15]. Lastly, the increasing availability of multi-modality sensor systems has generated larger amounts of data in which the EMG signal is considered one of the most important sources of information [16,17]. Here, we present the current state of existing shared EMG datasets, highlighting the opportunities and challenges in the development of truly big EMG data.…”

Section: Introductionmentioning

confidence: 99%

EMG Pattern Recognition in the Era of Big Data and Deep Learning

Phinyomark

Scheme

2018

BDCC

191

105

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Abstract:The increasing amount of data in electromyographic (EMG) signal research has greatly increased the importance of developing advanced data analysis and machine learning techniques which are better able to handle "big data". Consequently, more advanced applications of EMG pattern recognition have been developed. This paper begins with a brief introduction to the main factors that expand EMG data resources into the era of big data, followed by the recent progress of existing shared EMG data sets. Next, we provide a review of recent research and development in EMG pattern recognition methods that can be applied to big data analytics. These modern EMG signal analysis methods can be divided into two main categories: (1) methods based on feature engineering involving a promising big data exploration tool called topological data analysis; and (2) methods based on feature learning with a special emphasis on "deep learning". Finally, directions for future research in EMG pattern recognition are outlined and discussed.

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Section: Introductionmentioning

confidence: 99%

EMG Pattern Recognition in the Era of Big Data and Deep Learning

Phinyomark

Scheme

2018

BDCC

191

105

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“…In general, accuracy ( ACC ) is a common evaluation indicator of the goodness of a classifier [51]. However, it only offers us rough information about the classifier’s performance.…”

Section: Methodsmentioning

confidence: 99%

Construction of Metabolism Prediction Models for CYP450 3A4, 2D6, and 2C9 Based on Microsomal Metabolic Reaction System

Zhang

et al. 2016

IJMS

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During the past decades, there have been continuous attempts in the prediction of metabolism mediated by cytochrome P450s (CYP450s) 3A4, 2D6, and 2C9. However, it has indeed remained a huge challenge to accurately predict the metabolism of xenobiotics mediated by these enzymes. To address this issue, microsomal metabolic reaction system (MMRS)—a novel concept, which integrates information about site of metabolism (SOM) and enzyme—was introduced. By incorporating the use of multiple feature selection (FS) techniques (ChiSquared (CHI), InfoGain (IG), GainRatio (GR), Relief) and hybrid classification procedures (Kstar, Bayes (BN), K-nearest neighbours (IBK), C4.5 decision tree (J48), RandomForest (RF), Support vector machines (SVM), AdaBoostM1, Bagging), metabolism prediction models were established based on metabolism data released by Sheridan et al. Four major biotransformations, including aliphatic C-hydroxylation, aromatic C-hydroxylation, N-dealkylation and O-dealkylation, were involved. For validation, the overall accuracies of all four biotransformations exceeded 0.95. For receiver operating characteristic (ROC) analysis, each of these models gave a significant area under curve (AUC) value >0.98. In addition, an external test was performed based on dataset published previously. As a result, 87.7% of the potential SOMs were correctly identified by our four models. In summary, four MMRS-based models were established, which can be used to predict the metabolism mediated by CYP3A4, 2D6, and 2C9 with high accuracy.

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“…The SCL signal was detected with a rate of 10 Hz and between 0.1 and 39.9 mMho. Due to the adequate data quality, no other preprocessing steps were necessary, and the mean of the signal (SCL-M) as well as the area under the curve (SCL-AUC) were calculated (36,37).…”

Section: Electrophysiological Datamentioning

confidence: 99%

Effects of Expectancy on Cognitive Performance, Mood, and Psychophysiology in Healthy Adolescents and Their Parents in an Experimental Study

Watolla¹,

Mazurak²,

Gruss

et al. 2020

Front. Psychiatry

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Objective: Placebo effects on cognitive performance and mood and their underlying mechanisms have rarely been investigated in adolescents. Therefore, the following hypotheses were investigated with an experimental paradigm: (1) placebo effects could be larger in adolescents than in adults, (2) parents' expectations influence their adolescents' expectations and placebo effects, and (3) a decrease in stress levels could be an underlying mechanism of placebo effects.Methods: Twenty-six healthy adolescents (13.8 ± 1.6 years, 14 girls) each with a parent (45.5 ± 4.2 years, 17 mothers) took part in an experimental within-subjects study. On two occasions, a transdermal patch was applied to their hips and they received an envelope containing either the information that it is a Ginkgo patch to improve cognitive performance and mood, or it is an inactive placebo patch, in counterbalanced order. Cognitive performance and mood were assessed with a parametric Go/No-Go task (PGNG), a modification of California Verbal Learning Test, and Profile of Mood Scales (POMS). Subjects rated their expectations about Ginkgo's effects before patch application as well as their subjective assessment of its effects after the tests. An electrocardiogram and skin conductance levels (SCLs) were recorded and root mean square of successive differences (RMSSD), high-frequency power (HF), and the area under the curve of the SCL (AUC) were analyzed as psychophysiological stress markers.Results: Expectations did not differ between adolescents and parents and were correlated concerning reaction times only. Overall, expectations did not influence placebo effects. There was only one significant placebo effect on the percentage of correct inhibited trials in one level of the PGNG in adolescents, but not in parents. RMSSD and HF significantly increased, and AUC decreased from pre-to post-patch application in adolescents, but not in parents.Conclusion: With this experimental paradigm, we could not induce relevant placebo effects in adolescents and parents. This could be due to aspects of the study design such as application form and substance, and that healthy subjects were employed. Nevertheless, we could show that adolescents are more sensitive to psychophysiological reactions related with interventions which could be part of the underlying mechanisms of placebo effects in adolescents.

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Pain Intensity Recognition Rates via Biopotential Feature Patterns with Support Vector Machines

Cited by 112 publications

References 31 publications

EMG Pattern Recognition in the Era of Big Data and Deep Learning

EMG Pattern Recognition in the Era of Big Data and Deep Learning

Construction of Metabolism Prediction Models for CYP450 3A4, 2D6, and 2C9 Based on Microsomal Metabolic Reaction System

Effects of Expectancy on Cognitive Performance, Mood, and Psychophysiology in Healthy Adolescents and Their Parents in an Experimental Study

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