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Feature Extraction and Selection for Pain Recognition Using Peripheral Physiological Signals

In pattern recognition, the selection of appropriate features is paramount to both the performance and the robustness of the system. Over-reliance on machine learning-based feature selection methods can, therefore, be problematic; especially when conducted using small snapshots of data. The results...

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Autores principales: Campbell, Evan, Phinyomark, Angkoon, Scheme, Erik
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6513974/
https://www.ncbi.nlm.nih.gov/pubmed/31133782
http://dx.doi.org/10.3389/fnins.2019.00437
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author Campbell, Evan
Phinyomark, Angkoon
Scheme, Erik
author_facet Campbell, Evan
Phinyomark, Angkoon
Scheme, Erik
author_sort Campbell, Evan
collection PubMed
description In pattern recognition, the selection of appropriate features is paramount to both the performance and the robustness of the system. Over-reliance on machine learning-based feature selection methods can, therefore, be problematic; especially when conducted using small snapshots of data. The results of these methods, if adopted without proper interpretation, can lead to sub-optimal system design or worse, the abandonment of otherwise viable and important features. In this work, a deep exploration of pain-based emotion classification was conducted to better understand differences in the results of the related literature. In total, 155 different time domain and frequency domain features were explored, derived from electromyogram (EMG), skin conductance levels (SCL), and electrocardiogram (ECG) readings taken from the 85 subjects in response to heat-induced pain. To address the inconsistency in the optimal feature sets found in related works, an exhaustive and interpretable feature selection protocol was followed to obtain a generalizable feature set. Associations between features were then visualized using a topologically-informed chart, called Mapper, of this physiological feature space, including synthesis and comparison of results from previous literature. This topological feature chart was able to identify key sources of information that led to the formation of five main functional feature groups: signal amplitude and power, frequency information, nonlinear complexity, unique, and connecting. These functional groupings were used to extract further insight into observable autonomic responses to pain through a complementary statistical interaction analysis. From this chart, it was observed that EMG and SCL derived features could functionally replace those obtained from ECG. These insights motivate future work on novel sensing modalities, feature design, deep learning approaches, and dimensionality reduction techniques.
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spelling pubmed-65139742019-05-27 Feature Extraction and Selection for Pain Recognition Using Peripheral Physiological Signals Campbell, Evan Phinyomark, Angkoon Scheme, Erik Front Neurosci Neuroscience In pattern recognition, the selection of appropriate features is paramount to both the performance and the robustness of the system. Over-reliance on machine learning-based feature selection methods can, therefore, be problematic; especially when conducted using small snapshots of data. The results of these methods, if adopted without proper interpretation, can lead to sub-optimal system design or worse, the abandonment of otherwise viable and important features. In this work, a deep exploration of pain-based emotion classification was conducted to better understand differences in the results of the related literature. In total, 155 different time domain and frequency domain features were explored, derived from electromyogram (EMG), skin conductance levels (SCL), and electrocardiogram (ECG) readings taken from the 85 subjects in response to heat-induced pain. To address the inconsistency in the optimal feature sets found in related works, an exhaustive and interpretable feature selection protocol was followed to obtain a generalizable feature set. Associations between features were then visualized using a topologically-informed chart, called Mapper, of this physiological feature space, including synthesis and comparison of results from previous literature. This topological feature chart was able to identify key sources of information that led to the formation of five main functional feature groups: signal amplitude and power, frequency information, nonlinear complexity, unique, and connecting. These functional groupings were used to extract further insight into observable autonomic responses to pain through a complementary statistical interaction analysis. From this chart, it was observed that EMG and SCL derived features could functionally replace those obtained from ECG. These insights motivate future work on novel sensing modalities, feature design, deep learning approaches, and dimensionality reduction techniques. Frontiers Media S.A. 2019-05-07 /pmc/articles/PMC6513974/ /pubmed/31133782 http://dx.doi.org/10.3389/fnins.2019.00437 Text en Copyright © 2019 Campbell, Phinyomark and Scheme. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Campbell, Evan
Phinyomark, Angkoon
Scheme, Erik
Feature Extraction and Selection for Pain Recognition Using Peripheral Physiological Signals
title Feature Extraction and Selection for Pain Recognition Using Peripheral Physiological Signals
title_full Feature Extraction and Selection for Pain Recognition Using Peripheral Physiological Signals
title_fullStr Feature Extraction and Selection for Pain Recognition Using Peripheral Physiological Signals
title_full_unstemmed Feature Extraction and Selection for Pain Recognition Using Peripheral Physiological Signals
title_short Feature Extraction and Selection for Pain Recognition Using Peripheral Physiological Signals
title_sort feature extraction and selection for pain recognition using peripheral physiological signals
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6513974/
https://www.ncbi.nlm.nih.gov/pubmed/31133782
http://dx.doi.org/10.3389/fnins.2019.00437
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