Cargando…
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...
Autores principales: | , , |
---|---|
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 |
_version_ | 1783417799748616192 |
---|---|
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. |
format | Online Article Text |
id | pubmed-6513974 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT campbellevan featureextractionandselectionforpainrecognitionusingperipheralphysiologicalsignals AT phinyomarkangkoon featureextractionandselectionforpainrecognitionusingperipheralphysiologicalsignals AT schemeerik featureextractionandselectionforpainrecognitionusingperipheralphysiologicalsignals |