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Multimodal physiological sensing for the assessment of acute pain
Raul Fernendez-Rojas
, Niraj Hirachan
, Nick Brown
,
Gordon Waddington
, Luke Murtagh
, Ben Seymour
, Roland Goecke
Information Systems
Research output
:
Contribution to journal
›
Article
›
peer-review
43
Citations (Scopus)
61
Downloads (Pure)
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Dive into the research topics of 'Multimodal physiological sensing for the assessment of acute pain'. Together they form a unique fingerprint.
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Keyphrases
Acute Pain
100%
Electrodermal Activity
100%
Physiological Sensing
100%
Pain Assessment
50%
Forearm
50%
Pain Location
50%
High Risk
25%
Decision Tree
25%
Challenging Tasks
25%
Nonverbal
25%
Support Vector Machine
25%
Feature Selection
25%
Clinical Setting
25%
Multi-class
25%
Classification Results
25%
Sensing Technology
25%
Linear Discriminant Analysis
25%
Machine Learning Models
25%
Patient Report
25%
Physiological Changes
25%
Anatomical Location
25%
Realistic Scenario
25%
Multi-class Problem
25%
Pain Intensity
25%
Pain Conditions
25%
Objective Measurement
25%
Three-machine
25%
Machine Decisions
25%
Multiplexed Sensing
25%
Photoplethysmography Signal
25%
Respiration Signal
25%
Nursing and Health Professions
Self Report
100%
Pain Assessment
100%
Decision Tree
50%
Support Vector Machine
50%
Gold Standard
50%
Patient Monitor
50%
Pain Intensity
50%
Photoelectric Plethysmography
50%
Feature Extraction
50%
Discriminant Analysis
50%
Medicine and Dentistry
Electrodermal Activity
100%
Clinician
50%
Self Report
50%
Pain Assessment
50%
Feature Extraction
25%
Photoelectric Plethysmography
25%
Discriminant Analysis
25%
Psychology
Electrodermal Activity
100%
Self-Report
50%
Pain Condition
25%
Learning Model
25%