Files and links (2)
Published, Version of Record (VoR)CC BY V4.0, Open
Journal article
Application of functional data analysis in classification and clustering of functional near-infrared spectroscopy signal in response to noxious stimuli
Journal of biomedical optics, v 21(10), pp 101411-101411
Oct 2016
PMID: 27155020
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
We introduce the application of functional data analysis (fDA) on functional near-infrared spectroscopy (fNIRS) signals for the development of an accurate and clinically practical assessment method of pain perception. We used the cold pressor test to induce different levels of pain in healthy subjects while the fNIRS signal was recorded from the frontal regions of the brain. We applied fDA on the collected fNIRS data to convert discrete samples into continuous curves. This method enabled us to represent the curves as a linear combination of basis functions. We utilized bases coefficients as features that represent the shape of the signals (as opposed to extracting defined features from signal) and used them to train a support vector machine to classify the signals based on the level of induced pain. We achieved 94% of accuracy to classify low-pain and high-pain signals. Moreover applying hierarchical clustering on the coefficients, we found three clusters in the data which represented low-pain (one cluster) and high-pain groups (two clusters) with an accuracy of 91.2%. The center of these clusters can represent the prototype fNIRS response of that pain level.
Metrics
Details
- Title
- Application of functional data analysis in classification and clustering of functional near-infrared spectroscopy signal in response to noxious stimuli
- Creators
- Ahmad Pourshoghi - Drexel UniversityIssa Zakeri - Drexel UniversityKambiz Pourrezaei - Drexel University
- Publication Details
- Journal of biomedical optics, v 21(10), pp 101411-101411
- Publisher
- Society of Photo-Optical Instrumentation Engineers (SPIE)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- School of Biomedical Engineering, Science, and Health Systems; Epidemiology and Biostatistics
- Web of Science ID
- WOS:000388213300013
- Scopus ID
- 2-s2.0-84973622005
- Other Identifier
- 991019168434204721
UN Sustainable Development Goals (SDGs)
This publication has contributed to the advancement of the following goals:
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Biochemical Research Methods
- Optics
- Radiology, Nuclear Medicine & Medical Imaging