An fNIRS-Based Feature Learning and Classification Framework to Distinguish Hemodynamic Patterns in Children Who Stutter

Stuttering is a communication disorder that affects approximately 1% of the population. Although 5-8% of preschool children begin to stutter, the majority will recover with or without intervention. There is a significant gap, however, in our understanding of why many children recover from stuttering...

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Bibliographic Details
Published in:IEEE transactions on neural systems and rehabilitation engineering 2018-06, Vol.26 (6), p.1254-1263
Main Authors: Hosseini, Rahilsadat, Walsh, Bridget, Tian, Fenghua, Wang, Shouyi
Format: Article
Language:English
Subjects:
Adolescent
Algorithms
Biological system modeling
Biomarkers
Brain
Cerebrovascular Circulation
Child
Child, Preschool
Children
data mining
Delays
Feature extraction
feature extraction and selection
Female
functional near-infrared spectroscopy (fNIRS)
Hemodynamics
Humans
Infrared imaging
Infrared spectroscopy
Learning
Machine Learning
Male
mutual information
Near infrared radiation
Neuroimaging
Neurophysiology
Preschool children
Production
Remission, Spontaneous
Reproducibility of Results
sparse modeling
Spectroscopy, Near-Infrared - methods
Speech disorders
speech production
Stuttering
Stuttering - physiopathology
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Summary:Stuttering is a communication disorder that affects approximately 1% of the population. Although 5-8% of preschool children begin to stutter, the majority will recover with or without intervention. There is a significant gap, however, in our understanding of why many children recover from stuttering while others persist and stutter throughout their lives. Detecting neurophysiological biomarkers of stuttering persistence is a critical objective of this paper. In this paper, we developed a novel supervised sparse feature learning approach to discover discriminative biomarkers from functional near infrared spectroscopy (fNIRS) brain imaging data recorded during a speech production experiment from 46 children in three groups: children who stutter (n = 16); children who do not stutter (n = 16); and children who recovered from stuttering (n = 14). We made an extensive feature analysis of the cerebral hemodynamics from fNIRS signals and selected a small number of important discriminative features using the proposed sparse feature learning framework. The selected features are capable of differentiating neural activation patterns between children who do and do not stutter with an accuracy of 87.5% based on a five-fold cross-validation procedure. The discovered set cerebral hemodynamics features are presented as a set of promising biomarkers to elucidate the underlying neurophysiology in children who have recovered or persisted in stuttering and to facilitate future data-driven diagnostics in these children.
ISSN:1534-4320
1558-0210
DOI:10.1109/TNSRE.2018.2829083