Novel method to classify hemodynamic response obtained using multi-channel fNIRS measurements into two groups: Exploring the combinations of channels

Near-infrared spectroscopy (NIRS) in psychiatric studies has widely demonstrated that cerebral hemodynamics differs among psychiatric patients. Recently we found that children with attention attention-deficit / hyperactivity disorder (ADHD) and children with autism spectrum disorders (ASD) showed di...

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Main Authors: Hiroko eIchikawa, Jun eKitazono, Kenji eNagata, Akira eManda, Keiichi eShimamura, Ryoichi eSakuta, Masato eOkada, Masami K Yamaguchi, So eKanazawa, Ryusuke eKakigi
Format: Article
Language:English
Published: Frontiers Media S.A. 2014-07-01
Series:Frontiers in Human Neuroscience
Subjects:
Online Access:http://journal.frontiersin.org/Journal/10.3389/fnhum.2014.00480/full
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spelling doaj-28987c0fa97b473cbabe8de3af05e8582020-11-25T02:20:39ZengFrontiers Media S.A.Frontiers in Human Neuroscience1662-51612014-07-01810.3389/fnhum.2014.0048089072Novel method to classify hemodynamic response obtained using multi-channel fNIRS measurements into two groups: Exploring the combinations of channelsHiroko eIchikawa0Hiroko eIchikawa1Jun eKitazono2Jun eKitazono3Kenji eNagata4Akira eManda5Keiichi eShimamura6Ryoichi eSakuta7Masato eOkada8Masato eOkada9Masami K Yamaguchi10So eKanazawa11Ryusuke eKakigi12Japan Society for the Promotion of ScienceChuo UniversityJapan Society for the Promotion of ScienceThe University of TokyoThe University of TokyoThe University of TokyoDokkyo Medical University Koshigaya HospitalDokkyo Medical University Koshigaya HospitalThe University of TokyoRIKEN Brain Science InstituteChuo UniversityJapan Women's UniversityNational Institute for Physiological SciencesNear-infrared spectroscopy (NIRS) in psychiatric studies has widely demonstrated that cerebral hemodynamics differs among psychiatric patients. Recently we found that children with attention attention-deficit / hyperactivity disorder (ADHD) and children with autism spectrum disorders (ASD) showed different hemodynamic responses to their own mother’s face. Based on this finding, we may be able to classify their hemodynamic data into two those groups and predict which diagnostic group an unknown participant belongs to. In the present study, we proposed a novel statistical method for classifying the hemodynamic data of these two groups. By applying a support vector machine (SVM), we searched the combination of measurement channels at which the hemodynamic response differed between the two groups; ADHD and ASD. The SVM found the optimal subset of channels in each data set and successfully classified the ADHD data from the ASD data. For the 24-dimentional hemodynamic data, two optimal subsets classified the hemodynamic data with 84% classification accuracy while the subset contains all 24 channels classified with 62% classification accuracy. These results indicate the potential application of our novel method for classifying the hemodynamic data into two groups and revealing the combinations of channels that efficiently differentiate the two groups.http://journal.frontiersin.org/Journal/10.3389/fnhum.2014.00480/fullAutism spectrum disorders (ASD)Near-infrared spectroscopy (NIRS)Attention-deficit / hyperactivity disorder (ADHD)hemodynamic datasupport vector machine (SVM)sparse modeling
collection DOAJ
language English
format Article
sources DOAJ
author Hiroko eIchikawa
Hiroko eIchikawa
Jun eKitazono
Jun eKitazono
Kenji eNagata
Akira eManda
Keiichi eShimamura
Ryoichi eSakuta
Masato eOkada
Masato eOkada
Masami K Yamaguchi
So eKanazawa
Ryusuke eKakigi
spellingShingle Hiroko eIchikawa
Hiroko eIchikawa
Jun eKitazono
Jun eKitazono
Kenji eNagata
Akira eManda
Keiichi eShimamura
Ryoichi eSakuta
Masato eOkada
Masato eOkada
Masami K Yamaguchi
So eKanazawa
Ryusuke eKakigi
Novel method to classify hemodynamic response obtained using multi-channel fNIRS measurements into two groups: Exploring the combinations of channels
Frontiers in Human Neuroscience
Autism spectrum disorders (ASD)
Near-infrared spectroscopy (NIRS)
Attention-deficit / hyperactivity disorder (ADHD)
hemodynamic data
support vector machine (SVM)
sparse modeling
author_facet Hiroko eIchikawa
Hiroko eIchikawa
Jun eKitazono
Jun eKitazono
Kenji eNagata
Akira eManda
Keiichi eShimamura
Ryoichi eSakuta
Masato eOkada
Masato eOkada
Masami K Yamaguchi
So eKanazawa
Ryusuke eKakigi
author_sort Hiroko eIchikawa
title Novel method to classify hemodynamic response obtained using multi-channel fNIRS measurements into two groups: Exploring the combinations of channels
title_short Novel method to classify hemodynamic response obtained using multi-channel fNIRS measurements into two groups: Exploring the combinations of channels
title_full Novel method to classify hemodynamic response obtained using multi-channel fNIRS measurements into two groups: Exploring the combinations of channels
title_fullStr Novel method to classify hemodynamic response obtained using multi-channel fNIRS measurements into two groups: Exploring the combinations of channels
title_full_unstemmed Novel method to classify hemodynamic response obtained using multi-channel fNIRS measurements into two groups: Exploring the combinations of channels
title_sort novel method to classify hemodynamic response obtained using multi-channel fnirs measurements into two groups: exploring the combinations of channels
publisher Frontiers Media S.A.
series Frontiers in Human Neuroscience
issn 1662-5161
publishDate 2014-07-01
description Near-infrared spectroscopy (NIRS) in psychiatric studies has widely demonstrated that cerebral hemodynamics differs among psychiatric patients. Recently we found that children with attention attention-deficit / hyperactivity disorder (ADHD) and children with autism spectrum disorders (ASD) showed different hemodynamic responses to their own mother’s face. Based on this finding, we may be able to classify their hemodynamic data into two those groups and predict which diagnostic group an unknown participant belongs to. In the present study, we proposed a novel statistical method for classifying the hemodynamic data of these two groups. By applying a support vector machine (SVM), we searched the combination of measurement channels at which the hemodynamic response differed between the two groups; ADHD and ASD. The SVM found the optimal subset of channels in each data set and successfully classified the ADHD data from the ASD data. For the 24-dimentional hemodynamic data, two optimal subsets classified the hemodynamic data with 84% classification accuracy while the subset contains all 24 channels classified with 62% classification accuracy. These results indicate the potential application of our novel method for classifying the hemodynamic data into two groups and revealing the combinations of channels that efficiently differentiate the two groups.
topic Autism spectrum disorders (ASD)
Near-infrared spectroscopy (NIRS)
Attention-deficit / hyperactivity disorder (ADHD)
hemodynamic data
support vector machine (SVM)
sparse modeling
url http://journal.frontiersin.org/Journal/10.3389/fnhum.2014.00480/full
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