Novel method to classify hemodynamic response obtained using multi-channel fNIRS measurements into two groups: exploring the combinations of channels.
Novel method to classify hemodynamic response obtained using multi-channel fNIRS measurements into two groups: exploring the combinations of channels.
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DOI：
10.3389/fnhum.2014.00480
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发表时间：
2014
影响因子：
2.9
通讯作者：
Kakigi R
中科院分区：
文献类型：
--
作者：
Ichikawa H;Kitazono J;Nagata K;Manda A;Shimamura K;Sakuta R;Okada M;Yamaguchi MK;Kanazawa S;Kakigi R
精神病学研究中的近红外光谱(NIRS)已广泛表明，不同精神病患者的脑血流动力学有所不同。最近我们发现，注意力缺陷多动障碍(ADHD)儿童和自闭症谱系障碍(ASD)儿童对自己母亲的脸表现出不同的血流动力学反应。基于这一发现，我们可能能够将血流动力学数据分为两组，并预测未知参与者属于哪个诊断组。在本研究中，我们提出了一种新的统计方法来对这两组的血流动力学数据进行分类。通过应用支持向量机，我们搜索了ADHD和ASD儿童之间血流动力学反应不同的测量通道组合。支持向量机在每个数据集中找到最优的频道子集，并成功地从ASD数据中分类ADHD数据。对于24维血流动力学数据，两个最优子集对血流动力学数据的分类正确率为84%，而子集包含所有24个通道，分类正确率为62%。这些结果表明，我们的新方法有可能应用于将血流动力学数据分成两组，并揭示有效区分这两组的通道组合。
Near-infrared spectroscopy (NIRS) in psychiatric studies has widely demonstrated that cerebral hemodynamics differs among psychiatric patients. Recently we found that children with 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 the hemodynamic data into two those groups and predict to which diagnostic group an unknown participant belongs. 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 ADHD and the ASD children. 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-dimensional hemodynamic data, two optimal subsets classified the hemodynamic data with 84% classification accuracy, while the subset contained 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.
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