Bahrami Sheyda, Shamsi Mousa
Department of Electrical Engineering, Sahand University of Technology, Tabriz, Iran.
J Med Signals Sens. 2017 Jul-Sep;7(3):153-162.
Functional magnetic resonance imaging (fMRI) is a popular method to probe the functional organization of the brain using hemodynamic responses. In this method, volume images of the entire brain are obtained with a very good spatial resolution and low temporal resolution. However, they always suffer from high dimensionality in the face of classification algorithms. In this work, we combine a support vector machine (SVM) with a self-organizing map (SOM) for having a feature-based classification by using SVM. Then, a linear kernel SVM is used for detecting the active areas. Here, we use SOM for feature extracting and labeling the datasets. SOM has two major advances: (i) it reduces dimension of data sets for having less computational complexity and (ii) it is useful for identifying brain regions with small onset differences in hemodynamic responses. Our non-parametric model is compared with parametric and non-parametric methods. We use simulated fMRI data sets and block design inputs in this paper and consider the contrast to noise ratio (CNR) value equal to 0.6 for simulated datasets. fMRI simulated dataset has contrast 1-4% in active areas. The accuracy of our proposed method is 93.63% and the error rate is 6.37%.
功能磁共振成像（fMRI）是一种利用血液动力学反应来探究大脑功能组织的常用方法。在这种方法中，能够以非常高的空间分辨率和较低的时间分辨率获取整个大脑的体积图像。然而，面对分类算法时，这些图像总是存在高维度问题。在这项工作中，我们将支持向量机（SVM）与自组织映射（SOM）相结合，通过使用SVM进行基于特征的分类。然后，使用线性核SVM来检测活跃区域。在这里，我们使用SOM进行特征提取和数据集标记。SOM有两个主要优势：（i）它降低了数据集的维度，从而降低计算复杂度；（ii）它有助于识别血液动力学反应中起始差异较小的脑区。我们的非参数模型与参数化和非参数化方法进行了比较。在本文中，我们使用模拟的fMRI数据集和组块设计输入，并将模拟数据集的对比噪声比（CNR）值设为0.6。fMRI模拟数据集在活跃区域的对比度为1 - 4%。我们提出的方法的准确率为93.63%，错误率为6.37%。
