Takagi Shinya, Sakuma Shigemitsu, Morita Ichizo, Sugimoto Eri, Yamaguchi Yoshihiro, Higuchi Naoya, Inamoto Kyoko, Ariji Yoshiko, Ariji Eiichiro, Murakami Hiroshi
Department of Fixed Prosthodontics, School of Dentistry, Aichi Gakuin University, Nagoya 464-8651, Japan.
Japanese Red Cross Toyota College of Nursing, Toyota 471-8565, Japan.
J Clin Med. 2020 Oct 28;9(11):3475. doi: 10.3390/jcm9113475.
In fields using functional near-infrared spectroscopy (fNIRS), there is a need for an easy-to-understand method that allows visual presentation and rapid analysis of data and test results. This preliminary study examined whether deep learning (DL) could be applied to the analysis of fNIRS-derived brain activity data. To create a visual presentation of the data, an imaging program was developed for the analysis of hemoglobin (Hb) data from the prefrontal cortex in healthy volunteers, obtained by fNIRS before and after tooth clenching. Three types of imaging data were prepared: oxygenated hemoglobin (oxy-Hb) data, deoxygenated hemoglobin (deoxy-Hb) data, and mixed data (using both oxy-Hb and deoxy-Hb data). To differentiate between rest and tooth clenching, a cross-validation test using the image data for DL and a convolutional neural network was performed. The network identification rate using Hb imaging data was relatively high (80‒90%). These results demonstrated that a method using DL for the assessment of fNIRS imaging data may provide a useful analysis system.
在使用功能性近红外光谱技术（fNIRS）的领域中，需要一种易于理解的方法，以便直观呈现和快速分析数据及测试结果。这项初步研究探讨了深度学习（DL）是否可应用于分析源自fNIRS的大脑活动数据。为了直观呈现数据，开发了一个成像程序，用于分析健康志愿者在紧咬牙前后通过fNIRS获取的前额叶皮质血红蛋白（Hb）数据。准备了三种类型的成像数据：氧合血红蛋白（oxy-Hb）数据、脱氧血红蛋白（deoxy-Hb）数据和混合数据（同时使用oxy-Hb和deoxy-Hb数据）。为区分静息状态和紧咬牙状态，利用深度学习的图像数据和卷积神经网络进行了交叉验证测试。使用Hb成像数据的网络识别率相对较高（80% - 90%）。这些结果表明，使用深度学习评估fNIRS成像数据的方法可能提供一个有用的分析系统。
