A semi-learning algorithm for noise rejection: an fNIRS study on ADHD children

Stephanie Sutoko; Tsukasa Funane; Takusige Katura; Hiroki Sato; Masashi Kiguchi; Atsushi Maki; Yukifumi Monden M.D.; Masako Nagashima M.D.; Takanori Yamagata M.D.; Ippeita Dan

doi:10.1117/12.2249742

17 February 2017 A semi-learning algorithm for noise rejection: an fNIRS study on ADHD children

Stephanie Sutoko, Tsukasa Funane, Takusige Katura, Hiroki Sato, Masashi Kiguchi, Atsushi Maki, Yukifumi Monden M.D., Masako Nagashima M.D., Takanori Yamagata M.D., Ippeita Dan

Author Affiliations +

Proceedings Volume 10059, Optical Tomography and Spectroscopy of Tissue XII; 1005914 (2017) https://doi.org/10.1117/12.2249742
Event: SPIE BiOS, 2017, San Francisco, California, United States

Abstract

In pediatrics studies, the quality of functional near infrared spectroscopy (fNIRS) signals is often reduced by motion artifacts. These artifacts likely mislead brain functionality analysis, causing false discoveries. While noise correction methods and their performance have been investigated, these methods require several parameter assumptions that apparently result in noise overfitting. In contrast, the rejection of noisy signals serves as a preferable method because it maintains the originality of the signal waveform. Here, we describe a semi-learning algorithm to detect and eliminate noisy signals. The algorithm dynamically adjusts noise detection according to the predetermined noise criteria, which are spikes, unusual activation values (averaged amplitude signals within the brain activation period), and high activation variances (among trials). Criteria were sequentially organized in the algorithm and orderly assessed signals based on each criterion. By initially setting an acceptable rejection rate, particular criteria causing excessive data rejections are neglected, whereas others with tolerable rejections practically eliminate noises. fNIRS data measured during the attention response paradigm (oddball task) in children with attention deficit/hyperactivity disorder (ADHD) were utilized to evaluate and optimize the algorithm’s performance. This algorithm successfully substituted the visual noise identification done in the previous studies and consistently found significantly lower activation of the right prefrontal and parietal cortices in ADHD patients than in typical developing children. Thus, we conclude that the semi-learning algorithm confers more objective and standardized judgment for noise rejection and presents a promising alternative to visual noise rejection

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Stephanie Sutoko, Tsukasa Funane, Takusige Katura, Hiroki Sato, Masashi Kiguchi, Atsushi Maki, Yukifumi Monden M.D., Masako Nagashima M.D., Takanori Yamagata M.D., and Ippeita Dan "A semi-learning algorithm for noise rejection: an fNIRS study on ADHD children", Proc. SPIE 10059, Optical Tomography and Spectroscopy of Tissue XII, 1005914 (17 February 2017); https://doi.org/10.1117/12.2249742

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KEYWORDS

Visualization

Algorithm development

Brain

Interference (communication)

Visual analytics

Error analysis

Statistical analysis

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