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Open AccessArticle

Application of Deep Learning in the Identification of Cerebral Hemodynamics Data Obtained from Functional Near-Infrared Spectroscopy: A Preliminary Study of Pre- and Post-Tooth Clenching Assessment

1
Department of Fixed Prosthodontics, School of Dentistry, Aichi Gakuin University, Nagoya 464-8651, Japan
2
Japanese Red Cross Toyota College of Nursing, Toyota 471-8565, Japan
3
Department of Pediatric Dentistry, School of Dentistry, Aichi Gakuin University, Nagoya 464-8651, Japan
4
Department of Endodontics, School of Dentistry, Aichi Gakuin University, Nagoya 464-8651, Japan
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Department of Oral and Maxillofacial Radiology, School of Dentistry, Aichi Gakuin University, Nagoya 464-8651, Japan
6
Department of Gerodontology and Home Care Dentistry, School of Dentistry, Aichi Gakuin University, Nagoya 464-8651, Japan
*
Authors to whom correspondence should be addressed.
J. Clin. Med. 2020, 9(11), 3475; https://doi.org/10.3390/jcm9113475
Received: 14 September 2020 / Revised: 14 October 2020 / Accepted: 27 October 2020 / Published: 28 October 2020
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. View Full-Text
Keywords: deep learning; deoxy-hemoglobin; functional near-infrared spectroscopy; oxy-hemoglobin deep learning; deoxy-hemoglobin; functional near-infrared spectroscopy; oxy-hemoglobin
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Takagi, S.; Sakuma, S.; Morita, I.; Sugimoto, E.; Yamaguchi, Y.; Higuchi, N.; Inamoto, K.; Ariji, Y.; Ariji, E.; Murakami, H. Application of Deep Learning in the Identification of Cerebral Hemodynamics Data Obtained from Functional Near-Infrared Spectroscopy: A Preliminary Study of Pre- and Post-Tooth Clenching Assessment. J. Clin. Med. 2020, 9, 3475.

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