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Mitsuhiro Ogawa

Researcher at Kanazawa University

Publications -  18
Citations -  111

Mitsuhiro Ogawa is an academic researcher from Kanazawa University. The author has contributed to research in topics: Partial least squares regression & Support vector machine. The author has an hindex of 6, co-authored 18 publications receiving 103 citations.

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Proceedings ArticleDOI

A New Non-invasive Method for Measuring Blood Glucose Using Instantaneous Differential Near Infrared Spectrophotometry

TL;DR: Improved the performance of the spectrophotometer is improved and new in vivo measurements carried out in 23 healthy volunteers undergoing glucose tolerance tests are described, encouraging the precision and accuracy of the non-invasive determinations.
Proceedings ArticleDOI

A fully automated health-care monitoring at home without attachment of any biological sensors and its clinical evaluation

TL;DR: By evaluation on 3 patients with cardiac infarct or sleep apnea syndrome, patients’ health condition such as body and excretion weight in the toilet and apnea and hypopnea during sleeping were successfully monitored, indicating that the system appears useful for monitoring the health condition during daily living.
Proceedings ArticleDOI

Multivariate regression and discreminant calibration models for a novel optical non-invasive blood glucose measurement method named pulse glucometry

TL;DR: The results show that the regression calibration model based on the support vector machines can provide a good regression for the 101 paired data, in which the BGLs ranged from 89.0–219 mg/dl (4.94–12.2 mmol/l).
Book ChapterDOI

Development of a New Vascular Endoscopic System for Observing Inner Wall of Aorta Using Intermittent Saline Jet

TL;DR: A prototype endoscopic system for observing inner wall of aorta was developed and tested in vivo using swine, suggesting an availability of the present method as an assistive technology for the endovascular interventions in aorte.

Support vector machines as multivariate calibration model for prediction of blood glucose concentration using a new non-invasive optical method named pulse glucometry

TL;DR: In this article, a novel optical non-invasive in vivo blood glucose concentration (BGL) measurement technique, named Pulse Glucometry, was combined with a kernel method; support vector machines; and a calibration model using paired data of a family of DeltaODlambdas and the corresponding known BGLs was constructed with support vector machine regression instead of using calibration by a conventional partial least squares regression (PLS).