Masamichi Nogawa

Assessing the Effectiveness of Increased F I O 2 for Enhancing Driver's Activation State Using Simulated Monotonous Driving

Lowering of what we term a drivers Activation State (AS) during monotonous driving conditions may increase the risk of an accident. To develop an in-car environment that allows active driving - Biofee dforward System - we have investigated the effects of applying a stimulus of increased inspired oxygen fraction (FIO2) supply on a drivers AS, using simulated monotonous driving. We used our previously substantiated index of As derived from beat-by-beat blood pressure (BP) response following an electrical stimulus. We have made physiological measurements including BP and found that the increased FIO2stimulus is effective in enhancing the AS. This finding was also confirmed in terms of the autonomic activity balance as well as the lengthening in time for active, safer, driving.

Attempt of a novel calibration method of pulse oximetry using support vector machines regression

A novel calibration method using support vector machines regression (SVMsR) of pulse oximetry was proposed and attempted. Conventional calibration method of pulse oximetry that based on an optical density ratio of transmitted visible red light and infrared radiation whereas a proposed method here was not based on the optical density ratio directly. In theory, conventional calibrations using the ratio can be considered as a technique for nonlinear problem: nonlinear relation between two optical densities (red and IR) and oxygen saturation could be linearized by the ratio calculation. We thought, that nonlinear problem could be solved by using nonlinear analyses. Among them, the support vector machines regression method that has been studied well in this decade was attempted to be applied for pulse oximetry calibration. As an experiment, two photo plethysmograms (PPGs) by red and IR were measured on five subjects. Simultaneously, oxygen saturation (SpO2) level was measured by a commercial pulse oxymeter. SpO2 level was controlled by breathing 10% oxygen gas obtaining 98-75% SpO2 level. Sequentially, feature points of two PPGs were extracted in beat by beat. Convex peaks and concave valleys on waveform and DC levels of PPGs were selected as feature points. Then, nonlinear regression using SVMs were attempted to obtain relationship between SpO2 by meter (regressand) and feature points of PPG (regressor). In result, a regression model was constructed from training data that is three fourths of measured cardiac data by using SVMsR. Finally, the constructed calibration model was evaluated by other one third data (validation data). The root mean squared error for the validation data is 1.90 [SpO2 level %] and 89% of validation data fell within ± 2 % of SpO2 level by the meter. In conclusion, SVMsR might be applicable on calibration for pulse oximetry.