高齢者の心血管特性を考慮したクラス分類による光電脈波信号を用いたカフレス血圧推定

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Blood Pressure (BP) is a very important factor for monitoring the cardiovascular condition. In general, non-invasive BP measurements need a cuff. However, such measurement techniques can hardly monitor BP continuously. Recently it has gotten easier to measure biological signals daily because sensor technologies have well-developed, and because of availability of many kinds of miniaturized measurement instruments consuming less power. This study suggests a method of estimating Systolic Blood Pressure (SBP) with a wearable sensor instead of a cuff. In particular, our study depends on only one pulse wave signal detected by a Photoplethysmograph (PPG) sensor since the PPG sensor is very small. Moreover, the human subject just wears the sensor on the surface of the body to measure the signal. Cardiovascular peculiarities keep changing as people get older. Additionally, the peculiarities vary among individuals according to the advanced rate of arteriosclerosis. Hence, it is necessary for estimating the SBP to divide the data into several classes, by parameters that relate to individual cardiovascular peculiarities. In this study, the regression equation of SBP was calculated from individual information and from features of the PPG signal in each class. As a result, the estimation accuracy was improved. This technique would make cuffless SBP monitoring become more convenient and helpful as only one device is required for monitoring, which is smaller than traditional measurements.
社団法人電気学会の論文
2010-02-01

高齢者の心血管特性を考慮したクラス分類による光電脈波信号を用いたカフレス血圧推定

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