To date, the applications of photoplethysmograms in the estimation of cuff-less blood pressure, arteriosclerosis, and so on have been studied. Photoplethysmogram waveform features have often been used to estimate the target volumes. For these estimations, it is necessary to acquire photoplethysmogram waveforms and changes in their derivative waveform details, for which the photoplethysmograms measured at comparatively high sampling rates have been used. The performance of smartphone cameras and wearable photoplethysmogram sensors has improved; with regard to mobile health technology, photoplethysmograms measured at lower sampling rates offer considerable advantages. These include lower computational resources, compression of accumulated data, and lower sensor power consumption. However, compared to photoplethysmograms measured at a high sampling rate, photoplethysmogram measurement at a low sampling rate will result in waveform signal degradation. This paper investigates the possibility of using photoplethysmograms measured at a low sampling rate. To this end, we statistically compared photoplethysmogram waveform features obtained from 63 male subjects free of circulatory diseases, at a sampling rate of 240 Hz, with waveform features obtained at low sampling rates (120, 60, 30, 20, and 10 Hz) through downsampling, and evaluated possible commercial use.INDEX TERMS Big data, mobile health, photoplethysmogram, waveform feature.

This paper proposes an inverse-model-based cuffless method for estimating blood pressure using a single photoplethysmography sensor. The proposed method, which is based on the relationship between blood pressure and the features of pulse waves, employs an inverse estimation and uses the blood pressure as the explanatory variable. Using this method, the blood pressure can be estimated with high accuracy even in situations where the pulse wave features are scattered, as the method uses the dynamic signal-to-noise ratio of the Taguchi method. In order to verify the effectiveness of the proposed method, we employed it to measure the systolic blood pressure. It could be confirmed that the estimation accuracy of the proposed method is higher than that of similar methods.

This paper proposes a cuff-less systolic blood pressure (SBP) estimation method using partial least-squares (PLS) regression. Level-crossing features (LCFs) were used in this method, which were extracted from the contour lines arbitrarily drawn on the second-derivative photoplethysmography waveform. Unlike conventional height ratio features (HRFs), which are extracted on the basis of the peaks in the waveform, LCFs can be reliably extracted even if there are missing peaks in the waveform. However, the features extracted from adjacent contour lines show similar trends; thus, there is a strong correlation between the features, which leads to multicollinearity when conventional multiple regression analysis (MRA) is used. Hence, we developed a multivariate estimation method based on PLS regression to address this issue and estimate the SBP on the basis of the LCFs. Two-hundred-and-sixty-five subjects (95 males and 170 females [(Mean ± Standard Deviation) SBP: 133.1 ± 18.4 mmHg; age: 62.8 ± 16.8 years] participated in the experiments. Of the total number of subjects, 180 were considered as learning data, while 85 were considered as testing data. The values of the correlation coefficient between the measured and estimated values were found to be 0.78 for the proposed method (LCFs + PLS), 0.58 for comparison method 1 (HRFs + MRA), and 0.62 for comparison method 2 (HRFs + MRA). The proposed method was therefore found to demonstrate the highest accuracy among the three methods being compared.

This paper proposes an improvement to Taguchi's T-method. In product development, it is difficult to gather sufficient samples at the beginning of the development process.Taguchi's T-method was proposed as a method for constructing an estimation model in such a situation. However, if an outlier exists in the training data, Taguchi's T-method cannot obtain a correct estimation model. This is because Taguchi's T-method uses the least squares. Therefore, in this paper, we propose an improved version of Taguchi's T-method that does not degrade the accuracy of estimation by using a median-median line even for small training samples with outliers. The effectiveness of the proposed method is confirmed through experiments. We confirmed that the accuracy of the proposed method is superior to that of similar methods. K E Y W O R D S median-median line, multiple regression analysis, outlier, small sample, Taguchi's T-method Electron Comm Jpn. 2019;102:49-56.

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