Using the massive MIMIC physiological database, we tried to validate pulse wave analysis (PWA) based on multiparameters model whether it can continuously estimate blood pressure (BP) values on single site of one hand. In addition, to consider the limitation of insufficient data acquirement for home user, we used pulse arrival time (PAT) driven BP information to determine the individual scale factors of the PWA-BP estimation model. Experimental results indicate that the accuracy of the average regression model has error standard deviations of  mmHg (PAT),  mmHg (PWA) for SBP and  mmHg (PAT),  mmHg (PWA) for DBP on 23 subjects over a 1 day period. We defined a local-model which is extracted regression model from sparsely selected small dataset, contrast to full dataset for 24h (average-model). The limit of BP estimation accuracy from the local-model of PWA is lower than that of PAT-BP average-model. Whereas the error of the BP estimation local-model was reduced using more data for scaling, it required more than four times the 1 min data extracted over the 12 h calibration period to predict BP for 1 day. This study shows that PWA has possibility to estimate BP value and PAT-driven BP information could be used to determine the individual scale factors of the PWA-BP estimation model for home users.

[Purpose]The purpose of this study was to develop a regression model to estimate the heart rate at the lactate threshold (HRLT) and the heart rate at the ventilatory threshold (HRVT) using the heart rate threshold (HRT), and to test the validity of the regression model.[Methods]We performed a graded exercise test with a treadmill in 220 normal individuals (men: 112, women: 108) aged 20–59 years. HRT, HRLT, and HRVT were measured in all subjects. A regression model was developed to estimate HRLT and HRVT using HRT with 70% of the data (men: 79, women: 76) through randomization (7:3), with the Bernoulli trial. The validity of the regression model developed with the remaining 30% of the data (men: 33, women: 32) was also examined.[Results]Based on the regression coefficient, we found that the independent variable HRT was a significant variable in all regression models. The adjusted R2 of the developed regression models averaged about 70%, and the standard error of estimation of the validity test results was 11 bpm, which is similar to that of the developed model.[Conclusion]These results suggest that HRT is a useful parameter for predicting HRLT and HRVT.

[Purpose]The purpose of this study was to investigate the effect of diet plus exercise training and detraining for 12 weeks on body composition, aerobic performance, and stress-related variables in obese women.[Methods]Twenty-five women in their 20s-40s with 30% body fat and body mass indices above 25 kg/m2 were divided into HRLT (heart rate at lactate threshold) and HRLT + 5% groups. Dietary intervention of 70% recommended dietary allowance (RDA) and exercise treatment composed of aerobic exercises on a bicycle (30 min) and treadmill (30 min) were then performed. These interventions were performed three times a week for 12 weeks.[Results]Dietary intake was significantly decreased, while daily activity significantly increased within the 12-week intervention period, and this effect was sustained after 12 weeks of detraining. Exercise training based on dietary intake and daily activity presented a significantly decreased weight and % body fat, improvement of aerobic performance, and a significant increase in heart rate variability (HRV) (e.g., average of all RR intervals and the square root mean squared differences of successive RR intervals) as stress-related variables. It was also confirmed that the improvement of body composition and stress-related variables were maintained even after detraining.[Conclusion]Our results suggest that 70% RDA of dietary intervention and exercise training corresponding to HRLT and HRLT + 5% for 12 weeks were effective in improving body composition and aerobic performance, and relieving stress. In particular, enhanced HRV persisted for up to 12 weeks after the end of exercise training in obese women.

This paper describes photoplethysmography (PPG)-based pulse direction determination algorithm on a site of the radial artery using a wrist band. It has been well known that PPG is susceptible to noise and motion artifacts in the mobile environment and many research efforts have been made to focus on rejection of the noise and motion artifacts. However, no research has been performed to find PPG pulses when PPG is inverted by wrist movement. We present an algorithm, which accurately yields which direction PPG pulses face regardless of wrist movement. The algorithm is one step closer to robust real-time PPG pulse direction determination for continuous PPG monitoring regardless of body movements.

We present a QRS detection algorithm for wearable ECG applications using a proportional-derivative (PD) control. ECG data of arrhythmia have irregular intervals and magnitudes of QRS waves that impede correct QRS detection. To resolve the problem, PD control is applied to avoid missing a small QRS wave followed from a large QRS wave and to avoid falsely detecting noise as QRS waves when an interval between two adjacent QRS waves is large (e.g. bradycardia, pause, and arioventricular block). ECG data was obtained from 78 patients with various cardiovascular diseases and tested for the performance evaluation of the proposed algorithm. The overall sensitivity and positive predictive value were 99.28% and 99.26%, respectively. The proposed algorithm has low computational complexity, so that it can be suitable to apply mobile ECG monitoring system in real time.