Sang-Kon Bae

Patient-Specific Identification of Optimal Ubiquitous Electrocardiogram (U-ECG) Placement Using a Three-Dimensional Model of Cardiac Electrophysiology

A bipolar mini-ECG for ubiquitous healthcare (U-ECG) has been introduced, and various studies using the U-ECG device are in progress. Because it uses two electrodes within a small torso surface area, the design of the U-ECG must be suitable for detecting ECG signals. Using a 3-D model of cardiac electrophysiology, we have developed a simulation method for identifying the optimal placement of U-ECG electrodes on the torso surface. We simulated the heart-torso model to obtain a body surface potential map and ECG waveforms, which were compared with the empirical data. Using this model, we determined the optimal placement of the two U-ECG electrodes, spaced 5 cm apart, for detecting the P, R, and T waves. The ECG data, obtained using the optimal U-ECG placement for a specific wave, showed a clear shape for the target wave, but equivocal shapes for the other waves. The present study provides an efficient simulation method to identify the optimal attachment position and direction of the U-ECG electrodes on the surface of the torso.

Factors affecting the accuracy of volume-oscillometric blood pressure measurement during partial pressurization of the wrist

We compared the volume-oscillometric responses of the airbag pressure sensor and the contact force sensor across and along the radial artery on the wrist during partial pressurization by an airbag. Because of the anatomic structure and non-uniform pressurization pressure distribution, elongated and shifted oscillometric pressure waveform envelope variations are observed. For the contact force sensors directly above the radial artery, S-shaped pressurization curves can be seen possibly due to temporal softening of the radial artery stiffness at near zero transmural pressure. These differences in the shape of oscillometric envelope as well as pressurization curve may be the leading factors for inaccuracies of volume-oscillometric blood pressure measurement by partial pressurization method using an airbag.

Development of an anaerobic threshold (HRLT, HRVT) estimation equation using the heart rate threshold (HRT) during the treadmill incremental exercise test

[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.

A preliminary study of a running speed based heart rate prediction during an incremental treadmill exercise

This preliminary study investigates feasibility of a running speed based heart rate (HR) prediction. It is basically motivated from the assumption that there is a significant relationship between HR and the running speed. In order to verify the assumption, HR and running speed data from 217 subjects of varying aerobic capabilities were simultaneously collected during an incremental treadmill exercise. A running speed was defined as a treadmill speed and its corresponding heart rate was calculated by averaging the last one minute HR values of each session. The feasibility was investigated by assessing a correlation between the heart rate and the running speed using inter-subject (between-subject) and intra-subject (within-subject) datasets with regression orders of 1, 2, 3, and 4, respectively. Furthermore, HR differences between actual and predicted HRs were also employed to investigate the feasibility of the running speed in predicting heart rate. In the inter-subject analysis, a strong positive correlation and a reasonable HR difference (r = 0.866, 16.55±11.24 bpm @ 1st order; r = 0.871, 15.93±11.49 bpm @ 2nd order; r = 0.897, 13.98±10.80 bpm @ 3rd order; and r = 0.899, 13.93±10.64 bpm @ 4th order) were obtained, and a very high positive correlation and a very low HR difference (r = 0.978, 6.46±3.89 bpm @ 1st order; r = 0.987, 5.14±2.87 bpm @ 2nd order; r = 0.996, 2.61±2.03 bpm @ 3rd order; and r = 0.997, 2.04±1.73 bpm @ 4th order) were obtained in the intra-subject analysis. It can therefore be concluded that 1) heart rate is highly correlated with a running speed; 2) heart rate can be approximately estimated by a running speed with a proper statistical model (e.g., 3rd-order regression); and 3) an individual HR-speed calibration process may improve the prediction accuracy.This preliminary study investigates feasibility of a running speed based heart rate (HR) prediction. It is basically motivated from the assumption that there is a significant relationship between HR and the running speed. In order to verify the assumption, HR and running speed data from 217 subjects of varying aerobic capabilities were simultaneously collected during an incremental treadmill exercise. A running speed was defined as a treadmill speed and its corresponding heart rate was calculated by averaging the last one minute HR values of each session. The feasibility was investigated by assessing a correlation between the heart rate and the running speed using inter-subject (between-subject) and intra-subject (within-subject) datasets with regression orders of 1, 2, 3, and 4, respectively. Furthermore, HR differences between actual and predicted HRs were also employed to investigate the feasibility of the running speed in predicting heart rate. In the inter-subject analysis, a strong positive correlation and a reasonable HR difference (r = 0.866, 16.55±11.24 bpm @ 1st order; r = 0.871, 15.93±11.49 bpm @ 2nd order; r = 0.897, 13.98±10.80 bpm @ 3rd order; and r = 0.899, 13.93±10.64 bpm @ 4th order) were obtained, and a very high positive correlation and a very low HR difference (r = 0.978, 6.46±3.89 bpm @ 1st order; r = 0.987, 5.14±2.87 bpm @ 2nd order; r = 0.996, 2.61±2.03 bpm @ 3rd order; and r = 0.997, 2.04±1.73 bpm @ 4th order) were obtained in the intra-subject analysis. It can therefore be concluded that 1) heart rate is highly correlated with a running speed; 2) heart rate can be approximately estimated by a running speed with a proper statistical model (e.g., 3rd-order regression); and 3) an individual HR-speed calibration process may improve the prediction accuracy.

Effect of pressurization methods on the accuracy of wrist blood pressure measurement

In developing a wrist blood pressure monitor of high and reliable accuracy, the effect of different pressurization methods on the accuracy of blood pressure measurement at the wrist using oscillometry is investigated in this paper. 30 volunteers are recruited and blood pressure readings are taken with three different methods of pressurizing the wrist. It was found that measurement of mean arterial pressure (MAP) is more accurate when the wrist is locally compressed directly over the radial artery (−2.6 ± 11.4 mmHg) or with a region of surrounding tissue (10.3 ± 6.0 mmHg) than when the whole wrist is compressed by a conventional, constricting cuff (−11.4 ± 16.4 mmHg). Characteristics of accuracy, however, differ between the two local pressurization methods. While a square airbag that compresses the wrist directly over the radial artery may measure the most accurate MAP on average, the range of errors among individuals is large. Contrarily, measurements taken by pressurizing a region over the radial artery with a bladder are least affected by individual variability. In order to measure blood pressure accurately at the wrist while unbiased by the population-based algorithmic compensation to ensure accuracy among different individuals, therefore, the use of local pressurization method may be the most appropriate.