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.

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.

Motion artifact reduction in electrocardiogram using adaptive filtering based on half cell potential monitoring.

The electrocardiogram (ECG) is the main measurement parameter for effectively diagnosing chronic disease and guiding cardio-fitness therapy. ECGs contaminated by noise or artifacts disrupt the normal functioning of the automatic analysis algorithm. The objective of this study is to evaluate a method of measuring the HCP variation in motion artifacts through direct monitoring. The proposed wearable sensing device has two channels. One channel is used to measure the ECG through a differential amplifier. The other is for monitoring motion artifacts using the modified electrode and the same differential amplifier. Noise reduction was performed using adaptive filtering, based on a reference signal highly correlated with it. Direct measurement of HCP variations can eliminate the need for additional sensors.

Numerical simulation of motion-induced dynamic noise in a ubiquitous ECG application.

Wearable ubiquitous biomedical applications, such as ECG monitors, can generate dynamic noise as a person moves. However, the source of this noise is not clear. We postulated that the dynamic ECG noise has two causes: the change in displacement of the heart during motion and the change in the electrical impedance of the skin-gel interface due to motion-induced deformation of the skin-gel interface. Using a three-dimensional electrophysiological heart model coupled with a torso model, dynamic noise was simulated, while the displacement of the heart was changed in the vertical and horizontal directions, independently and while the skin-gel interface was deformed during motion. To determine the deformation rate of the skin and sol-gel layers, motion-induced deformation of the two layers was simulated using a three-dimensional finite element method.

Predicting the optimal position and direction of a ubiquitous ECG using a multi-scale model of cardiac electrophysiology.

In this study, we determined the optimal position and direction of a one-channel bipolar electrocardiogram (ECG), used ubiquitously in healthcare. To do this, we developed a three-dimensional (3D) electrophysiological model of the heart coupled with a torso model that can generate a virtual body surface potential map (BSPM). Finite element models of the atria and ventricles incorporated the electrophysiological dynamics of atrial and ventricular myocytes, respectively. The torso model, in which the electric wave pattern on the cardiac tissue is reflected onto the body surface, was implemented using a boundary element method. Using the model, we derived the optimal positions of two electrodes, 5 cm apart, of the bipolar ubiquitous ECG (U-ECG) for detecting the P, R, and T waves. This model can be used as a simulation tool to design U-ECG device for use for various arrhythmia and normal patients.