Analytical Methods for Assessment of Temporal Changes in Heart Function

Abstract

Quick and automatic detection of abnormal signals in electroencephalogram (ECG) can help cardiovascular patients. We firstly focused on the judgment of ST depression as an abnormal ECG signal. The optimal threshold was explored by the modified cross-validation analysis based on a correlation coefficient between ECG data on the ST depression as a template and the other disease (i.e., ventricular fibrillation or abnormal T waves). The optimal threshold of the correlation coefficient was around 0.8. The calculated threshold was little affected by the type of linear or spline interpolation and data length (i.e., 100, 200, and 300 points for the normalization). These results could reduce the computation time in online analysis of e-healthcare applications. Next, we assessed the temporal change in individual's heart function during the advanced trail making test (ATMT). The heart rate variability (HRV) analysis was performed in the time or frequency domain, and it was able to reflect healthy and unhealthy conditions during the ATMT. This result will be significantly affected by the activity of the autonomic nervous system. The indices for the HRV could be applied to a home healthcare system to find potential patients from daily temporal changes in heart function.