Baseline wander removal is one important part of electrocardiogram (ECG) filtering. This can be achieved by many different approaches. This work investigates the influence of three different baseline wander removal techniques on ST changes. The chosen filters were phase-free Butterworth filtering, median filtering and baseline correction with cubic spline interpolation. 289 simulated ECGs containing ischemia were used to determine the influence of these filtering processes on the ST segment. Synthetic baseline wander and offsets were added to the simulated signals. All methods proved to be good approaches by removing most of the baseline wander in all signals. Correlation coefficients between the original signals and the filtered signals were greater than 0.93 for all ECGs. Cubic spline interpolation performed best regarding the preservation of the ST segment amplitude change when compared to the original signal. The approach modified the ST segment by 0.10mV±0.06mV at elevated K points. Median filtering introduced a variation of 0.33mV±0.29mV, Butterworth filtering reached 0.16mV±0.14mV at elevated K points. Thus, all methods manipulate the ST segment.
The electric conductivity of human tissue could be used as an additional diagnostic parameter or might be helpful for the prediction of the local SAR during MR measurements. In this study, the approach Electric Properties Tomography (MR-EPT) is applied, which derives the patients electric conductivity using a standard MR system. To this goal, the spatial transmit sensitivity distribution of the applied RF coil is measured. This sensitivity distribution represents the positive circularly polarized component of the magnetic field. It can be post-processed utilizing Faradays and Amperes law, yielding an estimation of the spatial distribution of the patients electric conductivity. Thus, MR-EPT does not apply externally mounted electrodes, currents, or RF probes. In this study, phantom experiments underline the principle feasibility of MR-EPT. Furthermore, initial conductivity measurements in the brain allow distinguishing cerebrospinal fluid from the surrounding grey and white matter.