Biomedical subjects

Lutz Trahms

Publications and source records attributed to Lutz Trahms.

2 recordsLinked to original sources

PubMed2002

Errors in repolarization measurement using magnetocardiography.

Multichannel magnetocardiography (MCG) noninvasively measures variations in magnetic field strength from many sites at the body surface, potentially providing useful regional information about ventricular repolarization. MCGs contain features similar to ECGs, and although errors associated with repolarization measurement have been quantified for ECGs, no comparative data exists for MCGs. In this study, errors in manual measurement of repolarization interval in the MCG were determined. Sixteen MCG channels and three ECG leads were recorded simultaneously in eight healthy subjects. Each recording was displayed in a random order on a computer screen, in presentations with different noise levels, time display widths, and amplitude display heights. In total, manual measurement of repolarization intervals in 2,048 (eight subjects x 16 channels x eight presentations x two repeats) MCGs were made by each of four analysts. Measured repolarization intervals were reduced by 3 ms when noise was added and by a further 3 ms when this noise was doubled. Intervals were shortened by 9 ms when the time display width was doubled and by a further 10 ms when the display width was doubled again. Measurements increased by 7 ms for a doubling of amplitude display height, equivalent to a doubling of T wave height. There were also consistent differences between analysts; amounting to a greatest mean difference of 24 ms. Display characteristics, added noise, and different analysts thus affect manual repolarization interval measurements in MCG. The errors detected demonstrate the importance of a standard presentation for repolarization measurement in the MCG.

Electrocardiography↗

PubMed2002

Cardiac artifact subspace identification and elimination in cognitive MEG data using time-delayed decorrelation.

To reduce physiological artifacts in magnetoencephalographic (MEG) and electroencephalographic recordings, a number of methods have been applied in the past such as principal component analysis, signal-space projection, regression using secondary information, and independent component analysis. This method has become popular as it does not have constraints such as orthogonality between artifact and signal or the need for a priori information. Applying the time-delayed decorrelation algorithm to raw data from a visual stimulation MEG experiment, we show that several of the independent components can be attributed to the cardiac artifact. Calculating an average cardiac activity shows that physiologically different excitation states of the heart produce similar field distributions in the MEG sensor system. This is equivalent to differing spectral properties of cardiac field distributions in the raw data. As a consequence, the algorithm combines, e.g., the R peak and the T wave of the cardiac cycle into a single component and the one-to-one assignment of each independent component with a physiological source is not justified in this case. To improve the signal quality of visually evoked fields, the multidimensional cardiac artifact subspace is suppressed from the data. To assess the preservation of the evoked signal after artifact suppression, a geometrical and a temporal measure are introduced. The suppression of cardiac and alpha wave artifacts allows, in our experimental setting, the reduction of the number of epochs to one half while preserving the visually evoked signal.

Algorithms↗