A novel approach for the averaging of magnetocardiographically recorded heart beats

Abstract

Performing signal averaging in an efficient and correct way is indispensable since it is a prerequisite for a broad variety of magnetocardiographic (MCG) analysis methods. One of the most common procedures for performing the signal averaging to increase the signal-to-noise ratio (SNR) in magnetocardiography, as well as in electrocardiography (ECG), is done by means of spatial or temporal techniques. In this paper, an improvement of the temporal averaging method is presented. In order to obtain an accurate signal detection, temporal alignment methods and objective classification criteria are developed. The processing technique based on hierarchical clustering is introduced to take into account the non-stationarity of the noise and, to some extent, the biological variability of the signals reaching the optimum SNR. The method implemented is especially designed to run fast and does not require any interaction from the operator. The averaging procedure described in this work is applied to the averaging of MCG data as an example, but with its intrinsic properties it can also be applied to the averaging of ECG recording, averaging of body-surface-potential mapping (BSPM) and averaging of magnetoencephalographic (MEG) or electroencephalographic (EEG) signals.

Development of integrative autonomic nervous system function: an investigation based on time correlation in fetal heart rate patterns

“…The maternal ECG was registered to distinguish between fetal and maternal cardiac activity. After eliminating the maternal QRS complexes using blind source separation [2,7] , the fetal QRS complexes were detected in the magnetic channels with the highest signal-to-noise ratio [6] . In all data sets analyzed, the portion of ectopic beats and incorrect detections was < 5 % .…”

Quantitative evaluation of signal integrity for magnetocardiography

“…The investigation was performed to the optimization on the window length, and apparently, the method is quite versatile. It is also applicable to evaluate other procedures for the MCG signal processing, such as adaptive filter, adaptive frequency-dependent gradiometry, signal space separation, some novel filters, etc (Ken et al 2002, Zhu et al 2005, Taulu et al 2005, DiPietroPaolo et al 2005. The evaluation can be further enhanced by using multiple coils of different magnitude and orientation.…”

Impact of the control for corrupted diffusion tensor imaging data in comparisons at the group level: an application in Huntington disease

“…Ideally, the quality of the measured signal (i.e. the signal to noise ratio: SNR) can be improved by signal accumulation [16]. If applied to DTI data, the repeated recording of diffusion encoding volumes should lead to an improvement of the accurateness of the diffusion tensor and the FA value in each voxel [17, 18].…”