D DiPietroPaolo

Publications of D DiPietroPaolo

• Noise reduction in magnetocardiography by singular value decomposition and independent component analysis.

  Authors: D DiPietroPaolo, H P Müller, G Nolte, S N Erné

  Medical & biological engineering & computing. 07/2006; 44(6):489-99.

  In the routine recording of magnetocardiograms (MCGs), it is necessary to underline the problem of noise cancellation. Source separation has often been suggested to solve this problem. In this paper,In the routine recording of magnetocardiograms (MCGs), it is necessary to underline the problem of noise cancellation. Source separation has often been suggested to solve this problem. In this paper, blind source separation (BSS), by means of singular value decomposition (SVD) and independent component analysis (ICA), was used for noise reduction in MCG data to improve the signal to noise ratio. Special techniques, based on statistical parameters, for identifying noise and disturbances, have been introduced to automatically eliminate noise-related and disturbance-related components before reconstructing cleaned data sets. The results show that ICA and SVD can detect and remove a variety of noise and artefact sources from MCG data, as well as from stress MCG.

• A novel approach for the averaging of magnetocardiographically recorded heart beats.

  Authors: D DiPietroPaolo, H P Müller, S N Erné

  Physics in medicine and biology. 06/2005; 50(10):2415-26.

  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 commonPerforming 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.