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

Comparison of JADE and canonical correlation analysis for ECG de-noising

This paper explores differences between two methods for blind source separation within frame of ECG de-noising. First method is joint approximate diagonalization of eigenmatrices, which is based on estimation of fourth order cross-cummulant tensor and its diagonalization. Second one is the statistical method known as canonical correlation analysis, which is based on estimation of correlation matrices between two multidimensional variables. Both methods were used within method, which combines the blind source separation algorithm with decision tree. The evaluation was made on large database of 382 long-term ECG signals and the results were examined. Biggest difference was found in results of 50 Hz power line interference where the CCA algorithm completely failed. Thus main power of CCA lies in estimation of unstructured noise within ECG. JADE algorithm has larger computational complexity thus the CCA perfomed faster when estimating the components.

Baseline drift removal and denoising of MCG data using EEMD: role of noise amplitude and the thresholding effect

We adopt the Ensemble Empirical Mode Decomposition (EEMD) method, with an appropriate thresholding on the Intrinsic Mode Functions (IMFs), to denoise the magnetocardiography (MCG) signal. To this end, we discuss the two associated problems that relate to: (i) the amplitude of noise added to the observed signal in the EEMD method with a view to prevent mode mixing and (ii) the effect of direct thresholding that causes discontinuities in the reconstructed denoised signal. We then denoise the MCG signals, having various signal-to-noise ratios, by using this method and compare the results with those obtained by the standard wavelet based denoising method. We also address the problem of eliminating the high frequency baseline drift such as the sudden and discontinuous changes in the baseline of the experimentally measured MCG signal using the EEMD based method. We show that the EEMD method used for denoising and the elimination of baseline drift is superior in performance to other standard methods such as wavelet based techniques and Independent Component Analysis (ICA).

Multichannel system based on a high sensitivity superconductive sensor for magnetoencephalography

We developed a multichannel system based on superconducting quantum interference devices (SQUIDs) for magnetoencephalography measurements. Our system consists of 163 fully-integrated SQUID magnetometers, 154 channels and 9 references, and all of the operations are performed inside a magnetically-shielded room. The system exhibits a magnetic field noise spectral density of approximatively 5 fT/Hz(1=2). The presented magnetoencephalography is the first system working in a clinical environment in Italy.

Independent component analysis and decision trees for ECG holter recording de-noising

We have developed a method focusing on ECG signal de-noising using Independent component analysis (ICA). This approach combines JADE source separation and binary decision tree for identification and subsequent ECG noise removal. In order to to test the efficiency of this method comparison to standard filtering a wavelet- based de-noising method was used. Freely data available at Physionet medical data storage were evaluated. Evaluation criteria was root mean square error (RMSE) between original ECG and filtered data contaminated with artificial noise. Proposed algorithm achieved comparable result in terms of standard noises (power line interference, base line wander, EMG), but noticeably significantly better results were achieved when uncommon noise (electrode cable movement artefact) were compared.

Spatiotemporal properties of intracellular calcium signaling in osteocytic and osteoblastic cell networks under fluid flow

Mechanical stimuli can trigger intracellular calcium (Ca(2+)) responses in osteocytes and osteoblasts. Successful construction of bone cell networks necessitates more elaborate and systematic analysis for the spatiotemporal properties of Ca(2+) signaling in the networks. In the present study, an unsupervised algorithm based on independent component analysis (ICA) was employed to extract the Ca(2+) signals of bone cells in the network. We demonstrated that the ICA-based technology could yield higher signal fidelity than the manual region of interest (ROI) method. Second, the spatiotemporal properties of Ca(2+) signaling in osteocyte-like MLO-Y4 and osteoblast-like MC3T3-E1 cell networks under laminar and steady fluid flow stimulation were systematically analyzed and compared. MLO-Y4 cells exhibited much more active Ca(2+) transients than MC3T3-E1 cells, evidenced by more Ca(2+) peaks, less time to the 1st peak and less time between the 1st and 2nd peaks. With respect to temporal properties, MLO-Y4 cells demonstrated higher spike rate and Ca(2+) oscillating frequency. The spatial intercellular synchronous activities of Ca(2+) signaling in MLO-Y4 cell networks were higher than those in MC3T3-E1 cell networks and also negatively correlated with the intercellular distance, revealing faster Ca(2+) wave propagation in MLO-Y4 cell networks. Our findings show that the unsupervised ICA-based technique results in more sensitive and quantitative signal extraction than traditional ROI analysis, with the potential to be widely employed in Ca(2+) signaling extraction in the cell networks. The present study also revealed a dramatic spatiotemporal difference in Ca(2+) signaling for osteocytic and osteoblastic cell networks in processing the mechanical stimulus. The higher intracellular Ca(2+) oscillatory behaviors and intercellular coordination of MLO-Y4 cells provided further evidences that osteocytes may behave as the major mechanical sensor in bone modeling and remodeling processes.

Influence of shell properties on high-frequency ultrasound imaging and drug delivery using polymer-shelled microbubbles

This two-part study investigated shell rupture of ultrasound contrast agents (UCAs) under static overpressure conditions and the subharmonic component from UCAs subjected to 20-MHz tonebursts. Five different polylactide-shelled UCAs with shell-thickness-to-radius ratios (STRRs) of 7.5, 30, 40, 65, and 100 nm/¿m were subjected to static overpressure in a glycerol-filled test chamber. A video microscope imaged the UCAs as pressure varied from 2 to 330 kPa over 90 min. Images were postprocessed to obtain the pressure threshold for rupture and the diameter of individual microbubbles. Backscatter from individual UCAs was investigated by flowing a dilute UCA solution through a wall-less flow phantom placed at the geometric focus of a 20-MHz transducer. UCAs were subjected to 10- and 20-cycle tonebursts of acoustic pressures ranging from 0.3 to 2.3 MPa. A method based on singular-value decomposition (SVD) was employed to obtain a cumulative subharmonic score (SHS). Different UCA types exhibited distinctly different rupture thresholds that were linearly related to their STRR, but uncorrelated with UCA size. The rupture threshold for the UCAs with an STRR = 100 nm/μm was more than 4 times greater than the UCAs with an STRR = 7.5 nm/μm. The polymer-shelled UCAs produced substantial subharmonic response but the subharmonic response to 20- MHz excitation did not correlate with STRRs or UCA-rupture pressures. The 20-cycle excitation resulted in an SHS that was 2 to 3 times that of UCAs excited with 10-cycle tonebursts.

Cardiac signal extraction in patients with Implantable Cardioverter Defibrillators

According to the guidelines the indication for Implantable Cardioverter Defibrillator (ICD) implantation is based on the ejection fraction. However, only a fraction of patients with implanted ICD shows live threatening arrhythmic events followed by adequate shocks. For this reason, further research is needed to find a more sensitive risk stratificator for patients prone to ventricular tachycardia or fibrillation. Unfortunately, standard prospective studies are time consuming. An alternative approach is to perform retrospective studies on patients with already implanted ICDs. So far, an implanted ICD is an exclusion criterion for Magnetic Field Imaging (MFI) studies. To overcome this problem several Blind Source Separation (BSS) algorithms have been tested to find out whether it is possible to separate the disturbances from the cardiac signals, in spite of the extreme difference in amplitude. Not all the methods are able to separate cardiac signal and disturbances. Temporal Decorrelation source Separation (TDSEP) is found to be superior both from a separation and performing point of view. For the first time it is possible to extract cardiac signals from measurements disturbed by an ICD, offering the possibility for a QRS-fragmentation analysis in patients with already implanted ICDs.

Subharmonic analysis using singular-value decomposition of ultrasound contrast agents

Ultrasound contrast agents (UCAs) are designed to be used below 10 MHz, but interest is growing in studying the response of agents to high-frequency ultrasound. In this study, the subharmonic response of polymer-shelled UCAs with a mean diameter of 1.1 mum excited with 40-MHz tone-bursts of 1-20 cycles was analyzed. UCAs were diluted in water and streamed through a flow phantom that permitted single-bubble backscatter events to be acquired at peak-negative pressures from 0.75 to 5.0 MPa. At each exposure condition, 1000 single-bubble-backscatter events were digitized. Subharmonic content at 20 MHz was screened using a conventional and a singular-value-decomposition (SVD) method. The conventional method evaluated each event spectrum individually while the SVD method treated the 1000-event data set at one time. A subharmonic score (SHS) indicative of how much subharmonic content a 1000-event data set contained was computed for both methods. Empirical-simulation results indicated that SHSs obtained from the two methods were linearly related. Also, experimental data with both methods indicated that subharmonic likelihood increased with pulse duration and peaked near 2 MPa. The SVD method also yielded quantitative information about subharmonic events not available with the conventional method.

Independent component analysis: comparison of algorithms for the investigation of surface electrical brain activity

We compared the performance of 22 algorithms for independent component analysis with the aim to find suitable algorithms for applications in the field of surface electrical brain activity analysis. The quality of the separation is assessed with four performance measures: a correlation coefficient based index, a signal-to-interference ratio, a signal-to-distortion-ratio and the computational demand. Artificial data are used consisting of typical electroencephalogram and evoked potentials signal patterns, e.g. spikes, polyspikes, sharp waves and spindles. We evaluate different noise scenarios and the influence of pre-whitening. The comparisons reveal considerable differences between the algorithms, especially concerning the computational load. Algorithms based on the time structure of the data set seem to have advantages in separation quality especially for sine-shaped signals. Derivates of FastICA and Infomax also attain good results. Our results can serve as a reference for selecting a task-specific algorithm to analyze a large number of signal patterns occurring in the surface electrical brain activity.