Pseudo current density maps of electrophysiological heart, nerve or brain function and their physical basis

Enhancing the efficiency and cost-effectiveness of magnetocardiography by optimal channel selection for cardiac diagnosis

Background. Magnetocardiography (MCG) is a non-invasive and non-contact technique that measures weak magnetic fields generated by the heart. It is highly effective in the diagnosis of heart abnormalities. Multichannel MCG provides detailed spatio-temporal information of the measured magnetic fields. While multichannel MCG systems are costly, usage of the optimal number of measurement channels to characterize cardiac magnetic fields without any appreciable loss of signal information would be economically beneficial and promote the widespread use of MCG technology.Methods. An optimization method based on the sequential selection approach is used to choose channels containing the maximum signal information while avoiding redundancy. The study comprised 40 healthy individuals, along with two subjects having ischemic heart disease and one subject with premature ventricular contraction. MCG measured using a 37 channel MCG system. After revisiting the existing methods of optimization, the mean error and correlation of the optimal set of measurement channels with those of all 37 channels are evaluated for different sets, and it has been found that 18 channels are adequate.Results. The chosen 18 optimal channels exhibited a strong correlation (0.99 ± 0.006) between the original and reconstructed magnetic field maps for a cardiac cycle in healthy subjects. The root mean square error is 0.295 pT, indicating minimal deviation.Conclusion. This selection method provides an efficient approach for choosing MCG, which could be used for minimizing the number of channels as well as in practical unforeseen measurement conditions where few channels are noisy during the measurement.

First activity and interactions in thalamus and cortex using raw single-trial EEG and MEG elicited by somatosensory stimulation

Introduction

One of the primary motivations for studying the human brain is to comprehend how external sensory input is processed and ultimately perceived by the brain. A good understanding of these processes can promote the identification of biomarkers for the diagnosis of various neurological disorders; it can also provide ways of evaluating therapeutic techniques. In this work, we seek the minimal requirements for identifying key stages of activity in the brain elicited by median nerve stimulation.

Methods

We have used a priori knowledge and applied a simple, linear, spatial filter on the electroencephalography and magnetoencephalography signals to identify the early responses in the thalamus and cortex evoked by short electrical stimulation of the median nerve at the wrist. The spatial filter is defined first from the average EEG and MEG signals and then refined using consistency selection rules across ST. The refined spatial filter is then applied to extract the timecourses of each ST in each targeted generator. These ST timecourses are studied through clustering to quantify the ST variability. The nature of ST connectivity between thalamic and cortical generators is then studied within each identified cluster using linear and non-linear algorithms with time delays to extract linked and directional activities. A novel combination of linear and non-linear methods provides in addition discrimination of influences as excitatory or inhibitory.

Results

Our method identifies two key aspects of the evoked response. Firstly, the early onset of activity in the thalamus and the somatosensory cortex, known as the P14 and P20 in EEG and the second M20 for MEG. Secondly, good estimates are obtained for the early timecourse of activity from these two areas. The results confirm the existence of variability in ST brain activations and reveal distinct and novel patterns of connectivity in different clusters.

Discussion

It has been demonstrated that we can extract new insights into stimulus processing without the use of computationally costly source reconstruction techniques which require assumptions and detailed modeling of the brain. Our methodology, thanks to its simplicity and minimal computational requirements, has the potential for real-time applications such as in neurofeedback systems and brain-computer interfaces.

Non-Invasive Electroanatomical Mapping: A State-Space Approach for Myocardial Current Density Estimation

Electroanatomical mapping is a method for creating a model of the electrophysiology of the human heart. Medical professionals routinely locate and ablate the site of origin of cardiac arrhythmias with invasive catheterization. Non-invasive localization takes the form of electrocardiographic (ECG) or magnetocardiographic (MCG) imaging, where the goal is to reconstruct the electrical activity of the human heart. Non-invasive alternatives to catheter electroanatomical mapping would reduce patients' risks and open new venues for treatment planning and prevention. This work introduces a new system state-based method for estimating the electrical activity of the human heart from MCG measurements. Our model enables arbitrary propagation paths and velocities. A Kalman filter optimally estimates the current densities under the given measurements and model parameters. In an outer optimization loop, these model parameters are then optimized via gradient descent. This paper aims to establish the foundation for future research by providing a detailed mathematical explanation of the algorithm. We demonstrate the feasibility of our method through a simplified one-layer simulation. Our results show that the algorithm can learn the propagation paths from the magnetic measurements. A threshold-based segmentation into healthy and pathological tissue yields a DICE score of 0.84, a recall of 0.77, and a precision of 0.93.

Magnetocardiography-based coronary artery disease severity assessment and localization using spatiotemporal features

Objective.This study aimed to develop an automatic and accurate method for severity assessment and localization of coronary artery disease (CAD) based on an optically pumped magnetometer magnetocardiography (MCG) system.Approach.We proposed spatiotemporal features based on the MCG one-dimensional signals, including amplitude, correlation, local binary pattern, and shape features. To estimate the severity of CAD, we classified the stenosis as absence or mild, moderate, or severe cases and extracted a subset of features suitable for assessment. To localize CAD, we classified CAD groups according to the location of the stenosis, including the left anterior descending artery (LAD), left circumflex artery (LCX), and right coronary artery (RCA), and separately extracted a subset of features suitable for determining the three CAD locations.Main results.For CAD severity assessment, a support vector machine (SVM) achieved the best result, with an accuracy of 75.1%, precision of 73.9%, sensitivity of 67.0%, specificity of 88.8%, F1-score of 69.8%, and area under the curve of 0.876. The highest accuracy and corresponding model for determining locations LAD, LCX, and RCA were 94.3% for the SVM, 84.4% for a discriminant analysis model, and 84.9% for the discriminant analysis model.Significance. The developed method enables the implementation of an automated system for severity assessment and localization of CAD. The amplitude and correlation features were key factors for severity assessment and localization. The proposed machine learning method can provide clinicians with an automatic and accurate diagnostic tool for interpreting MCG data related to CAD, possibly promoting clinical acceptance.

A new wearable multichannel magnetocardiogram system with a SERF atomic magnetometer array

In this study, a wearable multichannel human magnetocardiogram (MCG) system based on a spin exchange relaxation-free regime (SERF) magnetometer array is developed. The MCG system consists of a magnetically shielded device, a wearable SERF magnetometer array, and a computer for data acquisition and processing. Multichannel MCG signals from a healthy human are successfully recorded simultaneously. Independent component analysis (ICA) and empirical mode decomposition (EMD) are used to denoise MCG data. MCG imaging is realized to visualize the magnetic and current distribution around the heart. The validity of the MCG signals detected by the system is verified by electrocardiogram (ECG) signals obtained at the same position, and similar features and intervals of cardiac signal waveform appear on both MCG and ECG. Experiments show that our wearable MCG system is reliable for detecting MCG signals and can provide cardiac electromagnetic activity imaging.

Effects of magnetogastrography sensor configurations in tracking slow wave propagation

Magnetogastrography (MGG) is a non-invasive method of assessing gastric slow waves (SWs) by recording the resultant magnetic fields. MGG can capture both SW frequency and propagation, and identify SW dysrhythmias that are associated with motility disorders. However, the impact of the restricted spatial coverage and sensor density on SW propagation tracking performance is unknown. This study simulated MGG using multiple anatomically specific torso geometries and two realistic SW propagation patterns to determine the effect of different sensor configurations on tracking SW propagation. The surface current density mapping and center-of-gravity tracking methods were used to compare four magnetometer array configurations: a reference system currently used in GI research and three hypothetical higher density and coverage arrays. SW propagation patterns identified with two hypothetical arrays (with coverage over at least the anterior of the torso) correlated significantly higher with simulated realistic 3 cycle-per-minute SW activity than the reference array (p = 0.016, p = 0.005). Furthermore, results indicated that most of the magnetic fields that contribute to the performance of SW propagation tracking were located on the anterior of the torso as further increasing the coverage did not significantly increase performance. A 30% decrease in sensor spacing within the same spatial coverage of the reference array also significantly increased correlation values by approximately 0.50 when the signal-to-noise ratio was 5 dB. This study provides evidence that higher density and coverage sensor layouts will improve the utility of MGG. Further work is required to investigate optimum sensor configurations across larger anatomical variations and other SW propagation patterns.

Choice of Magnetometers and Gradiometers after Signal Space Separation

BACKGROUND

Modern Elekta Neuromag MEG devices include 102 sensor triplets containing one magnetometer and two planar gradiometers. The first processing step is often a signal space separation (SSS), which provides a powerful noise reduction. A question commonly raised by researchers and reviewers relates to which data should be employed in analyses: (1) magnetometers only, (2) gradiometers only, (3) magnetometers and gradiometers together. The MEG community is currently divided with regard to the proper answer.

METHODS

First, we provide theoretical evidence that both gradiometers and magnetometers result from the backprojection of the same SSS components. Then, we compare resting state and task-related sensor and source estimations from magnetometers and gradiometers in real MEG recordings before and after SSS.

RESULTS

SSS introduced a strong increase in the similarity between source time series derived from magnetometers and gradiometers (r² = 0.3-0.8 before SSS and r² > 0.80 after SSS). After SSS, resting state power spectrum and functional connectivity, as well as visual evoked responses, derived from both magnetometers and gradiometers were highly similar (Intraclass Correlation Coefficient > 0.8, r² > 0.8).

CONCLUSIONS

After SSS, magnetometer and gradiometer data are estimated from a single set of SSS components (usually ≤ 80). Equivalent results can be obtained with both sensor types in typical MEG experiments.

An Integrated Maximum Current Density Approach for Noninvasive Detection of Myocardial Infarction

We present a new approach of integrated maximum current density (IMCD) for the noninvasive detection of myocardial infarction (MI) using magnetocardiography (MCG) data acquired from a superconducting quantum interference device (SQUID) system. In this paper, we investigated the relationship of the maximum current density (MCD) in the current density map and the underlying equivalent current dipole (ECD) based on a novel method of reconstructing the ECD in the extremum circle of the magnetic field map. The performance of IMCD and the integrated ECD (IECD) approaches were also evaluated by using 61-channel MCG data from 39 healthy subjects and 102 patients with ST elevation myocardial infarction (STEMI). Statistical analysis of the healthy and STEMI groups demonstrate that the IMCD approach obtains sensitivity and specificity up to 91.2% and 84.6%, somewhat higher than that of IECD, respectively. The results indicate that IMCD provides spatiotemporal information regarding cardiac electrical activity during ventricular repolarization. This approach may be helpful to diagnose MI in clinic application. The physical concept of the approach is also explained in this paper.

Characterization of gastric electrical activity using magnetic field measurements: a simulation study

Gastric disorders are often associated with abnormal propagation of gastric electrical activity (GEA). The identification of clinically relevant parameters of GEA using noninvasive measures would therefore be highly beneficial for clinical diagnosis. While magnetogastrograms (MGG) are known to provide a noninvasive representation of GEA, standard methods for their analysis are limited. It has previously been shown in simplistic conditions that the surface current density (SCD) calculated from multichannel MGG measurements provides an estimate of the gastric source location and propagation velocity. We examine the accuracy of this technique using more realistic source models and an anatomically realistic volume conductor model. The results showed that the SCD method was able to resolve the GEA parameters more reliably when the dipole source was located within 100 mm of the sensor. Therefore, the theoretical accuracy of SCD method would be relatively diminished for patients with a larger body habitus, and particularly in those patients with significant truncal obesity. However, many patients with gastric motility disorders are relatively thin due to food intolerance, meaning that the majority of the population of gastric motility patients could benefit from the methods developed here. Large errors resulted when the source was located deep within the body due to the distorting effects of the secondary sources on the magnetic fields. Larger errors also resulted when the dipole was oriented normal to the sensor plane. This was believed to be due to the relatively small contribution of the dipole source when compared to the field produced by the volume conductor. The use of three orthogonal magnetic field components rather than just one component to calculate the SCD yielded marginally more accurate results when using a realistic dipole source. However, this slight increase in accuracy may not warrant the use of more complex vector channels in future superconducting quantum interference device designs. When multiple slow waves were present in the stomach, the SCD map contained only one maximum point corresponding to the more dominant source located in the distal stomach. Parameters corresponding to the slow wave in the proximal stomach were obtained once the dominant slow terminated at the antrum. Additional validation studies are warranted to address the utility of the SCD method to resolve parameters related to gastric slow waves in a clinical setting.

Biomagnetic signatures of uncoupled gastric musculature

Gastric slow waves propagate in the electrical syncytium of the healthy stomach, being generated at a rate of approximately three times per minute in a pacemaker region along the greater curvature of the antrum and propagating distally towards the pylorus. Disease states are known to alter the normal gastric slow wave. Recent studies have suggested the use of biomagnetic techniques for assessing parameters of the gastric slow wave that have potential diagnostic significance. We present a study in which the gastric syncytium was uncoupled by mechanical division as we recorded serosal electric potentials along with multichannel biomagnetic signals and cutaneous potentials. By computing the surface current density (SCD) from multichannel biomagnetic recordings, we were able to quantify gastric slow wave propagation as well as the frequency and amplitude of the slow wave and to show that these correlate well with similar parameters from serosal electrodes. We found the dominant slow wave frequency to be an unreliable indicator of gastric uncoupling as uncoupling results in the appearance of multiple slow wave sources at various frequencies in external recordings. The percentage of power distributed in specific frequency ranges exhibited significant postdivision changes. Propagation velocity determined from SCD maps was a weak indicator of uncoupling in this work; we believe that the relatively low spatial resolution of our 19-channel biomagnetometer confounds the characterization of spatial variations in slow wave propagation velocities. Nonetheless, the biomagnetic technique represents a non-invasive method for accurate determination of clinically significant parameters of the gastric slow wave.

Surface current density mapping for identification of gastric slow wave propagation

The magnetogastrogram (MGG) records clinically relevant parameters of the electrical slow wave of the stomach noninvasively. Besides slow wave frequency, gastric slow wave propagation velocity is a potentially useful clinical indicator of the state of health of gastric tissue, but it is a difficult parameter to determine from noninvasive bioelectric or biomagnetic measurements. We present a method for computing the surface current density from multichannel MGG recordings that allows computation of the propagation velocity of the gastric slow wave. A moving dipole source model with hypothetical as well as realistic biomagnetometer parameters demonstrates that while a relatively sparse array of magnetometer sensors is sufficient to compute a single average propagation velocity, more detailed information about spatial variations in propagation velocity requires higher density magnetometer arrays. Finally, the method is validated with simultaneous MGG and serosal electromyography measurements in a porcine subject.

Non-invasive detection of conduction pathways to left atrium using magnetocardiography: validation by intra-cardiac electroanatomic mapping

AIMS

Alteration in conduction from right to left atrium (LA) is linked to susceptibility to atrial fibrillation (AF). We examined whether different inter-atrial conduction pathways can be identified non-invasively by magnetocardiographic mapping (MCG).

METHODS AND RESULTS

In 27 patients undergoing catheter ablation of paroxysmal AF, LA activation sequence was determined during sinus rhythm using invasive electroanatomic mapping. Before this, 99-channel magnetocardiography was recorded over anterior chest. The orientation of the magnetic fields during the early (40-70 ms from P onset) and later part (last 50%) of LA depolarization was determined using pseudocurrent conversion. Breakthrough of electrical activation to LA occurred through Bachmann bundle (BB) in 14, margin of fossa ovalis (FO) in 3, coronary sinus ostial region (CS) in 2, and their combinations in 10 cases by invasive reference in total of 29 different P-waves. Based on the combination of pseudocurrent angles over early and late parts of LA activation, the MCG maps were divided to three types. These types correctly identified the LA breakthrough sites to BB, CS, FO, or their combinations in 27 of 29 (93%) cases.

CONCLUSION

Magnetocardiographic mapping seems capable of distinguishing inter-atrial conduction pathways. Recognizing the inter-atrial conduction pattern may assist in understanding the pathogenesis of AF and identifying the subgroups for patient-tailored therapy.