Magnetocardiography Research Articles

Localization of magnetocardiographic sources for myocardial infarction cases using deterministic and Bayesian approaches

In this paper, the inverse problems of cardiac sources using analytical and probabilistic methods are solved and discussed. The standard Tikhonov regularization technique is solved initially to estimate the under-determined heart surface potentials from Magnetocardiographic (MCG) signals. The results of the deterministic method subjected to noise in the measurements are discussed and compared with the probabilistic models. Hierarchical Bayesian modeling with fixed Gaussian prior is employed to quantify the uncertainties in source reconstructions. A novel application of Variational Bayesian inference approach has been presented to estimate the heart sources. The reconstruction results of Variational Bayesian model with non-stationary priors are compared with solutions of simplistic Bayesian approach; and the performances are evaluated using Root Mean Square Error (RMSE) and correlation co-efficient metrics. The Bayesian solutions in the study are also extended to localize the MCG sources for two types of Myocardial infarction cases.

Feasibility of novel unshielded portable magnetocardiography: Insights from the prospective multicenter MAGNETO-SCD trial

Sudden cardiac death(SCD) causes >4million deaths globally/year, predominantly through ventricular arrhythmia(VA)1 caused by vulnerable-substrate via heterogenous conduction during cardiac-depolarisation/repolarization and survival is poor. 1 Current risk-stratifiers using cardiac-imaging/ECG are sub-optimal.1 Magnetocardiography(MCG) may be superior in detecting SCD/VA-risk,2,3,4 however, its use has been limited by significant financial/logistic restrictions.

Detection of Extremely Weak and Wideband Bio-Magnetic Signals in Non-Shielded Environments Using Passive Coil Sensors

We report a coil-based passive sensing system and associated Digital Signal Processing (DSP) capable of detecting extremely weak and wideband bio-magnetic signals without the need for shielding. Our previous work showed potential in this direction but was limited to detecting magnetocardiography (MCG) signals that are the strongest emanated by the human body as well as narrowband. In a major step forward, we advance our DSP with notch and Empirical Mode Decomposition filters, in addition to bandpass filtering and averaging utilized in the past and refine our coil design. We characterize the system's noise performance, analyze effectiveness of the DSP methods, and validate performance <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in vitro</i> . The proposed system considerably outperforms our previous design: noise levels at 10 Hz and 100 Hz are reduced by ∼46% and ∼92%, respectively, while detection sensitivity is improved by ∼94% across a ∼3333% wider signal bandwidth. That is, signals as low as magnetomyography (MMG) and evoked Compound Action Potentials (eCAP) and of frequency range comparable to magnetoneurography (MNG) can now be retrieved passively, in non-shielded environments. As a proof-of-concept, we utilize a single sensor and 24 minutes of recording; however, these parameters are scalable, as is the strength and frequency range of detectable bio-magnetic signals. To this end, we include discussions on how the hardware and DSP components can be parameterized and adjusted to fit diverse clinical needs. The proposed system can empower seamless detection of MCG, MMG, eCAP, and MNG, among others, opening unexplored opportunities for the future of medical diagnostics, monitoring, and treatment.

DHA Supplementation During Pregnancy Enhances Maternal Vagally Mediated Cardiac Autonomic Control in Humans

DHA is an essential omega-3 (ω-3; n-3) fatty acid that has well-established benefits for the fetus. DHA also has the potential to influence the health of the mother, but this area is understudied. The objective of this secondary analysis was to determine if DHA was related to maternal heart rate (HR) and heart rate variability (HRV) metrics in a large cohort of pregnant women. In the parent trial (1R01HD086001) eligible participants (≥18 y old, English speaking, carrying a singleton pregnancy, 12-20 wk of gestation) were randomly assigned to consume 200mg/d or 800mg/d DHA for the duration of their pregnancy (n=300). Weight, blood pressure, and magnetocardiograms (MCGs) were collected at 32 wk and 36 wk of gestation (n=221). Measures of HR and HRV in time-, frequency-, and nonlinear-domains were determined from the isolated maternal MCG. Treatment group and timepoint were examined as predictors in association with HR and HRV metrics using random-intercept mixed-effects ANOVA unadjusted and adjusted models accounting for weight and dietary DHA intake. Women receiving the higher dose of DHA (800mg/d) during pregnancy had lower HR, lower sympathetic index, higher vagally mediated HRV indices, and greater HRV complexity when compared with the women who received the lower dose (200mg/d; all P<0.05). All the dose relations remained significant even after controlling for the effect of time, maternal weight, and dietary DHA intake. DHA supplementation increases vagal tone in pregnant women. Longitudinal studies examining the potential link between DHA, enhanced vagal tone, and reported reduction in early preterm birth are warranted.

Application of VCSEL in Bio-Sensing Atomic Magnetometers.

Recent years have seen rapid development of chip-scale atomic devices due to their great potential in the field of biomedical imaging, namely chip-scale atomic magnetometers that enable high resolution magnetocardiography (MCG) and magnetoencephalography (MEG). For atomic devices of this kind, vertical cavity surface emitting lasers (VCSELs) have become the most crucial components as integrated pumping sources, which are attracting growing interest. In this paper, the application of VCSELs in chip-scale atomic devices are reviewed, where VCSELs are integrated in various atomic bio-sensing devices with different operating environments. Secondly, the mode and polarization control of VCSELs in the specific applications are reviewed with their pros and cons discussed. In addition, various packaging of VCSEL based on different atomic devices in pursuit of miniaturization and precision measurement are reviewed and discussed. Finally, the VCSEL-based chip-scale atomic magnetometers utilized for cardiac and brain magnetometry are reviewed in detail. Nowadays, biosensors with chip integration, low power consumption, and high sensitivity are undergoing rapid industrialization, due to the growing market of medical instrumentation and portable health monitoring. It is promising that VCSEL-integrated chip-scale atomic biosensors as featured applications of this kind may experience extensive development in the near future.

Measurement of T wave in magnetocardiography using tunnel magnetoresistance sensor

Several critical clinical applications of magnetocardiography (MCG) involve its T wave. The T wave’s accuracy directly affects the diagnostic accuracy of MCG for ischemic heart disease and arrhythmogenic. Tunnel magnetoresistance (TMR) attracts attention as a new MCG measurement technique. However, the T waves measured by TMR are often drowned in noise. The accuracy of T waves needs to be discussed to determine the clinical value of MCG measured by TMR. This study uses an improved empirical mode decomposition (EMD) algorithm and averaging to eliminate the noise in the MCG measured by TMR. The MCG signals measured by TMR are compared with MCG measured by the optically pumped magnetometer (OPM) to judge its accuracy. Using the MCG measured by OPM as a reference, the relative errors in time and amplitude of the T wave measured by TMR are 3.4% and 1.8%, respectively. This is the first demonstration that TMR can accurately measure the time and amplitude of MCG T waves. The ability to provide reliable T wave data illustrates the significant clinical application value of TMR in MCG measurement.

Quantifying Cognitive Workload Using a Non-Contact Magnetocardiography (MCG) Wearable Sensor

Quantifying cognitive workload, i.e., the level of mental effort put forth by an individual in response to a cognitive task, is relevant for healthcare, training and gaming applications. However, there is currently no technology available that can readily and reliably quantify the cognitive workload of an individual in a real-world environment at a seamless way and affordable price. In this work, we overcome these limitations and demonstrate the feasibility of a magnetocardiography (MCG) sensor to reliably classify high vs. low cognitive workload while being non-contact, fully passive and low-cost, with the potential to have a wearable form factor. The operating principle relies on measuring the naturally emanated magnetic fields from the heart and subsequently analyzing the heart rate variability (HRV) matrix in three time-domain parameters: standard deviation of RR intervals (SDRR); root mean square of successive differences between heartbeats (RMSSD); and mean values of adjacent R-peaks in the cardiac signals (MeanRR). A total of 13 participants were recruited, two of whom were excluded due to low signal quality. The results show that SDRR and RMSSD achieve a 100% success rate in classifying high vs. low cognitive workload, while MeanRR achieves a 91% success rate. Tests for the same individual yield an intra-subject classification accuracy of 100% for all three HRV parameters. Future studies should leverage machine learning and advanced digital signal processing to achieve automated classification of cognitive workload and reliable operation in a natural environment.

Abstract 11307: Longer Outflow Tract Conduction Time by Magnetocardiography as a Novel Non-Invasive Maker for Predicting Lethal Events in Patients With Brugada Syndrome Without Ventricular Fibrillation

Introduction: Many of the Brugada syndrome (BrS) patients have no symptom or only syncope without ventricular fibrillation (VF)/sudden cardiac death (SCD), thus to whom we should recommend implantable cardioverter defibrillator (ICD) for primary prevention of SCD is still the biggest concern in the BrS. Here we investigated whether a high spatial resolution magnetocardiography (MCG) could predict lethal events of the BrS without history of VF/SCD. Methods: This study retrospectively enrolled 157 BrS patients without VF or cardiopulmonary arrest (CPA) (150 male, 42±13 years old, 118 spontaneous and 39 drug-induced type-1 ECG). Outflow tract conduction time (OT-CT) from 2-D current map of MCG was measured in all patients as well as the standard 12-lead ECG and signal-averaged ECG (SAECG). Programmed electrical stimulation (PES) for VF induction was performed in 47 patients. Results: Patients with spontaneous type-1 ECG had longer OT-CT compared with type-2 BrS patients. Na channel blocker, pilsicainide (25-50mg) unmasked type-1 ECG as well as increased OT-CT (54±18 to 127±62 ms; p&lt;0.05), whereas additional isoproterenol (1ug/kg/min) reversed the pilsicainide-induced delayed OT-CT, thus normalized the ST elevation of ECG. During follow-up (mean 72 [6-147] months), 10 (6%) patients suffered VF/CPA, and those had a higher incidence of previous syncope (50% vs. 14.4%, p=0.01) and longer baseline OT-CT (83±31ms vs. 55±24ms, p=0.01) compared with event-free patients. Receiver operating curve (ROC) revealed OT-CT=65ms was a cut-off value of VF/CPA (AUC=0.78), whereas, spontaneous type-1 ECG, late potential of SAECG, family history, and VF inducibility by PES were not associated with VF/CPA events during follow-up. Multivariate Cox proportional analysis revealed that previous syncope (HR=1.70, 95%CI:1.01-3.05, p=0.04) and OT-CT≥65ms (HR=1.72, 95%CI=1.16-2.62) were the independent predictor for lethal events in the BrS without VF/CPA. Among asymptomatic BrS patients (n=131), OT-CT≥65ms was also an independent predictor for VF/CPA (HR=1.69, 95%CI=1.04-2.79, p=0.03). Conclusion: Longer OT-CT in the MCG may noninvasively demonstrate substrate of VF in BrS and stratify the risk of lethal events in patients with BrS without history of VF or CPA.

Imbalanced ECG signal-based heart disease classification using ensemble machine learning technique.

The machine learning (ML)-based classification models are widely utilized for the automated detection of heart diseases (HDs) using various physiological signals such as electrocardiogram (ECG), magnetocardiography (MCG), heart sound (HS), and impedance cardiography (ICG) signals. However, ECG-based HD identification is the most common one used by clinicians. In the current investigation, the ECG records or subjects have been sampled and are used as inputs to the classification model to distinguish between normal and abnormal patients. The study has employed an imbalanced number of ECG samples for training the various classification models. Few ML methods such as support vector machine (SVM), logistic regression (LR), and adaptive boosting (AdaBoost) which have been rarely used for HD detection have been selected. The performance of the developed model has been evaluated in terms of accuracy, F1-score, and area under curve (AUC) values using ECG signals of subjects given in publicly available (PTB-ECG, MIT-BIH) datasets. Ranking of the models has been assigned based on these performance metrics and it is found that the AdaBoost and LR classifiers stand in first and second positions. These two models have been ensembled based on the majority voting principle and the performance measure of this ensemble model has also been determined. It is, in general, observed that the proposed ensemble model demonstrates the best HD detection performance of 0.946, 0.949, and 0.951 for the PTB-ECG dataset and 0.921, 0.926, and 0.950 for the MIT-BIH dataset in terms of accuracy, F1-score, and AUC, respectively. The proposed methodology can also be employed for the classification of HD using ICG, MCG, and HS signals as inputs. Further, the proposed methodology can also be applied to the detection of other diseases.

Magnetocardiography as a noninvasive diagnostic strategy for suspected coronary microvascular dysfunction

Abstract Background Coronary microvascular dysfunction (CMD) is increasingly recognized as an independent predictor of mortality with a 4-fold higher risk. However current diagnostic modalities are limited by the need for an invasive procedure, access, cost, and exposure of ionizing radiation. Purpose To investigate the ability of magnetocardiography (MCG) to identify CMD in patients with suspected ischemia and no obstructive coronary artery disease (INOCA). Methods This is an observational, prospective pilot study of patients scheduled for coronary functional angiography (CFA), gold standard for evaluation for CMD (defined as coronary flow reserve (CFR) ≤2). 13 patients underwent both CFA and a noninvasive 36-channel MCG scan. A machine learning model was developed to characterize the presence of CMD in these patients against age matched controls (AMCs). The model consists of a logistic regression classifier which takes features representing the relative strengths of the “characteristic dipoles” of the MCG scan as input. The characteristic dipoles are parameterizations of the three strongest magnetic field map components resulting from a singular value decomposition of the MCG signal. A total of 37 patients were included in this analysis including 13 patients who completed CFA (7 had CMD and 6 had CFR &amp;gt;2 and included in the AMCs group). An additional 24 asymptomatic, healthy patients that did not undergo CFR were also included in the AMC group. Results The mean age for AMCs was 57 years (70% women) and mean age for CMD patients was 54 years (100% women). The performance of the model was evaluated using repeated stratified cross-validation with 5 folds and 3 repeats, resulting in 15 different 80%/20% train/test splits. Figure 1 shows the distribution of samples belonging to the CMD and AMC groups in a 2-dimensional representation of the feature space. The clear separation of the two groups and the clustering of the AMCs demonstrates the ability of the model to identify patients with CMD. We found that MCG had a mean accuracy of 94.8% (±6.4%), sensitivity of 100% (±0.0%) and specificity of 93.3% (±8.2%) for the detection of CMD using gold standard CFR ≤2 as reference. Conclusion(s) First study to show that MCG can be used with 94.8% accuracy to identify CMD among patients suspicious for INOCA with no exposure to ionization, 90 seconds of scan time and minimal cost. The use of this noninvasive modality to identify CMD warrants further investigation. Funding Acknowledgement Type of funding sources: Private company. Main funding source(s): Genetesis

Analysis and Optimization of Signal Detection Noise of Single-Beam Modulated Atomic Magnetometer Array

Highly sensitive atomic magnetometers (AMs) have been wildly used to form magnetocardiography (MCG) or magnetoencephalography (MEG) devices recently. However, signal detection noise, including light noise due to pump light fluctuations and electrical noise introduced by the detection circuit, will inevitably affect the sensitivity of the AM. Therefore, this article proposes a novel method to suppress signal detection noise and improve the sensitivity of AM. We first propose a low-noise preamplifier circuit to suppress the electrical noise of the signal detection part. Then, based on the equivalent magnetic noise amplitude, a method to optimize the pump light intensity is proposed. The experimental setup has been established. Both the analysis and experimental results can be evaluated that the magnetometer sensitivity can always be maintained at a relatively high-sensitivity level (around 24 fT/Hz <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{1/2}$ </tex-math></inline-formula> ) over a wide range of light intensities. We hope that the method proposed in this article can contribute to the improvement of sensor sensitivity in array AM systems.

Optimization of low-pass filters in a flux transformer of a SQUID magnetometer for MCG studies

Research and optimization of a passive low-pass filter (LPF) for a magnetic flux transformer of superconducting quantum interferometric detector (SQUID) operating in a magnetocardiographic (MCG) system, in an unshielded room, under the influence of electromagnetic interference of significant intensity have been carried out. The amplitude-frequency characteristics (AFC) of the LPF in the SQUID magnetic flux transformer are calculated for various shunt circuits, including the LPF circuit recommended by “Supracon” group for its CE2 blue model of the SQUID. The analysis of LPF circuits and their frequency response are carried out in order to ensure maximum protection of the input circuits of the SQUID from the effects of electromagnetic interference. It is shown that the optimal LPF for the MCG system is a filter with an active resistance of about 1 Ω in the shunt circuit of magnetic flux transformer. The performed calculations of the LPF intrinsic noise in the SQUID magnetic flux transformer showed that the additional noise of the shunt circuit with a 1 Ω resistor is significantly less than external noise for MCG system operating in unshielded rooms in the industrial city.

Triaxial precise magnetic field compensation of a zero-field optically pumped magnetometer based on a single-beam configuration.

Triaxial magnetic field compensation is crucial for a zero-field optically pumped magnetometer (OPM) in pursuit of a zero-field environment. In this work, we demonstrate a triaxial magnetic field compensation method for zero-field OPM based on single-beam configuration. It consists of two procedures: (1) pre-compensation to preliminarily cancel out ambient residual magnetic field by low-frequency magnetic field modulation; and (2) precise compensation to further compensate the residual magnetic field by high-frequency magnetic field modulation. This scheme enables rapid and precise compensation of a large-scale magnetic field and supports real null-point acquisition of the triaxial residual magnetic fields with simple processes. The experimental results show that the compensation resolution on the sensitive axis is better than 1 pT and significantly less than the fluctuation of experimental environments. Our work targets on the quick generation of a zero-field environment for high precision OPM, which is especially advantageous for emerging applications including magnetocardiography (MCG) and magnetoencephalography (MEG).

Myocardial Ischemia Detection by a Sensitive Pump-Probe Atomic Magnetometer.

Introduction: Magnetocardiography (MCG) based on optical atomic magnetometers has shown promise for detecting heart diseases accurately. Different methods were introduced to improve the sensitivity of detecting magnetic fields during cardiac activity. Methods: In this paper, an optical pump-probe magnetometer operated on the ground-state Hanle effect based on the zero-field level crossing technique was developed and the laser output signal was optimized in an unshielded environment. Then, the optical magnetometer was utilized to record the simulated MCG trace of different stages of myocardial ischemia. Results: The probe output light intensity followed the variation of cardiac magnetic field (MCG trace) generated by Helmholtz coil accurately. Conclusion: Based on the results, the feasibility of our highly sensitive optical magnetometer in tracing showed no change in the P-QRS-T waveform associated with ischemic heart disease (IHD), where P indicates atrial depolarization, QRS is responsible for ventricular depolarization, and T represents ventricular repolarization.

The Physical Origin of Waves in Magnetocardiography Technique and their Applications in Imaging

In this research, we propose a theoretical model which helps us to consider evolutions of some special heart cells by using superconducting quantum interface devices (SQUID) in a magnetocardiography (MCG) technique. In this model, each cell has its own special electrical structure including ions and charges. These charges move within or outside the cell and emit some magnetic fields. Also, some charges have spins which emit spinning magnetic fields. Summing over these biofields produces the real biofield of a cell. All cellular magnetic fields are summed and enter into the sensor (SQUID) and form the observed pulse on the scope. On the other hand, each biofield induces a current on the superconductor of detector. To consider evolutions of a special cell, one can produce some currents, equal and in opposite directions of currents which are induced by other cells. These currents cancel effects of other cells and only the current and magnetic field of a desired cell is remained. Thus, one can analyze the behavior of one special cell. For example, if a cell converts to a tumor one, its radiated charges and magnetic fields are changed. These changes could be detected by SQUID and tumors could be diagnosed fast.

Magnetocardiography Using a Novel Analysis System (Cardioflux) in ED Observation Unit Chest Pain Patients – A Feasibility Study

SUMMARY Background: Cardiovascular disease is currently the leading cause of death in the United States and is the second most common chief complaint among Emergency Department (ED) patients. Magnetocardiography (MCG) has emerged as a viable, non-invasive option for the evaluation of coronary artery stenosis. Cardioflux (CF) is a novel MCG imaging and analysis system. This study aims to evaluate the feasibility of using MCG using CF (MCG-CF) in our Emergency Department Observation Unit (EDOU). Methods: This is a single-center, retrospective, observational study of ED patients placed in the EDOU to evaluate of symptoms of possible cardiac ischemia from July 1, 2017 to October 31, 2017 to evaluate if they would have been eligible for MCG-CF scanning. Results: Of the 225 patients that met inclusion criteria, 69% were eligible for an MCG-CF scan (n = 156). The average patient was 55 years, 54% were women and 59% were Black. The most common exclusion was having a BMI of greater than or equal to 35kg/m2 making up 86% of exclusions. Conclusions: This feasibility study demonstrates that most patients in our EDOU are eligible for a MCG-CF scan.

Residual field suppression for magnetocardiography measurement inside a thin magnetically shielded room using bi-planar coil

For a magnetocardiography (MCG) system inside a magnetically shielded room (MSR), the residual field should be further suppressed to obtain high-quality MCG signals. In this paper, a compensation system has been developed based on the bi-planar coil and the proportional-integral-derivative (PID) controller. The bi-planar coil, derived from the target-field theory and the Tikhonov regularization method, is utilized to generate magnetic field with high uniformity in the pre-defined target region. And the PID controller is introduced to provide dynamic compensation current for the coil, according to the residual field change monitored by a reference SQUID magnetometer. The measurement results show that the noise suppression ratio (NSR) can reach above 20 dB in the low-frequency range from 0.1 Hz to 50 Hz. The DC component and the fluctuation of the residual field in time-domain can be respectively suppressed to 0 pT and 4 pT, indicating that this proposed compensation method is useful for the MCG measurement.

MCG-Net: End-to-End Fine-Grained Delineation and Diagnostic Classification of Cardiac Events From Magnetocardiographs.

In this paper, we propose an end-to-end deep learning architecture, referred as MCG-Net, integrating convolutional neural network (CNN) with transformer-based global context block for fine-grained delineation and diagnostic classification of four cardiac events from magnetocardiogram (MCG) data, namely Q-, R-, S- and T-waves. MCG-Net takes advantage of a multi-resolution CNN backbone as well as the state-of-the-art (SOTA) transformer encoders that facilitate global temporal feature aggregation. Besides the novel network architecture, we introduce a multi-task learning scheme to achieve simultaneous delineation and classification. Specifically, the problem of MCG delineation is formulated as multi-class heatmap regression. Meanwhile, a binary diagnostic classification label as well as a duration are jointly estimated for each cardiac event using features that are temporally aligned by event heatmaps. The framework is evaluated on a clinical MCG dataset, containing data collected from 270 subjects with cardiac anomalies and 108 control subjects. We designed and conducted a two-fold cross-validation study to validate the proposed method and to compare its performance with the SOTA methods. Experimental results demonstrated that our method outperformed counterparts on both event delineation and diagnostic classification tasks, achieving respectively an average ECG-F1 of 0.987 and an average Event-F1 of 0.975 for MCG delineation, and an average accuracy of 0.870, an average sensitivity of 0.732, an average specificity of 0.914 and an average AUC of 0.903 for diagnostic classification. Comprehensive ablation experiments are additionally performed to investigate effectiveness of different network components.

Abstract TP217: Potential Utility Of Magnetocardiogram For Patients With Embolic Stroke Of Undetermined Source

Background: The magnetocardiogram (MCG) is a promising medical tool for detecting and visualizing abnormal cardiac electrical activation in cardiovascular disease patients. MCG has higher spatial resolution than the electrocardiogram (ECG) because the magnetic field is not distorted by flow through tissues. Taking advantage of these features, MCG has been applied for studying arrhythmia, such as atrial fibrillation (AF) and long-QT syndrome. Paroxysmal AF is the most common sources of thromboembolism for ESUS. Since factors that reflect left atrial overload (LAO) are independent predictors of paroxysmal AF, evaluation of these factors in ESUS patients is essential to improve their prognosis. We aimed to investigate the potential utility of MCG in ESUS patients for evaluation of the factors that reflect LAO. Methods: ESUS patients who underwent MCG (MC-6400 MCG system with 64 magnetic sensors) in our hospital were enrolled between September 2018 and July 2021. The peak P-wave magnitude of the right atrium segment and the left atrium segment on the MCG (MCG-RA, MCG-LA) were measured. Subsequently, the ratio of peak magnitude of the LA segment to that of the RA segment (MCG-LA/RA) was used to evaluate LAO. Other factors that reflect LAO, such as PAC frequency on Holter monitoring, LAD in transthoracic echocardiography (TTE), serum level of brain natriuretic peptide, left ventricular ejection fraction (LVEF) in TTE, and P-wave terminal force in lead V1 (PTFV1) on ECG were also evaluated. Results: Fifty-two ESUS patients with a mean age of 67.9±14.3 years (females, 42%) were included. The mean MCV-RA and MCG-LA were 7.70±12.82 and 4.50±7.74 pico-Tesla, respectively. The mean MCG-LA/RA was 0.66±0.31. The MCG-LA/RA was significantly correlated with the PAC frequency (r=0.293, p=0.039), although either MCG-RA or MCG-LA was not correlated. Also, the MCG-LA/RA ratio was significantly correlated with LAD (r=0.301, p=0.03) but not with brain natriuretic peptide (r= 0.25, p= 0.09), LVEF (r= -0.06, p= 0.654), or PTFV1 (r= 0.02, p=0.91). Conclusion: This is the first study that evaluated the relationship of the MCG-LA/RA ratio with factors that reflect LAO in ESUS patients. These results suggest that the MCG-LA/RA ratio is a novel biomarker for LAO in ESUS patients.

Quantitative Evaluation for Magnetoelectric Sensor Systems in Biomagnetic Diagnostics.

Dedicated research is currently being conducted on novel thin film magnetoelectric (ME) sensor concepts for medical applications. These concepts enable a contactless magnetic signal acquisition in the presence of large interference fields such as the magnetic field of the Earth and are operational at room temperature. As more and more different ME sensor concepts are accessible to medical applications, the need for comparative quality metrics significantly arises. For a medical application, both the specification of the sensor itself and the specification of the readout scheme must be considered. Therefore, from a medical user’s perspective, a system consideration is better suited to specific quantitative measures that consider the sensor readout scheme as well. The corresponding sensor system evaluation should be performed in reproducible measurement conditions (e.g., magnetically, electrically and acoustically shielded environment). Within this contribution, an ME sensor system evaluation scheme will be described and discussed. The quantitative measures will be determined exemplarily for two ME sensors: a resonant ME sensor and an electrically modulated ME sensor. In addition, an application-related signal evaluation scheme will be introduced and exemplified for cardiovascular application. The utilized prototype signal is based on a magnetocardiogram (MCG), which was recorded with a superconducting quantum-interference device. As a potential figure of merit for a quantitative signal assessment, an application specific capacity (ASC) is introduced. In conclusion, this contribution highlights metrics for the quantitative characterization of ME sensor systems and their resulting output signals in biomagnetism. Finally, different ASC values and signal-to-noise ratios (SNRs) could be clearly presented for the resonant ME sensor (SNR: dB, ASC: dB Hz) and also the electrically modulated ME sensor (SNR: dB, ASC: 23 dB Hz), showing that the electrically modulated ME sensor is better suited for a possible MCG application under ideal conditions. The presented approach is transferable to other magnetic sensors and applications.