Comparison of magnetocardiography and electrocardiography: a study of automatic measurement of dispersion of ventricular repolarization

Synchronous recording of magnetocardiographic and electrocardiographic signals

We present a system for simultaneous recording of the electrocardiogram and the magnetocardiogram. The measurement system contained of printed carbon electrodes and SERF magnetometer. The use of this system confirms that the position of the end of the magnetic T wave extends further than the electric T wave, which is an important indicator for the diagnosis of cardiological patients and for drug arrhythmogenicity. We analyze this phenomenon in depth, and demonstrate, that it originates from the fundamental difference between electric and magnetic measurements. The measured value is always bipolar since the electric measurements require two electrodes. We demonstrate how the dual electric and magnetic measuring system adds a new information to the commonly used electrocardiographic diagnosis. The ECG should be interpreted as the spatial asymmetry of the electric cardiac potential, and not as the potential itself. The results seem to prove, that the relation between the magnetic and the electric imaging of neural activities may be broadly applied for the benefit of medical diagnosis in cardiology and many other fields, where the neural activity is measured. This is a pilot study which requires further confirmation at the clinical level.

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.

Unshielded magnetocardiography: Repeatability and reproducibility of automatically estimated ventricular repolarization parameters in 204 healthy subjects

BACKGROUND

Magnetocardiographic mapping (MCG) provides quantitative assessment of the magnetic field (MF) induced by cardiac ionic currents, is more sensitive to tangential currents, and measures vortex currents undetectable by ECG, with higher reported sensitivity of MCG ventricular repolarization (VR) parameters for earlier detection of acute myocardial ischemia. Aims of this study were to validate the feasibility of in-hospital unshielded MCG and to assess repeatability and reproducibility of quantitative VR parameters, considering also possible gender- and age-related variability.

METHODS

MCG of 204 healthy subjects [114 males-mean age 43.4 ± 17.3 and 90 females-mean age 40.2 ± 15.7] was retrospectively analyzed, with a patented proprietary software automatically estimating twelve VR parameters derived from the analysis of the dynamics of the T-wave MF extrema (five parameters) and from the inverse solution with the effective magnetic dipole model giving the effective magnetic vector components (seven parameters). MCG repeatability was calculated as coefficient of variation (CV) ±standard error of the mean (SEM). Reproducibility was assessed as intraclass correlation coefficient (ICC).

RESULTS

The repeatability of all MCG parameters was 16 ± 1.2 (%) (average CV ± SEM). Optimal (ICC > 0.7) reproducibility was found for 11/12 parameters (mean values) and in 8/12 parameters (single values). No significant gender-related difference was observed; six parameters showed a strong/moderate correlation with age.

CONCLUSION

Reliable MCG can be performed into an unshielded hospital ambulatory, with repeatability and reproducibility of quantitative assessment of VR adequate for clinical purposes. Wider clinical use is foreseen with the development of multichannel optical magnetometry.

Note: optical receiver system for 152-channel magnetoencephalography

An optical receiver system composing 13 serial data restore/synchronizer modules and a single module combiner converted optical 32-bit serial data into 32-bit synchronous parallel data for a computer to acquire 152-channel magnetoencephalography (MEG) signals. A serial data restore/synchronizer module identified 32-bit channel-voltage bits from 48-bit streaming serial data, and then consecutively reproduced 13 times of 32-bit serial data, acting in a synchronous clock. After selecting a single among 13 reproduced data in each module, a module combiner converted it into 32-bit parallel data, which were carried to 32-port digital input board in a computer. When the receiver system together with optical transmitters were applied to 152-channel superconducting quantum interference device sensors, this MEG system maintained a field noise level of 3 fT/√Hz @ 100 Hz at a sample rate of 1 kSample/s per channel.

Optical transmission modules for multi-channel superconducting quantum interference device readouts

We developed an optical transmission module consisting of 16-channel analog-to-digital converter (ADC), digital-noise filter, and one-line serial transmitter, which transferred Superconducting Quantum Interference Device (SQUID) readout data to a computer by a single optical cable. A 16-channel ADC sent out SQUID readouts data with 32-bit serial data of 8-bit channel and 24-bit voltage data at a sample rate of 1.5 kSample/s. A digital-noise filter suppressed digital noises generated by digital clocks to obtain SQUID modulation as large as possible. One-line serial transmitter reformed 32-bit serial data to the modulated data that contained data and clock, and sent them through a single optical cable. When the optical transmission modules were applied to 152-channel SQUID magnetoencephalography system, this system maintained a field noise level of 3 fT/√Hz @ 100 Hz.

Early detection of coronary artery disease in patients studied with magnetocardiography: an automatic classification system based on signal entropy

We propose an automatic system for the classification of coronary artery disease (CAD) based on entropy measures of MCG recordings. Ten patients with coronary artery narrowing ≥ or ≤ 50% were categorized by a multilayer perceptron (MLP) neural network based on Linear Discriminant Analysis (LDA). Best results were obtained with MCG at rest: 99% sensitivity, 97% specificity, 98% accuracy, 96% and 99% positive and negative predictive values for single heartbeats. At patient level, these results correspond to a correct classification of all patients. The classifier's suitability to detect CAD-induced changes on the MCG at rest was validated with surrogate data.

An increase in right atrial magnetic strength is a novel predictor of recurrence of atrial fibrillation after radiofrequency catheter ablation

BACKGROUND

Differences in electrical properties between left and right atria (LA and RA) after pulmonary vein isolation (PVI) for atrial fibrillation (AF) are currently poorly understood. Magnetocardiograms were used to investigate the effect of PVI on bi-atrial magnetic field changes and their relationship to clinical outcomes.

METHODS AND RESULTS

This study included 71 patients undergoing PVI for paroxysmal AF. Magnetocardiograms were recorded at baseline and 1 day, 8 weeks, and 24 weeks after ablation. Peak magnitude of LA and RA segments on P waves was separately compared before and after PVI. During a 16-month post-ablation period, 53 (75%) patients were free from AF recurrences. LA magnetic strength in patients without recurrence persistently decreased for 24 weeks and was significantly lower at 8 weeks than that in patients with recurrence (1.28±0.69 vs. 1.74±0.71 pico-Tesla, P=0.02). RA magnetic strength in patients with recurrence persistently rose for 24 weeks and was significantly higher at 8 weeks than that in patients without recurrence (2.17±0.82 vs. 3.00±1.12 pico-Tesla, P=0.001). Multivariate analysis showed RA magnetic strength at 8 weeks to be the strongest predictor of AF recurrence (odds ratio=3.335; 95% confidence interval=1.181-9.416; P=0.02).

CONCLUSIONS

PVI resulted in distinct changes in magnetic strength in both the LA and the RA. A persistent rise in RA magnetic strength might be a robust predictor of AF recurrence after ablation.

Detection of U wave activity in healthy volunteers by high-resolution magnetocardiography

INTRODUCTION

The purpose of our study was to prove the existence of the U wave using magnetocardiograms (MCGs).

METHODS

The 31-channel MCGs of 25 healthy volunteers were recorded. The onset of the U wave was defined by newly developed spatial correlation analysis; and the end, by different approaches.

RESULTS

A U wave could be proved in all volunteers. In 10 volunteers (heart rate, 57 +/- 19 beats/min) in whom the U wave was found to be separated from the following P wave, the U wave's end could be determined as a threshold value (U wave duration, 310 +/- 24 milliseconds). In 15 volunteers (heart rate, 70 +/- 38 beats/min), the end of the U waves was concealed by a continuous transition of the U waves into the following P waves.

CONCLUSIONS

The U wave seems to be a regular phenomenon and has a distinct spatiotemporal assembly.

Detection of non-ST-elevation myocardial infarction using magnetocardiogram: new information from spatiotemporal electrical activation map

BACKGROUND AND AIM

Non-ST-segment elevation myocardial infarction (NSTEMI) cannot be easily detected in the emergency room. We evaluate a method to detect NSTEMI using 64-channel magnetocardiography (MCG).

METHODS

MCG recordings were made in 20 NSTEMI patients (aged 59.7+/-12.4 years), 15 young (aged 26.8+/-3.4 years), and 13 age-matched control subjects (aged 57.3+/-3.6). We evaluated three approaches to analysis, including 1) determination when individual subjects' MCG results fell outside normal ranges for ten MCG parameters, 2) the magnetic field map at the T-wave peak (T-MFM), and 3) a pair of spatiotemporal activation graphs (STAGs) showing two projections of electrical excitation during repolarization.

RESULTS

Significant differences were found between normal controls and patients for all MCG parameters. None of the healthy controls had more than four MCG abnormal parameters, whereas 19 NSTEMI patients (95%) were abnormal in more than four parameters. STAGs and T-MFM also showed clear differences between healthy controls and NSTEMI patients.

CONCLUSIONS

These results suggest that the MCG is sensitive to changes in the cardiac electrical pathway after myocardial infarction as described by these graphs and parameters, and therefore MCG may be a useful tool to detect severe ischemic patients.

Identification of ischemic heart disease via machine learning analysis on magnetocardiograms

Ischemic heart disease (IHD) is predominantly the leading cause of death worldwide. Early detection of IHD may effectively prevent severity and reduce mortality rate. Recently, magnetocardiography (MCG) has been developed for the detection of heart malfunction. Although MCG is capable of monitoring the abnormal patterns of magnetic field as emitted by physiologically defective heart, data interpretation is time-consuming and requires highly trained professional. Hence, we propose an automatic method for the interpretation of IHD pattern of MCG recordings using machine learning approaches. Two types of machine learning techniques, namely back-propagation neural network (BNN) and direct kernel self-organizing map (DK-SOM), were applied to explore the IHD pattern recorded by MCG. Data sets were obtained by sequential measurement of magnetic field emitted by cardiac muscle of 125 individuals. Data were divided into training set and testing set of 74 cases and 51 cases, respectively. Predictive performance was obtained by both machine learning approaches. The BNN exhibited sensitivity of 89.7%, specificity of 54.5% and accuracy of 74.5%, while the DK-SOM provided relatively higher prediction performance with a sensitivity, specificity and accuracy of 86.2%, 72.7% and 80.4%, respectively. This finding suggests a high potential of applying machine learning approaches for high-throughput detection of IHD from MCG data.