Usefulness of magnetocardiogram to detect unstable angina pectoris and non-ST elevation myocardial infarction

The magnetocardiogram

The magnetic field produced by the heart's electrical activity is called the magnetocardiogram (MCG). The first 20 years of MCG research established most of the concepts, instrumentation, and computational algorithms in the field. Additional insights into fundamental mechanisms of biomagnetism were gained by studying isolated hearts or even isolated pieces of cardiac tissue. Much effort has gone into calculating the MCG using computer models, including solving the inverse problem of deducing the bioelectric sources from biomagnetic measurements. Recently, most magnetocardiographic research has focused on clinical applications, driven in part by new technologies to measure weak biomagnetic fields.

Accurate diagnosis of severe coronary stenosis based on resting magnetocardiography: a prospective, single-center, cross-sectional analysis

OBJECTIVE

To evaluate the role of resting magnetocardiography in identifying severe coronary artery stenosis in patients with suspected coronary artery disease.

METHODS

A total of 513 patients with angina symptoms were included and divided into two groups based on the extent of coronary artery disease determined by angiography: the non-severe coronary stenosis group (< 70% stenosis) and the severe coronary stenosis group (≥ 70% stenosis). The diagnostic model was constructed using magnetic field map (MFM) parameters, either individually or in combination with clinical indicators. The performance of the models was evaluated using receiver operating characteristic curves, accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). Calibration plots and decision curve analysis were performed to investigate the clinical utility and performance of the models, respectively.

RESULTS

In the severe coronary stenosis group, QR_MCTDd, S_MDp, and TT_MAC50 were significantly higher than those in the non-severe coronary stenosis group (10.46 ± 10.66 vs. 5.11 ± 6.07, P < 0.001; 7.2 ± 8.64 vs. 4.68 ± 6.95, P = 0.003; 0.32 ± 57.29 vs. 0.26 ± 57.29, P < 0.001). While, QR_MVamp, R_MA, and T_MA in the severe coronary stenosis group were lower (0.23 ± 0.16 vs. 0.28 ± 0.16, P < 0.001; 55.06 ± 48.68 vs. 59.24 ± 53.01, P < 0.001; 51.67 ± 39.32 vs. 60.45 ± 51.33, P < 0.001). Seven MFM parameters were integrated into the model, resulting in an area under the curve of 0.810 (95% CI: 0.765-0.855). The sensitivity, specificity, PPV, NPV, and accuracy were 71.7%, 80.4%, 93.3%, 42.8%, and 73.5%; respectively. The combined model exhibited an area under the curve of 0.845 (95% CI: 0.798-0.892). The sensitivity, specificity, PPV, NPV, and accuracy were 84.3%, 73.8%, 92.6%, 54.6%, and 82.1%; respectively. Calibration curves demonstrated excellent agreement between the nomogram prediction and actual observation. The decision curve analysis showed that the combined model provided greater net benefit compared to the magnetocardiography model.

CONCLUSIONS

The novel quantitative MFM parameters, whether used individually or in combination with clinical indicators, have been shown to effectively predict the risk of severe coronary stenosis in patients presenting with angina-like symptoms. Magnetocardiography, an emerging non-invasive diagnostic tool, warrants further exploration for its potential in diagnosing coronary heart disease.

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.

Clinical magnetocardiography: the unshielded bet-past, present, and future

Magnetocardiography (MCG), which is nowadays 60 years old, has not yet been fully accepted as a clinical tool. Nevertheless, a large body of research and several clinical trials have demonstrated its reliability in providing additional diagnostic electrophysiological information if compared with conventional non-invasive electrocardiographic methods. Since the beginning, one major objective difficulty has been the need to clean the weak cardiac magnetic signals from the much higher environmental noise, especially that of urban and hospital environments. The obvious solution to record the magnetocardiogram in highly performant magnetically shielded rooms has provided the ideal setup for decades of research demonstrating the diagnostic potential of this technology. However, only a few clinical institutions have had the resources to install and run routinely such highly expensive and technically demanding systems. Therefore, increasing attempts have been made to develop cheaper alternatives to improve the magnetic signal-to-noise ratio allowing MCG in unshielded hospital environments. In this article, the most relevant milestones in the MCG's journey are reviewed, addressing the possible reasons beyond the currently long-lasting difficulty to reach a clinical breakthrough and leveraging the authors' personal experience since the early 1980s attempting to finally bring MCG to the patient's bedside for many years thus far. Their nearly four decades of foundational experimental and clinical research between shielded and unshielded solutions are summarized and referenced, following the original vision that MCG had to be intended as an unrivaled method for contactless assessment of the cardiac electrophysiology and as an advanced method for non-invasive electroanatomical imaging, through multimodal integration with other non-fluoroscopic imaging techniques. Whereas all the above accounts for the past, with the available innovative sensors and more affordable active shielding technologies, the present demonstrates that several novel systems have been developed and tested in multicenter clinical trials adopting both shielded and unshielded MCG built-in hospital environments. The future of MCG will mostly be dependent on the results from the ongoing progress in novel sensor technology, which is relatively soon foreseen to provide multiple alternatives for the construction of more compact, affordable, portable, and even wearable devices for unshielded MCG inside hospital environments and perhaps also for ambulatory patients.

Magnetocardiography for the detection of myocardial ischemia

Ischemic heart disease (IHD) continues to be a significant global public health concern and ranks among the leading causes of mortality worldwide. However, the identification of myocardial ischemia in patients suspected of having coronary artery disease (CAD) remains a challenging issue. Functional or stress testing is widely recognized as the gold standard method for diagnosing myocardial ischemia, but it is hindered by low diagnostic accuracy and limitations such as radiation exposure. Magnetocardiography (MCG) is a non-contact, non-invasive method that records magnetic fields produced by the electrical activity of the heart. Unlike electrocardiography (EKG) and other functional or stress testing, MCG offers numerous advantages. It is highly sensitive and can detect early signs of myocardial ischemia that may be missed by other diagnostic tools. This review aims to provide an extensive overview of the available evidence that establishes the utility of MCG as a valuable diagnostic tool for identifying myocardial ischemia, accompanied by a discussion of potential future research directions in this domain.

Detection of coronary artery disease in patients with chest pain: A machine learning model based on magnetocardiography parameters

BACKGROUD

Patients with chest pain and suspected of coronary artery disease(CAD) need further test to confirm the diagnosis. Magnetocardiography (MCG) is a non-invasive and emission-free technology which can detect and measure the weak magnetic fields created by the electrical activity of the heart.

OBJECTIVE

This study aimed to investigate the usefulness of the 10 MCG parameters to detect CAD in patients with chest pain by means of a machine learning method of multilayer perceptron(MLP) neural network.

METHODS

209 patients who were suffering from chest pain and suspected of CAD were enrolled in this cross-sectional study. In all patients, 12-lead electrocardiography(ECG) and MCG test were performed before percutaneous coronary angiography(PCA). 10 MCG parameters were analyzed by MLP neural networks.

RESULTS

11 diagnostic models(M1 to M11) were established after MLP analysis. The accuracies ranged from 71.2% to 90.5%. Two models(M10 and M11) were further analyzed. The accuracy, sensitivity, specificity, PPV, NPV, PLR and NLR were 89.5%, 89.8%, 88.9%, 92.7%, 84.7%, 11.10 and 0.11, of M10, and were 90.0%, 91.4%, 87.7%, 92.1%, 86.6%, 7.43 and 0.10, of M11.

CONCLUSIONS

By a method of MLP neural network, MCG is applicable in identifying CAD in patients with chest pain, which seems beneficial for detection of CAD.

Diagnostic accuracy of the magnetocardiograph for patients with suspected acute coronary syndrome

Background

We aimed to estimate the diagnostic accuracy of the VitalScan magnetocardiograph (MCG) for suspected acute coronary syndrome (ACS).

Methods

We undertook a prospective cohort study evaluating the diagnostic accuracy of the MCG in adults with suspected ACS. The reference standard of ACS was determined by an independent adjudication committee based on 30-day investigations and events. The cohort was split into a training sample, to derive the MCG algorithm and an algorithm combining MCG with a modified Manchester Acute Coronary Syndrome (MACS) clinical probability score, and a validation sample, to estimate diagnostic accuracy.

Results

We recruited 756 participants and analysed data from 680 (293 training, 387 validation), of whom 96 (14%) had ACS. In the training sample, the respective area under the receiver operating characteristic (AUROC) curves were the following: MCG 0.66 (95% CI 0.58 to 0.74), MACS 0.64 (95% CI 0.54 to 0.73) and MCG+MACS 0.70 (95% CI 0.63 to 0.77). MCG specificity was 0.16 (95% CI 0.12 to 0.21) at the threshold achieving acceptable sensitivity for rule-out (>0.98). In the validation sample (n=387), the respective AUROCs were the following: MCG 0.56 (95% CI 0.48 to 0.64), MACS 0.69 (95% CI 0.61 to 0.77) and MCG+MACS 0.64 (95% CI 0.56 to 0.72). MCG sensitivity was 0.89 (95% CI 0.77 to 0.95) and specificity 0.15 (95% CI 0.12 to 0.20) at the rule-out threshold. MCG+MACS sensitivity was 0.85 (95% CI 0.73 to 0.92) and specificity 0.30 (95% CI 0.25 to 0.35).

Conclusion

The VitalScan MCG is currently unable to accurately rule out ACS and is not yet ready for use in clinical practice. Further developmental research is required.

Effectiveness of magnetocardiography to identify patients in need of coronary artery revascularization: a cross-sectional study

BACKGROUND

Patients with angina-like symptoms need invasive or non-invasive angiography to determine whether revascularization is necessary. For patients in need of revascularization, undergoing coronary computed tomography angiography (CCTA) may delay the treatment of revascularization and increase exposure to contrast agents and radiation. The aim of this cross-sectional study was to accessed the effectiveness of magnetocardiography (MCG) to identify patients who should undergo coronary revascularization.

METHODS

A total of 203 patients who were suffering from angina-like symptoms and underwent percutaneous coronary angiography (PCA) between July 27, 2015 and April 10, 2017 at the 8th Medical Center of Chinese PLA General Hospital, were enrolled in this cross-sectional study. In all patients, 12-lead electrocardiography (ECG) and MCG test were performed before PCA. For each subject. The value at every single sampling point was extracted from T wave of each MCG channel in time sequence. Pearson's correlation coefficients were calculated for each two T-waves. A binary logistic regression diagnosis model of these coefficients was established to identify patients in need of revascularization.

RESULTS

Ten pairings of coefficients were entered into diagnostic regression model as covariates. The area under the receiver operating characteristic (ROC) curve (AUC) was 0.747 (95% CI: 0.680-0.815), and the asymptotic P value was less than 0.001. At the cut-off value of 0.55, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were 72.9%, 65.9%, 74.8%, 63.6% and 69.9%, and the positive and negative post-test probabilities were 65.9% and 25.7%. The accuracy, sensitivity, specificity, PPV and NPV for 12-lead ECG were 67.0%, 62.7%, 63.5%, 70.5% and 55.1%, respectively. However, when those acute myocardial infarction (AMI) patients were ruled out from both groups, the MCG model had an accuracy of 68.2%, a sensitivity of 70.1%, a specificity of 66.3%, a PPV of 68.5% and an NPV of 67.9%. But, the accuracy, sensitivity, specificity, PPV and NPV for 12-lead ECG were 60.0%, 55.2%, 65.1%, 62.3% and 58.1%, respectively.

CONCLUSIONS

Patients suffering from angina-like symptoms, with a logistic regression model value over 0.55, should be recommended for PCA.

Fast backward singular value decomposition (SVD) algorithm for magnetocardiographic signal reconstruction from pulsed atomic magnetometer data

In a pulse pump Rb atomic magnetometer, the magnetic field is associated with the Larmor frequency of the free induction decay (FID) signal. The reconstruction of the magnetic field from the collected signal, thereby, is crucial for magnetocardiography. In this study, we propose a backward singular value decomposition (BSVD) method for fast reconstruction of a magnetocardiographic signal. Experiments on the simulated and real data were performed to estimate its potential advantages over previous approaches, such as the fast Fourier transform (FFT) method, the zero-crossing means (ZM) method, etc. The results show the high accuracy of the BSVD method compared with other methods. More importantly, the BSVD method requires less sampled data than other methods while ensuring the accuracy. With the help of it, the recording time can be greatly reduced from the initial 3.6m s to the present 0.6m s. Thus, the time resolution of the magnetocardiograph could reach 2m s which is equivalent to that of conventional electrocardiogragh. This will bring the atomic magnetocardiography more practicable in clinic application.

Magnetocardiography for identification of coronary ischemia in patients with chest pain and normal resting 12-lead electrocardiogram

Background

Identification of coronary ischemia in patients presenting with chronic chest pain is difficult as resting ECG can be normal. Diagnosis of coronary ischemia requires evaluation during exercise or pharmacological stress. A noninvasive test to identify coronary ischemia at rest without the need for exercise is desirable. We studied the diagnostic accuracy of magnetocardiography (MCG) at rest to detect coronary ischemia in these patients.

Methods

Patients with chronic chest pain and suspected coronary ischemia with a normal ECG were included. Patients underwent treadmill test (TMT) and were divided into TMT positive and TMT negative groups. MCG was recorded in a magnetically shielded room. Iso‐field contour maps generated at the T‐wave peak were compared between the groups. From the magnetic field map (MFM), the magnetic field angle at T‐wave peak was calculated and was also compared across the two groups.

Results

There were a total of 29 patients, 12 with positive TMT and 17 with negative TMT. An abnormal magnetic field angle was more common in the TMT positive group (72% vs. 6%). Abnormal contour maps in the form of nondipole patterns or abnormal orientation were seen in 81.8% (9/11) patients in TMT positive group and 6.8% (1/17) patients in the TMT negative group (p < .001).

Conclusion

Abnormal magnetic field angle and abnormal magnetic field maps in MCG recorded at rest are able to identify the presence of coronary ischemia in patients with chronic chest pain and a normal resting ECG.

A portable prototype magnetometer to differentiate ischemic and non-ischemic heart disease in patients with chest pain

BACKGROUND

Magnetocardiography (MCG) is a non-invasive technique used to measure and map cardiac magnetic fields. We describe the predictive performance of a portable prototype magnetometer designed for use in acute and routine clinical settings. We assessed the predictive ability of the measurements derived from the magnetometer for the ruling-out of healthy subjects and patients whose chest pain has a non-ischemic origin from those with ischemic heart disease (IHD).

METHODS

MCG data were analyzed from a technical performance study, a pilot clinical study, and a young healthy reference group. Participants were grouped to enable differentiation of those with IHD versus non-IHD versus controls: Group A (70 IHD patients); Group B (69 controls); Group C (37 young healthy volunteers). Scans were recorded in an unshielded room. Between-group differences were explored using analysis of variance. The ability of 10 candidate MCG predictors to predict normal/abnormal cases was analyzed using logistic regression. Predictive performance was internally validated using repeated five-fold cross-validation.

RESULTS

Three MCG predictors showed a significant difference between patients and age-matched controls (P<0.001); eight predictors showed a significant difference between patients and young healthy volunteers (P<0.001). Logistic regression comparing patients with controls yielded a specificity of 35.0%, sensitivity of 95.4%, and negative predictive value for the ruling-out of IHD of 97.8% (area under the curve 0.78).

CONCLUSION

This analysis represents a preliminary indication that the portable magnetometer can help rule-out healthy subjects and patients whose chest pain has a non-ischemic origin from those with IHD.

Incremental diagnostic value of combined quantitative and qualitative parameters of magnetocardiography to detect coronary artery disease

BACKGROUND/OBJECTIVES

Magnetocardiography (MCG) has been proposed as a non-invasive and functional technique with high accuracy for diagnosis of myocardial ischemia. This study sought to investigate the incremental diagnostic value of combined quantitative and qualitative parameters of MCG to detect coronary artery disease (CAD).

METHODS

Ninety six patients with suspected CAD who underwent coronary angiography were enrolled in the analysis to test the diagnostic accuracy of 2 MCG parameters (a quantitative parameter of the percent change of ST-segment fluctuation score and a qualitative parameter of non-dipole phenomenon).

RESULTS

The best cut-off value for the percent change of ST-segment fluctuation score was -51.0%. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 78.1, 73.9, 82.0, 79.1, and 77.4, in the percent change of ST-segment fluctuation score and 86.5, 84.8, 88.0, 86.7, and 86.3 in non-dipole phenomenon. The area under the curve of receiver-operating characteristics was 0.79 for the percent change of ST-segment fluctuation score and 0.86 for non-dipole phenomenon (p<0.001). However, the incorporation of non-dipole phenomenon into a model with the percent change of ST-segment fluctuation score significantly improved C-statistics, indicating the enhancement of diagnostic performance in the detection of significant CAD (0.790 to 0.930; p<0.001).

CONCLUSIONS

Qualitative assessment of non-dipole phenomenon has a better diagnostic value than the quantitative parameter of percent change of ST-segment fluctuation score in the detection of significant CAD. Furthermore, this study found that the incorporation of non-dipole phenomenon into the percent change of ST-segment fluctuation score significantly improved the diagnostic performance of CAD detection.

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.

Repolarization Heterogeneity of Magnetocardiography Predicts Long-Term Prognosis in Patients with Acute Myocardial Infarction

PURPOSE

Magnetocardiography (MCG) has been proposed as a noninvasive, diagnostic tool for risk-stratifying patients with acute myocardial infarction (AMI). This study evaluated whether MCG predicts long-term prognosis in AMI.

MATERIALS AND METHODS

In 124 AMI patients (95 males, mean age 60±11 years), including 39 with ST-elevation myocardial infarction, a 64-channel MCG was performed within 2 days after AMI. During a mean follow-up period of 6.1 years, major adverse cardiac events (MACE) were evaluated.

RESULTS

MACE occurred in 31 (25%) patients, including 20 revascularizations, 8 deaths, and 3 re-infarctions. Non-dipole patterns were observed at the end of the T wave in every patients. However, they were observed at T-peak in 77% (24/31) and 54% (50/93) of patients with and without MACE, respectively (p=0.03). Maximum current, field map angles, and distance dynamics were not different between groups. In the multivariate analysis, patients with non-dipole patterns at T-peak had increased age- and gender-adjusted hazard ratios for MACE (hazard ratio 2.89, 95% confidence interval 1.20-6.97, p=0.02) and lower cumulative MACE-free survival than those with dipole patterns (p=0.02).

CONCLUSION

Non-dipole patterns at T-peak were more frequently observed in patients with MACE and were related to poor long-term prognosis. Thus, repolarization heterogeneity measured by MCG may be a useful predictor for AMI prognosis.

Early Myocardial Repolarization Heterogeneity Is Detected by Magnetocardiography in Diabetic Patients with Cardiovascular Risk Factors

Multi-channel magnetocardiography (MCG) is a sensitive technique to map spatial ventricular repolarization with high resolution and reproducibility. Spatial ventricular repolarization heterogeneity measured by MCG has been shown to accurately detect and localize myocardial ischemia. Here, we explored whether these measurements correlated with cardiovascular risk factors in patients with type 2 diabetes. Two hundreds and seventy-seven type 2 diabetic patients without known coronary artery disease (CAD) and arrhythmia were recruited consecutively from the outpatient clinic of National Taiwan University Hospital. The spatially distributed QTc contour maps were constructed with 64-channel MCG using the superconducting quantum interference device (SQUID) system. Indices of myocardial repolarization heterogeneity including the smoothness index of QTc (SI-QTc) and QTc dispersion were derived and analyzed for association with conventional cardiovascular risk factors. SI-QTc correlated strongly with the QTc dispersion (r = 0.70, p <0.0001). SI-QTc was significantly higher in patients with presence of metabolic syndrome in comparison to those without metabolic syndrome (8.56 vs. 7.96 ms, p = 0.02). In univariate correlation analyses, QTc dispersion was associated with smoking status (average 79.90, 83.83, 86.51, and 86.00 ms for never smokers, ex-smokers, current smokers reporting less than 10 cigarettes daily, and current smoker reporting more than 10 cigarettes daily, respectively, p = 0.03), body weight (r = 0.15, p = 0.01), and hemoglobin A1c (r = 0.12, p = 0.04). In stepwise multivariate regression analyses, QTc dispersion was associated with smoking (p = 0.02), body weight (p = 0.04), total cholesterol levels (p = 0.05), and possibly estimated glomerular filtration rate (p = 0.07). In summary, spatial heterogeneity of myocardial repolarization measured by MCG is positively associated cardiovascular risk factors including adiposity, smoking, and total cholesterol levels.

Spatial repolarization heterogeneity detected by magnetocardiography correlates with cardiac iron overload and adverse cardiac events in beta-thalassemia major

Background

Patients with transfusion-dependent beta-thalassemia major (TM) are at risk for myocardial iron overload and cardiac complications. Spatial repolarization heterogeneity is known to be elevated in patients with certain cardiac diseases, but little is known in TM patients. The purpose of this study was to evaluate spatial repolarization heterogeneity in patients with TM, and to investigate the relationships between spatial repolarization heterogeneity, cardiac iron load, and adverse cardiac events.

Methods and Results

Fifty patients with TM and 55 control subjects received 64-channel magnetocardiography (MCG) to determine spatial repolarization heterogeneity, which was evaluated by a smoothness index of QT_c (SI-QT_c), a standard deviation of QT_c (SD-QT_c), and a QT_c dispersion. Left ventricular function and myocardial T2* values were assessed by cardiac magnetic resonance. Patients with TM had significantly greater SI-QT_c, SD-QT_c, and QT_c dispersion compared to the control subjects (all p values<0.001). Spatial repolarization heterogeneity was even more pronounced in patients with significant iron overload (T2*<20 ms, n = 20) compared to those with normal T2* (all p values<0.001). Log_e cardiac T2* correlated with SI-QT_c (r = −0.609, p<0.001), SD-QT_c (r = −0.572, p<0.001), and QT_c dispersion (r = −0.622, p<0.001), while all these indices had no relationship with measurements of the left ventricular geometry or function. At the time of study, 10 patients had either heart failure or arrhythmia. All 3 indices of repolarization heterogeneity were related to the presence of adverse cardiac events, with areas under the receiver operating characteristic curves (ranged between 0.79 and 0.86), similar to that of cardiac T2*.

Conclusions

Multichannel MCG demonstrated that patients with TM had increased spatial repolarization heterogeneity, which is related to myocardial iron load and adverse cardiac events.

Diagnostic value of magnetocardiography in coronary artery disease and cardiac arrhythmias: a review of clinical data

Despite the availability of several advanced non-invasive diagnostic tests such as echocardiography and magnetic resonance imaging, electrocardiography (ECG) remains as the most widely used diagnostic technique in clinical cardiology. ECG detects electrical potentials that are generated by cardiac electrical activity. In addition to electrical potentials, the same electrical activity of the heart also induces magnetic fields. These extremely weak cardiac magnetic signals are detected by a non-invasive, contactless technique called magnetocardiography (MCG), which has been evaluated in a number of clinical studies for its usefulness in diagnosing heart diseases. We reviewed the basic principles, history and clinical data on the diagnostic role of MCG in coronary artery disease and cardiac arrhythmias.

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.

Changes in dipolar structure of cardiac magnetic field maps after ST elevation myocardial infarction

BACKGROUND

Pathological changes in cardiac electrophysiology have been investigated in coronary artery disease using magnetocardiography. Aim of this work was to examine the structure of cardiac magnetic field maps (MFM) during ventricular depolarization and repolarization in patients with acute ST elevation myocardial infarction (STEMI).

METHODS

Magnetocardiograms were recorded in 39 healthy subjects and 97 patients who had been successfully revascularized after STEMI. Using the Karhunen-Loeve transform, 12 eigenmaps were constructed for six intervals within the QT interval of each subject's signal-averaged data. The relative information content of the eigenmaps was compared between STEMI patients and healthy subjects.

RESULTS

Relative nondipolar content was between 0.03% and 0.52% higher in the STEMI group, (P < 0.001 for the repolarization intervals). Information content of the first dipolar eigenmap in the STEMI group was reduced by 2.6%-11.7% (P < 0.001 for the repolarization intervals). STT interval was best able to discriminate between groups: area-under-the-curve for nondipolar content was 85.8% (P < 0.001), for the first eigenmap 91.7% (P < 0.001). Severity of infarction was reflected in lower STT interval map 1 content for patients with anterior versus posterior infarction (83%± 11% vs. 87%± 10%, P < 0.05), with wall motion disturbances (84%± 11% vs. 92%± 7%, P < 0.001) and with microvascular obstruction (81%± 12% vs. 87%± 10%, P < 0.05). Regression analysis showed that patients with lower ejection fraction tended to have less information content (P < 0.001).

CONCLUSION

STEMI is associated with a loss of spatial coherence during repolarization, as quantified by principal component analysis of cardiac MFM.

Subtraction magnetocardiogram for detecting coronary heart disease

BACKGROUND

A large-scale magnetocardiogram (MCG) database was produced, and standard MCG waveforms of healthy patients were calculated by using this database. It was clarified that the standard MCG waveforms are formed with the same shape and current distribution in healthy patients. A new subtraction method for detecting abnormal ST-T waveforms in coronary heart disease (CHD) patients by using the standard MCG waveform was developed.

METHODS

We used MCGs of 56 CHD patients (63 ± 3 years old) and 101 age-matched normal control patients (65 ± 5 years old). To construct a subtracted ST-T waveform, we used standard MCG waveforms produced from 464 normal MCGs (male: 268, female: 196). The standard MCG waveforms were subtracted from each subject's measured MCGs, which were shortened or lengthened and normalized to adjust to the data length and magnitude of the standard waveform. We evaluated the maximum amplitude and maximum current-arrow magnitude of the subtracted ST-T waveform.

RESULTS

The maximum magnetic field, maximum magnitude of current arrows, and maximum magnitude of total current vector increased according to the number of coronary artery lesions. The sensitivity and specificity of detecting CHD and normal control patients were 74.6% and 84.1%, respectively.

CONCLUSIONS

The subtraction MCG method can be used to detect CHD with high accuracy, namely, sensitivity of 74.6% and specificity of 84.1% (in the case of maximum amplitude of total current vector). Furthermore, the subtraction MCG magnitude and its current distribution can reflect the expanse of the ischemic lesion area and the progress from ischemia to myocardial infarction.

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.