Assessing heart disease using a novel magnetocardiography device

Coronary artery disease severity and location detection using deep-mining-based magnetocardiography pattern features

BACKGROUND AND OBJECTIVE

The objective of this study was to develop an automated, accurate method of assessing coronary artery disease (CAD), including its severity and location, using deep-mining-based magnetocardiography (MCG) pattern features.

METHODS

The pattern information of MCG was mined deeply, and features were extracted from multiple perspectives. The curl, gradient, and divergence fields were extended based on the current field to visualize hidden pattern information before the singular value decomposition, main field, and image class features were proposed. Finally, the statistical parameters of fine granularity and compound features were introduced. To estimate the CAD severity, stenosis was classified as none, mild, moderate, or severe, and a suitable subset of features for machine learning (ML) modeling was presented. To localize CAD, it was categorized according to the stenosis location, including the left anterior descending (LAD), left circumflex artery (LCX), and right coronary artery (RCA), and the selected subsets of features appropriate for each localization ML model.

RESULTS

The test set exhibited an accuracy, precision, sensitivity, specificity, F1 score, and area under the receiver operating characteristic curve of 85.1 %, 77.8 %, 75.9 %, 95.6 %, 75.9 %, and 0.853, respectively, for CAD severity. The model test sets to detect the LAD, LCX, and RCA locations exhibited accuracies of 97.6 %, 81.2 %, and 85.9 %, respectively.

CONCLUSIONS

Deep-mining-based MCG features can effectively reflect the severity and location of CAD. The proposed ML method can serve as an automated, accurate diagnostic tool for clinicians to improve the interpretation and application of MCG technology in clinical settings.

Accurate diagnosis of ischemic heart disease without exposure to radiation using non-stress unshielded magnetocardiography

Study objectives

To evaluate the capability and accuracy of magnetocardiography (MCG) to identify patients with ischemic chest pain from those with non-ischemic pain and to verify normalcy in the MCG in healthy subjects.

Design

We studied 133 patients (mean age 59 ± 14 years, 69 % male) with chronic or acute chest pain syndrome and 63 healthy subjects (mean age 41.7 ± 12.2 years, 51 % male) using unshielded cryogenically cooled MCG systems (Cardiomag Imaging Inc., 9 and 36 channels) in a general clinical setting. Scan time was 90 s to 6 min. Interventions: The MCG data were processed with the same automated analysis software and results were immediately available. All patients were chest pain free at the time of scanning.

Results

A diagnosis of ischemic chest pain was established in 41 % after non-invasive and invasive testing. Rest MCG was normal in all healthy subjects. An abnormal rest MCG was strongly associated with ischemic chest pain, p < 0.0001 (sensitivity of 86 %, specificity of 80 %, positive (PPV) and negative predictive value (NPV) of 75 % and 89 %, respectively). In comparison, the sensitivity, specificity, PPV and NPV of stress SPECT was 93 %, 72 %, 77 % and 91 %, respectively.

Conclusion

Resting MCG is a rapid risk-free method for the detection of ischemic chest pain without the use of radiation or contrast with results comparable with stress SPECT.

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.

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.

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.