A 90-second magnetocardiogram using a novel analysis system to assess for coronary artery stenosis in Emergency department observation unit chest pain patients

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.

The role of beat-by-beat cardiac features in machine learning classification of ischemic heart disease (IHD) in magnetocardiogram (MCG)

Cardiac electrical changes associated with ischemic heart disease (IHD) are subtle and could be detected even in rest condition in magnetocardiography (MCG) which measures weak cardiac magnetic fields. Cardiac features that are derived from MCG recorded from multiple locations on the chest of subjects and some conventional time domain indices are widely used in Machine learning (ML) classifiers to objectively distinguish IHD and control subjects. Most of the earlier studies have employed features that are derived from signal-averaged cardiac beats and have ignored inter-beat information. The present study demonstrates the utility of beat-by-beat features to be useful in classifying IHD subjects (n = 23) and healthy controls (n = 75) in 37-channel MCG data taken under rest condition of subjects. The study reveals the importance of three features (out of eight measured features) namely, the field map angle (FMA) computed from magnetic field map, beat-by-beat variations of alpha angle in the ST-T region and T wave magnitude variations in yielding a better classification accuracy (92.7 %) against that achieved by conventional features (81 %). Further, beat-by-beat features are also found to augment the accuracy in classifying myocardial infarction (MI) Versus control subjects in two public ECG databases (92 % from 88 % and 94 % from 77 %). These demonstrations summarily suggest the importance of beat-by-beat features in clinical diagnosis of ischemia.

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.

An epoch based methodology to denoise magnetocardiogram (MCG) signals and its application to measurements on subjects with implanted devices

Magnetocardiograms (MCG) provide clinically useful diagnostic information in a variety of cardiac dysfunctions. Low frequency baseline drifts and high frequency noise are inevitably present in routine MCG even for those measured inside magnetically shielded rooms. These interferences sometimes exceed subtle cardiac features in MCG recorded on subjects with implanted devices like cardiac pacemakers; this makes interpretation of cardiac magnetic fields difficult. The present study proposes a correlation-based beat-by-beat approach and principal component analysis to eliminate drifts and high frequency noise respectively; the approach is suitable for denoising both single and multi-channel MCG data. The methodology is critically evaluated on simulated noisy measurements using a 37 channel MCG system, when objects such as implantable permanent pacemaker and stainless-steel wire are sequentially kept externally on the chests of five healthy subjects. By characterizing the noise introduced by each of these objects, the deterioration in the quality of MCG and its subsequent restoration by using the proposed method is assessed. The performance of the proposed method is also compared with other conventional denoising techniques namely, bandpass filters, wavelets and ensemble empirical mode decomposition. The proposed method not only exhibits least distortion, but also preserves the beat-by-beat dynamics of cardiac time series. The method has also been illustrated on actual MCG measurements on two subjects with implanted pacemaker which highlight the ability of the proposed method for denoising MCG in general and during extremely noisy measurement situations. Keywords: Magnetocardiography (MCG), pacemaker, baseline correction, PCA.