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.

Correlation of fetal heart rate dynamics to inflammatory markers and brain-derived neurotrophic factor during pregnancy.

OBJECTIVES: This study aims to show the relation between biomarkers in maternal and cord-blood samples and fetal heart rate variability (fHRV) metrics through a non-invasive fetal magnetocardiography (fMCG) technique. METHODS: Twenty-three women were enrolled for collection of maternal serum and fMCG tracings immediately prior to their scheduled cesarean delivery. The umbilical cord blood was collected for measurement of biomarker levels. The fMCG metrics were then correlated to the biomarker levels from the maternal serum and cord blood. RESULTS: Brain-derived neurotrophic factor (BDNF) had a moderate correlation with fetal parasympathetic activity (0.416) and fetal sympathovagal ratios (-0.309; -0.356). Interleukin (IL)-6 also had moderate-sized correlations but with an inverse relationship as compared to BDNF. These correlations were primarily in cord-blood samples and not in the maternal blood. CONCLUSIONS: In this small sample-sized exploratory study, we observed a moderate correlation between fHRV and cord-blood BDNF and IL-6 immediately preceding scheduled cesarean delivery at term. These findings need to be validated in a larger population.

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.

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.

Exploring the Influence of Fetal Sex on Heart Rate Dynamics Using Fetal Magnetocardiographic Recordings.

Fetal sex has been associated with different development trajectories that cause structural and functional differences between the sexes throughout gestation. Fetal magnetocardiography (fMCG) recordings from 123 participants (64 females and 59 males; one recording/participant) from a database consisting of low-risk pregnant women were analyzed to explore and compare fetal development trajectories of both sexes. The gestational age of the recordings ranged from 28 to 38 weeks. Linear metrics in both the time and frequency domains were applied to study fetal heart rate variability (fHRV) measures that reveal the dynamics of short- and long-term variability. Rates of linear change with GA in these metrics were analyzed using general linear model regressions with assessments for significantly different variances and GA regression slopes between the sexes. The fetal sexes were well balanced for GA and sleep state. None of the fHRV measures analyzed exhibited significant variance heterogeneity between the sexes, and none of them exhibited a significant sex-by-GA interaction. The absence of a statistically significant sex-by-GA interaction on all parameters resulted in none of the regression slope estimates being significantly different between the sexes. With high-precision fMCG recordings, we were able to explore the variation in fHRV parameters as it relates to fetal sex. The fMCG-based fHRV parameters did not show any significant difference in rates of change with gestational age between sexes. This study provides a framework for understanding normal development of the fetal autonomic nervous system, especially in the context of fetal sex.

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.

Real-Time Magnetocardiography with Passive Miniaturized Coil Array in Earth Ambient Field.

We demonstrate a magnetocardiography (MCG) sensor that operates in non-shielded environments, in real-time, and without the need for an accompanying device to identify the cardiac cycles for averaging. We further validate the sensor's performance on human subjects. Our approach integrates seven (7) coils, previously optimized for maximum sensitivity, into a coil array. Based on Faraday's law, magnetic flux from the heart is translated into voltage across the coils. By leveraging digital signal processing (DSP), namely, bandpass filtering and averaging across coils, MCG can be retrieved in real-time. Our coil array can monitor real-time human MCG with clear QRS complexes in non-shielded environments. Intra- and inter-subject variability tests confirm repeatability and accuracy comparable to gold-standard electrocardiography (ECG), viz., a cardiac cycle detection accuracy of >99.13% and averaged R-R interval accuracy of <5.8 ms. Our results confirm the feasibility of real-time R-peak detection using the MCG sensor, as well as the ability to retrieve the full MCG spectrum as based upon the averaging of cycles identified via the MCG sensor itself. This work provides new insights into the development of accessible, miniaturized, safe, and low-cost MCG tools.

A method for noninvasive beat-by-beat visualization of His bundle signals.

BACKGROUND: Invasive recording of His bundle signals (HBS) in electrophysiological study (EPS) is important in determining HV interval, the time taken to activate the ventricles from the His bundle. Noninvasive surface measurements of HBS are attempted by averaging typically 100-200 cardiac cycles of ECG time series in body surface potential mapping (BSPM) and in magnetocardiography (MCG) which records weak cardiac magnetic fields by highly sensitive detectors. However, noninvasive beat-by-beat extraction of HBS is challenged by ramp-like atrial signals and noise in PR segment of the cardiac cycle. METHODS: By making use of a signal-averaged trace showing prominent HBS as a guide trace, we developed a method combining interval-dependent wavelet thresholding (IDWT) and signal space projection (SSP) technique to eliminate artifacts from single beats. The method was applied on MCG recorded on 21 subjects with known HV intervals based on EPS and noninvasive signal-averaging, including five subjects with BSPM recorded subsequently. The method was also applied on stress-MCG of a subject featuring autonomic dynamics. RESULTS: HBS could be extracted from 19 out of 21 subjects by signal-averaging whose timing differed from EPS between -8 and 11 ms as tested by 2 observers. HBS in single beats were seen as aligned patterns in inter-beat contours and were appreciable in stress-MCG and conspicuous than BSPM. The performance of the method was evaluated on simulated and measured MCG to be adequate if the signal-to-noise ratio was at least 20 dB. CONCLUSIONS: These results suggest the use of this method for noninvasive assessments on HBS.

A movable unshielded magnetocardiography system.

Magnetocardiography (MCG), which uses high-sensitivity magnetometers to record magnetic field signals generated by electrical activity in the heart, is a noninvasive method for evaluating heart diseases such as arrhythmia and ischemia. The MCG measurements usually require the participant keeping still in a magnetically shielded room due to the immovable sensor and noisy external environments. These requirements limit MCG applications, such as exercise MCG tests and long-term MCG observations, which are useful for early detections of heart diseases. Here, we introduce a movable MCG system that can clearly record MCG signals of freely behaving participants in an unshielded environment. On the basis of optically pumped magnetometers with a sensitivity of 140 fT/Hz1/2, we successfully demonstrated the resting MCG and the exercise MCG tests. Our method is promising to realize a practical movable multichannel unshielded MCG system that nearly sets no limits to participants and brings another kind of insight into the medical diagnosis of heart disease.

Application of Magnetocardiography to Screen for Inflammatory Cardiomyopathy and Monitor Treatment Response.

Background Inflammatory cardiomyopathy is one of the most common causes of sudden cardiac death in young adults. Diagnosis of inflammatory cardiomyopathy remains challenging, and better monitoring tools are needed. We present magnetocardiography as a method to diagnose myocardial inflammation and monitor treatment response. Methods and Results A total of 233 patients were enrolled, with a mean age of 45 (±18) years, and 105 (45%) were women. The primary analysis included 209 adult subjects, of whom 66 (32%) were diagnosed with inflammatory cardiomyopathy, 17 (8%) were diagnosed with cardiac amyloidosis, and 35 (17%) were diagnosed with other types of nonischemic cardiomyopathy; 91 (44%) did not have cardiomyopathy. The second analysis included 13 patients with inflammatory cardiomyopathy who underwent immunosuppressive therapy after baseline magnetocardiography measurement. Finally, diagnostic accuracy of magnetocardiography was tested in 3 independent cohorts (total n=23) and 1 patient, who developed vaccine-related myocarditis. First, we identified a magnetocardiography vector to differentiate between patients with cardiomyopathy versus patients without cardiomyopathy (vector of ≥0.051; sensitivity, 0.59; specificity, 0.95; positive predictive value, 93%; and negative predictive value, 64%). All patients with inflammatory cardiomyopathy, including a patient with mRNA vaccine-related myocarditis, had a magnetocardiography vector ≥0.051. Second, we evaluated the ability of the magnetocardiography vector to reflect treatment response. We observed a decrease of the pathologic magnetocardiography vector toward normal in all 13 patients who were clinically improving under immunosuppressive therapy. Magnetocardiography detected treatment response as early as day 7, whereas echocardiographic detection of treatment response occurred after 1 month. The magnetocardiography vector decreased from 0.10 at baseline to 0.07 within 7 days (P=0.010) and to 0.03 within 30 days (P<0.001). After 30 days, left ventricular ejection fraction improved from 42.2% at baseline to 53.8% (P<0.001). Conclusions Magnetocardiography has the potential to be used for diagnostic screening and to monitor early treatment response. The method is valuable in inflammatory cardiomyopathy, where there is a major unmet need for early diagnosis and monitoring response to immunosuppressive therapy.

Development of Magnetocardiograph without Magnetically Shielded Room Using High-Detectivity TMR Sensors.

A magnetocardiograph that enables the clear observation of heart magnetic field mappings without magnetically shielded rooms at room temperatures has been successfully manufactured. Compared to widespread electrocardiographs, magnetocardiographs commonly have a higher spatial resolution, which is expected to lead to early diagnoses of ischemic heart disease and high diagnostic accuracy of ventricular arrhythmia, which involves the risk of sudden death. However, as the conventional superconducting quantum interference device (SQUID) magnetocardiographs require large magnetically shielded rooms and huge running costs to cool the SQUID sensors, magnetocardiography is still unfamiliar technology. Here, in order to achieve the heart field detectivity of 1.0 pT without magnetically shielded rooms and enough magnetocardiography accuracy, we aimed to improve the detectivity of tunneling magnetoresistance (TMR) sensors and to decrease the environmental and sensor noises with a mathematical algorithm. The magnetic detectivity of the TMR sensors was confirmed to be 14.1 pTrms on average in the frequency band between 0.2 and 100 Hz in uncooled states, thanks to the original multilayer structure and the innovative pattern of free layers. By constructing a sensor array using 288 TMR sensors and applying the mathematical magnetic shield technology of signal space separation (SSS), we confirmed that SSS reduces the environmental magnetic noise by -73 dB, which overtakes the general triple magnetically shielded rooms. Moreover, applying digital processing that combined the signal average of heart magnetic fields for one minute and the projection operation, we succeeded in reducing the sensor noise by about -23 dB. The heart magnetic field resolution measured on a subject in a laboratory in an office building was 0.99 pTrms and obtained magnetocardiograms and current arrow maps as clear as the SQUID magnetocardiograph does in the QRS and ST segments. Upon utilizing its superior spatial resolution, this magnetocardiograph has the potential to be an important tool for the early diagnosis of ischemic heart disease and the risk management of sudden death triggered by ventricular arrhythmia.

Towards a Practical Implementation of a Single-Beam All-Optical Non-Zero-Field Magnetic Sensor for Magnetoencephalographic Complexes.

We present a single-beam all-optical two-channel magnetic sensor scheme developed for biological applications such as non-zero-field magnetoencephalography and magnetocardiography. The pumping, excitation and detection of magnetic resonance in two cells are performed using a single laser beam with time-modulated linear polarization: the linear polarization of the beam switches to orthogonal every half-cycle of the Larmor frequency. Light with such characteristics can be transmitted over a single-mode polarization-maintaining fiber without any loss in the quality of the polarization characteristics. We also present an algorithm for calculating optical elements in a sensor scheme, the results of measuring the parametric dependences of magnetic resonance in cells, and the results of direct testing of a sensor in a magnetic shield. We demonstrate sensitivity at the level of 20 fT/√Hz in one sensor channel in the frequency range of 80-200 Hz.

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.