Relationship between metabolic and anthropometric maternal parameters and the fetal autonomic nervous system

Pre-pregnancy obesity, defined as a body mass index (BMI) greater than or equal to 30 kg/m2, can have adverse effects on the health of newborns and can also lead to metabolic, cardiovascular and neurological diseases in the offspring as they grow older. In the area of fetal origins and disease in adult life, a large number of studies have reported a critical role for maternal weight and metabolism before or during gestation in shaping the health of their offspring. Maternal obesity is recognised as a major modifiable contributor to obesity and metabolic syndrome in offspring, but the underlying factors remain unclear. The fetal autonomic nervous system (ANS) is subject to programming during developmental periods and is considered one of the processes by which early programming of disease can take place. The main goal of the present work was to use the fetal heart rate (HR) and heart rate variability (HRV) as proxies for the fetal ANS to study the effects of metabolic and anthropometric maternal (MAM) parameters before and during gestation on the fetuses of healthy, normoglycemic mothers. A total of 184 women in their second/third trimesters of uncomplicated pregnancies were included in this study. Pre-pregnancy BMI and maternal weight gain during pregnancy were recorded. In a subsample (n = 104), maternal insulin sensitivity was measured during an oral glucose tolerance test. Fetal HR and HRV were determined by magnetic recording in all subjects. The influence of pre-pregnancy BMI, maternal weight gain and maternal insulin sensitivity on fetal HR and HRV was evaluated. Associations between MAM parameters and maternal HR and HRV were also assessed. ANCOVA, partial correlation and mediation analysis were applied, all of which were adjusted for gestational age, gender and parity. A regression on fetal HR using a machine learning approach was tested to explore which maternal factor is the driving factor programming the fetal ANS. Four models were tested: Linear regression, Regression Tree, Support Vector Machine and Random Forest. The fetal HR was higher in fetuses of mothers with high pre-pregnancy BMI (overweight/obese) than in mothers with normal weight. The fetal HRV was lower in mothers with high weight gain than in mothers with normal weight gain. The fetal HR was negatively correlated with maternal weight gain and maternal insulin sensitivity. Pre-pregnancy BMI was positively correlated with fetal high frequency and negatively correlated with low frequency and the low to high frequency ratio. Maternal weight gain was associated indirectly with birth weight through fetal HR, while maternal insulin sensitivity was associated with fetal HR through fetal HRV. Separately, fetal HRV was associated with birth weight through the fetal HR. The Random Forest ensemble tree-based model outperformed linear regression as the fetal HR regression model. Fetal HR can be predicted using the following nine relevant variables (sorted from the most important to the least important): pre-pregnancy BMI, gender, maternal fasting insulin, maternal insulin sensitivity, gravidity, maternal age, maternal fasting glucose, gestational age and maternal weight gain. Pre-pregnancy BMI appeared to be the major factor predicting fetal HR. In conclusion, the fetal ANS is sensitive to maternal metabolic and anthropometric influences, and particularly maternal weight before pregnancy. These findings support the concept of the “Developmental Origin of Health and Disease” and increase our knowledge about the importance of the intrauterine environment in the programming of the ANS and the possible programming of disease in later life

Strategies for optimal design of biomagnetic sensor systems

Magnetic field imaging (MFI) is a technique to record contact free the magnetic field distribution and estimate the underlying source distribution in the heart. Currently, the cardiomagnetic fields are recorded with superconducting quantum interference devices (SQUIDs), which are restricted to the inside of a cryostat filled with liquid helium or nitrogen. New room temperature optical magnetometers allow less restrictive sensor positioning, which raises the question of how to optimally place the sensors for robust field reconstruction. The objective in this study is to develop a generic object-oriented framework for optimizing sensor arrangements (sensor positions and orientations) which supports the necessary constraints of a limited search volume (only outside the body) and the technical minimum distance of sensors (e.g. 1 cm). In order to test the framework, a new quasi-continuous particle swarm optimizer (PSO) component is developed as well as an exemplary goal function component using the condition number (CN) of the leadfield matrix. Generic constraint handling algorithms are designed and implemented, that decompose complex constraints into basic ones. The constraint components interface to an operational exemplary optimization strategy which is validated on the magnetocardiographic sensor arrangement problem. The simulation setup includes a three compartment boundary element model of a torso with a fitted multi-dipole heart model. The results show that the CN, representing the reconstruction robustness of the inverse problem, can be reduced with our optimization by one order of magnitude within a sensor plane (the cryostat bottom) in front of the torso compared to a regular sensor grid. Reduction of another order of magnitude is achieved by optimizing sensor positions on the entire torso surface. Results also indicate that the number of sensors may be reduced to 20-30 without loss of robustness in terms of CN. The original contributions are the generic reusable framework and exemplary components, the quasicontinuous PSO algorithm with constraint support and the composite constraint handling algorithms

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

High-temperature superconducting magnetometers for on-scalp MEG

In the growing field of on-scalp magnetoencephalography (MEG), brain activity is studied by non-invasively mapping the magnetic fields generated by neuronal currents with sensors that are flexibly placed in close proximity to the subject\u27s head. This thesis focuses on high-temperature superconducting magnetometers made from YBa2Cu3Ox-7 (YBCO), which enables a reduction in the sensor-to-room temperature standoff distance from roughly 2 cm (for conventional MEG systems) down to 1 mm. Because of the higher neuromagnetic signal magnitudes available to on-scalp sensors, simulations predict that even a relatively low-sensitivity (higher noise) full-head on-scalp MEG system can extract more information about brain activity than conventional systems.In the first part of this thesis, the development of high critical temperature (high-Tc) superconducting quantum interference device (SQUID) magnetometers for a 7-channel on-scalp MEG system is described. The sensors are single layer magnetometers with a directly coupled pickup loop made on 10 mm 7 10 mm substrates using bicrystal grain boundary Josephson junctions. We found that the kinetic inductance strongly varies with film quality and temperature. Determination of all SQUID parameters by combining measurements and inductance simulations led to excellent agreement between experimental results and theoretical predictions. This allowed us to perform an in-depth magnetometer optimization. The best magnetometers achieve a magnetic field noise level of 44 fT/√Hz at 78 K. Fabricated test SQUIDs provide evidence that noise levels below 30 fT/√Hz are possible for high quality junctions with fairly low critical currents and in combination with the optimized pickup loop design. Different feedback methods for operation in a densely-packed on-scalp MEG system were also investigated. Direct injection of current into the SQUID loop was identified as the best on-chip feedback method with feedback flux crosstalk below 0.5%. By reducing the operation temperature, the noise level can be further reduced, however, the effective area also decreases because of the decreasing kinetic inductance contribution. We present a method that allows for one-time sensor calibration independent of temperature.In the second part, the design, operation, and performance of the constructed 7-channel on-scalp MEG system based on the fabricated magnetometers is presented. With a dense (2 mm edge-to-edge) hexagonal head-aligned array, the system achieves a small sensor-to-head standoff distance of 1-3 mm and dense spatial sampling. The magnetic field noise levels are 50-130 fT/√Hz and the sensor-to-sensor feedback flux crosstalk is below 0.6%. MEG measurements with the system demonstrate the feasibility of the approach and indicate that our on-scalp MEG system allows retrieval of information unavailable to conventional MEG.In the third part, two alternative magnetometer types are studied for the next generation system. The first alternative is magnetometers based on Dayem bridge junctions instead of bicrystal grain boundary junctions. With a magnetometer based on the novel grooved Dayem bridge junctions, a magnetic field noise level of 63 fT/√Hz could be achieved, which shows that Dayem bridge junctions are starting to become a viable option for single layer magnetometers. The second alternative are high-Tc SQUID magnetometers with an inductively coupled flux transformer. The best device with bicrystal grain boundary junctions reaches a magnetic field noise level below 11 fT/√Hz and outperforms the best single layer device for frequencies above 20 Hz.In the last part, the potential of kinetic inductance magnetometers (KIMs) is investigated. We demonstrate the first high-Tc KIMs, which can be operated in fields of 9-28 \ub5T and achieve a noise level of 4 pT/√Hz at 10 kHz

Numerical methods for improved signal to noise ratios in spatiotemporal biomedical data

Magnetocardiography (MCG) is a technique to measure the magnetic fields produced by electrical activity in the heart. The interpretation of MCG signals is difficult because of different disturbances and noise. Several methods have been suggested for noise reduction in MCG data such as averaging, pass or stop band filters, and statistical based methods, but a unified framework that takes into account different typologies of MCG signals (rest, stress, and patients with an already ICD– Implanted Cardioverter Defibrillator- implanted) using an adequate number of recordings is still missing. Consequently, the main aim of the thesis is to develop methods for noise and artifacts treatment. Due to the non-stationarity (NS) of the noise, the conventional ensemble averaging of the data does not yield the theoretical improvement. In order to overcome this problem an average procedure that ignores the noisiest beats is applied. The results of this averaging procedure confirms that in case of NS, the Signal to Noise Ratio (SNR) does not behave as expected, but reaches a maximum after a certain number of selected beats. Furthermore, a theoretical proof of this result is given. The second part of the thesis deals with techniques based on Blind Source Separation (BSS), as preprocessing step for the averaging procedure, in case of MCG signals with low SNR. Different BSS algorithms are compared in order to find the best one in terms of noise reduction, separation, and computational time for each data typology. A drawback of BSS techniques is the order of the sources that cannot be determined a priori; for this reason 3 methods (based on different statistical principles) have been developed for the retrieval of cardiac signals. The last part of the thesis deals with the application of BSS methods to a category of signals not yet analyzed: patients with ICD implanted. It is shown that it is possible to extract the cardiac signal also in such noisy data, although not automatically. The Temporal Decorrelation source SEParation (TDSEP) algorithm outperforms the other BSS methods. This thesis shows that, applying novel automatic routines for the removal of noise and artifacts, MCG data could be used in clinical environments

Advanced Source Reconstruction and Volume Conductor Modeling for Fetal Magnetocardiography

Abstract Fetuses that are identified with cardiac hypotrophy, hypertension and metabolic anomalies have higher risk of suffering from various health problems in their later life. Therefore, the early detection of congenital heart anomalies is critical for monitoring or prompt interventions, which can reduce the risks of congestive heart failure. Compared to adult cardiac monitoring, fetal electrophysiological heart monitoring using fetal ECG is extremely difficult due to the low signal amplitude and interferences from the maternal cardiac signal and to the complex environment inside the mother's womb. This problem is even worse in conditions such as diabetic pregnancies because of further signal reduction due to maternal obesity. At the same time, the prevalence of congenital heart anomalies is higher for fetuses of diabetic mothers. The purpose of this thesis is to develop and test fetal magnetocardiography (fMCG) techniques as an alternative diagnostic tool for the detection and monitoring of the fetal heart. fMCG is a novel technique that records the magnetic fields generated by the fetal heart's electric activity. From the aspect of signal processing, magnetic signals generated by the fetal heart are less affected by the low electrical conductivity of the surrounding fetal and maternal tissues compared to the electric signals recorded over the maternal abdomen, and can provide reliable recordings as early as 12 weeks of gestation. However, the fetal heart signals recorded with an array of magnetic sensors at a small distance from the maternal abdomen are affected by the source-to-sensor distance as well as by the geometry of the volume conductor, which is variable in different subjects or in the same subject when recordings are made at different gestational ages. The scope of this thesis is to develop a novel methodology for modeling the fetal heart and volume conductor and to use advanced source reconstruction techniques that can reduce the effect of these confounding factors in evaluating heart magnetic signals. Furthermore, we aim to use these new methods for developing a normative database of fMCG metrics at different gestational ages and test their reliability to detect abnormal patterns of cardiac electrophysiology in pregnancies complicated by maternal diabetes. In the first part of the thesis, we review three current fetal heart monitoring modalities, including fetal electrocardiography (ECG), ultrasonography, and fetal magnetocardiography (fMCG). The advantages and drawbacks of each technique are comparatively discussed. Finally, we discuss the developmental changes of fetal heart through gestation as well as the electromagnetic characteristics of the fetal cardiac activation

Development of a single beam SERF magnetometer using caesium atoms for medical applications

This thesis describes the design and implementation of a compact zero-field optically pumped magnetometer for human biomagetic measurements. This project aimed to achieve lower operating temperatures and a higher sensor bandwidth than current commercial rubidium-based equivalent sensors. Through careful selection of the sensing alkali, caesium, and all constituent components of the sensor package design, both of these aims are achieved. All of the required systems and components for a single-beam zero-field magnetometer are discussed, including a high efficiency cell heating and monitoring system, multi-axis field control and the optical detection scheme. Through full understanding and development of these systems, miniaturised and microfabricated versions are developed that facilitate the construction of a sensor package with external dimensions of 25 × 25 × 50 mm3. A number of machine learning tools are developed and applied to directly optimise the sensor’s sensitivity through control of the appropriate operational parameters, yielding a factor of five improvement. These techniques also enabled the investigation of the effect of nitrogen buffer gas pressure on the sensor’s measured sensitivity, demonstrating a linear increase in sensitivity with increasing pressure. The prototype sensor demonstrated a significant advancement in terms of bandwidth achieving a linear frequency response up to ' 900 Hz. The external package temperature of the sensor for prolonged timescales (> 1 hour) maintained a skin-safe temperature (< 41 ◦C), with a biomagnetic field level sensitivity, 90 fT/√ Hz, and compact package footprint, less than a square inch. A practical measurement of the magnetic field of a cardiac signal successfully demonstrates the sensor as a suitable biomagnetic measurement tool.This thesis describes the design and implementation of a compact zero-field optically pumped magnetometer for human biomagetic measurements. This project aimed to achieve lower operating temperatures and a higher sensor bandwidth than current commercial rubidium-based equivalent sensors. Through careful selection of the sensing alkali, caesium, and all constituent components of the sensor package design, both of these aims are achieved. All of the required systems and components for a single-beam zero-field magnetometer are discussed, including a high efficiency cell heating and monitoring system, multi-axis field control and the optical detection scheme. Through full understanding and development of these systems, miniaturised and microfabricated versions are developed that facilitate the construction of a sensor package with external dimensions of 25 × 25 × 50 mm3. A number of machine learning tools are developed and applied to directly optimise the sensor’s sensitivity through control of the appropriate operational parameters, yielding a factor of five improvement. These techniques also enabled the investigation of the effect of nitrogen buffer gas pressure on the sensor’s measured sensitivity, demonstrating a linear increase in sensitivity with increasing pressure. The prototype sensor demonstrated a significant advancement in terms of bandwidth achieving a linear frequency response up to ' 900 Hz. The external package temperature of the sensor for prolonged timescales (> 1 hour) maintained a skin-safe temperature (< 41 ◦C), with a biomagnetic field level sensitivity, 90 fT/√ Hz, and compact package footprint, less than a square inch. A practical measurement of the magnetic field of a cardiac signal successfully demonstrates the sensor as a suitable biomagnetic measurement tool

SQUIDs in biomagnetism : A roadmap towards improved healthcare

This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 686865.Peer reviewedPublisher PD