Non-Invasive Electroanatomical Mapping: A State-Space Approach for Myocardial Current Density Estimation.

Electroanatomical mapping is a method for creating a model of the electrophysiology of the human heart. Medical professionals routinely locate and ablate the site of origin of cardiac arrhythmias with invasive catheterization. Non-invasive localization takes the form of electrocardiographic (ECG) or magnetocardiographic (MCG) imaging, where the goal is to reconstruct the electrical activity of the human heart. Non-invasive alternatives to catheter electroanatomical mapping would reduce patients' risks and open new venues for treatment planning and prevention. This work introduces a new system state-based method for estimating the electrical activity of the human heart from MCG measurements. Our model enables arbitrary propagation paths and velocities. A Kalman filter optimally estimates the current densities under the given measurements and model parameters. In an outer optimization loop, these model parameters are then optimized via gradient descent. This paper aims to establish the foundation for future research by providing a detailed mathematical explanation of the algorithm. We demonstrate the feasibility of our method through a simplified one-layer simulation. Our results show that the algorithm can learn the propagation paths from the magnetic measurements. A threshold-based segmentation into healthy and pathological tissue yields a DICE score of 0.84, a recall of 0.77, and a precision of 0.93.

Quantitative Evaluation for Magnetoelectric Sensor Systems in Biomagnetic Diagnostics.

Dedicated research is currently being conducted on novel thin film magnetoelectric (ME) sensor concepts for medical applications. These concepts enable a contactless magnetic signal acquisition in the presence of large interference fields such as the magnetic field of the Earth and are operational at room temperature. As more and more different ME sensor concepts are accessible to medical applications, the need for comparative quality metrics significantly arises. For a medical application, both the specification of the sensor itself and the specification of the readout scheme must be considered. Therefore, from a medical user's perspective, a system consideration is better suited to specific quantitative measures that consider the sensor readout scheme as well. The corresponding sensor system evaluation should be performed in reproducible measurement conditions (e.g., magnetically, electrically and acoustically shielded environment). Within this contribution, an ME sensor system evaluation scheme will be described and discussed. The quantitative measures will be determined exemplarily for two ME sensors: a resonant ME sensor and an electrically modulated ME sensor. In addition, an application-related signal evaluation scheme will be introduced and exemplified for cardiovascular application. The utilized prototype signal is based on a magnetocardiogram (MCG), which was recorded with a superconducting quantum-interference device. As a potential figure of merit for a quantitative signal assessment, an application specific capacity (ASC) is introduced. In conclusion, this contribution highlights metrics for the quantitative characterization of ME sensor systems and their resulting output signals in biomagnetism. Finally, different ASC values and signal-to-noise ratios (SNRs) could be clearly presented for the resonant ME sensor (SNR: -90 dB, ASC: 9.8×10-7 dB Hz) and also the electrically modulated ME sensor (SNR: -11 dB, ASC: 23 dB Hz), showing that the electrically modulated ME sensor is better suited for a possible MCG application under ideal conditions. The presented approach is transferable to other magnetic sensors and applications.

Active Magnetoelectric Motion Sensing: Examining Performance Metrics with an Experimental Setup.

Magnetoelectric (ME) sensors with a form factor of a few millimeters offer a comparatively low magnetic noise density of a few pT/Hz in a narrow frequency band near the first bending mode. While a high resonance frequency (kHz range) and limited bandwidth present a challenge to biomagnetic measurements, they can potentially be exploited in indirect sensing of non-magnetic quantities, where artificial magnetic sources are applicable. In this paper, we present the novel concept of an active magnetic motion sensing system optimized for ME sensors. Based on the signal chain, we investigated and quantified key drivers of the signal-to-noise ratio (SNR), which is closely related to sensor noise and bandwidth. These considerations were demonstrated by corresponding measurements in a simplified one-dimensional motion setup. Accordingly, we introduced a customized filter structure that enables a flexible bandwidth selection as well as a frequency-based separation of multiple artificial sources. Both design goals target the prospective application of ME sensors in medical movement analysis, where a multitude of distributed sensors and sources might be applied.

Magnetic Measurement of Electrically Evoked Muscle Responses With Optically Pumped Magnetometers.

OBJECTIVE: Electroneurography has been an essential method for assessing peripheral nerve disorders for decades. During this procedure, a nerve is briefly electrically excited, and nerve conduction properties are identified by indirect means from the behavior of the innervated muscle. The magnetic field of the resulting muscle response can also be recorded by novel, uncooled magnetometers, which have become very attractive for different medical applications over recent years. These highly sensitive magnetometers are called optically pumped magnetometers. METHODS: We performed unaveraged and averaged magnetic signal detection of electrically evoked muscle responses using optically pumped magnetometers. We then discussed the suitability of this procedure for clinical applications in the context of diagnostic value and in direct comparison with the current electrical gold standard. RESULTS: The magnetic detection of muscle responses is possible using optically pumped magnetometers. Our magnetic results (averaged and unaveraged) closely match those from electrical measurements. CONCLUSION: Optically pumped magnetometers provide an alternative, contactless technology for electrode-based motor studies, but they are currently not ready for routine clinical use. This costly technology requires additional earth magnetic shielding because this is a prerequisite for proper operation. Currently, there are no diagnostic advantages over electrical measurements. Additionally, the required measurement setup and procedure are much more complicated. SIGNIFICANCE: In contrast to already published proof-of-principle studies for magnetomyography, we report in detail the results of the magnetic measurements of electrically evoked muscle responses in a shielded environment by applying supramaximal stimulation and finally validate our findings with electroneurography data as a reference.

Signal Modeling and Simulation of Temporal Dispersion and Conduction Block in Motor Nerves.

OBJECTIVE: Electroneurography is a well-established diagnostic test for supporting the diagnosis of disorders of myelinated peripheral nerves. Neurophysiological quantities are automatically calculated and are used to determine the pathology of the nerve (axonal damage) or its sheath (myelin damage). Specific differential diagnostic criteria are derived from time-domain normative data, which result primarily from a computer simulation in the early 1990s based on animal data, namely rats. However, the rat signals studied differ significantly from those of humans because of anatomical differences. METHODS: We present a model-based simulation of nerve conduction in healthy and pathological motor nerves. In contrast to earlier simulations, the present model is based on motor unit action potentials extracted from real human measurements facilitating the generation of realistic signals, starting from a conduction velocity distribution. In addition to the modeling of healthy nerves, we model a hereditary peripheral nerve disease as well as an acute and a chronic inflammatory demyelinating condition. RESULTS: Quantitative signal differences based on standard variables in the time-domain are presented. The findings for the demyelinating conditions demonstrate amplitude reductions of 71% and 65% between the distal and proximal responses, which result from an increase in the variance of the nerve fiber conduction velocities. CONCLUSION: The simulation results closely match those of empirical measurements, indicating that the signal model captures relevant pathological mechanisms. An amplitude reduction of more than 50% in demyelinating conditions is in accordance with routine measurements and shows that temporal dispersion is quite well-modeled compared to previous simulation models. SIGNIFICANCE: The simulation outcomes can serve as the basis for an improved pathophysiological understanding of peripheral nerve disorders and should aid neurophysiologists to refine their diagnostic armamentarium resulting in a more precise differential diagnosis.