ECG Localization Method Based on Volume Conductor Model and Kalman Filtering

The 12-lead electrocardiogram was invented more than 100 years ago and is still used as an essential tool in the early detection of heart disease. By estimating the time-varying source of the electrical activity from the potential changes, several types of heart disease can be noninvasively identified. However, most previous studies are based on signal processing, and thus an approach that includes physics modeling would be helpful for source localization problems. This study proposes a localization method for cardiac sources by combining an electrical analysis with a volume conductor model of the human body as a forward problem and a sparse reconstruction method as an inverse problem. Our formulation estimates not only the current source location but also the current direction. For a 12-lead electrocardiogram system, a sensitivity analysis of the localization to cardiac volume, tilted angle, and model inhomogeneity was evaluated. Finally, the estimated source location is corrected by Kalman filter, considering the estimated electrocardiogram source as time-sequence data. For a high signal-to-noise ratio (greater than 20 dB), the dominant error sources were the model inhomogeneity, which is mainly attributable to the high conductivity of the blood in the heart. The average localization error of the electric dipole sources in the heart was 12.6 mm, which is comparable to that in previous studies, where a less detailed anatomical structure was considered. A time-series source localization with Kalman filtering indicated that source mislocalization could be compensated, suggesting the effectiveness of the source estimation using the current direction and location simultaneously. For the electrocardiogram R-wave, the mean distance error was reduced to less than 7.3 mm using the proposed method. Considering the physical properties of the human body with Kalman filtering enables highly accurate estimation of the cardiac electric signal source location and direction. This proposal is also applicable to electrode configuration, such as ECG sensing systems.

Ultra fast and highly realistic numerical modelling of the surface EMG

Modelling the biophysics underlying the generation and recording of electromyographic (EMG) signals has had a fundamental role in our understanding of muscle electrophysiology as well as in the validation of algorithms for information extraction from the EMG. Current EMG models differ for the complexity of the description of the volume conductor. Analytical solutions are computationally efficient for a small number of fibers but limited to simplified geometries. Numerical solutions are based on accurate anatomical descriptions but require long computational time and are therefore impractical for applications requiring a large number of simulations across a broad variety of conditions. Here, we propose a computationally efficient and realistic EMG model. The volume conductor is described from magnetic resonance images (MRI) or tissue surfaces by discretization in a tetrahedral mesh. The numerical solution of the forward model is optimized by reducing the main calculations to the solutions in a minimal number of basis points, from which the general solution can be obtained. This approach allows the lowest computational time than any current EMG models and also provides a scalable solution. New solutions for the same volume conductor can indeed be obtained without re-computing the volume conductor transformation. This property provides almost real-time simulations, without any constraints on the complexity of the volume conductor or of the transmembrane current source. Because of the high computational efficiency, the proposed model can be used as a basis for the solution of the inverse model or as a means to simulate a large number of data for artificial intelligence (AI) based EMG processing.

The effect of dipole housing and feeding wires in physical phantoms for EEG

Current dipoles are well established models in the localization of neuronal activity to electroencephalography (EEG) data. In physical phantoms, current dipoles can be used as signal sources. Current dipoles are often powered by constant current sources connected via twisted pair wires mostly consisting of copper. The poles are typically formed by platinum wires. These wires as well as the dipole housing might disturb the electric potential distributions in physical phantom measurements. We aimed to quantify this distortion by comparing simulation setups with and without the wires and the housing. The electric potential distributions were simulated using finite element method (FEM). We chose a homogenous volume conductor surrounding the dipoles, which was 100 times larger than the size of the dipoles. We calculated the difference of the electric potential at the surface of the volume conductor between the simulations with and without the connecting wires and the housing. Comparing simulations neglecting all connecting wires and the housing rod to simulations considering them, the electric potential at the surface of the volume conductor differed on average by 2.85 %. Both platinum and twisted pair copper wires had a smaller effect on the electric potentials with a maximum average change of 6.38 ppm. Consequently, source localization of measurements in physical head phantoms should consider these rods in the forward model.

Variation in Reported Human Head Tissue Electrical Conductivity Values

Electromagnetic source characterisation requires accurate volume conductor models representing head geometry and the electrical conductivity field. Head tissue conductivity is often assumed from previous literature, however, despite extensive research, measurements are inconsistent. A meta-analysis of reported human head electrical conductivity values was therefore conducted to determine significant variation and subsequent influential factors. Of 3121 identified publications spanning three databases, 56 papers were included in data extraction. Conductivity values were categorised according to tissue type, and recorded alongside methodology, measurement condition, current frequency, tissue temperature, participant pathology and age. We found variation in electrical conductivity of the whole-skull, the spongiform layer of the skull, isotropic, perpendicularly- and parallelly-oriented white matter (WM) and the brain-to-skull-conductivity ratio (BSCR) could be significantly attributed to a combination of differences in methodology and demographics. This large variation should be acknowledged, and care should be taken when creating volume conductor models, ideally constructing them on an individual basis, rather than assuming them from the literature. When personalised models are unavailable, it is suggested weighted average means from the current meta-analysis are used. Assigning conductivity as: 0.41 S/m for the scalp, 0.02 S/m for the whole skull, or when better modelled as a three-layer skull 0.048 S/m for the spongiform layer, 0.007 S/m for the inner compact and 0.005 S/m for the outer compact, as well as 1.71 S/m for the CSF, 0.47 S/m for the grey matter, 0.22 S/m for WM and 50.4 for the BSCR.

Corticomotoneuronal Model for Intraoperative Neurophysiological Monitoring During Direct Brain Stimulation

Intraoperative neurophysiological monitoring during brain surgery uses direct cortical stimulation to map the motor cortex by recording muscle activity induced by the excitation of alpha motor neurons (MNs). Computational models have been used to understand local brain stimulation. However, a computational model revealing the stimulation process from the cortex to MNs has not yet been proposed. Thus, the aim of the current study was to develop a corticomotoneuronal (CMN) model to investigate intraoperative stimulation during surgery. The CMN combined the following three processes into one system for the first time: (1) induction of an electric field in the brain based on a volume conductor model; (2) activation of pyramidal neuron (PNs) with a compartment model; and (3) formation of presynaptic connections of the PNs to MNs using a conductance-based synaptic model coupled with a spiking model. The implemented volume conductor model coupled with the axon model agreed with experimental strength-duration curves. Additionally, temporal/spatial and facilitation effects of CMN synapses were implemented and verified. Finally, the integrated CMN model was verified with experimental data. The results demonstrated that our model was necessary to describe the interaction between frequency and pulses to assess the difference between low-frequency and multi-pulse high-frequency stimulation in cortical stimulation. The proposed model can be used to investigate the effect of stimulation parameters on the cortex to optimize intraoperative monitoring.

Variation in reported human head tissue electrical conductivity values

ABSTRACTElectromagnetic source characterisation requires accurate volume conductor models representing head geometry and the electrical conductivity field. Head tissue conductivity is often assumed from previous literature, however, despite extensive research, measurements are inconsistent. A meta-analysis of reported human head electrical conductivity values was therefore conducted to determine significant variation and subsequent influential factors. Of 3,121 identified publications spanning three databases, 56 papers were included in data extraction. Conductivity values were categorised according to tissue type, and recorded alongside methodology, measurement condition, current frequency, tissue temperature, participant pathology and age. We found variation in electrical conductivity of the whole-skull, the spongiform layer of the skull, isotropic, perpendicularly- and parallelly-oriented white matter (WM) and the brain-to-skull-conductivity ratio (BSCR) could be significantly attributed to a combination of differences in methodology and demographics. This large variation should be acknowledged, and care should be taken when creating volume conductor models, ideally constructing them on an individual basis, rather than assuming them from the literature. When personalised models are unavailable, it is suggested weighted average means from the current meta-analysis are used. Assigning conductivity as: 0.41 S/m for the scalp, 0.02 S/m for the whole skull, or when better modelled as a 3-layer skull 0.048 S/m for the spongiform layer, 0.007 S/m for the inner compact and 0.005 S/m for the outer compact, as well as 1.71 S/m for the CSF, 0.47 S/m for the grey matter, 0.22 S/m for WM and 50.4 for the BSCR.

The study of the influence of heart ventricular wall thickness on pseudo-ECG

Electrocardiogram is a widespread method of diagnosis of heart diseases. Nevertheless, there are still issues related to connection of some physiological features of themyocardium with patterns observed on the electrocardiogram. In ourworkwe studied the effect of ventricular remodelling, i.e., thickening ofwalls of ventricles typical for hypertrophic cardiomyopathy (HCM), on the pseudo-electrocardiogram on the surface of a volume conductor during myocardial activation from different sources. A model of two ventricles of the heart was developed for this purpose allowing us to vary ventricular geometry. The volume conductor surrounding the heart was a cubic homogeneous volume conductor. Simulation of a pseudo-electrocardiogram was performed by using a realistic ionic model of cardiomyocytes of the ventricles of the human heart and the bidomain model of the myocardium [15]. The zone of initial activation in the model was given on a part of the subendocardial surface or at one or two points corresponding to positions of electrodes of most common implantable devices. In the course of the study we revealed an inversion of the T-wave when changing the thickness of the left ventricle wall regardless of changes of properties of cardiomyocytes or myocardium conductivity. A linear dependence between the wall thickness of the left ventricle and peak amplitudes and integrals under QRS complex and T wave of the electrocardiogram was shown. We have qualitatively shown that with a change in the wall thickness of the left ventricle the pseudo-electrocardiogram changes stronger in the case of activation from one point than in activation from two points or activation of the entire subendocardium.