Volume Conductor Research Articles

Robust Assessment of EEG Connectivity Patterns in Mild Cognitive Impairment and Alzheimer's Disease.

The prevalence of dementia, including Alzheimer's disease (AD), is on the rise globally with screening and intervention of particular importance and benefit to those with limited access to healthcare. Electroencephalogram (EEG) is an inexpensive, scalable, and portable brain imaging technology that could deliver AD screening to those without local tertiary healthcare infrastructure. We study EEG recordings of subjects with sporadic mild cognitive impairment (MCI) and prodromal familial, early-onset, AD for the same working memory tasks using high- and low-density EEG, respectively. A challenge in detecting electrophysiological changes from EEG recordings is that noise and volume conduction effects are common and disruptive. It is known that the imaginary part of coherency (iCOH) can generate functional connectivity networks that mitigate against volume conduction, while also erasing true instantaneous activity (zero or π-phase). We aim to expose topological differences in these iCOH connectivity networks using a global network measure, eigenvector alignment (EA), shown to be robust to network alterations that emulate the erasure of connectivities by iCOH. Alignments assessed by EA capture the relationship between a pair of EEG channels from the similarity of their connectivity patterns. Significant alignments-from comparison with random null models-are seen to be consistent across frequency ranges (delta, theta, alpha, and beta) for the working memory tasks, where consistency of iCOH connectivities is also noted. For high-density EEG recordings, stark differences in the control and sporadic MCI results are observed with the control group demonstrating far more consistent alignments. Differences between the control and pre-dementia groupings are detected for significant correlation and iCOH connectivities, but only EA suggests a notable difference in network topology when comparing between subjects with sporadic MCI and prodromal familial AD. The consistency of alignments, across frequency ranges, provides a measure of confidence in EA's detection of topological structure, an important aspect that marks this approach as a promising direction for developing a reliable test for early onset AD.

Comparison of MI-EEG Decoding in Source to Sensor Domain.

Brain-computer interface (BCI) system based on sensorimotor rhythm (SMR) is a more natural brain-computer interaction system. In this paper, we propose a new multi-task motor imagery EEG (MI-EEG) classification framework. Unlike traditional EEG decoding algorithms, we perform the decoding task in the source domain rather than the sensor domain. In the proposed algorithm, we first build a conduction model of the signal using the public ICBM152 head model and the boundary element method (BEM). The sensor domain EEG was then mapped to the selected cortex region using standardized low-resolution electromagnetic tomography (sLORETA) technology, which benefit to address volume conduction effects problem. Finally, the source domain features are extracted and classified by combining FBCSP and simple LDA. The results show that the classification-decoding algorithm performed in the source domain can well solve the classification task of MI-EEG. In addition, we found that the source imaging method can significantly increase the number of available EEG channels, which can be expanded at least double. The preliminary results of this study encourage the implementation of EEG decoding algorithms in the source domain. Clinical Relevance- This confirms that better results can be obtained by performing MI-EEG decoding in the source domain than in the sensor domain.

Electrophysiological correlates of confidence differ across correct and erroneous perceptual decisions

Every decision we make is accompanied by an estimate of the probability that our decision is accurate or appropriate. This probability estimate is termed our degree of decision confidence. Recent work has uncovered event-related potential (ERP) correlates of confidence both during decision formation and after a decision has been made. However, the interpretation of these findings is complicated by methodological issues related to ERP amplitude measurement that are prevalent across existing studies. To more accurately characterise the neural correlates of confidence, we presented participants with a difficult perceptual decision task that elicited a broad range of confidence ratings. We identified a frontal ERP component within an onset prior to the behavioural response, which exhibited more positive-going amplitudes in trials with higher confidence ratings. This frontal effect also biased measures of the centro-parietal positivity (CPP) component at parietal electrodes via volume conduction. Amplitudes of the error positivity (Pe) component that followed each decision were negatively associated with confidence for trials with decision errors, but not for trials with correct decisions, with Bayes factors providing moderate evidence for the null in the latter case. We provide evidence for both pre- and post-decisional neural correlates of decision confidence that are observed in trials with correct and erroneous decisions, respectively. Our findings suggest that certainty in having made a correct response is associated with frontal activity during decision formation, whereas certainty in having committed an error is instead associated with the post-decisional Pe component. These findings also highlight the possibility that some previously reported associations between decision confidence and CPP/Pe component amplitudes may have been a consequence of ERP amplitude measurement-related confounds. Re-analysis of existing datasets may be useful to test this hypothesis more directly.

Analysis of tissue electrical properties on bio-impedance variation of upper limps

Upper limb loss has a significant impact on individual socioeconomic life. Human-machine interface (HMI) using surface electromyography (sEMG) establishes a link between the user and a hand prosthesis to recognize hand gestures and motions which allows the control of robotic machines and prostheses to perform dexterous tasks. Numerous methods aimed to enhance hand gesture and motion recognition toward an HMI. Bio-impedance analysis (BIA) is a noninvasive way of assessing body compositions and has been recently used for hand motion interpretation using `brute force? pattern recognition. The impedance variation in the body mostly depends on the precise stimulation using appropriate electrical features of the associated tissue layers. It has been reported that the electrical properties of these layers varied significantly. Thus, it is essential to investigate the influence of these variations on the stimulator design for the hand motion interpretation. This may not be possible using experimental approaches. Alternatively, using highly advanced computational models, this can be readily investigated by attaining the available range of the electrical properties of each tissue layer and applying appropriate boundary conditions and simulation settings. The computational models are composed of a volume conductor of the human arm model and electrode settings. Also, two different computational study methods were used to determine the influence of the tissues? dielectric properties on the results. The quasistatic approximation was used by only considering the resistivity of the anatomical layers and the transient simulation was used to analyze the capacitive impact on the results. Finite element (FE) models were developed to simulate the potential distribution inside the skin, muscle, and bone layers of the upper arm for given electrode settings. Then, simulation results were recorded for various electrical properties and different study types. It was shown that the capacitive influence of the tissue may not be ignored for certain conditions due to significant variation in the induced electrical potential variation along with the target muscle. Also, the influence of the individual tissue?s electrical properties was investigated using a set of dielectric parameters. The results showed that the skin and muscle layers have a significant impact on the electrical potential variation across the muscle length.

Directed Functional Coordination Analysis of Swallowing Muscles in Healthy and Dysphagic Subjects by Surface Electromyography.

Swallowing is a complex sequence of highly regulated and coordinated skeletal and smooth muscle activity. Previous studies have attempted to determine the temporal relationship between the muscles to establish the activation sequence pattern, assessing functional muscle coordination with cross-correlation or coherence, which is seriously impaired by volume conduction. In the present work, we used conditional Granger causality from surface electromyography signals to analyse the directed functional coordination between different swallowing muscles in both healthy and dysphagic subjects ingesting saliva, water, and yoghurt boluses. In healthy individuals, both bilateral and ipsilateral muscles showed higher coupling strength than contralateral muscles. We also found a dominant downward direction in ipsilateral supra and infrahyoid muscles. In dysphagic subjects, we found a significantly higher right-to-left infrahyoid, right ipsilateral infra-to-suprahyoid, and left ipsilateral supra-to-infrahyoid interactions, in addition to significant differences in the left ipsilateral muscles between bolus types. Our results suggest that the functional coordination analysis of swallowing muscles contains relevant information on the swallowing process and possible dysfunctions associated with dysphagia, indicating that it could potentially be used to assess the progress of the disease or the effectiveness of rehabilitation therapies.

Functional Connectivity Analysis in EEG Source Space During Deception

Purpose: Deception is described as a deliberate endeavor to deceive others. In this research, the main purpose is to survey the brain network between deception and telling the truth.
 Materials and Methods: Electroencephalography (EEG) data were collected from 17 subjects during a deception task in which the subjects had to classify the target stimuli deceptively while responding truthfully to other stimuli (non-targets). Functional Connectivity (FC) analysis was carried out in source space in order to attenuate the volume conduction effect. The coherence criterion was applied for calculating FC.
 Results: The results revealed that deception is associated with significantly greater connectivity between distant regions, including frontal-occipital and frontal-parietal connectivity. In addition, Anterior Cingulate Cortex (ACC) demonstrated significantly greater connectivity with regions of the frontal and occipital lobes. Besides, deception was accompanied by high number of strong connectivity between the left parietal and frontal lobes.
 Conclusions: The findings demonstrated that the FC studies in source space can strikingly assist in the investigation of deception.

Floating EMG sensors and stimulators wirelessly powered and operated by volume conduction for networked neuroprosthetics

BackgroundImplantable neuroprostheses consisting of a central electronic unit wired to electrodes benefit thousands of patients worldwide. However, they present limitations that restrict their use. Those limitations, which are more adverse in motor neuroprostheses, mostly arise from their bulkiness and the need to perform complex surgical implantation procedures. Alternatively, it has been proposed the development of distributed networks of intramuscular wireless microsensors and microstimulators that communicate with external systems for analyzing neuromuscular activity and performing stimulation or controlling external devices. This paradigm requires the development of miniaturized implants that can be wirelessly powered and operated by an external system. To accomplish this, we propose a wireless power transfer (WPT) and communications approach based on volume conduction of innocuous high frequency (HF) current bursts. The currents are applied through external textile electrodes and are collected by the wireless devices through two electrodes for powering and bidirectional digital communications. As these devices do not require bulky components for obtaining power, they may have a flexible threadlike conformation, facilitating deep implantation by injection.MethodsWe report the design and evaluation of advanced prototypes based on the above approach. The system consists of an external unit, floating semi-implantable devices for sensing and stimulation, and a bidirectional communications protocol. The devices are intended for their future use in acute human trials to demonstrate the distributed paradigm. The technology is assayed in vitro using an agar phantom, and in vivo in hindlimbs of anesthetized rabbits.ResultsThe semi-implantable devices were able to power and bidirectionally communicate with the external unit. Using 13 commands modulated in innocuous 3 MHz HF current bursts, the external unit configured the sensing and stimulation parameters, and controlled their execution. Raw EMG was successfully acquired by the wireless devices at 1 ksps.ConclusionsThe demonstrated approach overcomes key limitations of existing neuroprostheses, paving the way to the development of distributed flexible threadlike sensors and stimulators. To the best of our knowledge, these devices are the first based on WPT by volume conduction that can work as EMG sensors and as electrical stimulators in a network of wireless devices.

Network-level permutation entropy of resting-state MEG recordings: A novel biomarker for early-stage Alzheimer's disease?

Increasing evidence suggests that measures of signal variability and complexity could present promising biomarkers for Alzheimer’s disease (AD). Earlier studies have however been limited to the characterization of local activity. Here, we investigate whether a network version of permutation entropy could serve as a novel biomarker for early-stage AD. Resting-state source-space magnetoencephalography was recorded in 18 subjects with subjective cognitive decline (SCD) and 18 subjects with mild cognitive impairment (MCI). Local activity was characterized by permutation entropy (PE). Network-level interactions were studied using the inverted joint permutation entropy (JPEinv), corrected for volume conduction. The JPEinv showed a reduction of nonlinear connectivity in MCI subjects in the theta and alpha band. Local PE showed increased theta band entropy. Between-group differences were widespread across brain regions. Receiver operating characteristic (ROC) analysis of classification of MCI versus SCD subjects revealed that a logistic regression model trained on JPEinv features (78.4% [62.5–93.3%]) slightly outperformed PE (76.9% [60.3–93.4%]) and relative theta power–based models (76.9% [60.4–93.3%]). Classification performance of theta JPEinv was at least as good as the relative theta power benchmark. The JPEinv is therefore a potential biomarker for early-stage AD that should be explored in larger studies.

Identifying Suspected Volume Conduction Contamination of External Anal Sphincter Motor Evoked Potentials in Lumbosacral Spine Surgery.

Iatrogenic injury to sacral nerve roots poses significant quality of life issues for patients. Motor evoked potential (MEP) monitoring can be used for intraoperative surveillance of these important structures. We hypothesized that volume conducted depolarizations from gluteus maximus (GM) may contaminate external anal sphincter (EAS) MEP results during lumbosacral spine surgery. Motor evoked potential from the EAS and medial GM in 40 patients were prospectively assessed for inter-muscle volume conduction during lumbosacral spine surgeries. Peak latency matching between the EAS and GM MEP recordings conditionally identified volume conduction (VC+) or no volume conduction (VC-). Linear regression and power spectral density analysis of EAS and medial GM MEP amplitudes were performed from VC+ and VC- data pairs to confirm intermuscle electrical cross-talk. Motor evoked potential peak latency matching identified putative VC+ in 9 of 40 patients (22.5%). Mean regression coefficients (r2) from peak-to-peak EAS and medial GM MEP amplitude plots were 0.83 ± 0.04 for VC+ and 0.34 ± 0.06 for VC- MEP (P < 0.001). Power spectral density analysis identified the major frequency component in the MEP responses. The mean frequency difference between VC+ EAS and medial GM MEP responses were 0.4 ± 0.2 Hz compared with 3.5 ± 0.6 Hz for VC- MEP (P < 0.001). Our data support using peak latency matching between EAS and GM MEP to identify spurious MEP results because of intermuscle volume conduction. Neuromonitorists should be aware of this possible cross-muscle conflict to avoid interpretation errors during lumbosacral procedures using EAS MEP.

Frontal-occipital network alterations while viewing 2D &amp; 3D movies: a source-level EEG and graph theory approach

Abstract Many researchers have measured the differences in electroencephalography (EEG) while viewing 2D and 3D movies to uncover the neuromechanism underlying distinct viewing experiences. Using whole-brain network analyses of scalp EEG, our previous study reported that beta and gamma bands presented higher global efficiencies while viewing 3D movies. However, scalp EEG is influenced by volume conduction, not allowing inference from a neuroanatomy perspective; thus, source reconstruction techniques are recommended. This paper is the first to measure the differences in the frontal-occipital networks in EEG source space during 2D and 3D movie viewing. EEG recordings from 40 subjects were performed during 2D and 3D movie viewing. We constructed frontal-occipital networks of alpha, beta, and gamma bands in EEG source space and analyzed network efficiencies. We found that the beta band exhibited higher global efficiency in 3D movie viewing than in 2D movie viewing; however, the alpha global efficiency was not statistically significant. In addition, a support vector machine (SVM) classifier, taking functional connectivities as classification features, was built to identify whether the frontal-occipital networks contain patterns that could distinguish 2D and 3D movie viewing. Using the 6 most important functional connectivity features of the beta band, we obtained the best accuracy of 0.933. Our findings shed light on uncovering the neuromechanism underlying distinct experiences while viewing 2D and 3D movies.

Theta waves, neural spikes and seizures can propagate by ephaptic coupling in vivo

Electric field coupling has been shown to be responsible for non-synaptic neural activity propagation in hippocampal slices and cortical slices. Epileptiform and slow-wave sleep activity can propagate by electric field coupling without using synaptic connections at speeds of ~0.1 m/s in vitro. However, the characteristics of the events that can propagate using electric field coupling through a volume conductor in vivo have not been studied. Thus, we tested the hypothesis that various types of neural signals such as interictal spikes, theta waves and seizures could propagate in vivo across a transection in the hippocampus. We induced epileptiform activity in 4 rats under anesthesia by injecting 4-aminopyridine in the temporal region of the hippocampus, four recording electrodes were inserted along the longitudinal axis of the hippocampus. A transection was made between the electrodes to study the propagation of the neural activity. Although 54% of the interictal spikes could propagate through the cut, only those spikes with a high amplitude and short duration had a high probability to do so. 70% of seizure events could propagate through the cut but parameters distinguishing between propagating and non-propagating seizure events could not be identified. Theta activity was also observed to propagate at a mean speed of 0.16 ± 0.12 m/s in the characteristic range of propagation using electric field coupling through the transection. The electric field volume conduction mechanism was confirmed by showing that propagation was blocked by placing a dielectric layer within the cut. The speed of propagation was not affected by the transection thereby providing further evidence that various types of neural signals including activity in the theta range can propagate by electric field coupling in-vivo.

Tensor electrical impedance myography identifies bulbar disease progression in amyotrophic lateral sclerosis

ObjectiveElectrical impedance myography (EIM) is a promising biomarker for amyotrophic lateral sclerosis (ALS). A key issue is how best to utilise the complex high dimensional, multi-frequency data output by EIM to fully characterise the progression of disease. MethodsMuscle volume conduction properties were obtained from EIM recordings of the tongue across three electrode configurations and 14 input frequencies (76 Hz–625 kHz). Analyses of individual frequencies, averaged EIM spectra and non-negative tensor factorisation were undertaken. Longitudinal data were collected from 28 patients and 17 healthy volunteers at 3-monthly intervals for a maximum of 9 months. EIM was evaluated against the Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised (ALSFRS-R) bulbar sub-score, tongue strength and an overall bulbar disease burden score. ResultsLongitudinal changes to individual patient EIM spectra demonstrated complex shifts in the spectral shape. At a group level, a clear pattern emerged over time, characterised by an increase in centre frequency and general shift to the right of the spectral shape. Tensor factorisation reduced the spectral data from a total of 168 data points per participant per recording to a single value which captured the complexity of the longitudinal data and which we call tensor EIM (T-EIM). The absolute change in tensor EIM significantly increased within 3 months and continued to do so over the 9-month study duration. In a hypothetical clinical trial scenario tensor EIM required fewer participants (n = 64 at 50% treatment effect), than single frequency measures (n range 87–802) or ALSFRS-R bulbar subscore (n = 298). ConclusionsChanges to tongue EIM spectra over time in ALS are complex. Tensor EIM captured and quantified disease progression and was more sensitive to changes than single frequency EIM measures and other biomarkers of bulbar disease. SignificanceObjective biomarkers for the assessment of bulbar disease in ALS are lacking. Tensor EIM enhances the biomarker potential of EIM data and can improve bulbar symptom monitoring in clinical trials.

High-quality biopotential acquisition without a reference electrode: power-line interference reduction by adaptive impedance balancing in a mixed analog-digital design.

Power-line-interference (PLI) is one of the major disturbing factors in almost all ground-free biopotential acquisition applications. The body is a volume conductor and collects PLI currents. Some of these currents pass through the sensing electrodes, then the electrode cables, and finally via the amplifier input impedances they reach the signal ground. The electrode impedances and the amplifier input impedances form an impedance bridge. Due to electrode impedance instability over time, the bridge tends to be imbalanced and produces differential PLI which is amplified together with the useful signal. This paper describes a powerful mixed analog-digital solution for automatic impedance bridge balance using software PLL for line synchronization. The approach is implemented and validated through recorded real ECG signals. The PLI is canceled by adding part of the common-mode voltage, with automatically adjusted amplitude and phase, to the useful differential biosignal. The described approach produces high-quality biosignals without the need for a common-mode reference electrode. It is applicable to all biosignals taken with surface electrodes like ECG, EEG, EMG, EOG, etc., and can benefit all diagnostic and therapeutic medical devices where these signals are in use.

From Coherence to Multivariate Causal Estimators of EEG Connectivity.

The paper concerns the development of methods of EEG functional connectivity estimation including short overview of the currently applied measures describing their advantages and flaws. Linear and non-linear, bivariate and multivariate methods are confronted. The performance of different connectivity measures in respect of robustness to noise, common drive effect and volume conduction is considered providing a guidance towards future developments in the field, which involve evaluation not only functional, but also effective (causal) connectivity. The time-varying connectivity measure making possible estimation of dynamical information processing in brain is presented. The methods of post-processing of connectivity results are considered involving application of advanced graph analysis taking into account community structure of networks and providing hierarchy of networks rather than the single, binary networks currently used.

Characterization and Modeling of Free Volume and Ionic Conduction in Multiblock Copolymer Proton Exchange Membranes.

Free volume plays a key role on transport in proton exchange membranes (PEMs), including ionic conduction, species permeation, and diffusion. Positron annihilation lifetime spectroscopy and electrochemical impedance spectroscopy are used to characterize the pore size distribution and ionic conductivity of synthesized PEMs from polysulfone/polyphenylsulfone multiblock copolymers with different degrees of sulfonation (SPES). The experimental data are combined with a bundle-of-tubes model at the cluster-network scale to examine water uptake and proton conduction. The results show that the free pore size changes little with temperature in agreement with the good thermo-mechanical properties of SPES. However, the free volume is significantly lower than that of Nafion®, leading to lower ionic conductivity. This is explained by the reduction of the bulk space available for proton transfer where the activation free energy is lower, as well as an increase in the tortuosity of the ionic network.

Learn to interpret voltage maps: an atlas of topographies.

Describing the location of EEG abnormalities, such as interictal epileptiform discharges, is an important step in the interpretation of EEG recordings and has clinical relevance, as it is expected to point out the region of the brain generating these abnormal signals. Traditionally, the location is reported by specifying the area on the scalp where maximum negativity is located. However, this only reflects the correct localization in the brain when the cortical generator is located on the convexity (radial orientation). When the cortical generator is in the wall of a sulcus (tangential orientation), due to current flow (volume conduction), the maximum negativity is not over the generator, but at a distance from it. Voltage maps are widely available in most EEG reader software programs. Simple rules for reading voltage maps help to estimate the orientation and location of the source in the brain, avoiding false lateralization and false localization. In this seminar in epileptology, using a didactic approach, we explain how to read voltage maps and provide an atlas of voltage maps.

Experimental and simulation studies of localization and decoding of single and double dipoles

Objective. Electroencephalography is a technique for measuring normal or abnormal neuronal activity in the human brain, but its low spatial resolution makes it difficult to locate the precise locations of neurons due to the volume conduction effect of brain tissue. Approach. The acoustoelectric (AE) effect has the advantage of detecting electrical signals with high temporal resolution and focused ultrasound with high spatial resolution. In this paper, we use dipoles to simulate real single and double neurons, and further investigate the localization and decoding of single and double dipoles based on AE effects from numerical simulations, brain tissue phantom experiments, and fresh porcine brain tissue experiments. Main results. The results show that the localization error of a single dipole is less than 0.3 mm, the decoding signal is highly correlated with the source signal, and the decoding accuracy is greater than 0.94; the location of double dipoles with an interval of 0.4 mm or more can be localized, the localization error tends to increase as the interval of dipoles decreases, and the decoding accuracy tends to decrease as the frequency of dipoles decreases. Significance. This study localizes and decodes dipole signals with high accuracy, and provides a technical method for the development of EEG.

Is sensor space analysis good enough? Spatial patterns as a tool for assessing spatial mixing of EEG/MEG rhythms

Analyzing non-invasive recordings of electroencephalography (EEG) and magnetoencephalography (MEG) directly in sensor space, using the signal from individual sensors, is a convenient and standard way of working with this type of data. However, volume conduction introduces considerable challenges for sensor space analysis. While the general idea of signal mixing due to volume conduction in EEG/MEG is recognized, the implications have not yet been clearly exemplified. Here, we illustrate how different types of activity overlap on the level of individual sensors. We show spatial mixing in the context of alpha rhythms, which are known to have generators in different areas of the brain. Using simulations with a realistic 3D head model and lead field and data analysis of a large resting-state EEG dataset, we show that electrode signals can be differentially affected by spatial mixing by computing a sensor complexity measure. While prominent occipital alpha rhythms result in less heterogeneous spatial mixing on posterior electrodes, central electrodes show a diversity of rhythms present. This makes the individual contributions, such as the sensorimotor mu-rhythm and temporal alpha rhythms, hard to disentangle from the dominant occipital alpha. Additionally, we show how strong occipital rhythms can contribute the majority of activity to frontal channels, potentially compromising analyses that are solely conducted in sensor space. We also outline specific consequences of signal mixing for frequently used assessment of power, power ratios and connectivity profiles in basic research and for neurofeedback application. With this work, we hope to illustrate the effects of volume conduction in a concrete way, such that the provided practical illustrations may be of use to EEG researchers to in order to evaluate whether sensor space is an appropriate choice for their topic of investigation.

Analytical Modelling of Surface EMG Signals Generated by Curvilinear Fibers With Approximate Conductivity Tensor.

Mathematical modelling of surface electromyographic (EMG) signals has been proven a valuable tool to interpret experimental data and to validate signal processing techniques. Most analytical EMG models only consider muscle fibers with specific arrangements. However, the fiber orientation may change along the fiber paths and differ from fiber to fiber. Here we propose a subject-specific EMG model that simulates the fiber trajectories in muscles of the upper arm and analytically derives the action potentials assuming an approximate conductivity tensor. Magnetic Resonance (MR) images were acquired to generate muscle fiber paths and to build the volume conductor. While the action potentials propagated along the identified curvilinear fibers, the conductivity tensor was approximated to be cylindrically anisotropic. Single fiber action potentials (SFAPs) were computed by simulating the generation, propagation, and extinction of membrane current sources. To validate the assumption of the approximate conductivity tensor, two numerical models with approximate or exact conductivity tensors were implemented. The motor unit action potentials generated by the proposed analytical model and the two numerical models were highly similar (cross-correlation 0.98, normalized root mean square error, nRMSE ≤ 0.04, relative error in the median frequency of the simulated waveforms of approximately 3%). The proposed analytical model was also evaluated by comparing simulated and experimentally recorded compound muscle action potentials (CMAPs). Finally, the proposed model was used to test the accuracy of an EMG decomposition algorithm, providing a realistic benchmark. The proposed analytical model generates action potentials that reflect the spatial distributions of muscle fibers with curvilinear paths.

Impulse Data Models for the Inverse Problem of Electrocardiography

To develop, train and test neural networks for predicting heart surface potentials (HSPs) from body surface potentials (BSPs). The method re-frames traditional inverse problems of electrocardiography into regression problems, constraining the solution space by decomposing signals with multidimensional Gaussian impulse basis functions. Impulse HSPs were generated with single Gaussian basis functions at discrete heart surface locations and projected to corresponding BSPs using a volume conductor torso model. Both BSP (inputs) and HSP (outputs) were mapped to regular 2D surface meshes and used to train a neural network. Predictive capabilities of the network were tested with unseen synthetic and experimental data. A dense full connected single hidden layer neural network was trained to map body surface impulses to heart surface Gaussian basis functions for reconstructing HSP. Synthetic pulses moving across the heart surface were predicted from the neural network with root mean squared error of 9.1±1.4%. Predicted signals were robust to noise up to 20 dB and errors due to displacement and rotation of the heart within the torso were bounded and predictable. A shift of the heart 40 mm toward the spine resulted in a 4% increase in signal feature localization error. The set of training impulse function data could be reduced, and prediction error remained bounded. Recorded HSPs from in-vitro pig hearts were reliably decomposed using space-time Gaussian basis functions. Activation times calculated from predicted HSPs for left-ventricular pacing had a mean absolute error of 10.4±11.4 ms. Other pacing scenarios were analyzed with similar success. Impulses from Gaussian basis functions are potentially an effective and robust way to train simple neural network data models for reconstructing HSPs from decomposed BSPs. The HSPs predicted by the neural network can be used to generate activation maps that non-invasively identify features of cardiac electrical dysfunction and can guide subsequent treatment options.