Estimation Of Conduction Velocity Research Articles

Estimating axon conduction velocity in vivo from microstructural MRI

The conduction velocity (CV) of action potentials along axons is a key neurophysiological property central to neural communication. The ability to estimate CV in humans in vivo from non-invasive MRI methods would therefore represent a significant advance in neuroscience. However, there are two major challenges that this paper aims to address: (1) Much of the complexity of the neurophysiology of action potentials cannot be captured with currently available MRI techniques. Therefore, we seek to establish the variability in CV that can be captured when predicting CV purely from parameters that have been reported to be estimatable from MRI: inner axon diameter (AD) and g-ratio. (2) errors inherent in existing MRI-based biophysical models of tissue will propagate through to estimates of CV, the extent to which is currently unknown. Issue (1) is investigated by performing a sensitivity analysis on a comprehensive model of axon electrophysiology and determining the relative sensitivity to various morphological and electrical parameters. The investigations suggest that 85% of the variance in CV is accounted for by variation in AD and g-ratio. The observed dependency of CV on AD and g-ratio is well characterised by the previously reported model by Rushton. Issue (2) is investigated through simulation of diffusion and relaxometry MRI data for a range of axon morphologies, applying models of restricted diffusion and relaxation processes to derive estimates of axon volume fraction (AVF), AD and g-ratio and estimating CV from the derived parameters. The results show that errors in the AVF have the biggest detrimental impact on estimates of CV, particularly for sparse fibre populations (AVF<0.3). For our equipment set-up and acquisition protocol, CV estimates are most accurate (below 5% error) where AVF is above 0.3, g-ratio is between 0.6 and 0.85 and AD is high (above 4μm). CV estimates are robust to errors in g-ratio estimation but are highly sensitive to errors in AD estimation, particularly where ADs are small. We additionally show CV estimates in human corpus callosum in a small number of subjects. In conclusion, we demonstrate accurate CV estimates are possible in regions of the brain where AD is sufficiently large. Problems with estimating ADs for smaller axons presents a problem for estimating CV across the whole CNS and should be the focus of further study.

Performance of Different Atrial Conduction Velocity Estimation Algorithms Improves with Knowledge about the Depolarization Pattern

Abstract Quantifying the atrial conduction velocity (CV) reveals important information for targeting critical arrhythmia sites that initiate and sustain abnormal electrical pathways, e.g. during atrial flutter. The knowledge about the local CV distribution on the atrial surface thus enhances clinical catheter ablation procedures by localizing pathological propagation paths to be eliminated during the intervention. Several algorithms have been proposed for estimating the CV. All of them are solely based on the local activation times calculated from electroanatomical mapping data. They deliver false values for the CV if applied to regions near scars or wave collisions. We propose an extension to all approaches by including a distinct preprocessing step. Thereby, we first identify scars and wave front collisions and provide this information for the CV estimation algorithm. In addition, we provide reliable CV values even in the presence of noise. We compared the performance of the Triangulation, the Polynomial Fit and the Radial Basis Functions approach with and without the inclusion of the aforementioned preprocessing step. The evaluation was based on different activation patterns simulated on a 2D synthetic triangular mesh with different levels of noise added. The results of this study demonstrate that the accuracy of the estimated CV does improve when knowledge about the depolarization pattern is included. Over all investigated test cases, the reduction of the mean velocity error quantified to at least 25 mm/s for the Radial Basis Functions, 14 mm/s for the Polynomial Fit and 14 mm/s for the Triangulation approach compared to their respective implementations without the preprocessing step. Given the present results, this novel approach can contribute to a more accurate and reliable CV estimation in a clinical setting and thus improve the success of radio-frequency ablation to treat cardiac arrhythmias.

A technique for measuring anisotropy in atrial conduction to estimate conduction velocity and atrial fibre direction

BackgroundCardiac conduction properties exhibit large variability, and affect patient-specific arrhythmia mechanisms. However, it is challenging to clinically measure conduction velocity (CV), anisotropy and fibre direction. Our aim is to develop a technique to estimate conduction anisotropy and fibre direction from clinically available electrical recordings. MethodsWe developed and validated automated algorithms for estimating cardiac CV anisotropy, from any distribution of recording locations on the atrial surface. The first algorithm is for elliptical wavefront fitting to a single activation map (method 1), which works well close to the pacing location, but decreases in accuracy further from the pacing location (due to spatial heterogeneity in the conductivity and fibre fields). As such, we developed a second methodology for measuring local conduction anisotropy, using data from two or three activation maps (method 2: ellipse fitting to wavefront propagation velocity vectors from multiple activation maps). ResultsEllipse fitting to CV vectors from two activation maps (method 2) leads to an improved estimation of longitudinal and transverse CV compared to method 1, but fibre direction estimation is still relatively poor. Using three activation maps with method 2 provides accurate estimation, with approximately 70% of atrial fibres estimated within 20∘. We applied the technique to clinical activation maps to demonstrate the presence of heterogeneous conduction anisotropy, and then tested the effects of this conduction anisotropy on predicted arrhythmia dynamics using computational simulation. ConclusionsWe have developed novel algorithms for calculating CV and measuring the direction dependency of atrial activation to estimate atrial fibre direction, without the need for specialised pacing protocols, using clinically available electrical recordings.

FPGA Implementation of a Single Step MFCV Estimator Based on EMG in Diabetic Neuropathy

This paper details the design and the hardware implementation of a real-time diagnostic system based on FPGA for the muscle fibre conduction velocity estimation (MFCV). The MFCV is considered as a principal monitoring index for diabetic neuropathy (DPN), as well as in muscle fatigue assessment, to evaluate the muscle fibre status. The FPGA platform evaluates the MFCV during dynamic contractions (e.g., gait), by exploiting a multichannel sensing system composed of 4 wireless surface EMG electrodes, placed in pair on each leg. Raw data are digitized and made binary to create two bitstreams for each monitored limb. Then, a comparison between the two-bit streamed EMGs extracted from the same leg is carried out. The comparison, which allows extracting the MFCV, exploits a computationally light version of the cross-correlation method. The overall architecture implemented and validated on an Altera Cyclone V FPGA is HPS-free and exploits 22.5% ALMs, 10,874 ALUTs, 9.81% registers, 3.36% block memory, and &lt;2.7% of the total wires available on the platform. The choice of FPGA as computing system lies in the possibility to determine resource utilization, related timing constraints for a future real-time ASIC implementation in wearable applications. From the actual muscle contraction during gait (cyclical starting point of the computing), the system spends about 316 ms to acquire useful data and 47.5 ms (on average) to process the signal and provide the output, dynamically dissipating 28.6 mW. The accuracy of the tool evaluation has been evaluated proving the repeatability of the measurements by in vivo test. In this context, 1250 contractions from each subject involved in a protocolled 10-meter walk have been acquired (n=10 subjects evaluated). On average, the same MFCV estimation has been extracted on 1184/1250 contractions (standard deviation of 11 contractions), reaching an accuracy of 94.7%. These estimations fully match the physiological value range reported in literature.

Temporal Performance of Laplacian Eigenmaps and 3D Conduction Velocity in Detecting Ischemic Stress

BackgroundMyocardial ischemia has a complex and time-varying electrocardiographic signature that is used to diagnose and stratify severity. Despite the ubiquitous clinical use of the ECG to detect ischemia, the sensitivity and specificity of ECG based detection of myocardial ischemia are still inadequate. PurposeThe purpose of this study was to compare, using animal models, the performance of several traditional ECG-based metrics for detecting acute ischemia against two novel metrics, the Laplacian Eigenmap (LE) parameters and a three-dimensional estimate of Conduction Velocity (CV). MethodsLE is a machine learning technique that reduces the dimensions of simultaneously recorded time signals using a non-linear embedding followed by an singular value decomposition to represent each multichannel recording as a single trajectory on a manifold. Perturbations in the trajectories suggest the presence of myocardial ischemia. CV was computed using a tetrahedral mesh created from the electrode locations of transmural plunge needles. To validate the results, we used electrograms collected over 95 episodes of acutely induced myocardial ischemia in 15 canine and 2 porcine subjects. The LE and CV metrics were compared against traditional metrics derived from the ST segment, the T wave, the QRS of the same electrograms. The response time and robustness of each metric was quantified using parameters we defined as time to threshold (TTT) and contrast ratio (CR). ResultsThe temporal performance of the metrics evaluated throughout the ischemic episodes showed a consistent relationship; the LE metrics changed earlier than those from the T wave, which were followed by those from the ST segment, and finally from the QRS.The CV results showed median drops in conduction velocity throughout the perfusion bed of more than 23% in canines and over 12% during half of the induced ischemia episodes in swine. The other half of the episodes in swine produced a 76% drop. ConclusionsOur results suggest that the LE metric is more sensitive to acute ischemia than traditional single parameters used in previous studies, likely because it incorporates the entire QRST across multiple electrodes in a way that captures their most salient features in a low-dimensional space. The estimates of conduction velocity suggest substantial, in some cases dramatic slowing of the spread of activation, a finding that is not surprising but has not been documented in such three-dimensional detail before. The experiments and these new metrics provide a means to both explore details of the acute ischemic response not available from humans and suggest a path to translate this knowledge into improvements in clinical scoring of ischemia.

Automated isochronal late activation mapping to identify deceleration zones: Rationale and methodology of a practical electroanatomic mapping approach for ventricular tachycardia ablation

Sinus rhythm surrogates for critical isthmus sites are highly desirable because the vast majority of VT is hemodynamically unstable. While many ablation strategies to decrease the arrhythmogenicity of scar have been shown to be effective, the predominant method for electroanatomic mapping relies on a voltage-based depiction of scar and abnormal electrograms. A functional prioritization of slow conduction, distinct from late activation, is feasible in clinical practice with the creation of isochronal late activation maps. Regions of slow conduction are easily visualized with isochronal displays of baseline intrinsic rhythm activation and deceleration zones, where isochrones crowd, have been observed to have strong correlation with successful ablation sites. Automated annotation of the offset of local electrograms was developed to create the propagation maps to incorporate electrogram width and completion of local activation. Simple conduction velocity estimates where three isochrones are seen within a 1 cm radium confirm that deceleration zones harbor conduction velocity of <0.6 m/s. We present a practical methodology of analyzing electroanatomic substrate in a voltage-independent manner with correlation to reentrant VT. Non-linear 3D transmyocardial conduction limits the validity of conduction velocity estimates that assume planar and tangential conduction and we show an example of a patient with 3D isthmus boundaries with an activation gap on the epicardial surface during tachycardia.

Investigating changes in global conduction velocity estimates from biceps brachii muscles of stroke survivors

It has been inferred that stroke-induced alterations of the lower motor neurons and their muscle fibres may contribute to muscle weakness and thus to motor impairment. Although previous accounts have investigated the mechanisms underpinning muscle weakness from muscle fiber conduction velocity estimates, reports about how the global conduction velocity (CV) differ between healthy and paretic muscles post-stroke are uncommon. Thus, in this study we examined side-differences in global CV estimates from biceps brachii muscles of stroke survivors, and their relation with differences between muscles’ functions. Seven chronic post-stroke patients participated in this study. Surface electromyograms were sampled with a grid of 32 electrodes (8 × 4; 10 mm inter-electrode distance) from biceps brachii. Stimulation electrodes were placed in bipolar configuration as proximally as possible over the muscle. CV values were estimated from differential M waves elicited by supramaximal current pulses. Impairment of biceps brachii functions was assessed with the Fugl-Meyer Assessment scale. Spearman's rank-order correlation was applied to verify the relationship of the healthy/paretic ratio values for CV and Fugl-Meyer values. There was a significant negative correlation (Rho = −0.96; P = 0.003; n = 7: Fig. 1 ) between side-differences in CV values and the functional scores. Marked differences in CV values estimated for the healthy and paretic limbs were associated with highly impaired, biceps brachii function. Conversely, patients with either preserved or recovered motor function exhibited similar CV values for both limbs. Whether these side-differences in CV may be explained by structural adaptations remains an open issue. Current results reveal however the CV values may constitute a relevant index to assess the degree of motor function in biceps brachii motor units.

Variability of estimates of muscle fiber conduction velocity and surface EMG amplitude across subjects and processing intervals

The force developed by the human neuromuscular system can change very rapidly (15–50 ms). When processing EMG signals for inferring neural control strategies, it is therefore necessary to extract estimates from short time intervals. In this study, we investigate the relation between joint torque and estimates of average muscle fibre conduction velocity (MFCV) and amplitude (RMS) from surface EMG signals, when varying the duration of the processing interval. Moreover, we assessed the inter-subject variability in RMS and MFCV estimates. Ten healthy subjects performed isometric linearly increasing ankle dorsiflexion contractions up to 70% MVC at a rate of 5%MVCs−1. High-density EMG signals were recorded from the tibialis anterior muscle and MFCV and RMS were estimated in eight time-intervals ranging from 15 to 2000 ms. MFCV and RMS were significantly correlated with force in all subjects and when using all time-intervals (MFCV = 0.77 ± 0.07, RMS = 0.79 ± 0.06 (R2), Pearson-P < 0.01). The variability around the regression line for both MFCV and RMS estimates significantly increased when using intervals <100 ms (P < 0.001). However, the slope of the regression between EMG variables and force did not change with the duration of the interval (P < 0.001). Moreover, MFCV showed a substantially smaller variability across subjects in its relation to force than RMS [average coefficient of variation of regression slopes across all time intervals, 24.48 ± 1.51 (%), whilst for the RMS it was 56.65 ± 0.69 (%)]. These results indicate that estimates of MFCV and RMS as a function of joint torque are unbiased with respect to processing interval duration. Moreover, they reveal that estimates of MFCV are more consistent across subjects than EMG amplitude.

Diversity of Cortico-descending Projections: Histological and Diffusion MRI Characterization in the Monkey.

The axonal composition of cortical projections originating in premotor, supplementary motor (SMA), primary motor (a4), somatosensory and parietal areas and descending towards the brain stem and spinal cord was characterized in the monkey with histological tract tracing, electron microscopy (EM) and diffusion MRI (dMRI). These 3 approaches provided complementary information. Histology provided accurate assessment of axonal diameters and size of synaptic boutons. dMRI revealed the topography of the projections (tractography), notably in the internal capsule. From measurements of axon diameters axonal conduction velocities were computed. Each area communicates with different diameter axons and this generates a hierarchy of conduction delays in this order: a4 (the shortest), SMA, premotor (F7), parietal, somatosensory, premotor F4 (the longest). We provide new interpretations for i) the well-known different anatomical and electrophysiological estimates of conduction velocity; ii) why conduction delays are probably an essential component of the cortical motor command; and iii) how histological and dMRI tractography can be integrated.

Optimal spatio-temporal filter for the reduction of crosstalk in surface electromyogram

Objective. Crosstalk can pose limitations to the applications of surface electromyogram (EMG). Its reduction can help in the identification of the activity of specific muscles. The selectivity of different spatial filters was tested in the literature both in simulations and experiments: their performances are affected by many factors (e.g. anatomy, conduction properties of the tissues and dimension/location of the electrodes); moreover, they reduce crosstalk by decreasing the detection volume, recording data that represent only the activity of a small portion of the muscle of interest. In this study, an alternative idea is proposed, based on a spatio-temporal filter. Approach. An adaptive method is applied, which filters both in time and among different channels, providing a signal that maximally preserves the energy of the EMG of interest and discards that of nearby muscles (increasing the signal to crosstalk ratio, SCR). Main results. Tests with simulations and experimental data show an average increase of the SCR of about 2 dB with respect to the single or double differential data processed by the filter. This allows to reduce the bias induced by crosstalk in conduction velocity and force estimation. Significance. The method can be applied to few channels, so that it is useful in applicative studies (e.g. clinics, gate analysis, rehabilitation protocols with EMG biofeedback and prosthesis control) where limited and not selective information is usually available.

Two dimensional electrophysiological characterization of human pluripotent stem cell-derived cardiomyocyte system

Stem cell-derived cardiomyocytes provide a promising tool for human developmental biology, regenerative therapies, disease modeling, and drug discovery. As human pluripotent stem cell-derived cardiomyocytes remain functionally fetal-type, close monitoring of electrophysiological maturation is critical for their further application to biology and translation. However, to date, electrophysiological analyses of stem cell-derived cardiomyocytes has largely been limited by biologically undefined factors including 3D nature of embryoid body, sera from animals, and the feeder cells isolated from mouse. Large variability in the aforementioned systems leads to uncontrollable and irreproducible results, making conclusive studies difficult. In this report, a chemically-defined differentiation regimen and a monolayer cell culture technique was combined with multielectrode arrays for accurate, real-time, and flexible measurement of electrophysiological parameters in translation-ready human cardiomyocytes. Consistent with their natural counterpart, amplitude and dV/dtmax of field potential progressively increased during the course of maturation. Monolayer culture allowed for the identification of pacemaking cells using the multielectrode array platform and thereby the estimation of conduction velocity, which gradually increased during the differentiation of cardiomyocytes. Thus, the electrophysiological maturation of the human pluripotent stem cell-derived cardiomyocytes in our system recapitulates in vivo development. This system provides a versatile biological tool to analyze human heart development, disease mechanisms, and the efficacy/toxicity of chemicals.

A new optical flow model for motor unit conduction velocity estimation in multichannel surface EMG

Many studies have demonstrated the feasibility and benefits of Conduction Velocity (CV) estimation from surface electromyograms (EMGs) in various experimental conditions. Among them, a method based on optical flow was proposed recently, demonstrating relatively accurate CV estimation for EMG signals acquired in monopolar mode. We extended this method by a new data model that compensates more realistically for the spatial Motor Unit Action Potential (MUAP) shape variability and enables accurate CV estimation also in single-differential acquisition mode. The proposed modification was validated on 5000 synthetic Motor Units (MUs) with known CV and direction of fibres. It was shown that, in the noiseless case, the mean CV estimation error was significantly lower for our proposed modification compared to the original CV estimation procedure by up to 2% in the case of monopolar EMG signals and by up to 18.6% for single-differential EMG signals. When estimating fibre directions, the mean error was lower by up to 2.4° (for monopolar EMG signals) and 9.6° (for single-differential EMG signals). The results of tests with 10dB and 20dB noise further demonstrated the robustness of the proposed algorithm to noise in MUAP estimation.

Detection of Multiple Innervation Zones from Multi-Channel Surface EMG Recordings with Low Signal-to-Noise Ratio Using Graph-Cut Segmentation.

Knowledge of the location of muscle Innervation Zones (IZs) is important in many applications, e.g. for minimizing the quantity of injected botulinum toxin for the treatment of spasticity or for deciding on the type of episiotomy during child delivery. Surface EMG (sEMG) can be noninvasively recorded to assess physiological and morphological characteristics of contracting muscles. However, it is not often possible to record signals of high quality. Moreover, muscles could have multiple IZs, which should all be identified. We designed a fully-automatic algorithm based on the enhanced image Graph-Cut segmentation and morphological image processing methods to identify up to five IZs in 60-ms intervals of very-low to moderate quality sEMG signal detected with multi-channel electrodes (20 bipolar channels with Inter Electrode Distance (IED) of 5 mm). An anisotropic multilayered cylinder model was used to simulate 750 sEMG signals with signal-to-noise ratio ranging from -5 to 15 dB (using Gaussian noise) and in each 60-ms signal frame, 1 to 5 IZs were included. The micro- and macro- averaged performance indices were then reported for the proposed IZ detection algorithm. In the micro-averaging procedure, the number of True Positives, False Positives and False Negatives in each frame were summed up to generate cumulative measures. In the macro-averaging, on the other hand, precision and recall were calculated for each frame and their averages are used to determine F1-score. Overall, the micro (macro)-averaged sensitivity, precision and F1-score of the algorithm for IZ channel identification were 82.7% (87.5%), 92.9% (94.0%) and 87.5% (90.6%), respectively. For the correctly identified IZ locations, the average bias error was of 0.02±0.10 IED ratio. Also, the average absolute conduction velocity estimation error was 0.41±0.40 m/s for such frames. The sensitivity analysis including increasing IED and reducing interpolation coefficient for time samples was performed. Meanwhile, the effect of adding power-line interference and using other image interpolation methods on the deterioration of the performance of the proposed algorithm was investigated. The average running time of the proposed algorithm on each 60-ms sEMG frame was 25.5±8.9 (s) on an Intel dual-core 1.83 GHz CPU with 2 GB of RAM. The proposed algorithm correctly and precisely identified multiple IZs in each signal epoch in a wide range of signal quality and is thus a promising new offline tool for electrophysiological studies.

Cardiac conduction velocity estimation from sequential mapping assuming known Gaussian distribution for activation time estimation error.

In this paper, we study the problem of the cardiac conduction velocity (CCV) estimation for the sequential intracardiac mapping. We assume that the intracardiac electrograms of several cardiac sites are sequentially recorded, their activation times (ATs) are extracted, and the corresponding wavefronts are specified. The locations of the mapping catheter's electrodes and the ATs of the wavefronts are used here for the CCV estimation. We assume that the extracted ATs include some estimation errors, which we model with zero-mean white Gaussian noise values with known variances. Assuming stable planar wavefront propagation, we derive the maximum likelihood CCV estimator, when the synchronization times between various recording sites are unknown. We analytically evaluate the performance of the CCV estimator and provide its mean square estimation error. Our simulation results confirm the accuracy of the proposed method and the error analysis of the proposed CCV estimator.

Maximum likelihood cardiac conduction velocity estimation from sequential mapping in the presence of activation time noise with unknown variances.

The cardiac conduction velocity (CV) can be estimated by analysing the activation times (ATs) and the locations of the electrodes that are used for the intracardiac electrogram (IEGM) recording. Here, we study the problem of the CV estimation in sequential mapping without using any independent electrogram as a time alignment reference. We assume that the IEGMs are sequentially recorded from several sites, where at each site, at least two of the catheter's electrodes are in contact with the cardiac tissue. We consider the planar wavefront with stable CV that propagates within the recording sites throughout our data collection period. Assuming the zero-mean Gaussian AT estimation error, we derive the maximum likelihood estimations of the CV and AT at a desired location on the cardiac shell. The CV is estimated when the variance of the AT estimation error and the time delay between the sequential recordings are unknown variables. Our simulation results show that the proposed method can precisely estimate the CV of the planar wavefront.

Motor unit action potential conduction velocity estimated from surface electromyographic signals using image processing techniques.

In surface electromyography (surface EMG, or S-EMG), conduction velocity (CV) refers to the velocity at which the motor unit action potentials (MUAPs) propagate along the muscle fibers, during contractions. The CV is related to the type and diameter of the muscle fibers, ion concentration, pH, and firing rate of the motor units (MUs). The CV can be used in the evaluation of contractile properties of MUs, and of muscle fatigue. The most popular methods for CV estimation are those based on maximum likelihood estimation (MLE). This work proposes an algorithm for estimating CV from S-EMG signals, using digital image processing techniques. The proposed approach is demonstrated and evaluated, using both simulated and experimentally-acquired multichannel S-EMG signals. We show that the proposed algorithm is as precise and accurate as the MLE method in typical conditions of noise and CV. The proposed method is not susceptible to errors associated with MUAP propagation direction or inadequate initialization parameters, which are common with the MLE algorithm. Image processing -based approaches may be useful in S-EMG analysis to extract different physiological parameters from multichannel S-EMG signals. Other new methods based on image processing could also be developed to help solving other tasks in EMG analysis, such as estimation of the CV for individual MUs, localization and tracking of innervation zones, and study of MU recruitment strategies.

Wavelet analysis of cardiac optical mapping data

BackgroundOptical mapping technology is an important tool to study cardiac electrophysiology. Transmembrane fluorescence signals from voltage-dependent dyes need to be preprocessed before analysis to improve the signal-to-noise ratio. Fourier analysis, based on spectral properties of stationary signals, cannot directly provide information on the spectrum changes with respect to time. Fourier filtering has the disadvantage of causing degradation of abrupt waveform changes such as those in action potential signals. Wavelet analysis has the ability to offer simultaneous localization in time and frequency domains, suitable for the analysis and reconstruction of irregular, non-stationary signals like the fast action-potential upstroke, and better than conventional filters for denoising. MethodsWe applied discrete wavelet transformation for temporal processing of optical mapping signals and wavelet packet analysis approaches to process activation maps from simulated and experimental optical mapping data from canine right atrium. We compared the results obtained with the wavelet approach to a variety of other methods (Fast Fourier Transformation (FFT) with finite or infinite response filtering, and Gaussian filters).Results: Temporal wavelet analysis improved signal-to-noise ratio (SNR) better than FFT filtering for 5–10dB SNR, and caused less distortion of the action potential waveform over the full range of simulated noise (5–20dB). Spatial wavelet filtering produced more efficient denoising and/or more accurate conduction velocity estimates than Gaussian filtering. Propagation patterns were also best revealed by wavelet filtering.Conclusions: Wavelet analysis is a promising tool, facilitating accurate action potential characterization, activation map formation, and conduction velocity estimation.

Differences in age-related fiber atrophy between vastii muscles of active subjects: a multichannel surface EMG study

The aim of the study was to non-invasively determine if vastus lateralis (VL) and vastus medialis obliquus (VM) muscles are equally affected by age-related fiber atrophy. Multichannel surface electromyography was used since it allows to estimate muscle fiber conduction velocity (CV), which has been demonstrated to be related to the size of recruited muscle fibers. Twelve active elderly men (age 69 ± 4 years) and 12 active young men (age 23 ± 2 years) performed isometric knee extension at 30%, 50%, and 70% of maximal voluntary contraction. Electromyographic signals were recorded from VL and VM muscles of the dominant limb using arrays with eight electrodes and CVs were estimated for each contraction. CV estimates showed a different behavior in the two muscles: in VL at 50% and 70% of maximum voluntary contraction they were greater in young than in elderly; whereas such a difference was not observed in VM. This finding suggest that in active elderly VM seems to be less affected by the age-related fibers atrophy than VL. Hence, the common choice of studying VL as a muscle representative of the whole quadriceps could generate misleading findings. Indeed, it seemed that the sarcopenic ageing effects might be heterogeneous within quadriceps muscle.

Techniques for automated local activation time annotation and conduction velocity estimation in cardiac mapping

Measurements of cardiac conduction velocity provide valuable functional and structural insight into the initiation and perpetuation of cardiac arrhythmias, in both a clinical and laboratory context. The interpretation of activation wavefronts and their propagation can identify mechanistic properties of a broad range of electrophysiological pathologies. However, the sparsity, distribution and uncertainty of recorded data make accurate conduction velocity calculation difficult. A wide range of mathematical approaches have been proposed for addressing this challenge, often targeted towards specific data modalities, species or recording environments. Many of these algorithms require identification of activation times from electrogram recordings which themselves may have complex morphology or low signal-to-noise ratio. This paper surveys algorithms designed for identifying local activation times and computing conduction direction and speed. Their suitability for use in different recording contexts and applications is assessed.

On the nature of the electromyographic signals recorded during vibration exercise.

Surface electromyography (EMG) has been widely used to measure neuromuscular activity during vibration exercise (VE) to investigate the underlying mechanisms elicited by VE. However, the EMG spectrum recorded during VE shows sharp peaks at the vibration frequency whose interpretation remains controversial. Some authors considered those peaks as a result of motion artifacts, while others interpreted them as due to vibration-induced neuromuscular activity. The aim of the present study is to clarify the nature of those sharp peaks observed during VE. Three independent EMG measurements were performed during VE: in vitro (IVT), in vivo at rest ([Formula: see text]), and in vivo during voluntary contraction ([Formula: see text]). The amplitudes of the EMG vibration frequency components ([Formula: see text]) were extracted for all measurements. The conduction velocity (CV) of the vibration frequency components and the full EMG spectrum were also estimated during voluntary contraction. Our spectrum analysis revealed small [Formula: see text] for IVT and [Formula: see text], accounting for only 3.3 and 7.6 % of that obtained from [Formula: see text]. Moreover, the CV estimation indicated the EMG vibration components to propagate along the muscle fiber with CV [Formula: see text] 6.5 m/s, comparable to the CV estimated using the full EMG spectrum (5.7 m/s). We may therefore conclude that the sharp spectral peaks observed during VE are mainly due to vibration-induced muscle activity rather than motion artifacts.