Edward A. Clancy

Electrical alternans and cardiac electrical instability.

We investigated the relationship between electrical alternans and cardiac electrical stability in a series of 20 dog experiments and in a pilot clinical study. Electrical alternans was detected in both the QRS complex and the ST-T wave by use of a novel multidimensional spectral technique. The magnitude of the alteration was expressed as the alternating electrocardiographic morphology index (AEMI), expressed as parts per million of waveform energy. Electrical stability in the dog preparations was assessed via the ventricular fibrillation threshold measurement, and in the clinical studies via programmed stimulation. In 10 dog experiments, systemic hypothermia resulted in a 60% decrease in ventricular fibrillation threshold (VFT) (p less than .0001) and a significant increase in both AEMI(QRS) form 3.7 +/- 3.0 to 1448 +/- 548 (p less than .0001) and AEMI(ST-T) from 43.9 +/- 18.4 to 19,178 +/- 5579 (p less than .0001). In 10 dog experiments, transient coronary artery ligation also resulted in a 60% decrease in VFT (p less than .0001), an increase from 76.3 +/- 46.5 to 245 +/- 11 in AEMI(QRS) (p less than .05), and an increase from 842 +/- 505 to 1365 +/- 392 in AEMI(ST-T) (p less than .002). In 119 observations in 20 animal experiments, the rank correlation between VFT and AEMI(QRS) was -.30 (p less than .001), with that between VFT and AEMI(ST-T) being -.55 (p less than .0001). In a double-blind pilot clinical trial consisting of 23 studies in 19 patients, the result of electrophysiologic testing was used as an independent measure of cardiac electrical stability. Alternation in waveform morphology identified the inducible patient population with a sensitivity of 92%, a positive predictivity of 70%, and a specificity of 50% (p less than .05). We conclude that analysis of subtle beat-to-beat variability in electrocardiographic morphology may provide a noninvasive measure of cardiac electrical stability.

Sampling, noise-reduction and amplitude estimation issues in surface electromyography

This paper reviews data acquisition and signal processing issues relative to producing an amplitude estimate of surface EMG. The paper covers two principle areas. First, methods for reducing noise, artefact and interference in recorded EMG are described. Wherever possible noise should be reduced at the source via appropriate skin preparation, and the use of well designed active electrodes and signal recording instrumentation. Despite these efforts, some noise will always accompany the desired signal, thus signal processing techniques for noise reduction (e.g. band-pass filtering, adaptive noise cancellation filters and filters based on the wavelet transform) are discussed. Second, methods for estimating the amplitude of the EMG are reviewed. Most advanced, high-fidelity methods consist of six sequential stages: noise rejection/filtering, whitening, multiple-channel combination, amplitude demodulation, smoothing and relinearization. Theoretical and experimental research related to each of the above topics is reviewed and the current recommended practices are described.

Probability density of the surface electromyogram and its relation to amplitude detectors

When the surface electromyogram (EMG) generated from constant-force, constant-angle, nonfatiguing contractions is modeled as a random process, its density is typically assumed to be Gaussian. This assumption leads to root-mean-square (RMS) processing as the maximum likelihood estimator of the EMG amplitude (where EMG amplitude is defined as the standard deviation of the random process). Contrary to this theoretical formulation, experimental work has found the signal-to-noise-ratio [(SNR), defined as the mean of the amplitude estimate divided by its standard deviation] using mean-absolute-value (MAV) processing to be superior to RMS. This paper reviews RMS processing with the Gaussian model and then derives the expected (inferior) SNR performance of MAV processing with the Gaussian model. Next, a new model for the surface EMG signal, using a Laplacian density, is presented. It is shown that the MAV processor is the maximum likelihood estimator of the EMG amplitude for the Laplacian model. SNR performance based on a Laplacian model is predicted to be inferior to that of the Gaussian model by approximately 32%. Thus, minor variations in the probability distribution of the EMG may result in large decrements in SNR performance. Lastly, experimental data from constant-force, constant-angle, nonfatiguing contractions were examined. The experimentally observed densities fell in between the theoretic Gaussian and Laplacian densities. On average, the Gaussian density best fit the experimental data, although results varied with subject. For amplitude estimation, MAV processing had a slightly higher SNR than RMS processing.

Relating agonist-antagonist electromyograms to joint torque during isometric, quasi-isotonic, nonfatiguing contractions

Describes an experimental study which relates simultaneous elbow flexor-extensor electromyogram (EMG) amplitude to joint torque. Investigation was limited to the case of isometric, quasi-isotonic (slowly force-varying), nonfatiguing contractions. For each of the flexor and extensor muscle groups, the model relationship between muscle group torque contribution and EMG amplitude was constrained to be a sum of basis functions which had a linear dependence on a set of fit parameters. With these constraints, the problem of identifying the EMG-to-torque relationship was reduced to a linear least squares problem. Surface EMGs from elbow flexors and extensors, and joint torque were simultaneously recorded for nonfatiguing, quasi-isotonic, isometric contractions spanning 0-50% maximum voluntary contraction. Single-/multiple-channel unwhitened/whitened/adaptively-whitened EMG amplitude processors were used to identify an EMG-to-torque relation, and then estimate joint torque based on this relation. Each unwhitened multiple-channel EMG-to-torque estimator had a standard error (SE) approximately 70% of its respective single-channel estimator. The adaptively whitened multiple-channel joint torque estimator had an SE approximately 90% of the unwhitened multiple-channel estimator, providing an estimation error /spl ap/3% of the combined flexion/extension torque range. The experimental studies demonstrated that higher fidelity EMG amplitude processing led to improved joint torque estimation.

Single site electromyograph amplitude estimation

Previous investigators have experimentally demonstrated and/or analytically predicted that temporal whitening of the surface electromyograph (EMG) waveform prior to demodulation improves the EMG amplitude estimate. However, no systematic study of the influence of various whitening filters upon amplitude estimate performance has been reported. The authors describe a phenomenological mathematical model of a single site of the surface EMG waveform and reports on experimental studies which examined the performance of several temporal whitening filters. Surface EMG waveforms were sampled during nonfatiguing, constant-force, isometric contractions of the biceps or triceps muscles, over the range of 10-75% maximum voluntary contraction. A signal-to-noise ratio (SNR) was computed from each amplitude estimate (deviations about the mean value of the estimate were considered as noise). A moving average root mean square estimator (245 ms window) provided an average/spl plusmn/standard deviation (A/spl plusmn/SD) SNR of 10.7/spl plusmn/3.3 for the individual recordings. Temporal whitening with one fourth-order whitening filter designed per site improved the A/spl plusmn/SD SNR to 17.6/spl plusmn/6.0.<<ETX>>

Multiple site electromyograph amplitude estimation

Temporal whitening of individual surface electromyograph (EMG) waveforms and spatial combination of multiple recording sites have separately been demonstrated to improve the performance of EMG amplitude estimation. This investigation combined these two techniques by first whitening, then combining the data from multiple EMG recording sites to form an EMG amplitude estimate. A phenomenological mathematical model of multiple sites of the surface EMG waveform, with analytic solution for an optimal amplitude estimate, is presented. Experimental surface EMG waveforms were then sampled from multiple sites during nonfatiguing, constant-force, isometric contractions of the biceps or triceps muscles, over the range of 10-75% maximum voluntary contraction. A signal-to-noise ratio (SNR) was computed from each amplitude estimate (deviations about the mean value of the estimate were considered as noise). Results showed that SNR performance: 1) increased with the number of EMG sites, 2) was a function of the sampling frequency, 3) was predominantly invariant to various methods of determining spatial uncorrelation filters, 4) was not sensitive to the intersite correlations of the electrode configuration investigated, and 5) was best at lower levels of contraction. A moving average root mean square estimator (245-ms window) provided an average /spl plusmn/ standard deviation (A/spl plusmn/SD) SNR of 10.7/spl plusmn/3.3 for single site unwhitened recordings. Temporal whitening and four combined sites improved the A/spl plusmn/SD SNR to 24.6/spl plusmn/10.4. On one subject, eight whitened combined sites were achieved, providing an A/spl plusmn/SD SNR of 35.0/spl plusmn/13.4.<<ETX>>

Identification of Constant-Posture EMG–Torque Relationship About the Elbow Using Nonlinear Dynamic Models

The surface electromyogram (EMG) from biceps and triceps muscles of 33 subjects was related to elbow torque, contrasting EMG amplitude (EMGσ) estimation processors, linear/nonlinear model structures, and system identification techniques. Torque estimation was improved by 1) advanced EMGσ processors (i.e., whitened, multiple-channel signals); 2) longer duration training sets (52 s versus 26 s); and 3) determination of model parameters via pseudoinverse and ridge regression methods. Dynamic, nonlinear parametric models that included second- or third-degree polynomial functions of EMGσ outperformed linear models and Hammerstein/Weiner models. A minimum error of 4.65 ± 3.6% maximum voluntary contraction (MVC) flexion was attained using a third-degree polynomial, 28th-order dynamic model, with model parameters determined using the pseudoinverse method with tolerance 5.6 × 10-3 on 52 s of four-channel whitened EMG data. Similar performance (4.67 ± 3.7% MVC flexion error) was realized using a second-degree, 18th-order ridge regression model with ridge parameter 50.1.

Influence of smoothing window length on electromyogram amplitude estimates

A systematic, experimental study of the influence of smoothing window length on the signal-to-noise ratio (SNR) of electromyogram (EMG) amplitude estimates is described. Surface EMG waveforms were sampled during nonfatiguing, constant-force, constant-angle contractions of the biceps or triceps muscles, over the range of 10%-75% maximum voluntary contraction. EMG amplitude estimates were computed with eight different EMG processor schemes using smoothing length durations spanning 2.45-500 ms. An SNR was computed from each amplitude estimate (deviations about the mean value of the estimate were considered as noise). Over these window lengths, average /spl plusmn/ standard deviation SNRs ranged from 1.4/spl plusmn/0.28 to 16.2/spl plusmn/5.4 for unwhitened single-channel EMG processing and from 3.2/spl plusmn/0.7 to 37.3/spl plusmn/14.2 for whitened, multiple-channel EMG processing (results pooled across contraction level). It was found that SNR increased with window length in a square root fashion. The shape of this relationship was consistent with classic theoretical predictions, however none of the processors achieved the absolute performance level predicted by the theory. These results are useful in selecting the length of the smoothing window in traditional surface EMG studies. In addition, this study should contribute to the development of EMG processors which dynamically tune the smoothing window length when the EMG amplitude is time varying.

Estimation Of Joint Torque From The Surface EMG

Torque exerted about the elbow was estimated from flexor and extensor muscle surface EMG waveforms. An algebraic relation between joint torque and simultaneous flexor/ extensor EMG amplitudes was assumed, and a least squares method for identifying the parameters of this relation investigated. Initially, a simulation study investigated the performance of the least squares method and served as a tool to determine an appropriate experimental design. Then, multiple channels of flexor/ extensor EMG waveforms and joint torque were measured during non-fatiguing, slowly force-varying (quasi-isotonic), isometric contractions. Single/ multiple channel techniques for computing an EMG amplitude were used to identify an EMG to torque relation, and then estimate joint torque based on this relation.

Electromyogram Whitening for Improved Classification Accuracy in Upper Limb Prosthesis Control

Time and frequency domain features of the surface electromyogram (EMG) signal acquired from multiple channels have frequently been investigated for use in controlling upper-limb prostheses. A common control method is EMG-based motion classification. We propose the use of EMG signal whitening as a preprocessing step in EMG-based motion classification. Whitening decorrelates the EMG signal and has been shown to be advantageous in other EMG applications including EMG amplitude estimation and EMG-force processing. In a study of ten intact subjects and five amputees with up to 11 motion classes and ten electrode channels, we found that the coefficient of variation of time domain features (mean absolute value, average signal length and normalized zero crossing rate) was significantly reduced due to whitening. When using these features along with autoregressive power spectrum coefficients, whitening added approximately five percentage points to classification accuracy when small window lengths were considered.