Statistical procedures for interference EMG power spectra estimation

Postural stability in patients with different durations of benign paroxysmal positional vertigo

Postural stability maintenance was investigated in patients with "idiopathic" benign paroxysmal positional vertigo of the posterior semicircular canal (BPPV-PSC) and compared to healthy subjects. To measure the postural stability during a quiet upright stance, we used static posturography in two conditions: with open and with closed eyes. The effect of the repositioning Epley's maneuver on the recovery of postural stability in patients with different durations of BPPV-PSC less than 60 days after the first attack of positioning vertigo (group I) and more than 60 days (group II) was examined. The investigation was made 1 h after the positive Dix-Hallpike test and 7 days after treatment with the Epley maneuver. "Sway velocity" (SV) and "relative power spectrum" (RPS) of the stabilograms were calculated to evaluate the postural stability for each subject and each experimental condition. We found a pronounced spectral density peak in the frequency range of 1.0-2.0 Hz and smaller spectral density in the range of 0.15-0.5 Hz compared to that in healthy subjects. Our results showed that the postural maintenance in BPPV-PSC patients depended on the disease duration. Patients with a duration of BPPV symptoms less than 60 days after the first attack demonstrated a high dependence on the visual input for postural stability. One week after the Epley maneuver, differences in the recovery of postural control in both groups of patients were also found. We assume that the disturbed otolith function together with the impaired dynamics of the semicircular canal generate a particular pattern of postural maintenance. The different degree of restoration of postural stability 1 week after the EM treatment in BPPV-PSC patients with different durations of the disease gives us reason to believe that after removing the otoconia from the semicircular canal, some stimulation of other sensory subsystems and adaptation mechanisms occur that lead to a new pattern of postural maintenance.

Influence of estimators of spectral density on the analysis of electromyographic and vibromyographic signals

Electromyographic (EMG) and vibromyographic (VMG) signals are related to electrical and mechanical muscle activity, respectively. It is known that variations in their frequency components are related to changes in muscle activity and fatigue. The aims of this study were: (1) to analyse the resolution, variance and bias of different estimations of power spectral density function (PSD); and (2) to evaluate the influence of the spectral estimation method on three indices calculated from the PSD of EMG and VMG signals: mean (f(m)) and median (f(c)) frequencies and the ratio of high and low frequency components (H/L ratio) to select the most suitable estimator. Myographic signals were recorded from the sternomastoid muscle, an accessory respiratory muscle, during breathing. For non-parametric methods, Welch periodograms and correlograms were analysed with different windows. Autoregressive (AR) moving average (MA) and ARMA models with different orders were evaluated in the parametric methods. The reproducibility of the results was also studied. Frequency indices, particularly the H/L ratio and f(c), changed considerably when varying the following parameters of the estimators: periodogram with segment durations longer than 150 ms in EMG and with any duration in VMG signals; correlogram with window length shorter than 10% of the total number of samples; and AR models with an order lower than 10, 20 and 40 in f(c), fm and H/L ratio, respectively, in both myographic signals.

Autoregressive and cepstral analyses of motor unit action potentials

Quantitative electromyographic signal analysis in the time domain for motor unit action potential (MUAP) classification and disease identification has been well documented over recent years. Considerable work has also been carried out in the frequency domain using classical power spectrum analysis techniques. Although MUAP autoregressive (AR) spectral analysis has been suggested as a diagnostic tool by a number of studies, it has not been thoroughly investigated yet. This work investigates the application of AR modeling and cepstral analysis for the diagnostic assessment of MUAPs recorded from normal (NOR) subjects and subjects suffering with motor neuron disease (MND) and myopathy (MYO). The following feature sets were extracted from the MUAP signal: (i) time domain measures, (ii) AR spectral measures, (iii) AR coefficients, and (iv) cepstral coefficients. Discriminative analysis of the individual features was carried out using the univariate and multiple covariance analysis methods. Both methods showed that: (i) the duration measure is the best discriminative feature among the time domain parameters, and (ii) the median frequency is the best discriminative feature among the AR spectral measures. Furthermore, the classification performance of the above four feature sets was investigated for three classes (NOR, MND and MYO) using artificial neural networks. Results showed that the highest diagnostic yield was obtained with the time domain measures followed by the cepstral coefficients, the AR spectral parameters, and the AR coefficients. In conclusion, MUAP autoregressive and cepstral analyses combined with time domain analysis provide useful information in the assessment of myopathology.