Biomedical subjects

Marco Gazzoni

Publications and source records attributed to Marco Gazzoni.

8 recordsLinked to original sources

PubMed2005

Conduction velocity of quiescent muscle fibers decreases during sustained contraction.

We tested the hypothesis that conduction velocity of quiescent muscle fibers decreases during sustained contraction due to the activity of the active motor units in the muscle. Ten subjects trained for the identification of a target motor unit in the abductor pollicis brevis with feedback on surface EMG signals detected with a two-dimensional array of 61 electrodes. The subjects activated the target motor unit in two 10-s long contractions, before (contraction C1) and after (C3) a 3-min contraction (C2), all in ischemic condition. The target motor unit was not activated during C2. Eight of the 10 subjects (control group) performed a second experimental session identical to the first but with a resting period of 3 min instead of the contraction C2. Exerted force and target motor unit discharge rate were not different between the two subject groups and between C1 and C3 (mean +/- SD, over C1 and C3; C2 group: 15.8 +/- 10.4% maximal voluntary contractions and 13.1 +/- 1.9 pps; control group: 15.6 +/- 22.1% maximal voluntary contractions and 14.5 +/- 1.9 pps, respectively). Muscle fiber conduction velocity of the target motor unit decreased in C3 with respect to C1 in the C2 group (3.59 +/- 0.57 and 3.34 +/- 0.47 m/s for C1 and C3, respectively; P < 0.05) but not in the control group (3.47 +/- 0.68 and 3.46 +/- 0.73 m/s). In the C2 group, the percent decrease in conduction velocity of the target motor unit between C1 and C3 (6.4 +/- 7.1%) was not significantly different from the percent decrease in the average conduction velocity of the motor units active during C2 (9.6 +/- 5.4%). In conclusion, the contraction-induced modifications in electrophysiological membrane properties of muscle fibers are partly independent on fiber activation.

Action Potentials↗

PubMed2004

Conduction velocity of low-threshold motor units during ischemic contractions performed with surface EMG feedback.

The aim of this study was to analyze the effect of ischemia on low-threshold motor unit conduction velocity. Nine subjects were trained to isolate the activity of a single motor unit (target motor unit) in the abductor pollicis brevis muscle with feedback on surface EMG signals recorded with a 16-electrode linear array. After training, the subjects activated the target motor unit at approximately 8 pulses per second (pps) for five 3-min-long contractions. During the third and fourth contractions, a cuff inflated at 180 mmHg around the forearm induced ischemia of the hand. The exerted force (mean +/- SE, 4.6 +/- 2.1% of the maximal voluntary contraction force), discharge rate (8.6 +/- 0.4 pps), interpulse interval variability (34.8 +/- 2.5%), and peak-to-peak amplitude of the target motor unit action potentials (176.6 +/- 18.2 microV) were not different among the five contractions. Conduction velocity, mean power spectral frequency, and action potential duration were the same in the beginning of the five contractions (2.8 +/- 0.2 m/s, 195.2 +/- 10.5 Hz, and 5.4 +/- 0.3 ms, respectively) and changed over the 3 min of sustained activation only during the fourth contraction. Conduction velocity and mean power spectral frequency decreased (10.05 +/- 1.8% and 8.50 +/- 2.18% during the 3 min, respectively) and action potential duration increased (8.2 +/- 4.6% in the 3 min) during the fourth contraction. In conclusion, 1) subjects were able to isolate the activity of a single motor unit with surface EMG visual feedback in ischemic conditions maintained for 16 min, and 2) the activation-induced decrease in single motor unit conduction velocity was significantly larger with ischemia than with normal circulation, probably due to the alteration of mechanisms of ion exchange across the fiber membrane.

Action Potentials↗

PubMed2004

A new method for the extraction and classification of single motor unit action potentials from surface EMG signals.

It has been shown that multi-channel surface EMG allows assessment of anatomical and physiological single motor unit (MU) properties. To get this information, the action potentials of single MUs should be extracted from the interference EMG signals. This study describes an automatic system for the detection and classification of MU action potentials from multi-channel surface EMG signals. The methods for the identification and extraction of action potentials from the raw signals and for their clustering into the MUs to which they belong are described. The segmentation phase is based on the matched Continuous Wavelet Transform (CWT) while the classification is performed by a multi-channel neural network that is a modified version of the multi-channel Adaptive Resonance Theory networks. The neural network can adapt to slow changes in the shape of the MU action potentials. The method does not require any interaction of the operator. The technique proposed was validated on simulated signals, at different levels of force, generated by a structure based surface EMG model. The MUs identified from the simulated signals covered almost the entire recruitment curve. Thus, the proposed algorithm was able to identify a MU sample representative of the muscle. Results on experimental signals recorded from different muscles and conditions are reported, showing the possibility of investigating anatomical and physiological properties of the detected MUs in a variety of practical cases. The main limitation of the approach is that complete firing patterns can be obtained only in specific cases due to MU action potential superpositions.

Action Potentials↗

PubMed2004

Reproducibility of muscle-fiber conduction velocity estimates using multichannel surface EMG techniques.

The aim of this study was to assess the reproducibility of muscle-fiber conduction velocity (MFCV) estimates obtained from multichannel surface electromyographic (EMG) recordings. Surface EMG signals were collected with a matrix of 61 electrodes during isometric, submaximal (50% of the maximal voluntary contraction torque) contractions of the biceps brachii muscle. Conduction velocity was estimated using multichannel maximum likelihood techniques. Reproducibility of MFCV estimates was assessed varying the number of signals (two to seven) used for the estimate and the distance between detection points (5-30 mm). Intraclass correlation coefficient (ICC) of both initial MFCV values and their rates of change with fatigue increased when increasing number of signals and distance between detection points. ICC of initial MFCV was negative using two signals for MFCV estimate, and it increased to approximately 75% with six to seven signals. Thus, reproducibility of MFCV estimates may be improved significantly using advanced multichannel estimation methods with respect to classic two-channel techniques.

Analysis of Variance↗

PubMed2003

Low-threshold motor unit membrane properties vary with contraction intensity during sustained activation with surface EMG visual feedback.

Single-motor unit (MU) activities were detected from the abductor pollicis and abductor digiti minimi muscles providing the subjects with visual feedback of multichannel surface electromyogram (EMG) signals. The subjects could modulate the force to observe on the surface recordings a single dominant MU and modulate its firing rate for contractions of 300 s with a noninvasive EMG feedback. The firing rate was maintained at approximately 8 pulses per second [low-frequency (LF) contraction] and at approximately 12 pulses per second [high-frequency (HF) contraction]. Single-MU conduction velocity (CV) decreased slightly but significantly over time, and it was possible to identify a significantly larger rate of decrease of CV during the HF with respect to the LF contractions. CV initial value significantly increased with the average firing rate, and CV values were significantly correlated to the instantaneous firing rate (R ranging from 0.21 to 0.39). Both additional MU recruitment and substitution were observed during the contractions. The study provides evidence that 1). it is possible to follow the same MU in a hand muscle at two different intensities (HF and LF) for 300-s durations by using visual feedback of surface EMG, 2). low-threshold single-MU CV changes over time since the beginning of the contraction, and 3). it is possible to distinguish between CV changes of the same MU at slightly different firing rates. The technique provides a practical method for the noninvasive assessment of both control and membrane properties of single MUs.

Adult↗

PubMed2003

The linear electrode array: a useful tool with many applications.

In this review we describe the basic principles of operation of linear electrode arrays for the detection of surface EMG signals, together with their most relevant current applications. A linear array of electrodes is a system which detects surface EMG signals in a number of points located along a line. A spatial filter is usually placed in each point for signal detection, so that the recording of EMG signals with linear arrays corresponds to the sampling in one spatial direction of a spatially filtered version of the potential distribution over the skin. Linear arrays provide indications on motor unit (MU) anatomical properties, such as the locations of the innervation zones and tendons, and the fiber length. Such systems allow the investigation of the properties of the volume conductor and its effect on surface detected signals. Moreover, linear arrays allow to estimate muscle fiber conduction velocity with a very low standard deviation of estimation (of the order of 0.1-0.2 m/s), thus providing reliable indications on muscle fiber membrane properties and their changes in time (for example with fatigue or during treatment). Conduction velocity can be estimated from a signal epoch (global estimate) or at the single MU level. In the latter case, MU action potentials are identified from the interference EMG signals and conduction velocity is estimated for each detected potential. In this way it is possible, in certain conditions, to investigate single MU control and conduction properties with a completely non-invasive approach. Linear arrays provide valuable information on the neuromuscular system properties and appear to be promising tools for applied studies and clinical research.

Electromyography↗

PubMed2003

Assessment of low back muscle fatigue by surface EMG signal analysis: methodological aspects.

This paper focuses on methodological issues related to surface electromyographic (EMG) signal detection from the low back muscles. In particular, we analysed (1) the characteristics (in terms of propagating components) of the signals detected from these muscles; (2) the effect of electrode location on the variables extracted from surface EMG; (3) the effect of the inter-electrode distance (IED) on the same variables; (4) the possibility of assessing fatigue during high and very low force level contractions. To address these issues, we detected single differential surface EMG signals by arrays of eight electrodes from six locations on the two sides of the spine, at the levels of the first (L1), the second (L2), and the fifth (L5) lumbar vertebra. In total, 42 surface EMG channels were acquired at the same time during both high and low force, short and long duration contractions. The main results were: (1) signal quality is poor with predominance of non-travelling components; (2) as a consequence of point (1), in the majority of the cases it is not possible to reliably estimate muscle fiber conduction velocity; (3) despite the poor signal quality, it was possible to distinguish the fatigue properties of the investigated muscles and the fatigability at different contraction levels; (4) IED affects the sensitivity of surface EMG variables to electrode location and large IEDs are suggested when spectral and amplitude analysis is performed; (5) the sensitivity of surface EMG variables to changes in electrode location is on average larger than for other muscles with less complex architecture; (6) IED influences amplitude initial values and slopes, and spectral variable initial values; (7) normalized slopes for both amplitude and spectral variables are not affected by IED and, thus, are suggested for fatigue analysis at different postures or during movement, when IED may change in different conditions (in case of separated electrodes); (8) the surface EMG technique at the global level of amplitude and spectral analysis cannot be used to characterize fatigue properties of low back muscles during very low level, long duration contractions since in these cases the non-stable MU pool has a major influence on the EMG variables. These considerations clarify issues only partially investigated in past studies. The limitations indicated above are important and should be carefully discussed when presenting surface EMG results as a means for low back muscle assessment in clinical practice.

Adult↗

PubMed2002

Effect of age on muscle functions investigated with surface electromyography.

The purpose of this study was to investigate changes in the surface electromyographic (EMG) signal as a means of defining age-related central and peripheral mechanisms affecting muscle fatigue. Spectral and temporal variables of the surface EMG signal were studied during voluntary isometric contractions of the dominant biceps brachii muscle in a group of 8 healthy elderly men (age range 67-86 years) and a group of 10 healthy young men (age range 23-34 years). The maximal torque developed and the rate of decrease (slope) of spectral variables and conduction velocity (CV) were statistically higher in the young subjects than in the elderly subjects. Motor unit (MU) CV distribution was also estimated from the surface EMG signal and no statistical difference was observed in its variance between the two groups. These results confirm previous findings from the tibialis anterior muscle. Thus, changes in fiber type distribution and decrease in MU firing rate with aging may be factors determining the decrease in maximal voluntary contraction torque and in myoelectric manifestations of muscle fatigue.

Action Potentials↗