sEMG wavelet-based indices predicts muscle power loss during dynamic contractions.

The purpose of this study was to investigate the sensitivity of new surface electromyography (sEMG) indices based on the discrete wavelet transform to estimate acute exercise-induced changes on muscle power output during a dynamic fatiguing protocol. Fifteen trained subjects performed five sets consisting of 10 leg press, with 2 min rest between sets. sEMG was recorded from vastus medialis (VM) muscle. Several surface electromyographic parameters were computed. These were: mean rectified voltage (MRV), median spectral frequency (F(med)), Dimitrov spectral index of muscle fatigue (FI(nsm5)), as well as five other parameters obtained from the stationary wavelet transform (SWT) as ratios between different scales. The new wavelet indices showed better accuracy to map changes in muscle power output during the fatiguing protocol. Moreover, the new wavelet indices as a single parameter predictor accounted for 46.6% of the performance variance of changes in muscle power and the log-FI(nsm5) and MRV as a two-factor combination predictor accounted for 49.8%. On the other hand, the new wavelet indices proposed, showed the highest robustness in presence of additive white Gaussian noise for different signal to noise ratios (SNRs). The sEMG wavelet indices proposed may be a useful tool to map changes in muscle power output during dynamic high-loading fatiguing task.

Linear vs. non-linear mapping of peak power using surface EMG features during dynamic fatiguing contractions.

This study compares a non-linear (neural network) and a linear (linear regression) power mapping using a set of features of the surface electromyogram recorded from the vastus medialis and lateralis muscles. Fifteen healthy participants performed 5 sets of 10 repetitions leg press using the individual maximum load corresponding what they could perform 10 times (10RM) with 120s of rest between them. The following sEMG variables were computed from each extension contraction and used as inputs to both approaches: mean average value (MAV), median frequency (Fmed), the spectral parameter proposed by Dimitrov (FInsm5), average (over the observation interval) of the instantaneous mean frequency obtained from a Choi-Williams distribution (MFM), and wavelet indices ratio between moments at different scales (WIRM1551, WIRM1M51, WIRM1522, WIRE51, and WIRW51). The non-linear mapping (neural network) provided higher correlation coefficients and signal-to-noise ratios values (although not significantly different) between the actual and the estimated changes of power compared to linear mapping (linear regression) using the sEMG variables alone and a combination of WIRW51 and MFM (obtained by a stepwise multiple linear regression). In conclusion, non-linear mapping of force loss during dynamic knee extension exercise showed higher signal-to-noise ratio and correlation coefficients between the actual and estimated power output compared to linear mapping. However, since no significant differences were observed between linear and non-linear approaches, both were equally valid to estimate changes in peak power during fatiguing repetitive leg extension exercise.

EMG spectral indices and muscle power fatigue during dynamic contractions.

The purpose of this study was to examine acute exercise-induced changes on muscle power output and surface electromyography (sEMG) parameters (amplitude and spectral indices of muscle fatigue) during a dynamic fatiguing protocol. Fifteen trained subjects performed five sets consisting of 10 leg presses (10RM), with 2min rest between sets. Surface electromyography was recorded from vastus medialis (VM) and lateralis (VL) and biceps femoris (BF) muscles. A number of EMG-based parameters were compared for estimation accuracy and sensitivity to detect peripheral muscle fatigue. These were: Mean Average Voltage, median spectral frequency, Dimitrov spectral index of muscle fatigue (FI(nsm5)), as well as other parameters obtained from a time-frequency analysis (Choi-Williams distributions) such as mean and variance of the instantaneous frequency and frequency variance. The log FI(nsm5) as a single parameter predictor accounted for 37% of the performance variance of changes in muscle power and the log FI(nsm5) and MFM as a two factor combination predictor accounted for 44%. Peripheral impairments assessed by sEMG spectral index FI(nsm5) may be a relevant factor involved in the loss of power output after dynamic high-loading fatiguing task.

Métodos de procesamiento y análisis de señales electromiográficas / Electromyographic signal processing and analysis methods

La electromiografía clínica es una metodología de registro y análisis de la actividad bioeléctrica del músculo esquelético orientada al diagnóstico de las enfermedades neuromusculares. Las posibilidades de aplicación y el rendimiento diagnóstico de la electromiografía han evolucionado paralelamente al conocimiento de las propiedades de la energía eléctrica y al desarrollo de la tecnología eléctrica y electrónica. A mediados del siglo XX se introdujo el primer equipo comercial de electromiografía para uso médico basado en circuitos electrónicos analógicos. El desarrollo posterior de la tecnología digital ha permitido disponer de sistemas controlados por microprocesadores cada vez más fiables y potentes para captar, representar, almacenar, analizar y clasificar las señales mioeléctricas. Es esperable que el avance de las nuevas tecnologías de la información y la comunicación pueda conducir en un futuro próximo a la aplicación de desarrollos de inteligencia artificial que faciliten la clasificación automática de señales así como sistemas expertos de apoyo al diagnóstico electromiográfico (AU)

Clinical electromyography is a methodology for recording and analysing the bioelectrical activity of the skeletal muscle tissue in order to diagnose neuromuscular pathology. The possibilities of application and the diagnostic performance of electromyography have evolved parallel to a growing understanding of the properties of electricity and the development of electrical and electronic technology. The first commercially available electromyography equipment for medical use was introduced in the middle of the 20th century. It was based on analog electronic circuits. The subsequent development of digital technology made available more powerful and accurate systems, controlled by microprocessors, for recording, displaying, storing, analysing, and classifying the myoelectric signals. In the near future, it is likely that advances in the new information and communication technologies could result in the application of artificial intelligence systems to the automatic classification of signals as well as expert systems for electromyographic diagnosis support (AU)

Neuromuscular fatigue after resistance training.

This study examined the effects of heavy resistance training on dynamic exercise-induced fatigue task (5 x 10RM leg-press) after two loading protocols with the same relative intensity (%) (5 x 10RM(Rel)) and the same absolute load (kg) (5 x 10RM(Abs)) as in pretraining in men (n=12). Maximal strength and muscle power, surface EMG changes [amplitude and spectral indices of muscle fatigue], and metabolic responses (i.e.blood lactate and ammonia concentrations) were measured before and after exercise. After training, when the relative intensity of the fatiguing dynamic protocol was kept the same, the magnitude of exercise-induced loss in maximal strength was greater than that observed before training. The peak power lost after 5 x 10RM(Rel) (58-62%, pre-post training) was greater than the corresponding exercise-induced decline observed in isometric strength (12-17%). Similar neural adjustments, but higher accumulated fatigue and metabolic demand were observed after 5 x 10RM(Rel). This study therefore supports the notion that similar changes are observable in the EMG signal pre- and post-training at fatigue when exercising with the same relative load. However, after training the muscle is relatively able to work more and accumulate more metabolites before task failure. This result may indicate that rate of fatigue development (i.e. power and MVC) was faster and more profound after training despite using the same relative intensity.

[Electromyographic signal processing and analysis methods]. / Métodos de procesamiento y análisis de señales electromiográficas.

Clinical electromyography is a methodology for recording and analysing the bioelectrical activity of the skeletal muscle tissue in order to diagnose neuromuscular pathology. The possibilities of application and the diagnostic performance of electromyography have evolved parallel to a growing understanding of the properties of electricity and the development of electrical and electronic technology. The first commercially available electromyography equipment for medical use was introduced in the middle of the 20th century. It was based on analog electronic circuits. The subsequent development of digital technology made available more powerful and accurate systems, controlled by microprocessors, for recording, displaying, storing, analysing, and classifying the myoelectric signals. In the near future, it is likely that advances in the new information and communication technologies could result in the application of artificial intelligence systems to the automatic classification of signals as well as expert systems for electromyographic diagnosis support.

Gamma band responses to target and non-target auditory stimuli in humans.

We studied the EEG oscillatory changes in the gamma band during auditory oddball paradigms in two different conditions (counting targets and reading). A time-frequency analysis was performed for standard and target stimuli. The study revealed an early (26-59 ms) phase-locked oscillation. Around 200 ms, a non-phase locked response was found for standard and target stimuli in temporal posterior electrodes. At about 360 ms, a phase-locked oscillation was observed only after target stimuli in the "counting targets" condition. During the "reading" task this late activity was not found, and energy increases were lower than during "counting" task. The early oscillation may be related to the sensory processing of the stimuli. The response around 200 ms may be involved in auditory mismatch and/or memory retrieval, and late activity is probably a P300-related response. Attention enhances all these activities.

Movement-related changes in cortical oscillatory activity in ballistic, sustained and negative movements.

We studied movement-related EEG oscillatory changes in the alpha, beta and low-gamma frequency bands in three different paradigms of movement, namely ballistic, sustained, and negative (muscle relaxation). A time-frequency analysis of non-phase-locked activity in the 7-47 Hz range was performed on movement-centred EEG sweeps using wavelet filters and Gabor transforms. All three movements were accompanied by a decrease in beta activity that began contralaterally about 1.5 s prior to the onset of movement but that extended to both sides near the beginning of the movement. This decrease was followed by a rebound after the end of the movement in the ballistic and negative movements. A decrease was also seen in the alpha band during the three paradigms, which began later (1 s before movement) and lasted longer. An increase in gamma activity was only seen during ballistic and sustained movements, while a decrease in gamma energy was observed during negative movements. It was concluded that changes in the beta band of the EEG before movement are related to the preparation for the movement, but an important afferent component may be present in the later changes. Gamma band activity may be just involved in the execution of the movement, related to muscle contraction.

Beta electroencephalograph changes during passive movements: sensory afferences contribute to beta event-related desynchronization in humans.

Non-phase-locked beta oscillatory changes during passive movements were studied in six healthy volunteers, and compared with those observed in a similar group during ballistic movements. Passive movements consisted of brisk wrist extensions done with the help of a pulley system. Changes in the beta band were determined by means of wavelet and Gabor transforms, and compared statistically with a pre-movement period. In this paradigm, a marked beta energy loss (event-related desynchronization, ERD) was present after the beginning of the movement, followed by a beta energy increase (event-related synchronization, ERS). The ERD/ERS was similar to that observed during ballistic movements, but without pre-movement components. Although both changes were maximal in the contralateral central electrode, the beta ERD showed a more bilateral topography. These findings suggest that afferent proprioceptive inputs may play a role in the final part of the beta ERD observed during voluntary movements.

Gamma band activity in an auditory oddball paradigm studied with the wavelet transform.

OBJECTIVES: To examine the characteristics of evoked and induced gamma band oscillatory responses occurring during P300 development in an auditory oddball paradigm. METHODS: A time-frequency analysis method was applied to an auditory oddball paradigm in 7 healthy subjects. This method combines a multiresolution wavelet algorithm for signal extraction and the Gabor transform to represent the temporal evolution of the selected frequency components. Phase-locked or evoked activity and also non-phase-locked activity were computed for both standard and target stimuli. RESULTS: The gamma band frequency components differed between target and non-target stimuli processing. The study showed an early and mainly phase-locked oscillatory response appearing around 26--28 ms after both standard and target stimuli onset. This response showed a spectral peak around 44 Hz for both stimuli. A late oscillatory activity peaking at 37 Hz with a latency around 360 ms was observed appearing only for target stimuli. The latency of this late oscillatory activity had a high correlation (P=0.002) to the latency of the P300 wave. CONCLUSIONS: EEG signal analysis with wavelet transform allows the identification of an early oscillatory cortical response in the gamma frequency range, as well as a late P300-related response.

Quantification of jiggle in real electromyographic signals.

Two parameters have been defined for quantifying jiggle: normalized consecutive amplitude differences (CAD) and the cross-correlational coefficient of consecutive discharges (CCC). In real recordings, artifacts from several sources may increase the variability of these parameters as they were originally defined. Two methodological modifications designed to overcome such a limitation are proposed: estimation of baseline fluctuation from segments of the recording free from nearby concurrent motor unit potentials (MUPs), and waveform alignment of consecutive discharges by correlation maximization (CM). The results obtained by the original and modified methods were compared for MUPs from normal subjects and patients with amyotrophic lateral sclerosis and chronic neurogenic diseases. With the modified method, CAD and CCC showed fewer extreme values and less scatter. The number of successfully aligned MUPs with the CM method was 18.8% higher (n = 394; Chi-square = 54.6; P < 0.001), including irregular and unstable MUPs. The proposed modifications improve our capability to quantify the jiggle of real signals and reduce the necessity of manual interventions although low-interference recordings and operator supervision are still required.