Comparison of sEMG processing methods during whole-body vibration exercise.

The objective was to investigate the influence of surface electromyography (sEMG) processing methods on the quantification of muscle activity during whole-body vibration (WBV) exercises. sEMG activity was recorded while the participants performed squats on the platform with and without WBV. The spikes observed in the sEMG spectrum at the vibration frequency and its harmonics were deleted using state-of-the-art methods, i.e. (1) a band-stop filter, (2) a band-pass filter, and (3) spectral linear interpolation. The same filtering methods were applied on the sEMG during the no-vibration trial. The linear interpolation method showed the highest intraclass correlation coefficients (no vibration: 0.999, WBV: 0.757-0.979) with the comparison measure (unfiltered sEMG during the no-vibration trial), followed by the band-stop filter (no vibration: 0.929-0.975, WBV: 0.661-0.938). While both methods introduced a systematic bias (P < 0.001), the error increased with increasing mean values to a higher degree for the band-stop filter. After adjusting the sEMG(RMS) during WBV for the bias, the performance of the interpolation method and the band-stop filter was comparable. The band-pass filter was in poor agreement with the other methods (ICC: 0.207-0.697), unless the sEMG(RMS) was corrected for the bias (ICC ⩾ 0.931, %LOA ⩽ 32.3). In conclusion, spectral linear interpolation or a band-stop filter centered at the vibration frequency and its multiple harmonics should be applied to delete the artifacts in the sEMG signals during WBV. With the use of a band-stop filter it is recommended to correct the sEMG(RMS) for the bias as this procedure improved its performance.

sEMG during Whole-Body Vibration Contains Motion Artifacts and Reflex Activity.

The purpose of this study was to determine whether the excessive spikes observed in the surface electromyography (sEMG) spectrum recorded during whole-body vibration (WBV) exercises contain motion artifacts and/or reflex activity. The occurrence of motion artifacts was tested by electrical recordings of the patella. The involvement of reflex activity was investigated by analyzing the magnitude of the isolated spikes during changes in voluntary background muscle activity. Eighteen physically active volunteers performed static squats while the sEMG was measured of five lower limb muscles during vertical WBV using no load and an additional load of 33 kg. In order to record motion artifacts during WBV, a pair of electrodes was positioned on the patella with several layers of tape between skin and electrodes. Spectral analysis of the patella signal revealed recordings of motion artifacts as high peaks at the vibration frequency (fundamental) and marginal peaks at the multiple harmonics were observed. For the sEMG recordings, the root mean square of the spikes increased with increasing additional loads (p < 0.05), and was significantly correlated to the sEMG signal without the spikes of the respective muscle (r range: 0.54 - 0.92, p < 0.05). This finding indicates that reflex activity might be contained in the isolated spikes, as identical behavior has been found for stretch reflex responses evoked during direct vibration. In conclusion, the spikes visible in the sEMG spectrum during WBV exercises contain motion artifacts and possibly reflex activity. Key pointsThe spikes observed in the sEMG spectrum during WBV exercises contain motion artifacts and possibly reflex activityThe motion artifacts are more pronounced in the first spike than the following spikes in the sEMG spectrumReflex activity during WBV exercises is enhanced with an additional load of approximately 50% of the body mass.

Determination of the optimal parameters maximizing muscle activity of the lower limbs during vertical synchronous whole-body vibration.

PURPOSE: To describe the most effective parameters maximizing muscle activity during whole-body vibration (WBV) exercises on a vertically vibrating (VV) platform. METHODS: The influence of (1) WBV vs. no vibration, (2) vibration frequency (25, 30, 35, 40 Hz), (3) platform peak-to-peak displacement (1.2, 2 mm), and (4) additional loading (no load, 17, 33 kg) on surface electromyographic (sEMG) activity of five lower limb muscles was investigated in eighteen participants. RESULTS: (1) Comparing WBV to no vibration, sEMGRMS of the calf muscles was significantly higher with an additional load of 33 kg independently of the displacement and the frequency (P < 0.05). During WBV, (2) muscle activity at 40 Hz WBV was significantly higher than at 25 Hz for the gastrocnemius lateralis (GL) for all loads, and for the vastii medialis and lateralis using the 33 kg load (P < 0.05); (3) sEMGRMS of all lower limb muscles was significantly increased with the 2 mm compared to the 1.2 mm peak-to-peak displacement (P < 0.05); (4) an effect of additional load was found in the GL, with significantly higher neuromuscular activation for the 33 kg load than no load (P < 0.05). CONCLUSIONS: On a VV platform, we recommend the use of a high platform displacement in combination with a high vibration frequency to provoke the highest muscle activity enhancement. Without maxing out the acceleration stimuli, calf muscles' sEMG can be enhanced with an additional load of 33 kg which corresponded to 50 % of the body mass.

Estimation and modeling of QT-interval adaptation to heart rate changes.

This paper introduces a new method for QT-interval estimation. It consists in a batch processing mode of the improved Woody's method. Performance of this methodology is evaluated using synthetic data. In parallel, a new model of QT-interval dynamics behavior related to heart rate changes is presented. Since two kinds of QT response have been pointed out, the main idea is to split the modeling process into two steps: 1) the modeling of the fast adaptation, which is inspired by the electrical behavior at the cellular level relative to the electrical restitution curve, and 2) the modeling of the slow adaptation, inspired by experimental works at the cellular level. Both approaches are based on a low-complexity autoregressive process whose parameters are estimated using an unbiased estimator. This new modeling of QT adaptation, combined with the presented QT-estimation process, is applied to several ECG recordings with various heart rate variability dynamics. Its potential is then illustrated on ECG recorded during rest, atrial fibrillation episodes, and exercise. Meaningful results in agreement with physiological knowledge at the cellular level are obtained.

Quantifying the PR interval pattern during dynamic exercise and recovery.

We present a novel analysis tool for time delay estimation in electrocardiographic signal processing. This tool enhances PR interval estimation (index of the atrioventricular conduction time) by limiting the distortion effect of the T wave overlapping the P wave at high heart rates. Our approach consists of modeling the T wave, cancelling its influence, and finally estimating the PR intervals during exercise and recovery with the proposed generalized Woody method. Different models of the T wave are presented and compared in a statistical summary that quantitatively justifies the improvements introduced by this study. Among the different models tested, we found that a piecewise linear function significantly reduces the T wave-induced bias in the estimation process. Combining this modeling with the proposed time delay estimation method leads to accurate PR interval estimation. Using this method on real ECGs recorded during exercise and its recovery, we found: 1) that the slopes of PR interval series in the early recovery phase are dependent on the subjects' training status (average of the slopes for sedentary men = 0.11 ms/s, and for athlete men = 0.28 ms/s), and 2) an hysteresis phenomenon exists in the relation PR/RR intervals when data from exercise and recovery are compared.