O. stamineus exhibited diuretic activity, but was less potent than furosemide and hydrochlorothiazide. Care should be taken when consuming this herb as slight increase of kidney function enzymes was recorded.
High quality of surface electromyography is vital during investigation on muscle activity. Low quality of surface EMG signals causes extracted signals to be inaccurate and lead to misinterpretation and misclassification of the signals. A surface EMG signal quality is determined by the ratio of muscle contraction to its baseline during muscle relaxation period. Baseline noises are originated from powerline, cable motion artefact, electronics of the amplification systems and skin-electrode interface. The noises are quite difficult to be removed by digital or active filter since they do not have specific frequency range like powerline interference and corner frequency noise. However, wavelet de-noising enables users to remove noise by accessing both frequency and time information. Baseline surface EMG noise is possible to be removed by estimating de-noise threshold based on mean absolute value and root mean square of its baseline. The result of this study shows that the proposed estimation of threshold method is better than the conventional threshold setting.
Electromyography (EMG) is one of the indirect tools in indexing fatigue. Fatigue can be detected when there are changes on amplitude and frequency. However, various outcomes from literature make researchers conclude that EMG is not a reliable tool to measure fatigue. This paper investigates EMG behavior of biceps femoris in median frequency and mean absolute value during five days of Bruce Protocol treadmill test. Before that, surface EMG signals are filtered using band pass filter cut-off at 20-500Hz and are de-noised using db45 1-decimated wavelet transform. Five participants achieved more than 85% of their maximal heart rate during the running activity. The authors also consider other markers of fatigue such as performance, muscle soreness and lethargy as indicators to adaptation and maladaptation conditions. Result shows that turning points of median frequency and mean absolute value are very significant in indexing fatigue and indicators to adaptation of resistive training.
Ability of wavelet transform in accessing time and frequency information at the same time make it widely used in analyzing bio-signals like electromyography (EMG). Discrete wavelet transforms (DWT) and stationary wavelet transform (SWT) are examples of analysis based on wavelet. Both analyses are based on decomposition technique and splitting signals into few frequency band. The different is DWT will down sample resolution into half at each decomposition level, while SWT is not. This paper is investigating both analyses in its ability on de-noising process of EMG using the same properties. The signals will be decomposed into five level of decomposition using 'db20', and de-noised using the same threshold setting. The performance will be evaluated based on its signals to noise ratio and muscle fatigue detection. Results show that de-noising process through SWT give better signals to ratio. Inability in DWT removed 20Hz corner frequency in several reading lead to misinterpretation in fatigue detection.
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