EXTRACTION OF EMG SIGNAL BURIED IN RMI NOISE Sofia Ben Jebara COSIM Lab., Ecole Sup´erieure des Communications de Tunis, Carthage University Route de Raoued 3.5 Km, Cit´e El Ghazala, Ariana, 2088, TUNISIA
[email protected]
ABSTRACT
a)
The electromyographic signal (EMG) describes the muscle activity. But, when acquired under special conditions, such as functional Resonance Magnetic Images (fMRI), an important amount of noise, hide completely the useful signal. The purpose of this paper is to analyze fMRI noise characteristics and to develop an approach to reduce it. The proposed idea is based on spectral subtraction of the noise spectrum estimate over the entire EMG spectrum. Evaluation of the technique performances reveal that spectral subtraction is a viable artifact reduction tool that should be used with EMG collected during fMRI data acquisition. 1. INTRODUCTION Electromyography signal (EMG) represents the electrical activity of muscles. It is a non-invasive and relatively inexpensive technique that can provide considerable quantitative information about muscle activation patterns. EMG signal analysis can be refined when combined with functional Resonance Magnetic Images (fRMI) and vice versa.In fact, the ability to simultaneously collect EMG and fMRI data permits to provide simultaneously information about muscle motor tasks and brain activities changes during muscle activity. The EMG signal can be detected directly from the muscle or from the skin by using surface electrodes. However, it is difficult to obtain high-quality electrical signals from EMG sources because the signals have low amplitude (in range of mV) and are easily corrupted by noise. There are different kinds of noises, we relate for example those due to EMG signal acquisition (driver amplifier, electrodes, cable movement artifact,...) and those due to cross talk (electrodes over an adjacent muscle pick-up a signal via skin conduction). Moreover, when EMG signal is acquired during an fRMI diagnosis, a significant drawback, due to oscillating magnetic fields in fMRI, create considerable interference noise, which can hide completely the EMG signal. Fig. 1.a shows an example of EMG signal acquired in normal conditions, where only acquisition noise exists. We can clearly see muscle contractions, of 2.5 seconds durations approximatively, separated by temporal intervals of muscle rest, where a small amount of noise exists. Fig. 1.b shows
(b)
Fig. 1. Temporal evolution of an EMG signal. a) normal acquisition conditions, (b) under fMRI. an other example of EMG signal acquired under fMRI conditions. We effectively observe the important amount of noise so that the desired signal is completely buried in noise. Only small peaks appear and we guess that they are related to muscle contraction. But, it is impossible to apply directly any rigourous visual analysis, that’s why a denoising technique should be applied to enhance the useful EMG signal. Many strategies have been developed for reducing artifact in EMG collected in normal conditions. Each strategy is devoted to some kinds of noise. We relate for example techniques based on notch/comb filtering, to remove power line electrical noise [1], band-pass filtering to remove noise as-
sociated to mechanical perturbations [2], adaptive filtering to suppress electrocardiogram (ECG) interference [3],... Since the apparition of the wavelet transform for denoising [4], it becomes the mostly used concept for many kinds of signals such as biomedical signals and images. For EMG signals, different versions are adjusted to reduce various kinds of noise (see for example [5]). When EMG signal is collected during fMRI. the denoising approaches differ from those developed for normal acquisition conditions. In fact, the noise characteristics are quite different so that classical approaches fail in fMRI conditions. Two main families of approaches are developed. The first one uses comb filtering [6] to remove particular frequencies from the EMG spectrum in which the noise is concentrated. The second family uses wavelet thresholding principle [7]. Although these techniques reduce fMRI artifact from EMG, they therefore, reduce EMG signal power and deteriorate EMG spectral components. In this paper, we propose a novel approach, directly implemented in the frequency domain which is based on the spectral subtraction principle. It, first, estimates the noise during time intervals where only-noise is present (during muscle relax) and then subtract it. The noise estimation is recursively updated during noise-only frames and kept constant during muscle activity. So, the approach needs a Muscle Activity Detector (MAD), that we propose to determine using the Spectral Flatness Measure (SF M ) descriptor. The paper is organized as follows. Section 2 is devoted to frequency and time-frequency analysis of the fMRI noise. Section 3 gives the details of the proposed approach which is based on a muscle activity detector, a noise estimator during muscle non-activity and spectral subtractor. In section 4, the proposed method is tested and validated on real data. Finally, a conclusion is given. 2. TIME-FREQUENCY SIGNAL/NOISE CHARACTERISTICS 2.1. Observation model
2.2. Frequency analysis The experience, used here, for muscles activity acquisition under fRMI consists of a series of rests and arm movements. The activity of the common extensor muscle is acquired for analysis. For such purpose, two kinds of frames are selected in order to characterize the noise and the EMG signal spectral content. The first one corresponds to a noise-only signal (selected during muscle relax) and the second one is selected during muscle activity. Fig.2 illustrates the spectral magnitudes |Y(m, l)| in the two cases. The frames size is equal to N = 2048 which corresponds to a duration of 2048 ms for a sampling rate of Fs = 1000 Hz. In the noise-only frame magnitude spectrum (Y (m, l) = N (m, l)), illustrated in Fig.2.a, we notice the presence of frequency bins situated at frequencies multiples of F0 = 1 = 0.1024 ms is 9.765 Hz. The fundamental period T 0 = F0 the repetition time of image acquisition. So, this high-voltage noise, due to varying magnetic fields and radio frequency (RF) pulses in the scanner, is constantly associated with the EMG signal. In other experiments, one common strategy has been to avoid EMG recording during scanning or to use intermittent recording during short intervals between scanning when the level of noise is low. But it becomes necessary to correctly synchronize the actions. The aim of this work is to characterize muscle activity, at any time, even if it is corrupted by important fMRI image scans noise. Fig.2.b shows a noisy EMG frame magnitude spectrum (Y (m, l) = S(m, l) + N (m, l)). We find the same kind of frequency peaks situated at the same positions and we also find other frequency bins uniformly distributed along the frequency axis. They correspond to some spectral contents of the noise-free EMG signal. The purpose of the following section is to apply a time-frequency analysis to better understand the spectral content of the EMG signal.
2.3. Time-frequency analysis
Let the corrupted EMG signal y(k) be presented as: y(k) = s(k) + n(k),
(1)
where s(k) is the clean EMG signal and n(k) is the noise signal which are assumed to be uncorrelated. The processing is done on a frame-by-frame basis. We denote y(m, n) the noisy EMG sample (m is the frame index and n is the temporal index (n = 0, ..N − 1, N is the frame length). The Short Time Fourier Transform (STFT) is used and the previous model is re-written: Y(m, l) = S(m, l) + N(m, l), where l is the frequency bin.
(2)
We selected a set of data, composed of five contractions of 5 seconds duration alternated with relax intervals of 50 seconds and we calculated the spectrogram, using frames of 512 ms duration. The spectrogram drawn in Fig.3 shows equally spaced horizontal white lines which correspond to the harmonic structure of the fRMI noise during muscle relax. This fact confirms the results observed in Fig. 2.a. We also observe 5 short intervals, where the spectrogram looks like quasiuniform vertical regions in low gray color. These parts correspond to the noisy harmonic structure during muscle activity. We hence can guess that the EMG signal during muscle activity has a quasi-constant spectrum which corresponds to a noise. This constatation is the source of the idea we’ll use to firstly detect the muscle activity and then to denoise it.
3. PROPOSED APPROACH FOR EMG SIGNAL DENOISING a)
3.1. Muscle activity detection From a noisy EMG signal, we propose a novel method to detect time intervals where muscle activity is present. The origin of the idea comes from the spectrogram structure. A quite clear harmonic structure characterize only-noise time intervals whereas a noisy harmonic structure exists during muscle activity. An indicator of tonality, classically used in audio processing, can be extended in use to characterize EMG signal. It is the Spectral Flatness Measure (SF M ) calculated in the spectral domain as follows: qQ
(b)
Fig. 2. Spectrum of a) noise-only frame, (b) noisy EMG frame.
SF M (m) =
L
L−1 l=0
|Y(m, l)|
1 L
PL−1
|Y(m, l)|
l=0
,
(3)
where L is the number of frequency bins. A high SFM (approaching one) characterizes white noise whereas a low SFM (approaching zero) indicates that the spectrum is concentrated in a relatively small number of bands. We used the same EMG signal as previously and we illustrate in Fig. 4 the temporal domain signal and its SF M (multiplied by five for figure clarity reasons). We notice two ranges of values, one belonging to the interval [0.45 0.6] and an other one belonging to the interval [0.25 0.35]. The first range is obtained during muscle activity while the second one concerns the noise-only part of the signal. A simple idea to obtain a Muscle Activity Detector (M AD) is to apply threshoding: 1 if SF M (m) ≥ T hr M AD(m) = , (4) 0 otherwise where T hr is the threshold that should be fixed empirically. In case of Fig.4, it is chosen equal to T hr = 0.4.
Fig. 3. Spectrogram of a noisy EMG signal. Fig. 4. A noisy EMG signal and its SF M .
3.2. Noise estimation The noise spectrum can not be calculated precisely for each frame. But it can be estimated according to previous frames. In fact, the fMRI noise seems stationary during one run so that we propose to estimate, in recursive manner, the noise spectrum during time intervals where no muscle activity is present. A commonly used method for noise spectrum estimation is to average over frames which do not contain EMG activity and to maintain the previous estimation when muscle activity is detected: b K|N(m − 1, l)|2 + |Y(m, l)|2 K +1 b , |N(m, l)|2 = if M AD(m) = 0 2 b |N(m − 1, l)| otherwise (5) where K is the total number of previous frames where no muscle activity exists. 3.3. Spectral subtraction b The denoised speech short-time power spectrum |S(m, f )|2 is obtained using a spectral denoising approach. In this paper, we use the spectral subtraction principle, which is inspired from works on speech denoising [8]. It is a method for restoration of the spectrum of a signal observed in additive noise, through subtraction of an estimate of the average noise spectrum from the noisy signal spectrum. The denoised EMG power spectrum is written as follows: b l)|2 |Y(m, l)|2 − β|N(m, 2 b b |S(m, l)| = if |Y(m, l)|2 ≥ β|N(m, l)|2 0 otherwise (6) The parameter β controls the amount of noise to be reduced. If it is too low, unwanted residual noise will remains. If it is too high, EMG signal will be distorted. So, a tradeoff must be considered. Furthermore, from the first line of Eq.6, spectral subtraction method can lead to negative values, resulting from differences between the estimated noise and the noise present in actual frame. A simple solution is to set the negative values to zero, ensuring a non negative power spectrum. The temporal domain enhanced EMG frame is obtained by inverse Fourier transform (IF F T ) of the enhanced magnitude spectrum combined with the phase of the original noisy amplitude spectrum. In fact, the clean EMG phase is unknown, that’s why it is approximated by the available one. The samples of the denoised EMG frame is written: h i ˆ sˆ(m, n) = IF F T |S(m, l)|.ej arg(Y(m,l)) . (7) Note that EMG signal was Hamming windowed using
a 512-ms window and a 256-ms overlap between frames is used. 4. EXPERIMENTAL RESULTS The EMG data that are used to demonstrate and evaluate the spectral subtraction denoising technique were recorded from four forearm muscles which are the common extensor muscle, the brachioradialis, the flexor superficialis and the flexor profundus. Volunteers performed three successive runs. In each run, five contractions of 5 seconds duration are alternated with relax intervals of 50 seconds. 4.1. Temporal domain evaluation To evaluate the ability of spectral subtraction algorithm to enhance EMG signal, we compare the temporal evolution a signals before and after denoising. Fig.5 shows the signals of the four muscles. From noisy observations, we notice that the amount and kind of noise is not the same for the four muscles because it depends on different characteristics: muscle size, its position in the forearm, electrodes placement,... From denoised observations, it clearly appears that noise is well reduced for the three first muscles but not so well for the flexor profundus. In fact, since it is deep in the forearm, it is more difficult to place the electrodes so that the signal acquisition is not so precise. 4.2. Evaluation using RM S One method used to produce waveforms that are more easily analyzable than the noisy EMG (in terms of muscle activation degree and force estimation), is the Root Mean Square (RM S). It is a technique for rectifying the raw signal and converting it to an amplitude envelope, It is defined as follows: v u N −1 u1 X y(m, n)2 , (8) RM S(m) = t N n=0 where y(m, n) is the nth sample of the frame m of length N. Fig.6 shows the evolution of the RM S versus frame number for the common extensor muscle shown in Fig. 5.a. The frames characteristics are the same than the ones used in spectral subtraction (N = 512, hamming windows and 256 samples overlap between frames). Before denoising, the RM S signal presents important values and after denoising, the RM S values are arranged to right values. We can effectively show quasi-null values during muscle relax and classic shape during muscle activity. 4.3. Comparison with a reference signal To better see the RM S signal, we select one contraction of 5 seconds duration and compare its RM S after denoising to the noisy one and to an other one of the same muscle acquired
Fig. 6. An example of RM S of noisy and denoised EMG signal.
Fig. 7. Evolution of the RM S during one contraction. Fig. 5. Noisy EMG signals and their denoised versions. (a) common extensor muscle, (b) brachioradialis, (c) flexor superficialis and (d) flexor profundus. in normal conditions (not during fRMI scanning), we call it reference signal. In fact, since the clean EMG signal could not be observed under fRMI, we choose this method of reference signal to have an idea about the precision of the denoising technique. Fig.7 illustrates the RM S values calculated for half overlapping frames of 64 ms duration. This figure shows that, contrary to noisy signal, the dynamic range and the behavior of the denoised signal RM S looks like the one of the reference signal. This fact confirms the validity of the proposed denoising technique. 5. CONCLUSION In this work, we introduced and evaluated a spectral subtractive technique for estimating useful information of EMG signal completely buried in fRMI noise. This paper is a start-
ing idea to find a better noise estimator when noise spectrum changes considerably from one frame to an other. Furthermore, the solution of spectral subtraction should be refined for profundus muscle where denoising quality is still to be improved. 6. REFERENCES [1] D. T. Mewett, H. Nazeran and K. J. Reynolds, “Removing power line noise from recorded EMG,” Proc. of the 23rd Annual Conference IEEE Engineering in Medicine and Biology Society (EMBS), pp. 91-94, October 2001, Istanbul-Turkey. [2] C. J. De Luca, L. D. Gilmore, M. Kuznetsov and S. H. Roy, “Filtering the surface EMG signal: movement artifact and baseline noise contamination,” Journal of biomechanics, vol. 43, pp. 1573-1579, 2010. [3] G. Lu, J. S. Brittain, P. Holland, J. Yianni,, A. L. Green,
J. F. Stein, T. Z. Aziz and S. Wang, “Removing ECG noise from surface EMG signals using adaptive filtering,” Neuroscience Letters, vol. 462, pp. 14-19, 2009. [4] D. L. Donoho, “De-noising by soft-thresholding,” IEEE Trans. Information Theory, vol. 41, no. 3, pp. 613-627, May 1995. [5] A. Phinyomark, P. Phukpattaranont and C. Limsakul, “The usefulness of wavelet transform to reduce noise in the SEMG signal,” EMG Methods for Evaluating Muscle and Nerve Function, book edited by Mark Schwartzvol, InTech, 2012. [6] G. Ganesh, D. W. Franklin, R. Gassert, H. Imamizu and M. Kawato, “Accurate real-time feedback of surface EMG during fMRI,” Journal of neurophysiology, vol. 97, pp. 912-920, 2007. [7] J. B. Dougherty, “A novel and comprehensive artifact reduction strategy for EMG collected during fMRI at 3 Tesla,” PhD thesis, Dreexel University, 2010. [8] S.F. Boll, “Suppression of acoustic noise in speech using spectral subtraction,”IEEE Trans. Acoust., Speech, Signal Process, vol.27, pp. 113-120, Apr. 1979.
