Techniques of Removal of Baseline Fluctuation from EMG Signal Recordings
نویسندگان
چکیده
Appropriate cancellation of the baseline fluctuation (BLF) is an important issue when recording EMG signals as it may degrade signal quality and distort qualitative and quantitative analysis. We present two techniques, statistical and filter-design approach for cancellation of the BLF based on several signal processing techniques used sequentially. The methodology is to estimate the spectral contents of the BLF, and then to use this estimation to design a high pass filter by using Bilinear Transformation that cancel the BLF present in the signal. Two merit figures are devised for measuring the degree of BLF present in an EMG record. These figures are used to compare both methods which naively consider the baseline without any fluctuation i.e. constant potential shift. Applications of the techniques on real and simulated EMG signals show the superior performance of our approach in terms of both visual inspection and the merit figures. KeywordsNeedle EMG, Baseline removal, MUAPs —————————— —————————— Introduction lectromyography is used to classify various neuromuscular disorders .The word “electromyography” is made up of three parts: “electro“+ “myo” + “graphy.” “Myo-” is from the Greek word “mys”, meaning muscle and “graphy” comes from the Greek word “grapho” meaning to write. Thus Electromyography is the writing (recording) of muscle electricity. EMG stands for electromyography and is the study of muscle electrical signals, and forms a valuable aid in the diagnosis of neuromuscular disorders. There are several hundred neuromuscular disorders that affect the brain and spinal cord, nerves, or muscles. Many of these diseases are hereditary and life expectancy. Early detection and diagnosis of these diseases by clinical examination and laboratory tests are essential for their management as well as their prevention through prenatal diagnosis and genetic counseling. Such information is also useful in research which may lead to the understanding of the nature and eventual treatment of these diseases.EMG examinations studies the electrical activity of the muscle and forms a valuable neurophysiologic test for the assessment of neuromuscular disorders. In humans, clinical EMG provides useful information in the electro-diagnostic examination of patients suffering from neuromuscular disorders. EMG is also particularly helpful in deciding the symptom of muscle weakness in the assessment of neuromuscular disorders. EMG findings are used to detect and describe different disease processes affecting the motor unit, the smallest functional unit of the muscle. _________________ Manish, Assistant Professor.ABES Engineering College, Ghaziabad Sadhana Pal, ABES Engineering College, Ghaziabad Anupam Bhardwaj, HOD of EC Deptt. SIT Meerut Gyan Prakash Pal, Assistant Professor, SIT Meerut. With voluntary muscle contraction, the action potential reflecting the electrical activity of a single anatomical motor unit is recorded. It is the compound motor unit action potential (MUAP) of those muscle fibers within the recording range of the needle electrode. The quality of EMG signal may be degraded by baseline oscillations, disturbing the process of MUAP extraction, classification and analysis. An adequate cancellation of baseline fluctuation would enhance the signal quality and accordingly make the diagnosis more reliable. EMG signal is the algebraic summation of the motor unit action potentials within the pick-up area of the electrode being used. The pick-up area of an electrode will almost always include more than one motor unit because muscle fibers of different motor units are intermingled throughout the entire muscle. Any portion of the muscle may contain fibers belonging to as many as 20-50 motor units. Material: Analysis of the recording of EMG signals from the muscles in healthy subjects at low force level, using concentric needle electrode. The signal was analogue band pass filtered at 3 Hz to 10 KHz and sampled at 20 KHz. The EMG signal was then low pass filtered at 8 KHz and down sampled by a factor of two at 10 KHz. The Recording equipment comprised an electromyography and disposable concentric needle electrodes. The electromyography amplifies the input signals according to a manually selected gain. An EMG signal with baseline fluctuation and low frequency noise is shown in Figure1. E International Journal of Scientific & Engineering Research, Volume 2, Issue 11, November-2011 2 ISSN 2229-5518 IJSER © 2011 http://www.ijser.org Figure1. Raw EMG Signal Techniques: There are two techniques are used for removal of baseline fluctuation, which is listed below. 1. Statistical technique based on threshold Statistical technique based on threshold method comprises several sequential phases: (a) Calculation of threshold (b) Segmentation of EMG signal (c) Removal of baseline fluctuation 2. Digital Filter designing for removal of BLF Digital Filter designing for removal of BLF method comprises several sequential phases: (a) Calculation of threshold (b) Segmentation of EMG signal (c) Interpolation of baseline points (d) Analysis of Power spectrum density (e) Filter designing & filtering of raw EMG signal Figure2.Flow chart shows the sequential steps of statistical technique. Figure3. Flow chart of filter designing method 0 0.5 1 1.5 2 2.5 3 3.5 x 10 4 -200 -100 0 100 200 300 400 Samples m ic r o v o lt International Journal of Scientific & Engineering Research, Volume 2, Issue 11, November-2011 3 ISSN 2229-5518 IJSER © 2011 http://www.ijser.org 1.1. Statistical technique based on threshold: 1.1(a) Calculation of thresholdThe calculation of threshold is used to find out the activity level of the EMG signal, classification and segmentation of whole EMG signal x(t). An algorithm used for the calculation of threshold T is given as If maximum x (t)>30* mean (abs x (t)) Then Threshold = 5 * mean (abs x (t)) Else Threshold =maximum x (t)/5 1.1(b) Segmentation of EMG signalThe process to cut the EMG signal into segment of possible MUAPs segment (active segment) and low activity areas or baseline segment (MUAPs free segment) is known as segmentation. Segmentation of EMG signal can be performed with the help of discrete wavelet transform (DWT) [4]. Another approach of segmentation is performed into two stages. In first stage AS are obtained and in second stage BLS are obtained. In the first step segmentation algorithm calculates the threshold; peaks over the calculated threshold are considered as candidate MUAPs. A window of constant width of 120 points is applied centered at the identified peak. If a greater peak is found in the window, the window is centered at the greater peak otherwise the 120 points are saved as a candidate MUAP waveform. In second stage to obtain the BLS of EMG signal, second threshold, named T1 is calculated. In this step a windows of constant width of 30 points is taken and calculates T1, then selects the next window of 30 samples and calculate the value of T1 again. Thus the whole length of the EMG signal is divided into the window of 30 samples and threshold is calculated each time. The value of threshold is change for every next window. The threshold T1 is also calculated on the basis of mean absolute value of whole samples present in a window of 30 samples. The BLS is performed by the comparison of threshold T1 with first threshold T. If threshold T1 is greater than the threshold T then the samples is again considered as the candidate of MUAPs waveform i.e. the active, otherwise the segment is baseline segment. The value of second threshold T1 is calculated as: T1=mean [abs(X (w))] Where w is the size of window. 1.1(c) Removal of baseline fluctuation From the segmentation of whole EMG signal the AS and BLS distinguished from the EMG signal. The removal of BLF present in the BLS of the EMG signal can be performed. The AS of the EMG signal will remain same, only the correction is required in the BLS of the signal. The oscillations or disturbance present in the baseline segment of EMG signal are removed by subtracting the value of threshold T1 from the absolute value of the each samples present in the BLS of the first window of the size of 30 samples and then take the next window and subtract the value of respective threshold T1 from the absolute value of each samples of this window. This sequential procedure is applied to the whole windows of BLS of the EMG signal. After applying all the above procedure, a new BLS is obtained, which is free from the BLF. Figure 2(a) and 2(b) show the BL with Fluctuation and without fluctuation. The EMG signal without BLF can be further used for required applications. In this way an adequate cancellation of BLF can be obtained, that can enhance the signal quality and accordingly make the process of extraction and analysis of EMG signal easier and reliable. Figure 2(a). EMG signal with BLF Figure 2(b). EMG signal without BLF 2. 1Digital Filter designing for removal of BLF: 0 0.5 1 1.5 2 2.5 3 3.5 x 10 4 -200 -100 0 100 200 300 400 Samples m ic ro v o lt 0 0.5 1 1.5 2 2.5 3 3.5 x 10 4 -200 -100 0 100 200 300 400 International Journal of Scientific & Engineering Research, Volume 2, Issue 11, November-2011 4 ISSN 2229-5518 IJSER © 2011 http://www.ijser.org 2.1(a) The Calculation of threshold and 2.1(b) the Segmentation of EMG signal are same as discussed in section 1.1(a) and 1.1(b) respectively. 2.1(c) Interpolation of baseline pointsInterpolation is the process of estimation of values between the data points. The previously averaged points are interpolated by means of cubic splines, which closely follows the BL through (along) its fluctuations (Fig 3a). The cubic splines technique interpolates signal points by means of concatenated cubic polynomials such that the obtained interpolation curve and its time derivative are both continuous throughout the whole time span, and the signal points to be interpolated are exactly on the curve (Fig 3b). Figure 3(a). Interpolation points Figure 3(b). Interpolated BLS 2.1(d) Analysis of power spectrum density: The power spectrum density of the interpolated baseline segments will be obtained to find out the frequency range of the baseline, so that the filter would be designed for the specified cut off frequency. There are various methods to estimate the frequency spectrum of baseline segment. AR spectral estimation can also be used on the interpolated signal to obtain a smooth and high resolution power spectral density but in this paper Fast Fourier transform is used to obtain the Power spectrum density of interpolated baseline segment. PSD Estimation using FFT: For a wide-sense stationary (WSS) process X (n), the power spectral density, Sxx(f), is the Fourier transform of the autocorrelation function rxx (k) (Wiener-Khintchine) [3]. The power spectral density, Sxx(f), is a real and nonnegative function and the signal variance is df f s r xx xx ) ( ) 0 ( 2 (1) A. Periodgram: Given a finite sequence x[n], of length N, the autocorrelation function can be estimated by
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Removal of Baseline Fluctuation from Emg Recordings
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تاریخ انتشار 2011