CHAPTER 7 INTERFERENCE CANCELLATION IN EMG SIGNAL
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1 131 CHAPTER 7 INTERFERENCE CANCELLATION IN EMG SIGNAL 7.1 INTRODUCTION Electromyogram (EMG) is the electrical activity of the activated motor units in muscle. The EMG signal resembles a zero mean random signal. The amplitude of this signal varies from 50 µv to a few mv depending on the location of the measuring electrodes with respect to the muscle and the activity of the muscle. The frequency of the EMG signal varies from 10 Hz to 3 KHz. The EMG signal depends on the anatomical and physiological properties of muscles. It acquires noise while traveling through different tissues. Moreover, the EMG detector which is placed on the surface of the skin, collects signals from different motor units at a time and the interaction of these signals generates artifacts. This chapter gives the applications of the EMG signal, artifacts and interferences present in the EMG signal and the use of AI techniques for canceling artifacts in EMG. 7.2 APPLICATIONS OF EMG SIGNAL The EMG is a very powerful diagnostic modality for evaluating the peripheral nervous system and it provides valuable information that is not obtainable with any other diagnostic test. The EMG signal is mainly applicable to the study of skeletal muscle which is attached to the bone. Its
2 132 contraction is responsible for supporting and moving the skeleton. The field of management and rehabilitation of motor disability is another important application area. The EMG signal is used to provide insight into musculoskeletal system function via estimation of muscle fiber conduction velocity, monitoring of localized changes in the EMG during muscle fatigue, and analysis of gait or motion trajectory studies. It is used as a control signal for powered upper limb prostheses. The amplitude of the EMG estimated from bipolar recordings at the surface of the skin is used to monitor muscular activation level and duration, and to estimate the forces produced by the muscles. The EMG is also used to distinguish primary muscle conditions from muscle weakness caused by neurological disorders. It is used to find causes of weakness, paralysis, involuntary twitching, and abnormal levels of muscle enzymes. It helps to diagnose neuromuscular disorders such as motor neuron disease, neuropathy, nerve damage and muscle damage. Electromyography is also employed in biofeedback studies and training in which patients learn to control muscle tension in the face, neck, and shoulders. However, this type of electromyography uses surface electrodes to record the electrical activity, rather than needle electrodes inserted directly into the muscle. The clinical applications of the EMG include neuromuscular diseases, low back pain assessment, kinesiology and disorders of motor control. 7.3 MEASUREMENT OF EMG SIGNAL The EMG signal is measured using either needle or surface electrodes. Needle EMG does not introduce any electrical stimulation but it records the intrinsic electrical activity of skeletal muscle fibers. Each muscle fiber that contracts produces an action potential. The presence, size, and shape of the waveform of the action potential provide information about the ability
3 133 of the muscle to respond to nervous stimulation. Needle electrode recording allows the monitoring of potentials generated by fibers that may belong to a few different motor units (MU). It provides local information with good morphological details that allow identification and separation of the contributions due to different MU, as well as the recognition of MU action potential shapes. It also has amplitudes of a few mv with most of the power comprised in the frequency range of 10 Hz to 1 KHz. This recording is painful to the patient, and the muscle may feel tender for a few days. Surface Electromyogram (SEMG) recordings provide information about many fibers in superficial muscles and have amplitudes ranges from 0 to 10 mv with most of the power lying between 10 Hz and 400 Hz. 7.4 ARTIFACTS IN EMG SIGNAL this section. The different artifacts that occur in EMG signal are described in Noise Due to Electrode All electronic equipments generate thermal noise. It is minimized by careful circuit design. In recording of SEMG, the electrode skin interface has reactive impedance which depends on electrode size, the signal frequency and the current density at the electrodes. High electrode skin impedance leads to reduced signal amplitude, waveform distortion and power line interference in the recorded EMG. Paste-coupled electrodes generally exhibit lower electrode skin impedance than dry electrodes, because the high impedance of the epidermal layer of the skin is reduced with the use of a conductive gel or paste. Careful skin preparation, including cleansing with alcohol or liquid solvents (e.g. ether) and rubbing a conductive paste or gel into the skin will reduce the electrode skin impedance to acceptable levels even with dry
4 134 electrodes. Ag / AgCl electrodes are widely used as surface recording electrodes Ambient Noise The ambient noise occurs due to electromagnetic radiation. It has amplitude of one to three times than that of the EMG signal. The ways to minimize the ambient noise are differential amplification, shielding the circuit, careful electrode design and moving the recording apparatus away from the interference source. It is reduced by using notch filter centered at the fundamental frequency 50 or 60 Hz. This approach is useful only if a rough EMG amplitude estimate is of interest, because notch filtering at 50 or 60 Hz will remove signal as well as power line components Motion Artifact Motion artifact occurs when there is relative movement between the electrodes or cables and the underlying skin. It causes irregularities in the data. Electrode motion artifact can be reduced by using a layer of conductive gel or paste, by reducing the skin impedance, and using a HPF with a cut off frequency of 10 Hz. Cable motion artifact has a frequency range of 1 to 50 Hz and it is minimized by reducing the electrode skin impedance through careful skin preparation and by using shielded cables which provide a low impedance path to ground, external to the measurement system Biological Artifacts The EMG signal is also contaminated by other biosignals like ECG and EEG. The most common of these is the ECG, which is frequently present when the EMG is recorded from electrode sites on the trunk and neck.
5 135 Four AI techniques are investigated in this work to remove these biological artifacts and also power line interference. 7.5 ECG INTERFERENCE CANCELLATION IN EMG A biokit equipment (Appendix 5) is used in this research work to acquire the EMG signal. This EMG signal is recorded from the forearm position. It acts as the measured (target) signal and is shown in Figure 7.1. The EMG signal which is shown in Figure 7.1 is contaminated mainly by ECG signal. Hence, it is necessary to cancel the ECG artifact. The ECG signal used for AIC is recorded using electrodes placed on the fingers and is shown in Figure 7.2. The frequencies of EMG signal are higher (up to 3 KHz) when compared to the ECG signal (1 Hz -100 Hz). The HPF can be used to filter out the ECG component. However, it may remove a portion of the required EMG signal also. Hence, AIC is required to cancel the ECG interference in the EMG signal. In this work, four AI techniques namely BPN, CCN, ANFIS and ANFIS-FCM are employed to cancel the ECG interference in the EMG signal. Figure 7.1 EMG signal recorded from the forearm position
6 136 Figure 7.2 ECG signal AIC of ECG using BPN The same flowchart given in Figure 5.2 is again adopted for the cancellation of ECG interference in EMG signal. The parameters used for training BPN to cancel the ECG interference are epochs =30, goal = 0.65, momentum =0.9, show = 5, time = infinity and learning rate = 0.5. The BPN architecture has two neurons in the input layer, 35 neurons in the only hidden layer and one neuron in the output layer. The known ECG signal and the delayed ECG signal are given as two inputs. The measured EMG signal is the target in the training process. The training stops as soon as the performance goal (mean square value of the estimated EMG) reaches a minimum or the maximum number of epochs is reached. The result of AIC using BPN is shown in Figure 7.3.
7 137 Figure 7.3 Results of AIC in EMG using BPN (a) Contaminated EMG (b) Reference ECG (c) Estimated ECG interference in EMG (d) Estimated EMG (e) Noise after AIC The ECG signal recorded using appropriate electrodes is shown in Figure 7.3 (b). The estimated ECG interference in EMG using BPN is shown in Figure 7.3(c). The estimated EMG signal using AIC is shown in Figure 7.3(d).It is passed through a Butterworth filter of order =2 and normalized frequency = 0.7 to get the noise. Since BPN gives a rough estimation of the interference, the amount of noise in Figure 7.3(e) is high AIC of ECG using CCN The parameter values used for training CCN are same as that used for training BPN. The CCN architecture has two input nodes and one output
8 138 node. It is assumed that 35 hidden nodes (arranged into 3 sets with each set containing 10, 10 and 15 nodes) are available for selection. The covariance for all the three sets is calculated. Then, the set which has the highest covariance is selected and the other two sets are rejected. The results of AIC using CCN are shown in Figure 7.4. It is noted from Figure 7.4 (e) that the noise generated using CCN is slightly less than that with BPN. Figure 7.4 Results of AIC in EMG using CCN (a) Contaminated EMG (b) Reference ECG (c) Estimated ECG in EMG (d) Estimated EMG (e) Noise after AIC
9 AIC of ECG using ANFIS The parameters used for ANFIS training are given below: Number of nodes = 35, number of linear parameters = 27, number of nonlinear parameters = 18, number of training data pairs = 1000 and number of fuzzy rules = 9. The results of AIC using ANFIS are shown in Figure 7.5. It is observed from Figure 7.5(e) that the magnitude of the noise is very much reduced. Figure 7.5 Results of AIC in EMG using ANFIS (a) Contaminated EMG (b) Reference ECG(c) Estimated ECG in EMG (d) Estimated EMG (e) Noise after AIC
10 AIC of ECG using ANFIS-FCM Four clusters are used in ANFIS-FCM in addition to the parameter values used for ANFIS. The results of AIC using ANFIS-FCM are shown in Figure 7.6. It is observed from 7.6 (e) that the noise after AIC in EMG signal is very less. Figure 7.6 Results of AIC in EMG using ANFIS-FCM (a) Contaminated EMG (b) Reference ECG (c) Estimated ECG in EMG (d) Estimated EMG (e) Noise after AIC
11 PERFORMANCE COMPARISON OF ECG CANCELLATION IN EMG Quantitative analysis of the different AI techniques used for ECG interference cancellation in EMG signal is given Table 7.1. It shows that the Mean Square value of the estimated EMG signal and convergence time is less when ANFIS-FCM technique is used. Also SNR is maximum for the same technique. Table 7.1 Quantitative analysis of various AI techniques used for AIC in EMG Sl.No. Technique Mean Square value of the estimated EMG SNR (db) Convergence time(s) 1 BPN e CCN e ANFIS e ANFIS-FCM e AIC OF EEG IN EMG The cancellation of EEG in EMG is carried out using ANFIS-FCM only as this technique is superior to other techniques. The training parameters used for canceling EEG interference in EMG signal are, number of nodes = 35, number of linear parameters = 27, number of nonlinear parameters = 18, number of training data pairs = 500 and number of fuzzy rules = 9. The results of AIC using ANFIS-FCM are shown in Figure 7.7. The EMG and EEG signals taken from the physionet database are shown in
12 142 Figure 7.7 (a) and (b) respectively. The EEG signal acts as the reference and the EMG signal is used as the target for training. ANFIS-FCM technique is used for estimating the unknown EEG interference in the EMG signal. The estimated EEG interference is shown in Figure 7.7 (c). The estimated EMG signal after AIC is shown in Figure 7.7 (d). The estimated EMG is passed through a Butterworth filter of order 2 and normalized frequency of 0.9. The filtered output is subtracted from the estimated EMG to get the noise, which is shown in Figure 7.7 (e). It is observed from Figure 7.7 (e) that the noise present in the retrieved EMG signal is very less. Figure 7.7 Results of AIC of EEG in EMG using ANFIS-FCM (a) Contaminated EMG (b) Reference EEG(c) Estimated EEG in EMG (d) Estimated EMG (e) Noise after AIC
13 AIC OF POWER LINE INTERFERENCE IN EMG The EMG signal taken from the website is shown in Figure 7.8. It has a sampling frequency of 20 KHz. The contaminated EMG signal is a mixture of EMG (Figure 7.8) and the distorted 50 Hz power line interference. The contaminated EMG signal is shown in Figure 7.9(a). The estimated 50 Hz power line interference in Figure 7.9(a) using ANFIS-FCM is shown in Figure 7.9(b). The estimated EMG signal which is the required signal to be retrieved from Figure 7.9(a) is shown in Figure 7.9(c). The difference between the EMG signal and the estimated EMG signal is called noise and is shown in Figure 7.9(d). It is observed that ANFIS-FCM successfully retrieves the EMG signal. Figure 7.8 EMG signal
14 144 Figure 7.9 Results of AIC of power line in EMG using ANFIS-FCM (a) Contaminated EMG (b) Estimated power line in EMG (c) Estimated EMG (d) Noise after AIC 7.9 CONCLUSION The EMG signal is contaminated by various noises such as electrode noise, power line interference, EEG and ECG. Conventional methods are used to remove the non-physiological noises. Since some of the characteristics of EMG signal are similar to ECG and EEG signals, it is necessary to use AIC. Four AI techniques are employed to cancel the ECG interference in EMG signal. Quantitative analysis reveals that ANFIS-FCM outperforms the other techniques. Hence, ANFIS-FCM is used to remove the EEG and power line interferences from the EMG signal. The results obtained indicate that ANFIS-FCM is a useful AI technique to cancel the nonlinear interferences from the biosignals.
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