Analysis of ALS and normal EMG signals based on empirical mode decomposition
2016; Institution of Engineering and Technology; Volume: 10; Issue: 8 Linguagem: Inglês
10.1049/iet-smt.2016.0208
ISSN1751-8830
AutoresVipin Mishra, Varun Bajaj, Anil Kumar, Girish Kumar Singh,
Tópico(s)Fault Detection and Control Systems
ResumoIET Science, Measurement & TechnologyVolume 10, Issue 8 p. 963-971 Research ArticlesFree Access Analysis of ALS and normal EMG signals based on empirical mode decomposition Vipin K. Mishra, Vipin K. Mishra Discipline of Electronics and Communication Engineering, PDPM Indian Institute of Information Technology, Design and Manufacturing Jabalpur, Jabalpur, IndiaSearch for more papers by this authorVarun Bajaj, Corresponding Author Varun Bajaj bajajvarun056@yahoo.co.in Discipline of Electronics and Communication Engineering, PDPM Indian Institute of Information Technology, Design and Manufacturing Jabalpur, Jabalpur, IndiaSearch for more papers by this authorAnil Kumar, Anil Kumar Discipline of Electronics and Communication Engineering, PDPM Indian Institute of Information Technology, Design and Manufacturing Jabalpur, Jabalpur, IndiaSearch for more papers by this authorGirish Kumar Singh, Girish Kumar Singh Department of Electrical Engineering, Indian Institute Technology Roorkee, Roorkee, Uttarakhand, IndiaSearch for more papers by this author Vipin K. Mishra, Vipin K. Mishra Discipline of Electronics and Communication Engineering, PDPM Indian Institute of Information Technology, Design and Manufacturing Jabalpur, Jabalpur, IndiaSearch for more papers by this authorVarun Bajaj, Corresponding Author Varun Bajaj bajajvarun056@yahoo.co.in Discipline of Electronics and Communication Engineering, PDPM Indian Institute of Information Technology, Design and Manufacturing Jabalpur, Jabalpur, IndiaSearch for more papers by this authorAnil Kumar, Anil Kumar Discipline of Electronics and Communication Engineering, PDPM Indian Institute of Information Technology, Design and Manufacturing Jabalpur, Jabalpur, IndiaSearch for more papers by this authorGirish Kumar Singh, Girish Kumar Singh Department of Electrical Engineering, Indian Institute Technology Roorkee, Roorkee, Uttarakhand, IndiaSearch for more papers by this author First published: 01 November 2016 https://doi.org/10.1049/iet-smt.2016.0208Citations: 37AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract Electromyogram (EMG) signals contain a lot of information about the neuromuscular diseases like amyotrophic lateral sclerosis (ALS). ALS progressively degenerates the motor neurons in spinal cord. In this study, a new technique for the analysis of normal and ALS EMG signals is proposed. EMG signals are decomposed into narrow band intrinsic mode functions (IMFs) by using empirical mode decomposition (EMD) technique. The area of complex plot, two bandwidths namely amplitude modulation bandwidth (BAM) and frequency modulation bandwidth (BFM), normalised instantaneous frequency (IFn), spectral momentum of power spectral density (SMPSD) and mean of first derivative of instantaneous frequency (MFDIF) are extracted from analytic IMFs obtained by EMD technique. These six features are used as input in least square support vector machine classifier for the classification of ALS and normal EMG signals. Experimental results and comparative analysis show that classification performance of the proposed method is better than other existing method in the same database. 1 Introduction Electromyography is a technique of collecting and processing electrical signals produced by the contraction of skeletal muscles. These collected electrical signals are known as an electromyogram (EMG) signal [1]. Electrical signal magnitude is proportional to contraction potential of the skeletal muscles. EMG signals are being used in many fields such as sports science, ergonomics, medical research and rehabilitation. In medical science, the EMG signals can be used for the analysis and identification of diseases related to voluntary muscles and motor neurons [2]. Brain produces electrical impulses to control the action of muscles. Motor neuron is a nerve cell, which forms the path for transfer of electrical impulses. Brain needs upper and lower motor neurons to send the signals to a particular muscle in the body [3]. To perform any action, upper motor neurons send signals to trigger lower motor neurons that further transfer the signals to other part of the body. Muscles present in the chest, legs, throat, face, arms and tongue are controlled by the lower motor neurons [4]. Motor neuron disorder creates a lot of diseases, especially amyotrophic lateral sclerosis (ALS) [5]. ALS results in weakness, atrophy, paralysis, respiratory failure and loss of control of brain over muscles. ALS affects both (upper and lower) motor neurons, due to which muscles cannot be controlled voluntarily and become weaker and smaller [6, 7]. Autonomic impairment as dysregulation of blood pressure, heart rate (HR) and increase in the risk of unusual death have been found in many ALS patients [8]. The EMG signal has been a valuable clinical tool to assess the changes of ALS. Diagnosis of ALS by visual inspection is a tedious task and requires trained professionals and clinicians. Therefore, the accurate and automatic methodology is required for diagnosis of ALS EMG signals. Parameters extracted from the EMG signals are very useful in diagnostics. Most of the parameters extracted from EMG signals are based on the time domain, frequency domain, and time–frequency domain. Time domain based features such as root mean square, spectrogram, kurtosis, entropy and power have been used for classification of ALS and healthy signals [9]. Mel-frequency cepstral coefficient based features have been used as input to K-nearest neighbourhood (KNN) classifier for classification of ALS and healthy EMG signals [10]. Discrete wavelet transform coefficients have been employed to classify the healthy and ALS signals [11]. Time–frequency based features such as autocorrelation, zero-crossing rate, and average value of spectral peak and mean frequency have been applied to KNN classifier for classification of ALS and healthy EMG signals [12]. The power level of spectrum, frequency shifting to higher region and number of onset peak features extracted from short-time Fourier transform have been used for classification of ALS and normal EMG signals [13]. Time based features, autoregression (AR) spectral measure, AR coefficients, cepstral coefficients feature sets extracted from motor unit action potentials (MUAPs) have been used with neuro-fuzzy based classifier to discriminate the normal, myopathy and neuropathy signals [14]. Cepstrum analysis, which is popular in speech signal processing has been employed for classification of MUAP based diseases [15]. The on–off timing of skeletal muscles at the time of movement, continuous wavelet transform has been employed for analysis of EMG signals [16]. Various features of signal amplitude, area, duration rise time, number of phases have been employed for classification of EMG signals [17]. Optimal wavelet packet method with neural network classifier has been used for the classification of surface EMG signals [18]. A method based on optimisation of mother wavelet has been used for the classification of EMG signals [19]. Method based on multiscale entropy of EMG signal has been employed with support vector machine (SVM) for the classification of EMG signals [20]. Multiscale principle component analysis has been used with k-NN, ANN and SVM classifier for the classification of EMG signals [21]. Multi fractal analysis on EMG signals has been made to classify the different arm movements [22]. A method based on pattern recognition technique has been used for the discrimination of electromyographic interference pattern in normal and myopathy [23]. In this paper, six new features namely area, BAM, BFM, SMPSD, MFDIF and normalised instantaneous frequency (IFn) are extracted from analytic IMFs for the classification of ALS and normal EMG signals. The rest of the paper is organised as follows: The EMG dataset, the empirical mode decomposition method, features extraction from IMFs, and LS-SVM classifier are presented in Section 2. Experimental results for the classification of ALS and normal EMG signals based on extracted features are given in Section 3. Section 4 presents the discussion. Finally, Section 5 concludes the paper. 2 Methodology 2.1 Dataset The dataset EMG signals are available online [24]. In normal group, ten subjects comprising six males and four females of age 21–37 were present. No patient of normal group had any history of neuro muscular disorders. ALS group covers eight subjects: four males and four females’ of age group 35–67 years. Five subjects having the sign of ALS died few years after the occurrence of disease. A standard needle electrode of leading off area 0.07 mm2 was used for the signal acquisition. Sampling rate of EMG signals was 23437.5 Hz and digitised by A/D convertor of 16-bit resolution. Recorded signals were filtered at 2 Hz and 10 kHz by high- and low-pass filters, respectively, (i) to monitor the signal quality, video and audio feedbacks were used, (ii) a standard concentric needle electrode was used, (iii) 2 and 10 kHz range high- and low-pass filters were used in EMG amplifier, (iv) three level of needle insertion was used to collect the signal from five places of a muscle and (v) the recording was conducted at constant and low level of contraction. In this study, 87 ALS EMG signals and 133 normal EMG signals are used. The processing steps for the analysis of EMG signals are presented in Fig. 1. Fig 1Open in figure viewerPowerPoint Block diagram of the required processing steps for classification of ALS and normal EMG signals 2.2 Empirical mode decomposition EMD is an adaptive signal decomposition method. It decomposes any non-stationary signal into some set of band limited functions known as intrinsic mode function (IMFs). After decomposition, it maintains the original pattern of data, therefore it is a highly efficient method [25]. For each IMF to be generated, two conditions must be satisfied: (i) the number of extrema in data set should be equal to or differ by at most one from the number of zero crossing and (ii) the mean value of both the envelopes (minima and maxima) should be zero at any point. EMD algorithm follows these steps to break the signal into IMFs [26, 27]: Detect the minima and maxima of . Use cubic spline curve to connect extrema and generate lower and upper envelopes and , respectively. Calculate the mean m(t) of upper and lower envelopes and check for zero mean Find if mean is not zero. Iterate processes 1–4 till becomes an IMF, (which can be checked by two conditions illustrated above) . Find residue . Repeat steps 1–5 taking this residue as new signal to generate other IMFs. Continue the process till the final residue is generated. Residue contains one local maxima and minima, through this no more IMF can be obtained. Finally, EMD produces n number of IMFs to with a residual signal . Now, can be shown as (1)where M denotes the total number of IMFs. Hilbert transform is used to convert these IMFs into analytic IMFs. An analytic IMF obtained from any IMF c(t) can be defined as (2)Amplitude A(t), phase and IF ω(t) of an analytic IMF can be defined as (3) (4) (5) IF is a measure of rate of rotation of analytic signal in complex plane. In proposed method, Hilbert transform has been applied on all the IMFs to get analytic IMFs. Two parameters IF and instantaneous amplitude are extracted from these analytic IMFs. Localisation of IF of IMFs is good in time–frequency domain and useful to extract some essential features of signal. Each IMF can be represented in analytic form as (6) 2.3 Area computation from analytic IMFs Plot of an analytic IMF is a circular structure having unique centre [28]. For fast summarisation of visual information in the graph, centre tendency measurement (CTM) is a useful method [29]. In this paper, CTM is used to get the radius (r) from circular plot of analytic IMFs. The imaginary part of analytic signal is plotted against the real part of signal. Analytic plot of IMFs fulfil these two conditions: plot should have rotation direction, and a fixed centre. EMG signals are non-stationary and complicated in nature, and do not fulfil above conditions. EMD algorithm is used to break any complicated EMG signal into number of IMFs, which satisfies the above conditions. Plot of analytic IMFs can be used to calculate area feature [29]. The area is calculated by using 95% radius of the circular plot. Variability of the signal is quantified by CTM. It is computed by selecting all the points coming inside the radius r and dividing it by the total number of points [30]. The CTM for analytic IMF can be expressed as (7)where N is the total number of points and (8)CTM represents a fraction of the total number of points lying inside the radius r. 2.4 IFn computation from analytic IMFs Normalised first derivative of IF can be represented as [31]: (9)where (modulus of first derivative of IF), IFn is normalised by the mean value of IF; therefore, it is independent of carrier frequency. Frequency of any single tone signal is constant, due to which after normalisation process, the mean value of IF difference (δf) should be small. 2.5 Bandwidth feature Bandwidth of any signal is a measure of the extent of frequency. This feature provides information about the share of amplitude and frequency modulated bandwidth on whole bandwidth of any signal. The aim of this feature is to provide the information, whether the spread of frequency in EMG signals is due to the amplitude modulation (AM), or frequency modulation (FM), or due to both [32]. Share of frequency due to AM and FM in total bandwidth of signal can be written as (10)Here Above (10) has two parts: first one is dependent on amplitude and second on the phase of a signal. AM bandwidth (BAM) and FM bandwidth (BFM) can be represented as (11)and (12) 2.6 Spectral momentum of power spectral density (SMPSD) Power spectral density (PSD) is a useful parameter to find the power and dominant frequency of any signal. PSD of any signal can be represented as (13)To find the greater order shape, spectral moment of PSD can be represented as [33] (14) 2.7 Mean of first derivative of IF (MFDIF) To find the difference between two consecutive values of IF, first derivative is calculated. Mean value of these differences is calculated to use it as feature. This feature can be expressed as [31] (15)Mean value of first derivative of IF can be represented as (16)where N is the total number of samples present in first derivative of the IF. 2.8 Least square SVM (LS-SVM) These features used as input to LS-SVM classifier for classification of ALS and normal EMG signals. The decision function for two-class problem is defined as (17) The optimisation problem can be formulated as (18) (19)where xi is the index of N inputs, yi is the index of outputs, which is 1 or −1 for input of class 1 or 2, respectively. b and γ are the bias and regularisation parameters, respectively. The αi is the Lagrangian multiplier. The solution of LS-SVM classifier is obtained as (20)The polynomial kernel function for LS-SVM is expressed as (21)where l is the order of polynomial kernel function. The detailed description of LS-SVM classifier is available in [34, 35]. 3 Results The non-stationary and non-linear EMG signals are decomposed into number of IMFs by using EMD algorithm. Hilbert transform is used to extract the analytic signals from IMFs, which enable to compute the analytic features namely area, IFn, BAM, BFM, SMPSD and MFDIF features. Figs. 2a and b depict ALS EMG signal its IMFs and IF of ALS EMG signal. This figure clearly shows that frequency decreases in higher IMFs. Kruskal–Wallis test has been performed on each feature separately, which provide the discrimination probability of the ALS and normal EMG signals. Lower values of probability demonstrate better discrimination ability. The proposed analytic features are used as input to LS-SVM classifier for classification of the ALS and normal EMG signals. Fig 2Open in figure viewerPowerPoint Example of a EMD of ALS EMG signal b IF plot for eight IMFs of ALS EMG signal Table 1 shows the probability values (p) of area and IFn features with mean and standard deviation of ALS and normal EMG signals. It is clear from the table that the small p values of first six IMFs of both features are statistically significant. Area feature of ALS EMG signals is higher as compared with normal EMG signals due to variation in the amplitude. Also IFn feature ALS EMG signals are higher as compared with normal EMG signals due to variation in frequency. Figs. 3a, b and 4a, b show the discrimination ability of area and IFn parameter, respectively. Table 1. Probability value of the area and IFn features of the IMFs used in test Features Area IFn IMF no. Signal type Mean ± standard deviation Probability Mean ± standard deviation Probability IMF1 ALS 1.68 × 106 ± 5.42 × 106 0.03 0.2427 ± 0.0971 10−5 normal 1.53 × 106 ± 4.65 × 106 0.196 ± 0.0875 IMF2 ALS 2.99 × 106 ± 8.98 × 106 10−9 0.2211 ± 0.0809 10−12 normal 2 × 106 ± 6.23 × 106 0.15 ± 0.0672 IMF3 ALS 3.89 × 106 ± 1.73 × 107 10−7 0.1490 ± 0.0599 10−11 normal 8.44 × 105 ± 1.32 × 106 0.1021 ± 0.0391 IMF4 ALS 2.8 × 106 ± 3.39 × 106 10−9 0.1179 ± 0.0381 10−15 normal 1.07 × 106 ± 1.44 × 106 0.0805 ± 0.0310 IMF5 ALS 3.64 × 106 ± 5.42 × 106 10−6 0.0968 ± 0.0543 10−10 normal 1.6 × 106 ± 2.34 × 106 0.0652 ± 0.0296 IMF6 ALS 4.28 × 106 ± 6.68 × 106 10−8 0.0802 ± 0.0444 10−11 normal 1.43 × 106 ± 2.34 × 106 0.0487 ± 0.0257 IMF7 ALS 2.72 × 106 ± 3.52 × 106 10−9 0.076 ± 0.0454 0.0001 normal 1.19 × 106 ± 1.54 × 106 0.0527 ± 0.0322 IMF8 ALS 2.34 × 106 ± 3.56 × 106 10−9 0.0678 ± 0.0466 0.89 normal 1.05 × 106 ± 1.81 × 106 0.0772 ± 0.0635 Table 2 shows the discrimination ability of BAM and BFM features. Small probability values (p) of first four IMFs indicate that these features are significant (p < 0.05). The BAM and BFM features of normal EMG signals are higher as compared with ALS EMG signals. The higher BAM in normal EMG signals may be due to change of rate of amplitude and higher BFM in normal EMG signals may be due to change of rate of frequency. Kruskal–Wallis plots of BAM and BFM features are shown in Figs. 5a, b and 6a, b, respectively. Table 2. Probability value of the BAM and BFM features of the IMFs used in test Features BAM BFM IMF no. Signal type Mean ± std. Probability Mean ± std. Probability IMF1 ALS 0.057 ± 0.0077 4.26 × 10−7 1.33 × 103 ± 358.36 5.65 × 10−6 normal 0.0189 ± 0.0077 1.52 × 108 ± 349.71 IMF2 ALS 0.012 ± 0.0075 6.72 × 10−7 646.87 ± 213.77 3.962 × 10−3 normal 0.0144 ± 0.0073 705.85 ± 175.85 IMF3 ALS 0.0114 ± 0.0106 3.7 × 10−4 421.35 ± 204.83 1.36 × 10−3 normal 0.0112 ± 0.0069 438.57 ± 125.50 IMF4 ALS 0.0103 ± 0.0095 2.134 × 10−3 285.83 ± 151.95 0.018045 normal 0.0106 ± 0.0081 305.55 ± 96.56 IMF5 ALS 0.0102 ± 0.009 0.013758 226.08 ± 121.92 0.218188 normal 0.0091 ± 0.0067 215.66 ± 92.13 IMF6 ALS 0.0086 ± 0.0069 0.504716 167.54 ± 95.31 0.762604 normal 0.0087 ± 0.0074 158.77 ± 85.01 IMF7 ALS 0.0101 ± 0.0079 0.1444821 136.49 ± 76.29 0.752903 normal 0.0084 ± 0.0073 118.61 ± 69.42 IMF8 ALS 0.009 ± 0.0077 0.9181117 103.39 ± 62.45 0.1257617 normal 0.0109 ± 0.0073 120.03 ± 62.21 Table 3 shows the performance of MFDIF and SMPSD features. From the table, it is clear that all the IMFs of SMPSD feature are significant due to p < 0.05. Whereas IMF2–IMF7 of MFDIF feature are significant. From Table 3 and Figs. 7a, b and 8a, b, it can be noticed that the MFDIF and SMPSD features of ALS EMG signals are higher as compared with normal EMG signals. Larger values of MFDIF parameter might be due to higher rate of change of IF, whereas larger values of SMPSD parameter are due to greater power intensity of ALS EMG signals compared with normal EMG signals. Table 3. Probability value of the SMPSD and MFDIF features of the IMFs used in test Features SMPSD MFDIF IMF no. Signal type Mean ± std. Probability Mean ± std. Probability IMF1 ALS 6.71 × 108 ± 1.88 × 109 8.73 × 10−7 812.09 ± 328.52 0.4776 normal 1.14 × 108 ± 4.38 × 108 806.59 ± 274.54 IMF2 ALS 5.99 × 108 ± 1.24 × 109 1.15 × 10−5 390.65 ± 164.12 0.027433 normal 2.81 × 108 ± 8.19 × 108 346.59 ± 135.83 IMF3 ALS 2.22 × 108 ± 4.78 × 108 4 × 10−4 176.43 ± 105.58 5.20 × 10−6 normal 1.43 × 108 ± 2.98 × 108 130.33 ± 77.89 IMF4 ALS 4.24 × 108 ± 8.88 × 108 3.18 × 10−5 86.95 ± 68.08 1.00 × 10−4 normal 2.08 × 108 ± 4.71 × 108 59.79 ± 41.44 IMF5 ALS 3.71 × 108 ± 6.23 × 108 0.0132 48.01 ± 38.27 8.60 × 10−3 normal 2.42 × 108 ± 4.84 × 108 33.05 ± 20.89 IMF6 ALS 8.18 × 108 ± 2.08 × 109 3.49 × 10−6 30.54 ± 19.54 9.60 × 10−3 normal 5.6 × 108 ± 2.81 × 109 23.51 ± 14.61 IMF7 ALS 2.53 × 109 ± 8.27 × 109 3.89 × 10−06 206.99 ± 258.38 6.64 × 10−05 normal 5.68 × 109 ± 5.69 × 1010 136.4 ± 142.38 IMF8 ALS 5.06 × 1011 ± 4.65 × 1012 0.002 96.25 ± 83.36 0.4085285 normal 9.07 × 109 ± 6.42 × 1010 83.5 ± 89.09 The small p value of these features motivates us to use these features for automatic classification of ALS and normal EMG signals. These six features are simultaneously employed to LS-SVM classifier to for classification of ALS and normal EMG signals. The classification performance evaluated by each IMF to validate the proposed features for classification of ALS and normal EMG signals. The performance evaluation fidelity parameter sensitivity, specificity, and accuracy of each IMF are shown in Table 4. The classification accuracy for classification of ALS and normal EMG signals is 95% for second IMFs with polynomial kernel function. Table 4. Sensitivity, specificity and accuracy of IMFs with polynomial kernels of the LS-SVM classifier for classification between ALS and normal EMG signals IMF no. Sensitivity, % Specificity, % Accuracy, % IMF1 93.43 91.72 94.75 IMF2 93 92.54 95 IMF3 93.71 92.54 94.5 IMF4 93.4 91.93 94.85 IMF5 92.10 89.12 92 IMF6 95.5 85.53 90.5 IMF7 94.36 86.16 89.12 IMF8 91.38 89.12 88 4 Discussion For the first time, an EMD method with six analytic features and LS-SVM classifier have been proposed in this paper to classify the ALS and normal EMG signals. EMD decomposes any non-stationary signal into symmetric and band limited signals known as IMF. IMF components are converted into analytic signals which facilitate the computation of instantaneous amplitude and IF. The proposed features are extracted using instantaneous amplitude and IF which is possible due to EMD. Kruskal–Wallis test is used on all the features separately to find the discrimination ability of each feature. Kruskal–Wallis plot in Figs. 3-8 shows that the proposed features are significant for discrimination of ALS and normal EMG signals. Fig 3Open in figure viewerPowerPoint Comparison of area parameter for ALS and normal EMG signals a IMF1–IMF4 b IMF5–IMF8 Fig 4Open in figure viewerPowerPoint Comparison of IFn parameter for ALS and normal EMG signals a IMF1–IMF4 b IMF5–IMF8 Fig 5Open in figure viewerPowerPoint Comparison of BAM feature for ALS and normal EMG signals a IMF1–IMF4 b IMF5–IMF8 Fig 6Open in figure viewerPowerPoint Comparison of BFM feature for ALS and normal EMG signals a IMF1–IMF4 b IMF5–IMF8 Fig 7Open in figure viewerPowerPoint Comparison of SMPSD feature for ALS and normal EMG signals a IMF1–IMF4 b IMF5–IMF8 Fig 8Open in figure viewerPowerPoint Comparison of MFDIF feature for ALS and normal EMG signals a IMF1–IMF4 b IMF5–IMF8 The LS-SVM classifier with polynomial kernel has been used for the classification ALS and normal EMG signals with ten-fold cross-validation. LS-SVM classifier has nice properties that it has moderate complexity, algorithm implements in non-linear decision regions, adaptive implementation, and converges to minimum mean square error solutions. The capabilities of LS-SVM classifier are controlled by the choice of kernel function. This enables the proper solution of complex regression and pattern recognition problems. The main objective of this classifier to choose a suitable hyper plane that maximises the separation margin between it and nearest data points of each class. Table 5 shows the comparison of proposed method with another existing classification method in the same database. From the result of Table 5, it is clear that classification performance of proposed method is better than another shown method [10]. Table 5. A comparison of performance of the classification between ALS and normal EMG signals method studied on the same dataset Method Sensitivity, % Specificity, % Accuracy, % Doulah and Fattah [10] 76 98 92.5 proposed method 93 92.54 95 5 Conclusion The analytic features such as area, IFn, BAM, BFM, SMPSD and MFDIF features with LS-SVM classifier have been explored in this paper for classification of ALS and normal EMG signals. EMD is a promising method to decompose EMG signals into some set of IMFs. Analytic representation of IMFs has capability to compute these features. Discrimination performance of all the features has been evaluated using Kruskal-Wallis test and found significant for classification purpose. Initial four IMFs of all features provide satisfactory and significant discrimination between the ALS and normal EMG signals. The six features together with LS-SVM classifier have shown the 95% classification accuracy in second IMFs with polynomial kernel function. Proposed method can be helpful for the clinicians to take the decision about ALS disease. Future work includes the use of these features to classify other neural diseases (essential tremor, Tourette's syndrome, multiple sclerosis etc.). 6 References 1Yousefi, J., Hamilton-Wright, A.: ‘Characterizing EMG data using machine-learning tools’, Comput. Biol. Med., 2014, 51, pp. 1– 13 (https://doi/org/10.1016/j.compbiomed.2014.04.018) 2Tsai, A.C., Luh, J.J., Lin, T.T.: ‘A novel STFT-ranking feature of multi-channel EMG for motion pattern recognition’, Expert Syst. Appl., 2015, 42, (7), pp. 3327– 3341 (https://doi/org/10.1016/j.eswa.2014.11.044) 3Rosenfeld, J., Swash, M.: ‘Lumping or splitting ALS, PLS, PMA, and the other motor neuron diseases’, Neurology, 2006, 66, (5), pp. 624– 625 (https://doi/org/10.1212/01.wnl.0000205597.62054.db) 4Cristini, J.: ‘Misdiagnosis and missed diagnoses in patients with ALS’, J. Am. Acad. Physician Assistants, 2006, 19, (7), pp. 29– 35 (https://doi/org/10.1097/01720610-200607000-00006) 5Ko, K.D., El-Ghazawi, T., Kim, D. et. al.,: ‘ Predicting the severity of motor neuron disease progression using electronic health record data with a cloud computing Big Data approach’. IEEE Conf. Computational Intelligence in Bioinformatics and Computational Biology, May 2014, pp. 1– 6 6Kasi, P.K., Krivickas, L.S., Meister, M. et. al.,: ‘ Motor unit firing characteristics in patients with amyotrophic lateral sclerosis’. Annual Northeast IEEE Conf. on Bioengineering, June 2009, pp. 1– 2 7Fattah, S.A., Doulah, A.B.M.S.U., Jumana, M.A. et. al.,: ‘Evaluation of different time and frequency domain features of motor neuron and musculoskeletal diseases’, Int. J. Comput. Appl., 2012, 43, (23), pp. 34– 40 8Kasi, P.K., Krivickas, L.S., Melvin, M. et. al.,: ‘ Characterization of motor unit behavior in patients with amyotrophic lateral sclerosis’. Fourth Int. IEEE/EMBS Conf. on Neural Engineering, April 2009, pp. 10– 13 9Pal, P., Mohanty, N., Kushwaha, A. et. al.,: ‘ Feature extraction for evaluation of Muscular Atrophy’. IEEE Int. Conf. on Computational Intelligence and Computing Research, December 2010, pp. 1– 4 10Doulah, A.B.M.S.U., Fattah, S.A.: ‘ Neuromuscular disease classification based on mel frequency cepstrum of motor unit action potential’. Int. Conf. on Electrical Engineering and Information & Communication Technology (ICEEICT), April 2014, pp. 1– 4 11Fattah, S.A., Doulah, A.B.M.S.U., Iqbal, M.A. et. al.,: ‘ Identification of motor neuron disease using wavelet domain features extracted from EMG signal’. IEEE Int. Symp. on Circuits and Systems (ISCAS), May 2013, pp. 1308– 1311 12Fattah, S.A., Iqbal, Md.A., Jumana, M.A. et. al.,: ‘Identifying the motor neuron disease in EMG signal using time and frequency domain features with comparison’, Int. J. Signal Image Process., 2012, 3, (2), pp. 99– 114 (https://doi/org/10.5121/sipij.2012.3207) 13Doulah, A.B.M.S., Jumana, M.A.: ‘ ALS disease detection in EMG using time-frequency method’. Int. Conf. on Informatics, Electronics & Vision (ICIEV), May 2012, pp. 648– 651 14Pattichis, C.S., Elia, A.G.: ‘Autoregressive cepstral analyses of motor unit action potentials’, Med. Eng. Phys., 1999, 21, (6-7), pp. 405– 419 (https://doi/org/10.1016/S1350-4533(99)00072-7) 15Subasi, A.: ‘Classification of EMG signals using combined features and soft computing techniques’, Appl. Soft Comput., 2012, 12, (8), pp. 2188– 2198 (https://doi/org/10.1016/j.asoc.2012.03.035) 16Merlo, A., Farina, D.: ‘A fast and reliable technique for muscle activity detection from surface EMG signals’, IEEE Trans. Biomed. Eng., 2003, 50, (3), pp. 316– 323 (https://doi/org/10.1109/TBME.2003.808829) 17Katsis, C.D., Exarchos, T.P., Papaloukas, C. et. al.,: ‘A two-stage method for MUAP classification based on EMG decomposition’, Comput. Biol. Med., 2007, 37, (9), pp. 1232– 1240 (https://doi/org/10.1016/j.compbiomed.2006.11.010) 18Wang, G., Wang, Z., Chen, W. et. al.,: ‘Classification of surface EMG signals using optimal wavelet packet method based on Davies-Bouldin criterion’, Med. Biol. Eng. Comput., 2006, 44, (10), pp. 865– 872 (https://doi/org/10.1007/s11517-006-0100-y) 19Maitrot, A., Lucas, M.F., Doncarli, C. et. al.,: ‘Signal-dependent wavelets for electromyogram classification’, Med. Biol. Eng. Comput., 2005, 43, (4), p. 487 (https://doi/org/10.1007/BF02344730) 20Istenic, R., Kaplanis, P.A., Pattichis, C.S. et. al.,: ‘Multiscale entropy-based approach to automated surface EMG classification of neuromuscular disorders’, Med. Biol. Eng. Comput., 2010, 48, (8), pp. 773– 781 (https://doi/org/10.1007/s11517-010-0629-7) 21Gokgoz, E., Subasi, A.: ‘Effect of multiscale PCA de-noising on EMG signal classification for diagnosis of neuromuscular disorders’, J. Med. Syst., 2014, 38, (4), pp. 1– 10 (https://doi/org/10.1007/s10916-014-0031-3) 22Wang, G., Ren, O.: ‘Classification of surface electromyographic signals by means of multifractal singularity spectrum’, Med. Biol. Eng. Comput., 2013, 51, pp. 277– 284 (https://doi/org/10.1007/s11517-012-0990-9) 23Fusfeld, R.: ‘Classification of the electromyogram by a pattern-recognition method’, Med. Biol. Eng. Comput., 1982, 20, pp. 496– 500 (https://doi/org/10.1007/BF02442412) 24Nikolic, M.: ‘ Detailed analysis of clinical electromyography signals EMG decomposition, findings and firing pattern analysis in controls and patients with myopathy and amytrophic lateral sclerosis’. PhD Thesis, Faculty of Health Science, University of Copenhagen, August 2001 25Huang, N.E., Shen, Z., Long, S.R. et. al.,: ‘The empirical mode decomposition method and the Hilbert spectrum for non-stationary time series analysis’, Proc. R. Soc. Lond. A, 1998, 454, pp. 903– 995 (https://doi/org/10.1098/rspa.1998.0193) 26Flandrin, P., Rilling, G., Goncalvés, P.: ‘Empirical mode decomposition as a filter bank’, IEEE Signal Process. Lett., 2004, 11, (2), pp. 112– 114 (https://doi/org/10.1109/LSP.2003.821662) 27Bajaj, V., Pachori, R.B.: ‘Classification of seizure and nonseizure EEG signals using empirical mode decomposition’, IEEE Trans. Inf. Technol. Biomed., 2012, 16, (6), pp. 1135– 1142 (https://doi/org/10.1109/TITB.2011.2181403) 28Pachori, R.B., Bajaj, V.: ‘Analysis of normal and epileptic seizure EEG signals using empirical mode decomposition’, Comput. Methods Programs Biomed., 2011, 104, (3), pp. 373– 381 (https://doi/org/10.1016/j.cmpb.2011.03.009) 29Cohen, M.E., Hudson, D.L., Deedwania, P.: ‘Applying continuous chaotic modeling to cardiac signal analysis’, IEEE Eng. Med. Biol. Mag., 1996, 15, pp. 97– 102 (https://doi/org/10.1109/51.537065) 30Pachori, R.B., Hewson, D., Snoussi, H. et. al.,: ‘ Postural time-series analysis using empirical mode decomposition and second-order difference plots’. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, April 2009, pp. 537– 540 31Ohsawa, Y., Tsumoto, S.: ‘ Chance discoveries in real world decision making: data- based interaction of human intelligence and artificial intelligence’ ( Springer Science & Business Media, 2006), pp. 109– 110 32Cohen, L., Lee, C.: ‘ Instantaneous bandwidth for signals and spectrogram’, 1990, pp. 2450– 2454 33Zhou, S.M., Gan, J.Q., Sepulveda, F.: ‘Classifying mental tasks based on features of higher-order statistics from EEG signals in brain–computer interface’, Inf. Sci., 2008, 178, (6), pp. 1629– 1640 (https://doi/org/10.1016/j.ins.2007.11.012) 34Vapnik, V.: ‘ The nature of statistical learning theory’ ( Springer-Verlag, New York, 1995) 35Sukens, J.A.K., Vandewalle, J.: ‘Least squares support vector machine classifiers’, Neural Process. Lett., 1999, 9, (3), pp. 293– 300 (https://doi/org/10.1023/A:1018628609742) Citing Literature Volume10, Issue8November 2016Pages 963-971 FiguresReferencesRelatedInformation
Referência(s)