- Email: [email protected]
Decision support system for focal EEG signals using tunable-Q wavelet transform
Accepted Manuscript Title: Decision Support System for Focal EEG Signals using Tunable-Q Wavelet Transform Author: Rajeev Sharma Mohit Kumar Ram Bilas Pachori U. Rajendra Acharya PII: DOI: Reference:
S1877-7503(17)30343-5 http://dx.doi.org/doi:10.1016/j.jocs.2017.03.022 JOCS 644
To appear in: Received date: Revised date: Accepted date:
27-2-2017 17-3-2017 28-3-2017
Please cite this article as: Rajeev Sharma, Mohit Kumar, Ram Bilas Pachori, U. Rajendra Acharya, Decision Support System for Focal EEG Signals using TunableQ Wavelet Transform, (2017), http://dx.doi.org/10.1016/j.jocs.2017.03.022 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Highights: 1. Different nonlinear features are used in TQWT framework to identify focal and non-focal EEG signals.
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2. These features reveal the complexity present in various subbands of F and NF EEG signals.
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3. K-NN entropy is found most suitable feature for the classification of focal and non-focal EEG signals.
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4. We have achieved highest classification accuracy of 95 % with three entropy based features using LS-SVM classifier.
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5. Our proposed system can be used to locate the region of surgery in focal epileptic patients accurately.
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Decision Support System for Focal EEG Signals using Tunable-Q Wavelet Transform
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Rajeev Sharmaa , Mohit Kumara,, Ram Bilas Pachoria , U. Rajendra Acharyab,c,d a
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Discipline of Electrical Engineering, Indian Institute of Technology Indore, Indore, India 452553 b Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore 599489 c Department of Biomedical Engineering, School of Science and Technology, SIM University, Singapore. d Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Malaysia
Abstract
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In the present work, we have proposed an automated system to identify focal electroencephalogram (EEG) signals. The nonlinearity present in the focal (F) and non-focal (NF) EEG signals is quantified in tunable-Q wavelet transform (TQWT) framework. First, the EEG signals of both classes are decomposed into different subbands using TQWT. Different nonlinear features namely, K-nearest neighbour entropy estimator (KnnEnt), centered correntropy (CCorrEnt), and fuzzy entropy (FzEnt), bispectral entropies, permutation entropy (PmEnt), sample entropy (SmEnt), fractal dimension (FracDm) and largest Lyapunov exponent (LLE) are computed from these subbands. These features reveal the complexity present in various subbands of F and NF EEG signals. Our proposed method showed highest classification accuracy of 94.06% with least squares-support vector machine (LS-SVM) classifier using only KnnEnt features. The results of classification increased to 95.00% using three entropies (KnnEnt, CCorrEnt, and FaEnt) with LSSVM classifier. We have obtained the highest classification performance in the classification of F and NF classes which can be used to locate the region
Email addresses: [email protected] (Rajeev Sharma), [email protected] (Mohit Kumar ), [email protected] (Ram Bilas Pachori), [email protected] (U. Rajendra Acharya)
Preprint submitted to Elsevier
March 17, 2017
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of surgery in focal epileptic patients accurately.
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Keywords: Electroencephalogram; TQWT; Entropy; Ranking methods; LS-SVM.
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1. Introduction
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Electroencephalogram (EEG) is the record of the brain’s electrical activity, and can be used to characterise various pathological states related to brain such as epilepsy. The epilepsy is a disorder related to the nervous system, and characterized by seizures. The quality of patient’s life is affected due to epilepsy [1]. It can be categorised as generalized and focal (partial) epilepsy. The partial epilepsy affects limited part of the brain as compared to the generalized epilepsy. During the treatment of epilepsy, about one third of the patients show resistance to drug therapy [1]. Percentage of drug resistant epileptic patients is about 20% and 60% for generalized and partial epilepsy respectively [1]. Hence, the only choice the doctors have for the treatment of these patients is to remove the brain area involved in epileptic seizure surgically. Therefore, it is primary requirement to locate the affected brain area of the epileptic patients before undergoing the surgical treatment. The presurgical identification of affected brain area can be performed by analysing EEG signals with signal processing techniques. These techniques may be very useful to decipher the subtle variations in the characteristics of EEG signals and help to locate the epileptogenic focus. In [2], a correlation is found between delta asymmetries and side of focus in 17 epileptic subjects. In [3], it is observed that 15 out of 22 epileptic patients have asymmetries in the EEG background activity. Intracranial EEG signals of patients with neocortical seizures are studied in [4]. High-frequency oscillations are found to be useful for locating the epileptic seizure onset zone. The EEG recordings obtained in seizure-free intervals are analysed in order to study characteristics and nonlinear dynamics of the epileptogenic focus [5, 6, 7]. The focal (F) and non-focal (NF) EEG signals are the intracranial recordings obtained from the patients of partial epilepsy. The F EEG signals are the signals recorded from the electrodes where the changes related to the epilepsy were observed [8]. In literature, the epileptic EEG signals are analysed using various signal decomposition techniques such and empirical mode decomposition (EMD)and discrete wavelet transform (DWT). The DWT decomposes the EEG signals in to various subbands. These subbands and their coefficients are used to 2
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obtain various features to perform the classification of EEG signals related to epileptic activities [9, 10, 11]. In the similar way, different intrinsic mode functions (IMFs) obtained by applying EMD on EEG signals are used to extract different features for epileptic seizure classification [12, 13, 14, 15, 16]. The entropy features extracted from IMFs of EEG signals are used for the classification of the F and NF EEG signals [17, 18]. In a study [19], the features computed in EMD-DWT domain are effectively used for the discrimination and classification of F and NF EEG signals. The EMD method is data dependent technique which decomposes a signal by estimating the envelope from maxima and minima present in the signal. On the contrary, the DWT decomposes a signal in to subbands using a predefined basis function. The features extracted from DWT decomposition of the EEG signals are also found to be effective for the analysis of the F and NF EEG signals [20]. In [21], the time-frequency localized wavelets are used for the classification of F and NF EEG signals. In another study [22], time-frequency localized three-band biorthogonal wavelet filter bank is used to analyse epileptic EEG signals [22]. It is shown in the previous studies that the information related to the epileptic activity is distributed in different DWT subbands [23, 24]. The tunable-Q wavelet transform (TQWT) [25] is a form of DWT which enables to specify the quality-factor (Q). The number of subbands obtained using TQWT of a signal depends on the values of Q and redundancy (r). Therefore, it would be of interest to analyse the information of F EEG signals in the different TQWT subbands using the different nonlinear parameters. In this work, our objective is to develop an automated system based on TQWT which can identify F EEG signals. Various nonlinear features are effectively used to extract information from the EEG signals [11, 26, 20, 21]. Therefore, we have computed different nonlinear features in the TQWT framework for the automated detection of F EEG signals. The performance of the extracted features are evaluated by performing different experiments. The different ranking methods are used with least squares-support vector machine (LS-SVM) classifier [27] to find the most suitable combination of the features. Flow chart of the proposed work can be seen in Figure 1. This paper is divided in to five sections including the introduction. In the second section, a brief description of dataset used, TQWT, various entropies and classification method is provided. Obtained results are presented in the third section. Discussion and comparison with the existing work is given in the fourth section. Fifth section provides the conclusion of this work.
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Signal decomposition into subbands using TQWT
Computation of nonlinear features from the subbands
Ranking of features
Classification using LS-SVM
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Computation of difference (x-y) time series
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Figure 1: Flow chart of the proposed work.
2. Methodology
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2.1. Dataset used in the present work In the present work, we have used public database (www.dtic.upf.edu/ ~ralph/sc/) which consists of multichannel intracranial EEG recordings of five patients. The dataset was recorded at the Department of Neurology, University of Bern, Switzerland. These patients were suffering from pharamcoresistant temporal lobe epilepsy, and were admitted for surgical operation. Each EEG signal consists of 10240 samples with sampling frequency of 512 Hz and is filtered using bandpass filter of frequency range from 0.5 to 150 Hz. Recordings corresponding to the seizure activities and three hours after the last seizure were not included in the dataset [8]. There are 3750 files belonging to each class (F and NF) in the dataset. Each file includes a pair of EEG signals represented by x and y, respectively. Each signal pair is collected by randomly selecting one patient out of five patients. The information related to patient, channel and window is not included in the dataset. In this work, we have used the entire dataset. More details of the dataset is provided in [8]. Typical plots of both the time-series (x and y) obtained from F and NF EEG signals are depicted in Figure 2 and 3 respectively. The x and y time series shown in these figures are recorded simultaneously from adjacent channels.
2.2. Signal decomposition using TQWT The TQWT is a technique in which transients and oscillations of a signal can be analysed by adjusting the quality Q and r [25]. The lower value of Q is suitable for transient analysis and higher value can be used for the analysis of oscillatory behaviour of the signal. As the value of Q increases, frequency responses of subbands become narrower [25, 28, 29]. On the other hand, localization of wavelet in time-domain can be controlled by parameter r [25, 28]. The TQWT method can be realized by using two channel filter 4
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bank, iteratively [25]. In order to decompose F and NF EEG signals using TWQT, we need to specify Q, r and J parameters. The parameter J is used to determine the number of decomposition levels. The selection procedure of these parameters is provided in the Results section. There are several advantages in performing signal decomposition using TQWT. The EEG signals are usually analysed using different rhythms which have specific ranges of frequencies [30]. The TQWT decomposes a signal into several bands of different frequency ranges. The width of each pass band depends on the values of Q for a fixed value of r [25, 28]. By changing different values of Q, the bandwidth of the passband can be varied. By changing the value of Q, the shape of the wavelet gets changed. This is not possible with DWT. The shape of wavelet can be tuned according to the characteristic wave pattern of the signal by varying the value of Q. Another advantage of TQWT is that wavelet is well localized in time domain due to its over-sampled filter-bank property. Recently, TQWT has been applied to segment the cardiac sound signals [31], to classify the heart sound signals [32], to perform the diagnosis of septal defects [28], detection of abnormal electromayogram (EMG) signals [33], and for the detection of coronary artery disease (CAD) [34].
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2.3. Feature extraction The extraction of features is an important step in the identification of characteristic patterns present in the signals. In this work, we have used K-nearest neighbor entropy estimate (KnnEnt) [35], centered correntropy (CCorrEnt) [36, 37], sample entropy (SmEnt) [38], fuzzy entropy (FzEnt) [39], permutation entropy (PmEnt) [40], bispectral entropies [41], fractal dimension (FracDm) [42] and largest Lyapunove exponent (LLE) [43] features with LS-SVM for automatic separation of F and NF classes. These features are summarised as follows: KnnEnt is a measure of scattering of the signal [44]. Its computation is based on the distances of a sample from its k nearest neighbours [35, 44]. The value of k = 1, 3, 5, 7 are considered in the experiment. In our work, maximum classification accuracy is obtained for k = 7. Correntropy quantifies the information related to both distribution and time structure present in the signal [45]. The CCorrEnt [36, 37] is selected as a feature in our work. We have used Gaussian kernel to compute the CCorrEnt, and used ITL toolbox (www.sohanseth.com/Home/codes). SmEnt [38] measures the complexity of time series. The experiments are performed for m = 2, 3, 4, 5, 7
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and maximum classification accuracy is achieved for m = 5 and tolerance parameter rt = 0.2. FzEnt measures the similarity in the time series based on fuzzy function [39]. In the present work, the values of gradient and width of the fuzzy function are selected as 2 and 0.2 respectively. The most suitable value of template length m is selected by computing classification accuracies for m = 2, 3, 4, 5. In our work, m = 2 gave the highest classification accuracy. The PmEnt estimates the complexity of the EEG signal based on the comparison of permutation patterns [40]. In this work, the value of embedding dimension of 3 and time lag of 1 is considered for the computation of PmEnt from the subbands [46, 26]. In this work, two bispectral entropies namely BEnt1 and BEnt2 are computed from the third order moments [41, 26]. The BEnt1 and BEnt2 represent the normalized bispectral entropy and normalised squared bispectral entropy [41, 26]. These entropies can quantify the interaction among its different frequency components [26]. The FracDm is used to detect transient features in the signal waveform including EEG [42]. The Higuchi’s FracDm algorithm [47] is used in this work to compute the FracDm in the subbands of EEG signals. LLE [43] is used in this work to measure the complexity in the time series. We have used the method presented in [43] to estimate the LLE in this work.
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In the present work, we have used LS-SVM classifier [27]. It maps the input vectors to higher dimensional space. Then, a hyperplane is obtained in this space to separate the different classes of data [27]. To map the data into the higher dimension, concept of kernels are utilized in SVM [27]. In this study, radial basis function (RBF), linear, and polynomial of order 2 (Poly2), order 3 (Poly3), order 4 (Poly4) [48] kernels are used. The performance of the classifier is measured in terms of accuracy (ACC), sensitivity (SEN), and specificity (SPE) [49]. 4. Results
Each signal of the dataset used in this work consists of two different time series namely x and y. In the first step, the difference (x-y) time series is computed. The features can be computed from x and y time series and can be used for classification. The difference (x − y) signals have been used effectively in [19]. Hence, 3750 pairs of x and y time series yielded 3750 8
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difference time series for each class. Plots of difference time series for F and NF classes are presented in Figure 4(a) and 4(b). Thereafter, the differ-
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ence time-series are subjected to TQWT method, resulting in coefficients at different subbands. Individual TQWT subband is reconstructed in the timedomain using inverse TQWT. The different nonlinear features are computed from these obtained subband signals. The optimum values of Q and J are determined by performing several experiments. In all the experiments, the value of r is fixed to 3 which is the minimum value suggested in [50]. The 9
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ACC (%) obtained using LS-SVM classifier 86.80 84.65 86.17 70.65 70.38 84.14 82.37 51.93 85.28
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Feature KnnEnt FzEnt CCorrEnt BEnt1 BEnt2 PmEnt SmEnt LLE FracDm
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Table 1: Classification accuracy obtained for different features computed from different TQWT subbands (Q = 1 and J = 5).
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higher values of r lead to increased overlapping in the adjacent frequency response. The minimum value of Q to be chosen is 1 [50]. Therefore, we started our experiment by decomposing F and NF EEG signals with Q = 1 and J = 5. Then, various nonlinear features are computed from the obtained subbands. The features are normalized before feeding to the LS-SVM classifier. The ten-fold cross-validation [51] procedure is employed during training and testing of the classifier. The RBF kernel is used with LSSVM classifier to determine best set of features by performing classification experiments. The RBF kernel parameter (σ), is varied from 0.1 to 2.5 in steps of 0.2. The maximum classification accuracy obtained for different features is listed in Table 1. It can be observed from this table that maximum classification accuracy is obtained using KnnEnt feature. Hence, it is the most suitable feature for the classification of F and NF classes.
4.1. Selection of TQWT parameters The selection of optimum value of Q and J is an important step in signal decomposition. To select the optimum value of Q and J, we performed further experiments by taking only KnnEnt features as it yielded highest classification accuracy. Further, the values of J is varied by keeping Q = 1. We noted that the maximum possible value of J with Q = 1 is 18. Hence, the value of J is varied from 5 to 18 and KnnEnt is computed for each subband. Then, the classification is performed by varying value of J from 5 10
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Figure 5: Plot classification accuracies versus J for KnnEnt feature with LS-SVM classifier.
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to 18. The classification accuracy versus values of J is plotted in Figure 5. It can be noticed that at J = 15 maximum classification accuracy is obtained. Therefore, J = 15 is chosen for further experiments. After selecting J = 15, the value of Q is varied in steps from 1 to 10 and KnnEnt is computed from 16 subbands for each value of Q. The plot of classification accuracies for various values of Q is depicted in Figure 6. The figure shows that highest classification accuracy is obtained for Q = 3. The classification accuracy decreases gradually with increasing Q value. Finally, the difference EEG signals are decomposed up to 15-th level of decomposition. Plots of these subband (Subband1 to Subband16 ) signals are shown in Figures 7 and 8 for F and NF classes respectively. The subscript in Subband1 denotes the first level of subband. The subbands from Subband1 to Subband16 are in decreasing order of frequencies. First 15 subbands are reconstructed from the detail coefficients, and 16th subband is reconstructed from the approximate coefficients. In this way, the values of Q and J are selected for further experiments.
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4.2. Selection of optimal number of features The different features are computed from all subbands and experiments are performed to find the best combination of features using various ranking methods and LS-SVM classifier. Further, with Q = 3 and J = 15 different nonlinear features are computed from each decomposed subband. All the computed features are combined to form a feature set of size 7500 × 144. The typical range of different features obtained from Subband16 are shown in Table 2. The Kruskal-Wallis statistical test [52, 53] is applied to examine 13
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Range (Mean ± standard deviation ) F NF -20.3732± 4.7826 -21.8131± 5.7226 0.1873± 0.0102 0.1828 ± 0.0178 0.2720± 0.0963 0.3069± 0.1258 0.2772± 0.0527 0.2982± 0.0594 0.1139± 0.0607 0.1264± 0.0680 0.8433± 0.0141 0.8590± 0.0146 0.1507± 0.0336 0.1760± 0.0425 1.0187± 0.0041 1.0197± 0.0039 14.1376± 1.3128 14.17± 1.3140
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Table 2: Range (mean ± standard deviation) and p-value of different features obtained from Subband16 .
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the discrimination ability of various features. This test results in the p-value. The p-values are also depicted in Table 2 for different nonlinear features obtained from Subband16 . Apart from LLE, all other features are found to be significant with less p-values (p < 0.05) indicating their suitability for good discrimination of F and NF EEG signals. Further, the features are ranked using Wilcoxon, Student’s t-test, entropy, Battacharyya, and receiver operating characteristics (ROC) [54, 55] methods. Then, the ranked features are applied to LS-SVM classifier. The optimal number of features for classification are found by performing the experiments with different number of ranked features. The plot of classification accuracy versus number of ranked features is shown in Figure 9. It shows that the maximum classification accuracy (93.59%) is obtained with first 52 features using entropy ranking method.
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4.3. Experiments with most suitable entropies It can be noticed from Figure 6, that maximum classification accuracy for KnnEnt is 94.06%. On the other hand, 52 ranked features resulted in classification accuracy of 93.59% which is lower than the classification accuracy obtained with KnnEnt. Therefore, classification is again performed using different nonlinear features. The maximum classification accuracies corresponding to different features computed from TQWT subbands are listed in Table 3 for Q = 3 and J = 15 parameters. It can be observed from Table 3 that KnnEnt provided maximum classification accuracy. The features are ranked in the descending order based on the classification accuracies. Then, the features are appended one by one starting from highly ranked feature to perform the classification. The resulting classification accuracies are listed in Table 4. It can be observed that the maximum classification accuracy of 94.92% is obtained when KnnEnt, CCorrEnt, and FzEnt used together (Table 4). The feature set using these three entropy features computed from 16 subbands is of size 7500 × 48. Further, these 48 features are ranked using different ranking methods. The plot of classification accuracy versus number of ranked features is shown in Figure 10. The classification accuracy increased to 95% using 47 features ranked with ROC method. There is no improvement observed in the maxuimum classification accuracy for the features ranked using other ranking methods. The classification performance is 15
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also compared for other kernels like linear and polynomial kernels with order 2, 3, 4 with RBF kernel. The classification results obtained using different kernels of the classifier using 48 features are listed in Table 5. The ROC curves and area under curve (AUC) [48, 49] for different kernels are shown in Figure 11. The performance of the polynomial kernel with order 2 produced results better than other polynomial and linear kernels. However, the RBF kernel produced the best results with AUC = 0.9883.
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Table 3: Classification accuracy corresponding to each entropy using LS-SVM classifier (Q = 3, J = 15).
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Table 4: Classification accuracy obtained using different entropy combinations wth LSSVM (Q = 3, J = 15).
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KnnEnt KnnEnt, CCorrEnt KnnEnt, CCorrEnt, FzEnt KnnEnt, CCorrEnt, FzEnt, PmEnt KnnEnt, CCorrEnt, FzEnt, PmEnt, SmEnt KnnEnt, CCorrEnt, FzEnt, PmEnt, SmEnt, BEnt1 KnnEnt, CCorrEnt, FzEnt, PmEnt, SmEnt, BEnt1, BEnt2 KnnEnt, CCorrEnt, FzEnt, PmEnt, SmEnt, BEnt1, BEnt2, FracDm KnnEnt, CCorrEnt, FzEnt, PmEnt, SmEnt BEnt1, BEnt2, FracDm, LLE
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Figure 10: Variation in classification accuracy with different number of ranked features.
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Poly 2 AUC=0.9694 Poly 3 AUC=0.7665 Poly 4 AUC=0.7875 Linear AUC=0.9054 RBF AUC=0.9883 0.8 0.9 1
Figure 11: Receiver operating characteristic curves for LS-SVM classifier obtained using first three features.
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ACC (%) 82.09 91.59 74.55 77.45 94.92
SPE (%) 81.17 90.83 76.64 76.64 93.47
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SEN (%) 83.01 92.35 72.45 78.27 96.37
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Kernel parameter order 1 order 2 order 3 order 4 σ = 1.1
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Table 5: Results obtained using different kernels with 48 features as input to the LS-SVM classifier.
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The EEG signals exhibit nonlinear and nonstationary nature [56, 57]. In this work, F and NF EEG signals are analysed using nonlinear entropy features coupled with TQWT method. DWT is frequently used to handle the nonstationarity present in the signal. The TQWT is a modification of DWT with tunable-Q factor. Although, TQWT is essentially a constant Q transform, it facilitates to select the Q value of the wavelet before the signal decomposition. It decomposes the EEG signal in to transient and oscillating components based on the value of Q. In our present work, we have computed nonlinear features namely KnnEnt, CCorrEnt, FzEnt, BEnt1, BEnt2, PmEnt, SmEnt, FracDm, and LLE for difference (x-y) time series of the entire dataset. These features are computed in TQWT domain. The different nonlinear features are extracted from these subbands to quantify the nonlinearity present in the subbands. Entropy is a nonlinear measure which quantifies the degree of complexity present in the signal. Usually, the higher value of entropy is an indication of less regularity and high uncertainty in the signal [26]. Different entropies evaluate the complexities present in the signals in various ways. In Table 2, the range of values for various features in the Subband16 are presented. This subband is reconstructed from approximate coefficients of EEG signals (Q = 3 and J = 15). The KnnEnt is basically developed based on the distance of sample from its k-nearest neighbors. Consequently, it is a measure of the degree to which data points are scattered. The value of KnnEnt is less for NF EEG signals signifying that these signals has lesser scattering. The SmEnt and FzEnt quantify the complexity of the signals. These features have lower values for F EEG signals as compared to NF EEG signals indicating reduced complexity of F
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EEG signals as compared to NF EEG signals. The complexity of the signal in bispectral domain can be analysed using BEnt1 and BEnt2. The value of these entropies are also less for F EEG signal as compared to NF EEG signals indicating reduced complexity of F class of EEG signals. Several methods are reported in the literature to analyze the F and NF EEG time series. They are briefly presented in Table 6. In [58], delay permutation entropy (DPE) is computed from 50 EEG signals (F and NF). Further, computed values of DPE with SVM has provided the classification accuracy of 84%. In [17], a methodology is proposed to discriminate F and NF EEG signals by computing sample entropies and average variance of instantaneous frequencies (AVIFs) from IMFs in EMD domain. The accuracy of 85% is obtained for 50 pairs of EEG time series with LS-SVM. In [18], six different entropies are extracted from different IMFs of two classes. These entropies with LS-SVM are used to distinguish the two classes. The methodology is able to discriminate the two classes with an accuracy of 87%. In [20], EEG signals of two classes are decomposed up to sixth level using DWT, and seven different entropies are computed from each level of decomposed signals. Highest classification accuracy of 84% is observed with LS-SVM classifier. In a recent study [59], the classification of F and NF EEG signals is performed using empirical wavelet transform. The area measure computed form the reconstructed phase space (RPS) plots of EEG rhythms is used as the feature for classification. They obtained classification accuracy of 90% for 50 pairs of F and NF EEG signals. In [19], difference (x-y) time series of entire dataset are used to classify F and NF EEG signals. Renyi, log-energy, and Shannon entropies are computed in EMD, DWT and EMD-DWT domains. Discrimination ability of features are found to be higher in EMD-DWT domain with classification accuracy of 89.4% using K-nearest neighbour (K-NN) classifier. Recently, in [21], orthogonal wavelet filter banks are used to decompose the F and NF EEG signals. These filters are well localized in time-frequency domain. Different entropies computed from the wavelet coefficients with LS-SVM achieved the classification accuracy of 94.25% using the entire database. In [60], multivariate subband fuzzy entropy is computed from the TQWT subbands of F and NF EEG signals. They used the entire database and obtained 84.67% classification accuracy. The advantage of our proposed methodology is the improved classification accuracy over other compared methods. The TFCV procedure is employed to compute the performance of the classifier. The limitation of our method is 20
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Table 6: Summary of automated classification of F and NF classes using same database.
Sharma et al. (2014) [18] Sharma et al. (2015) [20] Das et al. (2016) [19] Bhattacharyya et al. (2016) [59] Sharma et al. (2016) [21] Bhattacharyya et al. (2017) [60]
DPE
F and NF classes
feature
SVM
50 signals of
EMD
F and NF classes
Average sample entropy and AVIFs
50 signals of
EMD
F and NF classes
6 different entropies
50 signals of
DWT
F and NF classes
7 different entropies
LS-SVM LS-SVM
EMD, DWT
Entire dataset
3 non-linear features
50 signals of
EWT
F and NF classes
area computed from RPS of rhythms
Time-frequency localized orthogonal filter bank
Entire dataset
7 different entropies
3750 signals of
TQWT
F and NF classes
Multivariate fuzzy entropy TQWT
Entire dataset
3 entropies
LS-SVM
ACC
validation
%
No
84%
10-fold
85%
10-fold
87%
10-fold
84%
K-NN
No
89.4%
LS-SVM
10-fold
90%
LS-SVM
10-fold
94.25%
LS-SVM
10-fold
84.67%
LS-SVM
10-fold
95.00%
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50 signals of
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that only five patients are used in this study. In future, we plan to continue this work using more number of patients and can evaluate the patient specific performance strategy.
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6. Conclusion
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In the present work, an automatic diagnostic system for the identification of F EEG signals is proposed using TQWT decomposition method. In this study, difference (x-y) time series are subjected to the TQWT decomposition method. Difference (x-y) EEG time series of F and NF classes are decomposed into subbands and various nonlinear features namely KnnEnt, CCorrEnt, FzEnt, PmEnt, SmEnt, BEnt1, BEnt2, FracDm, and LLE are computed from these subbands. A strategy is presented to select the optimum values for Q, r, and J parameters to get highest classification accuracy. We have achieved best performance with Q = 3, r = 3, and J = 15 in this work. Among the extracted features, KnnEnt is found to be most suitable in the classification of F and NF EEG signals. The combination of KnnEnt, CCorrEnt, and FzEnt achieved the highest classification accuracy of 95%. In future, we intend to test our algorithm using more F and NF patients. Also, our method can be used to identify other neural diseases like autism, epilepsy, dementia, and alcoholism.
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Rajeev Sharma
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Rajeev Sharma received Ph.D. degree in the Electrical Engineering from Indian Institute of Technology Indore, Indore, India in 2017. He has completed B.E. degree in Electronics and Communication Engineering from Rajiv Gandhi Technological University, Bhopal, India in 2009, and M.Tech. degree in Electronic Instrumentation from National Institute of Technology, Warangal, India in 2011. His research interest is Signal Processing and its application in Biomedical signals like EEG and ECG, pattern recognition, and machine learning etc.
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Mohit Kumar
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Mohit Kumar received B. Tech. degree in Electronics and Communication Engineering from Uttar Pradesh Technical University, Lucknow, India in 2009, and M. E. degree in Electronic Instrumentation and Control Engineering from Thapar University, Patiala, India in 2013. He is currently working towards the Ph.D. degree in Electrical Engineering at the Indian Institute of Technology, Indore, India. His research interest is Signal Processing and its application in Biomedical signals.
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Ram Bilas Pachori
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Ram Bilas Pachori received the BE degree with honors in Electronics and Communication Engineering from Rajiv Gandhi Technological University, Bhopal, India in 2001, the M. Tech and PhD degrees in Electrical Engineering from Indian Institute of Technology Kanpur, Kanpur, India in 2003 and 2008 respectively. He worked as a Postdoctoral Fellow at Charles Delaunay Institute, University of Technology of Troyes, Troyes, France during 2007-08. He served as an Assistant Professor at Communication Research Center, International Institute of Information Technology, Hyderabad, India during 2008-09. He served as an Assistant Professor at Discipline of Electrical Engineering, School of Engineering, Indian Institute of Technology Indore, Indore, India during 2009- 13, where presently he has been working as an Associate Professor since 2013. He worked as a Visiting Scholar at Intelligent Systems Research Center, Ulster University, Northern Ireland, UK during December 2014. His research interests are in the areas of biomedical signal processing, non-stationary signal processing, speech signal processing, signal processing for communications, computer aided medical diagnosis, and signal processing applications. U. R. Acharya
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U. R. Acharya, PhD, DEng is a senior faculty member at Ngee Ann Polytechnic, Singapore. He is also (i) Adjunct Professor at University of Malaya, Malaysia, (ii) Adjunct Faculty at Singapore Institute of Technology- University of Glasgow, Singapore, and (iii) Associate faculty at SIM University, Singapore. He received his Ph.D. from National Institute of Technology Karnataka (Surathkal, India) and DEng from Chiba University (Japan). He has published more than 400 papers, in refereed international SCI-IF journals(345), international conference proceedings (42), books (17) with more than 11,500 citations in Google Scholar (with h-index of 55), and ResearchGate RG Score of 45.00. He is ranked in the top 1% of the Highly Cited Researchers (2016) in Computer Science according to the Essential Science Indicators of Thomson. He has worked on various funded projects, with grants worth more than 2 million SGD. He has three patents and in the editorial board of many journals. He has served as guest editor for many journals. His major academic interests are in biomedical signal processing, biomedical imaging, data mining, visualization and biophysics for better healthcare design, delivery and therapy. Please visit https://scholar.google.com.sg/ citations?user=8FjY99sAAAAJ&hl=enformoredetails.
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S1877-7503(17)30343-5 http://dx.doi.org/doi:10.1016/j.jocs.2017.03.022 JOCS 644
To appear in: Received date: Revised date: Accepted date:
27-2-2017 17-3-2017 28-3-2017
Please cite this article as: Rajeev Sharma, Mohit Kumar, Ram Bilas Pachori, U. Rajendra Acharya, Decision Support System for Focal EEG Signals using TunableQ Wavelet Transform, (2017), http://dx.doi.org/10.1016/j.jocs.2017.03.022 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Highights: 1. Different nonlinear features are used in TQWT framework to identify focal and non-focal EEG signals.
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2. These features reveal the complexity present in various subbands of F and NF EEG signals.
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3. K-NN entropy is found most suitable feature for the classification of focal and non-focal EEG signals.
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4. We have achieved highest classification accuracy of 95 % with three entropy based features using LS-SVM classifier.
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5. Our proposed system can be used to locate the region of surgery in focal epileptic patients accurately.
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Decision Support System for Focal EEG Signals using Tunable-Q Wavelet Transform
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Rajeev Sharmaa , Mohit Kumara,, Ram Bilas Pachoria , U. Rajendra Acharyab,c,d a
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Discipline of Electrical Engineering, Indian Institute of Technology Indore, Indore, India 452553 b Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore 599489 c Department of Biomedical Engineering, School of Science and Technology, SIM University, Singapore. d Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Malaysia
Abstract
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In the present work, we have proposed an automated system to identify focal electroencephalogram (EEG) signals. The nonlinearity present in the focal (F) and non-focal (NF) EEG signals is quantified in tunable-Q wavelet transform (TQWT) framework. First, the EEG signals of both classes are decomposed into different subbands using TQWT. Different nonlinear features namely, K-nearest neighbour entropy estimator (KnnEnt), centered correntropy (CCorrEnt), and fuzzy entropy (FzEnt), bispectral entropies, permutation entropy (PmEnt), sample entropy (SmEnt), fractal dimension (FracDm) and largest Lyapunov exponent (LLE) are computed from these subbands. These features reveal the complexity present in various subbands of F and NF EEG signals. Our proposed method showed highest classification accuracy of 94.06% with least squares-support vector machine (LS-SVM) classifier using only KnnEnt features. The results of classification increased to 95.00% using three entropies (KnnEnt, CCorrEnt, and FaEnt) with LSSVM classifier. We have obtained the highest classification performance in the classification of F and NF classes which can be used to locate the region
Email addresses: [email protected] (Rajeev Sharma), [email protected] (Mohit Kumar ), [email protected] (Ram Bilas Pachori), [email protected] (U. Rajendra Acharya)
Preprint submitted to Elsevier
March 17, 2017
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of surgery in focal epileptic patients accurately.
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Keywords: Electroencephalogram; TQWT; Entropy; Ranking methods; LS-SVM.
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1. Introduction
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Electroencephalogram (EEG) is the record of the brain’s electrical activity, and can be used to characterise various pathological states related to brain such as epilepsy. The epilepsy is a disorder related to the nervous system, and characterized by seizures. The quality of patient’s life is affected due to epilepsy [1]. It can be categorised as generalized and focal (partial) epilepsy. The partial epilepsy affects limited part of the brain as compared to the generalized epilepsy. During the treatment of epilepsy, about one third of the patients show resistance to drug therapy [1]. Percentage of drug resistant epileptic patients is about 20% and 60% for generalized and partial epilepsy respectively [1]. Hence, the only choice the doctors have for the treatment of these patients is to remove the brain area involved in epileptic seizure surgically. Therefore, it is primary requirement to locate the affected brain area of the epileptic patients before undergoing the surgical treatment. The presurgical identification of affected brain area can be performed by analysing EEG signals with signal processing techniques. These techniques may be very useful to decipher the subtle variations in the characteristics of EEG signals and help to locate the epileptogenic focus. In [2], a correlation is found between delta asymmetries and side of focus in 17 epileptic subjects. In [3], it is observed that 15 out of 22 epileptic patients have asymmetries in the EEG background activity. Intracranial EEG signals of patients with neocortical seizures are studied in [4]. High-frequency oscillations are found to be useful for locating the epileptic seizure onset zone. The EEG recordings obtained in seizure-free intervals are analysed in order to study characteristics and nonlinear dynamics of the epileptogenic focus [5, 6, 7]. The focal (F) and non-focal (NF) EEG signals are the intracranial recordings obtained from the patients of partial epilepsy. The F EEG signals are the signals recorded from the electrodes where the changes related to the epilepsy were observed [8]. In literature, the epileptic EEG signals are analysed using various signal decomposition techniques such and empirical mode decomposition (EMD)and discrete wavelet transform (DWT). The DWT decomposes the EEG signals in to various subbands. These subbands and their coefficients are used to 2
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obtain various features to perform the classification of EEG signals related to epileptic activities [9, 10, 11]. In the similar way, different intrinsic mode functions (IMFs) obtained by applying EMD on EEG signals are used to extract different features for epileptic seizure classification [12, 13, 14, 15, 16]. The entropy features extracted from IMFs of EEG signals are used for the classification of the F and NF EEG signals [17, 18]. In a study [19], the features computed in EMD-DWT domain are effectively used for the discrimination and classification of F and NF EEG signals. The EMD method is data dependent technique which decomposes a signal by estimating the envelope from maxima and minima present in the signal. On the contrary, the DWT decomposes a signal in to subbands using a predefined basis function. The features extracted from DWT decomposition of the EEG signals are also found to be effective for the analysis of the F and NF EEG signals [20]. In [21], the time-frequency localized wavelets are used for the classification of F and NF EEG signals. In another study [22], time-frequency localized three-band biorthogonal wavelet filter bank is used to analyse epileptic EEG signals [22]. It is shown in the previous studies that the information related to the epileptic activity is distributed in different DWT subbands [23, 24]. The tunable-Q wavelet transform (TQWT) [25] is a form of DWT which enables to specify the quality-factor (Q). The number of subbands obtained using TQWT of a signal depends on the values of Q and redundancy (r). Therefore, it would be of interest to analyse the information of F EEG signals in the different TQWT subbands using the different nonlinear parameters. In this work, our objective is to develop an automated system based on TQWT which can identify F EEG signals. Various nonlinear features are effectively used to extract information from the EEG signals [11, 26, 20, 21]. Therefore, we have computed different nonlinear features in the TQWT framework for the automated detection of F EEG signals. The performance of the extracted features are evaluated by performing different experiments. The different ranking methods are used with least squares-support vector machine (LS-SVM) classifier [27] to find the most suitable combination of the features. Flow chart of the proposed work can be seen in Figure 1. This paper is divided in to five sections including the introduction. In the second section, a brief description of dataset used, TQWT, various entropies and classification method is provided. Obtained results are presented in the third section. Discussion and comparison with the existing work is given in the fourth section. Fifth section provides the conclusion of this work.
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2. Methodology
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2.1. Dataset used in the present work In the present work, we have used public database (www.dtic.upf.edu/ ~ralph/sc/) which consists of multichannel intracranial EEG recordings of five patients. The dataset was recorded at the Department of Neurology, University of Bern, Switzerland. These patients were suffering from pharamcoresistant temporal lobe epilepsy, and were admitted for surgical operation. Each EEG signal consists of 10240 samples with sampling frequency of 512 Hz and is filtered using bandpass filter of frequency range from 0.5 to 150 Hz. Recordings corresponding to the seizure activities and three hours after the last seizure were not included in the dataset [8]. There are 3750 files belonging to each class (F and NF) in the dataset. Each file includes a pair of EEG signals represented by x and y, respectively. Each signal pair is collected by randomly selecting one patient out of five patients. The information related to patient, channel and window is not included in the dataset. In this work, we have used the entire dataset. More details of the dataset is provided in [8]. Typical plots of both the time-series (x and y) obtained from F and NF EEG signals are depicted in Figure 2 and 3 respectively. The x and y time series shown in these figures are recorded simultaneously from adjacent channels.
2.2. Signal decomposition using TQWT The TQWT is a technique in which transients and oscillations of a signal can be analysed by adjusting the quality Q and r [25]. The lower value of Q is suitable for transient analysis and higher value can be used for the analysis of oscillatory behaviour of the signal. As the value of Q increases, frequency responses of subbands become narrower [25, 28, 29]. On the other hand, localization of wavelet in time-domain can be controlled by parameter r [25, 28]. The TQWT method can be realized by using two channel filter 4
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bank, iteratively [25]. In order to decompose F and NF EEG signals using TWQT, we need to specify Q, r and J parameters. The parameter J is used to determine the number of decomposition levels. The selection procedure of these parameters is provided in the Results section. There are several advantages in performing signal decomposition using TQWT. The EEG signals are usually analysed using different rhythms which have specific ranges of frequencies [30]. The TQWT decomposes a signal into several bands of different frequency ranges. The width of each pass band depends on the values of Q for a fixed value of r [25, 28]. By changing different values of Q, the bandwidth of the passband can be varied. By changing the value of Q, the shape of the wavelet gets changed. This is not possible with DWT. The shape of wavelet can be tuned according to the characteristic wave pattern of the signal by varying the value of Q. Another advantage of TQWT is that wavelet is well localized in time domain due to its over-sampled filter-bank property. Recently, TQWT has been applied to segment the cardiac sound signals [31], to classify the heart sound signals [32], to perform the diagnosis of septal defects [28], detection of abnormal electromayogram (EMG) signals [33], and for the detection of coronary artery disease (CAD) [34].
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2.3. Feature extraction The extraction of features is an important step in the identification of characteristic patterns present in the signals. In this work, we have used K-nearest neighbor entropy estimate (KnnEnt) [35], centered correntropy (CCorrEnt) [36, 37], sample entropy (SmEnt) [38], fuzzy entropy (FzEnt) [39], permutation entropy (PmEnt) [40], bispectral entropies [41], fractal dimension (FracDm) [42] and largest Lyapunove exponent (LLE) [43] features with LS-SVM for automatic separation of F and NF classes. These features are summarised as follows: KnnEnt is a measure of scattering of the signal [44]. Its computation is based on the distances of a sample from its k nearest neighbours [35, 44]. The value of k = 1, 3, 5, 7 are considered in the experiment. In our work, maximum classification accuracy is obtained for k = 7. Correntropy quantifies the information related to both distribution and time structure present in the signal [45]. The CCorrEnt [36, 37] is selected as a feature in our work. We have used Gaussian kernel to compute the CCorrEnt, and used ITL toolbox (www.sohanseth.com/Home/codes). SmEnt [38] measures the complexity of time series. The experiments are performed for m = 2, 3, 4, 5, 7
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and maximum classification accuracy is achieved for m = 5 and tolerance parameter rt = 0.2. FzEnt measures the similarity in the time series based on fuzzy function [39]. In the present work, the values of gradient and width of the fuzzy function are selected as 2 and 0.2 respectively. The most suitable value of template length m is selected by computing classification accuracies for m = 2, 3, 4, 5. In our work, m = 2 gave the highest classification accuracy. The PmEnt estimates the complexity of the EEG signal based on the comparison of permutation patterns [40]. In this work, the value of embedding dimension of 3 and time lag of 1 is considered for the computation of PmEnt from the subbands [46, 26]. In this work, two bispectral entropies namely BEnt1 and BEnt2 are computed from the third order moments [41, 26]. The BEnt1 and BEnt2 represent the normalized bispectral entropy and normalised squared bispectral entropy [41, 26]. These entropies can quantify the interaction among its different frequency components [26]. The FracDm is used to detect transient features in the signal waveform including EEG [42]. The Higuchi’s FracDm algorithm [47] is used in this work to compute the FracDm in the subbands of EEG signals. LLE [43] is used in this work to measure the complexity in the time series. We have used the method presented in [43] to estimate the LLE in this work.
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In the present work, we have used LS-SVM classifier [27]. It maps the input vectors to higher dimensional space. Then, a hyperplane is obtained in this space to separate the different classes of data [27]. To map the data into the higher dimension, concept of kernels are utilized in SVM [27]. In this study, radial basis function (RBF), linear, and polynomial of order 2 (Poly2), order 3 (Poly3), order 4 (Poly4) [48] kernels are used. The performance of the classifier is measured in terms of accuracy (ACC), sensitivity (SEN), and specificity (SPE) [49]. 4. Results
Each signal of the dataset used in this work consists of two different time series namely x and y. In the first step, the difference (x-y) time series is computed. The features can be computed from x and y time series and can be used for classification. The difference (x − y) signals have been used effectively in [19]. Hence, 3750 pairs of x and y time series yielded 3750 8
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difference time series for each class. Plots of difference time series for F and NF classes are presented in Figure 4(a) and 4(b). Thereafter, the differ-
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ence time-series are subjected to TQWT method, resulting in coefficients at different subbands. Individual TQWT subband is reconstructed in the timedomain using inverse TQWT. The different nonlinear features are computed from these obtained subband signals. The optimum values of Q and J are determined by performing several experiments. In all the experiments, the value of r is fixed to 3 which is the minimum value suggested in [50]. The 9
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ACC (%) obtained using LS-SVM classifier 86.80 84.65 86.17 70.65 70.38 84.14 82.37 51.93 85.28
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Table 1: Classification accuracy obtained for different features computed from different TQWT subbands (Q = 1 and J = 5).
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higher values of r lead to increased overlapping in the adjacent frequency response. The minimum value of Q to be chosen is 1 [50]. Therefore, we started our experiment by decomposing F and NF EEG signals with Q = 1 and J = 5. Then, various nonlinear features are computed from the obtained subbands. The features are normalized before feeding to the LS-SVM classifier. The ten-fold cross-validation [51] procedure is employed during training and testing of the classifier. The RBF kernel is used with LSSVM classifier to determine best set of features by performing classification experiments. The RBF kernel parameter (σ), is varied from 0.1 to 2.5 in steps of 0.2. The maximum classification accuracy obtained for different features is listed in Table 1. It can be observed from this table that maximum classification accuracy is obtained using KnnEnt feature. Hence, it is the most suitable feature for the classification of F and NF classes.
4.1. Selection of TQWT parameters The selection of optimum value of Q and J is an important step in signal decomposition. To select the optimum value of Q and J, we performed further experiments by taking only KnnEnt features as it yielded highest classification accuracy. Further, the values of J is varied by keeping Q = 1. We noted that the maximum possible value of J with Q = 1 is 18. Hence, the value of J is varied from 5 to 18 and KnnEnt is computed for each subband. Then, the classification is performed by varying value of J from 5 10
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to 18. The classification accuracy versus values of J is plotted in Figure 5. It can be noticed that at J = 15 maximum classification accuracy is obtained. Therefore, J = 15 is chosen for further experiments. After selecting J = 15, the value of Q is varied in steps from 1 to 10 and KnnEnt is computed from 16 subbands for each value of Q. The plot of classification accuracies for various values of Q is depicted in Figure 6. The figure shows that highest classification accuracy is obtained for Q = 3. The classification accuracy decreases gradually with increasing Q value. Finally, the difference EEG signals are decomposed up to 15-th level of decomposition. Plots of these subband (Subband1 to Subband16 ) signals are shown in Figures 7 and 8 for F and NF classes respectively. The subscript in Subband1 denotes the first level of subband. The subbands from Subband1 to Subband16 are in decreasing order of frequencies. First 15 subbands are reconstructed from the detail coefficients, and 16th subband is reconstructed from the approximate coefficients. In this way, the values of Q and J are selected for further experiments.
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4.2. Selection of optimal number of features The different features are computed from all subbands and experiments are performed to find the best combination of features using various ranking methods and LS-SVM classifier. Further, with Q = 3 and J = 15 different nonlinear features are computed from each decomposed subband. All the computed features are combined to form a feature set of size 7500 × 144. The typical range of different features obtained from Subband16 are shown in Table 2. The Kruskal-Wallis statistical test [52, 53] is applied to examine 13
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Range (Mean ± standard deviation ) F NF -20.3732± 4.7826 -21.8131± 5.7226 0.1873± 0.0102 0.1828 ± 0.0178 0.2720± 0.0963 0.3069± 0.1258 0.2772± 0.0527 0.2982± 0.0594 0.1139± 0.0607 0.1264± 0.0680 0.8433± 0.0141 0.8590± 0.0146 0.1507± 0.0336 0.1760± 0.0425 1.0187± 0.0041 1.0197± 0.0039 14.1376± 1.3128 14.17± 1.3140
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Table 2: Range (mean ± standard deviation) and p-value of different features obtained from Subband16 .
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the discrimination ability of various features. This test results in the p-value. The p-values are also depicted in Table 2 for different nonlinear features obtained from Subband16 . Apart from LLE, all other features are found to be significant with less p-values (p < 0.05) indicating their suitability for good discrimination of F and NF EEG signals. Further, the features are ranked using Wilcoxon, Student’s t-test, entropy, Battacharyya, and receiver operating characteristics (ROC) [54, 55] methods. Then, the ranked features are applied to LS-SVM classifier. The optimal number of features for classification are found by performing the experiments with different number of ranked features. The plot of classification accuracy versus number of ranked features is shown in Figure 9. It shows that the maximum classification accuracy (93.59%) is obtained with first 52 features using entropy ranking method.
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4.3. Experiments with most suitable entropies It can be noticed from Figure 6, that maximum classification accuracy for KnnEnt is 94.06%. On the other hand, 52 ranked features resulted in classification accuracy of 93.59% which is lower than the classification accuracy obtained with KnnEnt. Therefore, classification is again performed using different nonlinear features. The maximum classification accuracies corresponding to different features computed from TQWT subbands are listed in Table 3 for Q = 3 and J = 15 parameters. It can be observed from Table 3 that KnnEnt provided maximum classification accuracy. The features are ranked in the descending order based on the classification accuracies. Then, the features are appended one by one starting from highly ranked feature to perform the classification. The resulting classification accuracies are listed in Table 4. It can be observed that the maximum classification accuracy of 94.92% is obtained when KnnEnt, CCorrEnt, and FzEnt used together (Table 4). The feature set using these three entropy features computed from 16 subbands is of size 7500 × 48. Further, these 48 features are ranked using different ranking methods. The plot of classification accuracy versus number of ranked features is shown in Figure 10. The classification accuracy increased to 95% using 47 features ranked with ROC method. There is no improvement observed in the maxuimum classification accuracy for the features ranked using other ranking methods. The classification performance is 15
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KnnEnt KnnEnt, CCorrEnt KnnEnt, CCorrEnt, FzEnt KnnEnt, CCorrEnt, FzEnt, PmEnt KnnEnt, CCorrEnt, FzEnt, PmEnt, SmEnt KnnEnt, CCorrEnt, FzEnt, PmEnt, SmEnt, BEnt1 KnnEnt, CCorrEnt, FzEnt, PmEnt, SmEnt, BEnt1, BEnt2 KnnEnt, CCorrEnt, FzEnt, PmEnt, SmEnt, BEnt1, BEnt2, FracDm KnnEnt, CCorrEnt, FzEnt, PmEnt, SmEnt BEnt1, BEnt2, FracDm, LLE
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ACC (%) 82.09 91.59 74.55 77.45 94.92
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The EEG signals exhibit nonlinear and nonstationary nature [56, 57]. In this work, F and NF EEG signals are analysed using nonlinear entropy features coupled with TQWT method. DWT is frequently used to handle the nonstationarity present in the signal. The TQWT is a modification of DWT with tunable-Q factor. Although, TQWT is essentially a constant Q transform, it facilitates to select the Q value of the wavelet before the signal decomposition. It decomposes the EEG signal in to transient and oscillating components based on the value of Q. In our present work, we have computed nonlinear features namely KnnEnt, CCorrEnt, FzEnt, BEnt1, BEnt2, PmEnt, SmEnt, FracDm, and LLE for difference (x-y) time series of the entire dataset. These features are computed in TQWT domain. The different nonlinear features are extracted from these subbands to quantify the nonlinearity present in the subbands. Entropy is a nonlinear measure which quantifies the degree of complexity present in the signal. Usually, the higher value of entropy is an indication of less regularity and high uncertainty in the signal [26]. Different entropies evaluate the complexities present in the signals in various ways. In Table 2, the range of values for various features in the Subband16 are presented. This subband is reconstructed from approximate coefficients of EEG signals (Q = 3 and J = 15). The KnnEnt is basically developed based on the distance of sample from its k-nearest neighbors. Consequently, it is a measure of the degree to which data points are scattered. The value of KnnEnt is less for NF EEG signals signifying that these signals has lesser scattering. The SmEnt and FzEnt quantify the complexity of the signals. These features have lower values for F EEG signals as compared to NF EEG signals indicating reduced complexity of F
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EEG signals as compared to NF EEG signals. The complexity of the signal in bispectral domain can be analysed using BEnt1 and BEnt2. The value of these entropies are also less for F EEG signal as compared to NF EEG signals indicating reduced complexity of F class of EEG signals. Several methods are reported in the literature to analyze the F and NF EEG time series. They are briefly presented in Table 6. In [58], delay permutation entropy (DPE) is computed from 50 EEG signals (F and NF). Further, computed values of DPE with SVM has provided the classification accuracy of 84%. In [17], a methodology is proposed to discriminate F and NF EEG signals by computing sample entropies and average variance of instantaneous frequencies (AVIFs) from IMFs in EMD domain. The accuracy of 85% is obtained for 50 pairs of EEG time series with LS-SVM. In [18], six different entropies are extracted from different IMFs of two classes. These entropies with LS-SVM are used to distinguish the two classes. The methodology is able to discriminate the two classes with an accuracy of 87%. In [20], EEG signals of two classes are decomposed up to sixth level using DWT, and seven different entropies are computed from each level of decomposed signals. Highest classification accuracy of 84% is observed with LS-SVM classifier. In a recent study [59], the classification of F and NF EEG signals is performed using empirical wavelet transform. The area measure computed form the reconstructed phase space (RPS) plots of EEG rhythms is used as the feature for classification. They obtained classification accuracy of 90% for 50 pairs of F and NF EEG signals. In [19], difference (x-y) time series of entire dataset are used to classify F and NF EEG signals. Renyi, log-energy, and Shannon entropies are computed in EMD, DWT and EMD-DWT domains. Discrimination ability of features are found to be higher in EMD-DWT domain with classification accuracy of 89.4% using K-nearest neighbour (K-NN) classifier. Recently, in [21], orthogonal wavelet filter banks are used to decompose the F and NF EEG signals. These filters are well localized in time-frequency domain. Different entropies computed from the wavelet coefficients with LS-SVM achieved the classification accuracy of 94.25% using the entire database. In [60], multivariate subband fuzzy entropy is computed from the TQWT subbands of F and NF EEG signals. They used the entire database and obtained 84.67% classification accuracy. The advantage of our proposed methodology is the improved classification accuracy over other compared methods. The TFCV procedure is employed to compute the performance of the classifier. The limitation of our method is 20
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Table 6: Summary of automated classification of F and NF classes using same database.
Sharma et al. (2014) [18] Sharma et al. (2015) [20] Das et al. (2016) [19] Bhattacharyya et al. (2016) [59] Sharma et al. (2016) [21] Bhattacharyya et al. (2017) [60]
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F and NF classes
6 different entropies
50 signals of
DWT
F and NF classes
7 different entropies
LS-SVM LS-SVM
EMD, DWT
Entire dataset
3 non-linear features
50 signals of
EWT
F and NF classes
area computed from RPS of rhythms
Time-frequency localized orthogonal filter bank
Entire dataset
7 different entropies
3750 signals of
TQWT
F and NF classes
Multivariate fuzzy entropy TQWT
Entire dataset
3 entropies
LS-SVM
ACC
validation
%
No
84%
10-fold
85%
10-fold
87%
10-fold
84%
K-NN
No
89.4%
LS-SVM
10-fold
90%
LS-SVM
10-fold
94.25%
LS-SVM
10-fold
84.67%
LS-SVM
10-fold
95.00%
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In the present work
50 signals of
Cross
Classifier used
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Sharma et al. (2014) [17]
Methodology
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Zhu et al. (2013) [58]
Dataset used
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Authors
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that only five patients are used in this study. In future, we plan to continue this work using more number of patients and can evaluate the patient specific performance strategy.
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6. Conclusion
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In the present work, an automatic diagnostic system for the identification of F EEG signals is proposed using TQWT decomposition method. In this study, difference (x-y) time series are subjected to the TQWT decomposition method. Difference (x-y) EEG time series of F and NF classes are decomposed into subbands and various nonlinear features namely KnnEnt, CCorrEnt, FzEnt, PmEnt, SmEnt, BEnt1, BEnt2, FracDm, and LLE are computed from these subbands. A strategy is presented to select the optimum values for Q, r, and J parameters to get highest classification accuracy. We have achieved best performance with Q = 3, r = 3, and J = 15 in this work. Among the extracted features, KnnEnt is found to be most suitable in the classification of F and NF EEG signals. The combination of KnnEnt, CCorrEnt, and FzEnt achieved the highest classification accuracy of 95%. In future, we intend to test our algorithm using more F and NF patients. Also, our method can be used to identify other neural diseases like autism, epilepsy, dementia, and alcoholism.
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Rajeev Sharma
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Rajeev Sharma received Ph.D. degree in the Electrical Engineering from Indian Institute of Technology Indore, Indore, India in 2017. He has completed B.E. degree in Electronics and Communication Engineering from Rajiv Gandhi Technological University, Bhopal, India in 2009, and M.Tech. degree in Electronic Instrumentation from National Institute of Technology, Warangal, India in 2011. His research interest is Signal Processing and its application in Biomedical signals like EEG and ECG, pattern recognition, and machine learning etc.
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Mohit Kumar
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Mohit Kumar received B. Tech. degree in Electronics and Communication Engineering from Uttar Pradesh Technical University, Lucknow, India in 2009, and M. E. degree in Electronic Instrumentation and Control Engineering from Thapar University, Patiala, India in 2013. He is currently working towards the Ph.D. degree in Electrical Engineering at the Indian Institute of Technology, Indore, India. His research interest is Signal Processing and its application in Biomedical signals.
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Ram Bilas Pachori
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Ram Bilas Pachori received the BE degree with honors in Electronics and Communication Engineering from Rajiv Gandhi Technological University, Bhopal, India in 2001, the M. Tech and PhD degrees in Electrical Engineering from Indian Institute of Technology Kanpur, Kanpur, India in 2003 and 2008 respectively. He worked as a Postdoctoral Fellow at Charles Delaunay Institute, University of Technology of Troyes, Troyes, France during 2007-08. He served as an Assistant Professor at Communication Research Center, International Institute of Information Technology, Hyderabad, India during 2008-09. He served as an Assistant Professor at Discipline of Electrical Engineering, School of Engineering, Indian Institute of Technology Indore, Indore, India during 2009- 13, where presently he has been working as an Associate Professor since 2013. He worked as a Visiting Scholar at Intelligent Systems Research Center, Ulster University, Northern Ireland, UK during December 2014. His research interests are in the areas of biomedical signal processing, non-stationary signal processing, speech signal processing, signal processing for communications, computer aided medical diagnosis, and signal processing applications. U. R. Acharya
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U. R. Acharya, PhD, DEng is a senior faculty member at Ngee Ann Polytechnic, Singapore. He is also (i) Adjunct Professor at University of Malaya, Malaysia, (ii) Adjunct Faculty at Singapore Institute of Technology- University of Glasgow, Singapore, and (iii) Associate faculty at SIM University, Singapore. He received his Ph.D. from National Institute of Technology Karnataka (Surathkal, India) and DEng from Chiba University (Japan). He has published more than 400 papers, in refereed international SCI-IF journals(345), international conference proceedings (42), books (17) with more than 11,500 citations in Google Scholar (with h-index of 55), and ResearchGate RG Score of 45.00. He is ranked in the top 1% of the Highly Cited Researchers (2016) in Computer Science according to the Essential Science Indicators of Thomson. He has worked on various funded projects, with grants worth more than 2 million SGD. He has three patents and in the editorial board of many journals. He has served as guest editor for many journals. His major academic interests are in biomedical signal processing, biomedical imaging, data mining, visualization and biophysics for better healthcare design, delivery and therapy. Please visit https://scholar.google.com.sg/ citations?user=8FjY99sAAAAJ&hl=enformoredetails.
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