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Fuzzy entropy based seizure detection algorithms for EEG data analysis
CHAPTER 11
Fuzzy entropy based seizure detection algorithms for EEG data analysis Geetika Srivastava1, Alpika Tripathi2 and P.K. Maurya3 1
Department of Physics & Electronics, Dr. Ram Manohar Lohia Avadh University, Faizabad, India Department of Computer Science & Engineering, ASET, Amity University, Lucknow, India Department of Neurology, RML Institute of Medical Sciences, Lucknow, India
2 3
11.1 Introduction Electroencephalogram (EEG) is a popular technique of measuring electric fields produced by different neural activities of the brain. The EEG measurements makes it possible to extract important information of different mental activities (e.g., motor imagery, motor planning). This information is extracted by using a variety of electrophysiological signals such as slow cortical potentials, and mu or beta rhythms recorded from the scalp. The cortical neuronal activity of the brain can be recorded by implanted electrodes [1,2]. The brain cells and nerves send messages to each other by electrical signals. These electrical signals produced inside the brain could be detected and recorded by the surface electrodes placed on the scalp with the help of EEG machine. The EEG test is performed for recording these activities, involves no harm to the patients and it is a painless process as well. The typical EEG recording waveform is shown in the Fig. 11.1. Various neurological diseases such as epilepsy and many others can be correctly diagnosed by analyzing the EEG signals [47]. Epilepsy is considered as a chronic neurological disorder of the brain that affects around 50 million people of all ages in every country in the world. According to the World Health Organization (WHO), Epilepsy is a medical condition or brain disorder in which a person experienced repeated seizures. Anything that disturbs the normal pattern of neuronal activity- from illness to brain damage to abnormal brain development can lead to seizures [8]. The recoding of the EEG signals is performed by fixing an electrode on the subject scalp using the standardized electrode placement scheme (Fig. 11.2) [911]. The sources of artifacts of these signals are the noise during signal recording. These artifacts adversely affect the useful feature in the original signal. The accurate recording is done when subject is at rest and lying in relaxed position. The muscular activities, blinking of eyes
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r 2020 Elsevier Inc. All rights reserved.
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Figure 11.1 EEG waveform [3].
Figure 11.2 Standardized electrode placement scheme [13].
during signal acquisition procedure are major source of artifacts along with the power line electrical noise [12]. Many methods have been introduced to eliminate these unwanted signals. Each of them has its advantages and disadvantages. However, there is a common path for EEG signal processing (Fig. 11.3). The first part is preprocessing which includes acquisition of signal, removal of artifacts, signal averaging, thresholding of the output, enhancement of the resulting signal.
Fuzzy entropy based seizure detection algorithms for EEG data analysis
Raw EEG Acquisition of EEG record (.CSV file)
Filtering the EEG signal Extraction of channel based features Feature selection on basis of range of EEG waveform (α, β, θ, δ) Feature selection for training (fuzzy entropy)
Quadratic SVM classifier
Evaluating the result set
Result
Figure 11.3 Stages of EEG signal processing.
The second step is the feature extraction which is used to determine a feature vector from a regular vector. Feature extraction has a process to choose the most important features or information for selection and classification [1416]. The third step is the feature selection process for identifying the most important variables or parameters which help in predicting the outcome with less computing time. The final stage is signal classification which can be solved by many methods like linear analysis, nonlinear analysis, adaptive algorithms, clustering and fuzzy techniques, and neural networks. This is done by exploiting the algorithmic characteristics of the feature vector of the data input. In this paper PSD was used for feature extraction, Fuzzy Entropy for feature selection and Quadratic SVM for the classification of EEG signals. For feature selection authors have used Fuzzy Entropy [1720]. Fuzzy entropy is used to express the mathematical values of the fuzziness of fuzzy sets. The concept of entropy, the basic subject of information theory and telecommunications, is a measure of fuzziness in fuzzy sets. In 2007, Chen et al. (2007) [18] proposed Fuzzy Entropy by doing modifications of the Sample Entropy based algorithm. Fuzzy Entropy has been successfully used for feature extraction and in the classification of surface EMG signals. The algorithm has several characteristics of the Sample Entropy like relative uniformity and the suitability for the processing of short datasets. Fuzzy Entropy can transit smoothly through varying parameters to overcome the limitations of the Sample Entropy.
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The SVM is a popular classifier which can handle linear as well as non-linear class boundaries with the help of kernel functions [21]. In this paper authors have used Quadratic SVM for data classification. The SVM is used to identify the maximummargin hyper plane for the separation of different classes. However, if the data cannot be linearly separated, non-linear kernel functions are used to transform the feature space, allowing a maximum-margin hyper plane to be established. Kernel functions are used to obtain the high-dimension features mapped into the data without computing the non-linear transformation [22]. There are different type of kernel functions i.e. linear, quadratic, polynomial and radial basis function (rbf) kernels. In this paper author has used quadratic kernel svm. The optimal separating hyper plane between classes of data CðxÞ 5 vTx 1 a 5 b; 2 1 , b , 1 is found by minimizing the objective function 1 2 qðvÞ 5 :v: 2 w:r:t yjðvTxj 1 aÞ . 5 1 which is linear in the inequality constraint but is a quadratic objective function due to the squared term. The square of the Euclidean norm :v: makes the optimization problem “quadratic programming.” The quadratic objective function with inequality constraints results in a function value that is unique, but the solutions are not unique.
11.2 Materials & methods 11.2.1 Materials Classification of the EEG waveforms is generally based on the frequency, amplitude, and shape of the waveform. The sites on the scalp at which they are recorded also plays a major role in the signal classification. Usually the frequency is considered as basic classification tool for EEG waveform and are classified as alpha, beta, theta, and delta waves(Fig. 11.4). The spectral frequencies of these EEG subsignals are:
Figure 11.4 EEG waveform frequency [24].
Fuzzy entropy based seizure detection algorithms for EEG data analysis
Table 11.1 Healthy & epileptic subjects. Subject ID
Data source
Duration
NS1-NS29 ES1-ES23
RMLIMS, Lucknow(India) RMLIMS, Lucknow(India)
10 s/subject 10 s/subjects
delta (14 Hz), theta (48 Hz), alpha (813 Hz) and beta (1330 Hz). The epileptic patients have higher frequencies of these components and their distribution over scalp is also different than normal, which show abnormal brain activity. These frequency sub-bands contain important information about underlying problem. The minute changes in these signals can be amplified by considering each sub band independently, which are not so obvious in the original full-spectrum signal, [23]. Since data collected belonged to one subject and had the same distribution, the Welch PSD on the mentioned frequency band (alpha, beta, delta and theta waves) was calculated as features. The two EEG data sets of normal & epileptic subjects are available by Dept. of Neurology, RML Institute of Medical Sciences, Lucknow (India) [25] were analyzed. The first EEG data set of 29 healthy subjects and the second data set are of 23 epileptic subjects (Table 11.1).
11.2.2 Methods In this paper, the authors have proposed a feature extraction method for the classification of EEG signals. In the following section the data is prepared in order to examine the power spectrum of the EEG signals of α, β, δ and θ waveform and to estimate the power spectral density of these waves. Once we extract the feature then we will use Fuzzy Entropy for feature selection and at last we will use quadratic SVM classifier for evaluating the result. After extracting the EEG signals in the first step with the sampling frequencies (F), descending windows are placed at the start of the signal. The average PSD of each window is calculated for each window using Welch method. Areas under the different frequencies such as delta, theta, alpha & beta bands are calculated using trapezoidal integration technique. The average power contained in each band is calculated in the third step. In the very next step normalized power of each sub band is calculated by dividing average power of these sub bands by the total power of complete band [26]. The last step is to slide the window and repeat step second, third & fourth for the whole signal duration. The algorithm is successfully written in MATLAB(R) (Fig. 11.5).
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Figure 11.5 Power spectral density estimation curve of 10 s signal represent the PSD estimated curve consequent to the average power of delta, theta alpha and beta bands.
11.2.3 Performance For performance evaluation four different parameters are used. They are specificity (to correctly identify the negative cases), sensitivity (to correctly identify the positive cases), selectivity (positive predictive value) and accuracy (the proportion of correctly classified instances) [31]. The formulas are given below: Sensitivity 5
TP 100 ðTP 1 FN Þ
Specificity 5
TN 100 ðTN 1 FP Þ
Selectivity 5
TP 100 ðTP 1 FP Þ
Accuracy 5
TP 1 TN 100 ðTP 1 TN 1 FP 1 FN Þ
The sensitivity, specificity, selectivity and accuracy are calculated by using confusion matrix, where TP, FP, TN, FN are true positive, false positive, true negative, and
Fuzzy entropy based seizure detection algorithms for EEG data analysis
false negative respectively. The specificity is define as true negative rate and measures the ability of a test to correctly exclude the condition (not detect the condition) when the condition is absent. The sensitivity is also called as true positive rate measures the ability of a test to detect the condition when the condition is present. The selectivity is also called positive predictive value and is calculated as the proportion of positives that correspond to the presence of the condition. Accuracy define as the number of all correct predictions divided by the total number of the dataset
11.3 Results This paper gave feature extraction results by calculating the normalized power (Pnorm) of normal cases and compared with epileptic patients data which indicates the activity of particular EEG Recording out of complete power in percentage. The normalized powers of all waves of healthy & unhealthy patients of channels (FP1-F7&FP2-F8) are shown in (Tables 11.2 and 11.3). Table 11.2 Normalized power values of 29 EEG signals of normal subjects of channel (FP1-F7 & FP2-F8). Pnorm values of 29 EEG signals
Pnorm values
EEG waveform frequency
δ
θ
Subjects
Channel (FP1-F7) of healthy subjects
NS1 NS2 NS3 NS4 NS5 NS6 NS7 NS8 NS9 NS10 NS11 NS12 NS13 NS14 NS15 NS16
0.5413537 0.4767184 0.2939254 0.4253149 0.5274243 0.4797155 0.5733298 0.5537195 0.564974 0.5475365 0.5840516 0.5429309 0.4409271 0.4254111 0.5772271 0.494469
0.2872455 0.2560685 0.2356267 0.2990406 0.2883039 0.2914022 0.2936023 0.271506 0.2903853 0.288925 0.294204 0.278729 0.2595523 0.2583051 0.3007157 0.2593165
α
β
0.1004033 0.100909 0.2059518 0.1743965 0.1133399 0.1376972 0.0867633 0.0699613 0.0906103 0.0919169 0.0793545 0.0943066 0.121778 0.1401161 0.0879972 0.0924897
0.0549041 0.1516202 0.2491762 0.084046 0.0546007 0.0746054 0.0301287 0.0898006 0.0379069 0.0556635 0.0262906 0.0682626 0.1627377 0.1607761 0.0176447 0.1389988 (Continued)
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Table 11.2 (Continued) Pnorm values of 29 EEG signals
Pnorm values
EEG waveform frequency
δ
θ
Subjects
Channel (FP1-F7) of healthy subjects
NS17 NS18 NS19 NS20 NS21 NS22 NS23 NS24 NS25 NS27 NS28 NS29
0.4760902 0.404047 0.551532 0.5133274 0.3423469 0.4702214 0.3022422 0.3781471 0.3467232 0.5198029 0.3777926 0.4468493
Subjects
Channel (FP2-F8) of healthy subjects
NS1 NS2 NS3 NS4 NS5 NS6 NS7 NS8 NS9 NS10 NS11 NS12 NS13 NS14 NS15 NS16 NS17 NS18 NS19 NS20 NS21 NS22 NS23
0.542309 0.4635146 0.4142268 0.455424 0.5477776 0.4300589 0.5675522 0.6026477 0.5427482 0.5308815 0.5736176 0.561036 0.4095928 0.464734 0.5764885 0.5303974 0.5810494 0.4343215 0.5728333 0.3962846 0.336031 0.4503725 0.3406244
0.2481818 0.2619032 0.2948771 0.2756824 0.2332728 0.2701072 0.1819259 0.2393114 0.2554684 0.2778376 0.2182882 0.2828378
0.2876595 0.2523885 0.2605427 0.2893796 0.2889696 0.274989 0.2919507 0.2875673 0.2861007 0.2897235 0.2887541 0.2844362 0.2407916 0.2742661 0.2996099 0.2720615 0.2833371 0.272232 0.295526 0.2250875 0.2194597 0.2716224 0.1787516
α
β
0.0980836 0.1507571 0.0956789 0.0971098 0.1637028 0.1311979 0.0934789 0.1419741 0.173686 0.1038362 0.1256813 0.1409216
0.1631938 0.1678155 0.0416824 0.0984433 0.2460461 0.1125902 0.4113957 0.2260465 0.2087424 0.0827499 0.2646949 0.1132077
0.1012647 0.1076913 0.1494957 0.1467621 0.0996784 0.1508269 0.0881056 0.061647 0.0981829 0.101529 0.0792797 0.0868363 0.1185299 0.1328746 0.0886994 0.0859439 0.0733208 0.138488 0.0837847 0.1114892 0.1258759 0.1438582 0.0894939
0.0526231 0.1617248 0.1601772 0.0918793 0.0474038 0.1280574 0.0362683 0.0325049 0.0569534 0.0618052 0.0424668 0.0518339 0.2168693 0.1122007 0.0187995 0.0963894 0.0466455 0.1392849 0.0317154 0.2536664 0.3055912 0.1180808 0.3798016 (Continued)
Fuzzy entropy based seizure detection algorithms for EEG data analysis
Table 11.2 (Continued) Pnorm values of 29 EEG signals
Pnorm values
EEG waveform frequency
δ
θ
Subjects
Channel (FP1-F7) of healthy subjects
NS24 NS25 NS27 NS28 NS29
0.4100158 0.3951881 0.5776967 0.4079492 0.4837167
0.2434204 0.2663458 0.2829119 0.2311423 0.2911485
α
β
0.1260991 0.1599746 0.0750429 0.1205292 0.1236057
0.2060265 0.1627236 0.048683 0.2263818 0.0852641
Table 11.3 Normalized power values of 23 EEG signals of epileptic subjects of channel (FP1-F7 & FP2-F8). Pnorm values of 23 EEG signals
Pnorm values
EEG waveform frequency
δ
θ
Subjects
Channel (FP1-F7) of unhealthy subjects
ES1 ES2 ES3 ES4 ES5 ES6 ES7 ES8 ES9 ES10 ES11 ES12 ES13 ES14 ES15 ES16 ES17 ES18 ES19 ES20
0.3421377 0.4742951 0.5793422 0.4678816 0.3306869 0.4131936 0.2944349 0.6015025 0.5816088 0.5009388 0.4903287 0.5914533 0.3789077 0.5561882 0.5683588 0.4965758 0.5696945 0.4211835 0.541524 0.3948502
0.2834877 0.2712509 0.3041304 0.3151458 0.2905602 0.2687825 0.2904368 0.2920809 0.3013284 0.2723085 0.3115546 0.302994 0.229372 0.3049554 0.2786344 0.3087101 0.3022929 0.3194771 0.3038178 0.2478928
α
0.2291174 0.1228368 0.085104 0.1505602 0.2230337 0.1548142 0.2679495 0.0663021 0.0853984 0.1101891 0.1369251 0.0780709 0.1250842 0.0965006 0.0755104 0.1351923 0.0905007 0.1866851 0.1077074 0.1255535
β
0.1277282 0.1159599 0.0149806 0.04904 0.1383617 0.1473079 0.1289547 0.0242627 0.0152678 0.1009326 0.0440619 0.0111395 0.2528642 0.0258101 0.0620241 0.0424187 0.0210667 0.0547045 0.0302755 0.2173386 (Continued)
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Table 11.3 (Continued) Pnorm values of 23 EEG signals
Pnorm values
EEG waveform frequency
δ
θ
Subjects
Channel (FP1-F7) of unhealthy subjects
ES21 ES22 ES23
0.5817724 0.4458387 0.4117115
Subjects
Channel (FP2-F8) of unhealthy subjects
ES1 ES2 ES3 ES4 ES5 ES6 ES7 ES8 ES9 ES10 ES11 ES12 ES13 ES14 ES15 ES16 ES17 ES18 ES19 ES20 ES21 ES22 ES23
0.3331861 0.4428823 0.5870865 0.4880558 0.3485177 0.3452487 0.4107111 0.6157103 0.5955744 0.5525959 0.5301458 0.5783455 0.34483 0.5681467 0.5379521 0.4926048 0.578369 0.4438163 0.5323904 0.3357469 0.4059924 0.4313254 0.4040865
0.2884618 0.3002204 0.2500198
0.2796609 0.2658551 0.3065013 0.3157325 0.2897896 0.2548184 0.2963829 0.2984086 0.3002367 0.2839076 0.3047195 0.3007521 0.1993141 0.3065303 0.2610826 0.3096217 0.297146 0.3088214 0.3048773 0.2185742 0.2723842 0.2942861 0.2519396
α
0.07896 0.1597868 0.1359176
0.2349981 0.1314447 0.081316 0.1406498 0.2104969 0.1834798 0.1897171 0.0638346 0.0766989 0.0879631 0.1109345 0.087288 0.0966619 0.0907102 0.0760903 0.1376689 0.0817463 0.1675756 0.1143287 0.1302265 0.1688786 0.1701833 0.1322717
β
0.0348779 0.0770944 0.1873887
0.134644 0.1443431 0.0086248 0.0382553 0.1339975 0.2007346 0.0858028 0.0059653 0.0112165 0.0597538 0.0375317 0.0172106 0.3473494 0.0180742 0.1100769 0.0429596 0.0265642 0.0623622 0.0316246 0.3022749 0.1364904 0.0870863 0.1969331
The same processing steps are further applied on remaining 15 channels of EEG recording for Normal and Epileptic subjects. For feature selection Fuzzy Entropy algorithm calculates a feature score for each feature which can then be applied to rank and select top scoring features. PSD is the frequency response of periodic random signal. It gives information of average power distribution in frequency component. For classifying the experimental EEG signals,
Fuzzy entropy based seizure detection algorithms for EEG data analysis
the Quadratic SVM concept has been used. The computed normalized values are used by Quadratic SVM classifier to train and test the system. The performance of proposed methodology is evaluated in terms of parameters of confusion matrix & ROC Curve [2731] shown in Figs. 11.6 and 11.7.
Figure 11.6 Confusion matrix.
Figure 11.7 ROC curve.
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Table 11.4 Comparison with other application. Authors
Sensitivity
Specificity
Selectivity
Accuracy
N.kannathal et al. (2005) Rajesh et al. (2011) Zhen Zhang et al. (2013) Saman Saraf (2017) Our Findings
100%
91.3%
93.5%
90% 90.8% 93.33% 70.5% 96.2%
In this work, the quadratic SVM classifier had gained the sensitivity, specificity; selectivity and accuracy are 100%, 91.3%, 93.5% and 96.2% respectively. Table 11.4 shows comparison with other applications
11.4 Conclusion The main goal of this paper is to develop a technique for the fast analysis and classification of epileptic patients. This fast automated approach will help to reduce the sudden death rate due to delay in diagnosis. This paper uses a unique combined approach of PSD, Fuzzy Entropy and Quadratic SVM classifier for differentiating the Epileptic EEG signal from normal ones. The data set is taken from Natus NeuroWorks EEG Recording Machine from RML Institute of Medical Sciences, Lucknow (U.P.), India. Our classification result shows sensitivity, specificity, selectivity and accuracy are 100%, 91.3%, 93.5% and 96.2% respectively.
Acknowledgment The authors are very thankful to the valuable guidance and support of Dr. A. K. Thacker, Head of Department, Department of Neurology, RML Institute of Medical Sciences and Ms. Nidhi Singh, Technical Staff EEG Lab, RMLIMS, Lucknow (U.P.).
References [1] K. Brigham, B.V.K. Vijaya Kumar, Subject identification from electroencephalogram (EEG) signals during imagined speech. Biometrics: Theory Applications and Systems (BTAS), 2010 Fourth IEEE International Conference on. IEEE, 2010. [2] J. Wolpaw, N. Birbaumer, D.J. McFarland, G. Pfurtscheller, T.M. Vaughan, Brain-computer interfaces for communication and control, Clin. Neurophysiol. 113 (2002) 767791. [3] A.D. Boro, S. Haut, EEG Waveform, The Saul R Korey Department of Neurology, Albert Einstein College of Medicine; Physician, Department of Neurology, Montefiore Medical Center, 2017. [4] A.S. Al-Fahoum, A.A. Al-Fraihat, Methods of EEG signal features / extraction using linear analysis in frequency and time-frequency domains, ISRN Neurosci. 2014 (2014). [5] E.D. Ubeyli, Statistics over features: EEG signals analysis, Comput. Biol. Med. 39 (8) (2009) 733741. [6] R. Agarwal, J. Gotman, D. Flanagan, B. Rosenblatt, Automatic EEG analysis during long-term monitoring in the ICU, Electroencephalogr. Clin. Neurophysiol. 107 (1) (1998) 4458.
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[7] A. Meyer-Lindenberg, The evolution of complexity in human brain development: an EEG study, Electroencephalogr. Clin. Neurophysiol. 99 (5) (1996) 405411. [8] L. Orosco, A. Garcés Correa, E. Laciar, A survey of performance and techniques for automatic epilepsy detection, J. Med. Biol. Eng. 33 (6) (2013) 526537. [9] A. Subasi, E. Erçelebi, Classification of EEG signals using neural network and logistic regression, Comput. Methods Prog. Biomed. 78 (2) (2005) 8799. [10] A. Subasi, EEG signal classification using wavelet feature extraction and a mixture of expert model, Exp. Syst. Appl. 32 (4) (2007) 10841093. [11] H. Jasper and L.D. Proctor, Reticular Formation of the Brain, 1958. [12] M.R.N. Kousarrizi, A.A. Ghanbari, M. Teshnehlab, M. Aliyari, A. Gharaviri, Feature extraction and classification of EEG signals using wavelet transform, SVM and artificial neural networks for brain computer interfaces, in: Proceedings of the International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing (IJCBS ’09), 2009, pp. 352355. [13] A.M. Ivanitsky, EEG recording, Cognitive EEG Laboratory, Institute of Higher Nervous Activity and Neurophysiology, 2001. [14] H. Kordylewski, D. Graupe, K. Liu, A novel large-memory neural network as an aid in medical diagnosis applications, IEEE Trans. Inf. Technol. Biomed. 5 (3) (2001) 202209. [15] I. Guler, E.D. Ubeyli, Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients, J. Neurosci. Methods 148 (2) (2005) 113121. [16] E.D. Ubeyli, Wavelet/mixture of experts network structure for EEG signals classification, Exp. Syst. Appl. 34 (3) (2008) 19541962. [17] J. Xiang, et al., The detection of epileptic seizure signals based on fuzzy entropy, J. Neurosci. Methods 243 (2015) 1825. [18] W. Chen, et al., Characterization of surface EMG signal based on fuzzy entropy, IEEE Trans. Neural Syst. Rehabil. Eng. 15 (2) (2007) 266272. [19] A. Anwar, An entropy-based feature in epileptic seizure prediction algorithm, IOSR Journal of Computer Engineering (IOSR-JCE) e-ISSN: 2278-0661, p-ISSN: 2278-8727, vol. 17, issue 6, Ver. I (Nov Dec. 2015), pp. 4754. [20] Y. Yeniyayla, Fuzzy entropy and its application. Diss. DEÜ Fen Bilimleri Enstitüsü, 2011. [21] G. James, D. Witten, T. Hastie, R. Tibshirani, An Introduction to Statistical Learning, Springer New York, New York, 2013. [22] R. Singla, et al., Comparison of SVM and ANN for classification of eye events in EEG, J. Biomed. Sci. Eng. 4 (01) (2011) 62. [23] D. Gajic, Z. Djurovic, S. Di Gennaro, F. Gustafsson, Classification of EEG signals for detection of epileptic seizures based on wavelets and statistical pattern recognition, Biomed. Eng.: Appl., Basis Commun. 26 (2) (2014) 145. [24] http://www.medicine.mcgill.ca/physio/vlab/biomed_signals/EEG_n.htm, Bio Signal Asquisition, The McGill Physiology Virtual Laboratory, McGill University, 2018. [25] DataSet, Department of Neurology, RMLIMS, Lucknow (India), 2018. [26] M.M. Siddiqui, G. Srivastava, S.H. Saeed, Detection of rapid eye movement behaviour disorder using short time frequency analysis of PSD approach applied on EEg signal (ROC-LOC), Biomed. Res. 26 (3) (2015) 587593. [27] S. Saraswat, G. Srivastava, S. Shukla, Classification of ECG signals using cross-recurrence quantification analysis and probabilistic neural network classifier for ventricular tachycardia patients, Int. J. Biomed. Eng. Technol. 26 (2) (2018) 141156. [28] S.M. Usman, M. Usman, S. Fong, Epileptic Seizures Prediction Using Machine Learning Methods, Computational and Mathematical Methods in Medicine, 2017. [29] M. Yildiz, E. Bergil, C. Oral, Comparison of different classification methods for the preictal stage detection in EEG signals, Biomed. Res. 28 (2017) 2. [30] M. Bandarabadi, et al., Epileptic seizure prediction using relative spectral power features, Clin. Neurophysiol. 126 (2) (2015) 237248. [31] J.S. Borer, et al., Sensitivity, specificity and predictive accuracy of radionuclide cineangiography during exercise in patients with coronary artery disease. Comparison with exercise electrocardiography, Circulation 60 (3) (1979) 572580.
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Fuzzy entropy based seizure detection algorithms for EEG data analysis Geetika Srivastava1, Alpika Tripathi2 and P.K. Maurya3 1
Department of Physics & Electronics, Dr. Ram Manohar Lohia Avadh University, Faizabad, India Department of Computer Science & Engineering, ASET, Amity University, Lucknow, India Department of Neurology, RML Institute of Medical Sciences, Lucknow, India
2 3
11.1 Introduction Electroencephalogram (EEG) is a popular technique of measuring electric fields produced by different neural activities of the brain. The EEG measurements makes it possible to extract important information of different mental activities (e.g., motor imagery, motor planning). This information is extracted by using a variety of electrophysiological signals such as slow cortical potentials, and mu or beta rhythms recorded from the scalp. The cortical neuronal activity of the brain can be recorded by implanted electrodes [1,2]. The brain cells and nerves send messages to each other by electrical signals. These electrical signals produced inside the brain could be detected and recorded by the surface electrodes placed on the scalp with the help of EEG machine. The EEG test is performed for recording these activities, involves no harm to the patients and it is a painless process as well. The typical EEG recording waveform is shown in the Fig. 11.1. Various neurological diseases such as epilepsy and many others can be correctly diagnosed by analyzing the EEG signals [47]. Epilepsy is considered as a chronic neurological disorder of the brain that affects around 50 million people of all ages in every country in the world. According to the World Health Organization (WHO), Epilepsy is a medical condition or brain disorder in which a person experienced repeated seizures. Anything that disturbs the normal pattern of neuronal activity- from illness to brain damage to abnormal brain development can lead to seizures [8]. The recoding of the EEG signals is performed by fixing an electrode on the subject scalp using the standardized electrode placement scheme (Fig. 11.2) [911]. The sources of artifacts of these signals are the noise during signal recording. These artifacts adversely affect the useful feature in the original signal. The accurate recording is done when subject is at rest and lying in relaxed position. The muscular activities, blinking of eyes
Smart Healthcare for Disease Diagnosis and Prevention DOI: https://doi.org/10.1016/B978-0-12-817913-0.00011-0
r 2020 Elsevier Inc. All rights reserved.
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Figure 11.1 EEG waveform [3].
Figure 11.2 Standardized electrode placement scheme [13].
during signal acquisition procedure are major source of artifacts along with the power line electrical noise [12]. Many methods have been introduced to eliminate these unwanted signals. Each of them has its advantages and disadvantages. However, there is a common path for EEG signal processing (Fig. 11.3). The first part is preprocessing which includes acquisition of signal, removal of artifacts, signal averaging, thresholding of the output, enhancement of the resulting signal.
Fuzzy entropy based seizure detection algorithms for EEG data analysis
Raw EEG Acquisition of EEG record (.CSV file)
Filtering the EEG signal Extraction of channel based features Feature selection on basis of range of EEG waveform (α, β, θ, δ) Feature selection for training (fuzzy entropy)
Quadratic SVM classifier
Evaluating the result set
Result
Figure 11.3 Stages of EEG signal processing.
The second step is the feature extraction which is used to determine a feature vector from a regular vector. Feature extraction has a process to choose the most important features or information for selection and classification [1416]. The third step is the feature selection process for identifying the most important variables or parameters which help in predicting the outcome with less computing time. The final stage is signal classification which can be solved by many methods like linear analysis, nonlinear analysis, adaptive algorithms, clustering and fuzzy techniques, and neural networks. This is done by exploiting the algorithmic characteristics of the feature vector of the data input. In this paper PSD was used for feature extraction, Fuzzy Entropy for feature selection and Quadratic SVM for the classification of EEG signals. For feature selection authors have used Fuzzy Entropy [1720]. Fuzzy entropy is used to express the mathematical values of the fuzziness of fuzzy sets. The concept of entropy, the basic subject of information theory and telecommunications, is a measure of fuzziness in fuzzy sets. In 2007, Chen et al. (2007) [18] proposed Fuzzy Entropy by doing modifications of the Sample Entropy based algorithm. Fuzzy Entropy has been successfully used for feature extraction and in the classification of surface EMG signals. The algorithm has several characteristics of the Sample Entropy like relative uniformity and the suitability for the processing of short datasets. Fuzzy Entropy can transit smoothly through varying parameters to overcome the limitations of the Sample Entropy.
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The SVM is a popular classifier which can handle linear as well as non-linear class boundaries with the help of kernel functions [21]. In this paper authors have used Quadratic SVM for data classification. The SVM is used to identify the maximummargin hyper plane for the separation of different classes. However, if the data cannot be linearly separated, non-linear kernel functions are used to transform the feature space, allowing a maximum-margin hyper plane to be established. Kernel functions are used to obtain the high-dimension features mapped into the data without computing the non-linear transformation [22]. There are different type of kernel functions i.e. linear, quadratic, polynomial and radial basis function (rbf) kernels. In this paper author has used quadratic kernel svm. The optimal separating hyper plane between classes of data CðxÞ 5 vTx 1 a 5 b; 2 1 , b , 1 is found by minimizing the objective function 1 2 qðvÞ 5 :v: 2 w:r:t yjðvTxj 1 aÞ . 5 1 which is linear in the inequality constraint but is a quadratic objective function due to the squared term. The square of the Euclidean norm :v: makes the optimization problem “quadratic programming.” The quadratic objective function with inequality constraints results in a function value that is unique, but the solutions are not unique.
11.2 Materials & methods 11.2.1 Materials Classification of the EEG waveforms is generally based on the frequency, amplitude, and shape of the waveform. The sites on the scalp at which they are recorded also plays a major role in the signal classification. Usually the frequency is considered as basic classification tool for EEG waveform and are classified as alpha, beta, theta, and delta waves(Fig. 11.4). The spectral frequencies of these EEG subsignals are:
Figure 11.4 EEG waveform frequency [24].
Fuzzy entropy based seizure detection algorithms for EEG data analysis
Table 11.1 Healthy & epileptic subjects. Subject ID
Data source
Duration
NS1-NS29 ES1-ES23
RMLIMS, Lucknow(India) RMLIMS, Lucknow(India)
10 s/subject 10 s/subjects
delta (14 Hz), theta (48 Hz), alpha (813 Hz) and beta (1330 Hz). The epileptic patients have higher frequencies of these components and their distribution over scalp is also different than normal, which show abnormal brain activity. These frequency sub-bands contain important information about underlying problem. The minute changes in these signals can be amplified by considering each sub band independently, which are not so obvious in the original full-spectrum signal, [23]. Since data collected belonged to one subject and had the same distribution, the Welch PSD on the mentioned frequency band (alpha, beta, delta and theta waves) was calculated as features. The two EEG data sets of normal & epileptic subjects are available by Dept. of Neurology, RML Institute of Medical Sciences, Lucknow (India) [25] were analyzed. The first EEG data set of 29 healthy subjects and the second data set are of 23 epileptic subjects (Table 11.1).
11.2.2 Methods In this paper, the authors have proposed a feature extraction method for the classification of EEG signals. In the following section the data is prepared in order to examine the power spectrum of the EEG signals of α, β, δ and θ waveform and to estimate the power spectral density of these waves. Once we extract the feature then we will use Fuzzy Entropy for feature selection and at last we will use quadratic SVM classifier for evaluating the result. After extracting the EEG signals in the first step with the sampling frequencies (F), descending windows are placed at the start of the signal. The average PSD of each window is calculated for each window using Welch method. Areas under the different frequencies such as delta, theta, alpha & beta bands are calculated using trapezoidal integration technique. The average power contained in each band is calculated in the third step. In the very next step normalized power of each sub band is calculated by dividing average power of these sub bands by the total power of complete band [26]. The last step is to slide the window and repeat step second, third & fourth for the whole signal duration. The algorithm is successfully written in MATLAB(R) (Fig. 11.5).
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Figure 11.5 Power spectral density estimation curve of 10 s signal represent the PSD estimated curve consequent to the average power of delta, theta alpha and beta bands.
11.2.3 Performance For performance evaluation four different parameters are used. They are specificity (to correctly identify the negative cases), sensitivity (to correctly identify the positive cases), selectivity (positive predictive value) and accuracy (the proportion of correctly classified instances) [31]. The formulas are given below: Sensitivity 5
TP 100 ðTP 1 FN Þ
Specificity 5
TN 100 ðTN 1 FP Þ
Selectivity 5
TP 100 ðTP 1 FP Þ
Accuracy 5
TP 1 TN 100 ðTP 1 TN 1 FP 1 FN Þ
The sensitivity, specificity, selectivity and accuracy are calculated by using confusion matrix, where TP, FP, TN, FN are true positive, false positive, true negative, and
Fuzzy entropy based seizure detection algorithms for EEG data analysis
false negative respectively. The specificity is define as true negative rate and measures the ability of a test to correctly exclude the condition (not detect the condition) when the condition is absent. The sensitivity is also called as true positive rate measures the ability of a test to detect the condition when the condition is present. The selectivity is also called positive predictive value and is calculated as the proportion of positives that correspond to the presence of the condition. Accuracy define as the number of all correct predictions divided by the total number of the dataset
11.3 Results This paper gave feature extraction results by calculating the normalized power (Pnorm) of normal cases and compared with epileptic patients data which indicates the activity of particular EEG Recording out of complete power in percentage. The normalized powers of all waves of healthy & unhealthy patients of channels (FP1-F7&FP2-F8) are shown in (Tables 11.2 and 11.3). Table 11.2 Normalized power values of 29 EEG signals of normal subjects of channel (FP1-F7 & FP2-F8). Pnorm values of 29 EEG signals
Pnorm values
EEG waveform frequency
δ
θ
Subjects
Channel (FP1-F7) of healthy subjects
NS1 NS2 NS3 NS4 NS5 NS6 NS7 NS8 NS9 NS10 NS11 NS12 NS13 NS14 NS15 NS16
0.5413537 0.4767184 0.2939254 0.4253149 0.5274243 0.4797155 0.5733298 0.5537195 0.564974 0.5475365 0.5840516 0.5429309 0.4409271 0.4254111 0.5772271 0.494469
0.2872455 0.2560685 0.2356267 0.2990406 0.2883039 0.2914022 0.2936023 0.271506 0.2903853 0.288925 0.294204 0.278729 0.2595523 0.2583051 0.3007157 0.2593165
α
β
0.1004033 0.100909 0.2059518 0.1743965 0.1133399 0.1376972 0.0867633 0.0699613 0.0906103 0.0919169 0.0793545 0.0943066 0.121778 0.1401161 0.0879972 0.0924897
0.0549041 0.1516202 0.2491762 0.084046 0.0546007 0.0746054 0.0301287 0.0898006 0.0379069 0.0556635 0.0262906 0.0682626 0.1627377 0.1607761 0.0176447 0.1389988 (Continued)
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Table 11.2 (Continued) Pnorm values of 29 EEG signals
Pnorm values
EEG waveform frequency
δ
θ
Subjects
Channel (FP1-F7) of healthy subjects
NS17 NS18 NS19 NS20 NS21 NS22 NS23 NS24 NS25 NS27 NS28 NS29
0.4760902 0.404047 0.551532 0.5133274 0.3423469 0.4702214 0.3022422 0.3781471 0.3467232 0.5198029 0.3777926 0.4468493
Subjects
Channel (FP2-F8) of healthy subjects
NS1 NS2 NS3 NS4 NS5 NS6 NS7 NS8 NS9 NS10 NS11 NS12 NS13 NS14 NS15 NS16 NS17 NS18 NS19 NS20 NS21 NS22 NS23
0.542309 0.4635146 0.4142268 0.455424 0.5477776 0.4300589 0.5675522 0.6026477 0.5427482 0.5308815 0.5736176 0.561036 0.4095928 0.464734 0.5764885 0.5303974 0.5810494 0.4343215 0.5728333 0.3962846 0.336031 0.4503725 0.3406244
0.2481818 0.2619032 0.2948771 0.2756824 0.2332728 0.2701072 0.1819259 0.2393114 0.2554684 0.2778376 0.2182882 0.2828378
0.2876595 0.2523885 0.2605427 0.2893796 0.2889696 0.274989 0.2919507 0.2875673 0.2861007 0.2897235 0.2887541 0.2844362 0.2407916 0.2742661 0.2996099 0.2720615 0.2833371 0.272232 0.295526 0.2250875 0.2194597 0.2716224 0.1787516
α
β
0.0980836 0.1507571 0.0956789 0.0971098 0.1637028 0.1311979 0.0934789 0.1419741 0.173686 0.1038362 0.1256813 0.1409216
0.1631938 0.1678155 0.0416824 0.0984433 0.2460461 0.1125902 0.4113957 0.2260465 0.2087424 0.0827499 0.2646949 0.1132077
0.1012647 0.1076913 0.1494957 0.1467621 0.0996784 0.1508269 0.0881056 0.061647 0.0981829 0.101529 0.0792797 0.0868363 0.1185299 0.1328746 0.0886994 0.0859439 0.0733208 0.138488 0.0837847 0.1114892 0.1258759 0.1438582 0.0894939
0.0526231 0.1617248 0.1601772 0.0918793 0.0474038 0.1280574 0.0362683 0.0325049 0.0569534 0.0618052 0.0424668 0.0518339 0.2168693 0.1122007 0.0187995 0.0963894 0.0466455 0.1392849 0.0317154 0.2536664 0.3055912 0.1180808 0.3798016 (Continued)
Fuzzy entropy based seizure detection algorithms for EEG data analysis
Table 11.2 (Continued) Pnorm values of 29 EEG signals
Pnorm values
EEG waveform frequency
δ
θ
Subjects
Channel (FP1-F7) of healthy subjects
NS24 NS25 NS27 NS28 NS29
0.4100158 0.3951881 0.5776967 0.4079492 0.4837167
0.2434204 0.2663458 0.2829119 0.2311423 0.2911485
α
β
0.1260991 0.1599746 0.0750429 0.1205292 0.1236057
0.2060265 0.1627236 0.048683 0.2263818 0.0852641
Table 11.3 Normalized power values of 23 EEG signals of epileptic subjects of channel (FP1-F7 & FP2-F8). Pnorm values of 23 EEG signals
Pnorm values
EEG waveform frequency
δ
θ
Subjects
Channel (FP1-F7) of unhealthy subjects
ES1 ES2 ES3 ES4 ES5 ES6 ES7 ES8 ES9 ES10 ES11 ES12 ES13 ES14 ES15 ES16 ES17 ES18 ES19 ES20
0.3421377 0.4742951 0.5793422 0.4678816 0.3306869 0.4131936 0.2944349 0.6015025 0.5816088 0.5009388 0.4903287 0.5914533 0.3789077 0.5561882 0.5683588 0.4965758 0.5696945 0.4211835 0.541524 0.3948502
0.2834877 0.2712509 0.3041304 0.3151458 0.2905602 0.2687825 0.2904368 0.2920809 0.3013284 0.2723085 0.3115546 0.302994 0.229372 0.3049554 0.2786344 0.3087101 0.3022929 0.3194771 0.3038178 0.2478928
α
0.2291174 0.1228368 0.085104 0.1505602 0.2230337 0.1548142 0.2679495 0.0663021 0.0853984 0.1101891 0.1369251 0.0780709 0.1250842 0.0965006 0.0755104 0.1351923 0.0905007 0.1866851 0.1077074 0.1255535
β
0.1277282 0.1159599 0.0149806 0.04904 0.1383617 0.1473079 0.1289547 0.0242627 0.0152678 0.1009326 0.0440619 0.0111395 0.2528642 0.0258101 0.0620241 0.0424187 0.0210667 0.0547045 0.0302755 0.2173386 (Continued)
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Table 11.3 (Continued) Pnorm values of 23 EEG signals
Pnorm values
EEG waveform frequency
δ
θ
Subjects
Channel (FP1-F7) of unhealthy subjects
ES21 ES22 ES23
0.5817724 0.4458387 0.4117115
Subjects
Channel (FP2-F8) of unhealthy subjects
ES1 ES2 ES3 ES4 ES5 ES6 ES7 ES8 ES9 ES10 ES11 ES12 ES13 ES14 ES15 ES16 ES17 ES18 ES19 ES20 ES21 ES22 ES23
0.3331861 0.4428823 0.5870865 0.4880558 0.3485177 0.3452487 0.4107111 0.6157103 0.5955744 0.5525959 0.5301458 0.5783455 0.34483 0.5681467 0.5379521 0.4926048 0.578369 0.4438163 0.5323904 0.3357469 0.4059924 0.4313254 0.4040865
0.2884618 0.3002204 0.2500198
0.2796609 0.2658551 0.3065013 0.3157325 0.2897896 0.2548184 0.2963829 0.2984086 0.3002367 0.2839076 0.3047195 0.3007521 0.1993141 0.3065303 0.2610826 0.3096217 0.297146 0.3088214 0.3048773 0.2185742 0.2723842 0.2942861 0.2519396
α
0.07896 0.1597868 0.1359176
0.2349981 0.1314447 0.081316 0.1406498 0.2104969 0.1834798 0.1897171 0.0638346 0.0766989 0.0879631 0.1109345 0.087288 0.0966619 0.0907102 0.0760903 0.1376689 0.0817463 0.1675756 0.1143287 0.1302265 0.1688786 0.1701833 0.1322717
β
0.0348779 0.0770944 0.1873887
0.134644 0.1443431 0.0086248 0.0382553 0.1339975 0.2007346 0.0858028 0.0059653 0.0112165 0.0597538 0.0375317 0.0172106 0.3473494 0.0180742 0.1100769 0.0429596 0.0265642 0.0623622 0.0316246 0.3022749 0.1364904 0.0870863 0.1969331
The same processing steps are further applied on remaining 15 channels of EEG recording for Normal and Epileptic subjects. For feature selection Fuzzy Entropy algorithm calculates a feature score for each feature which can then be applied to rank and select top scoring features. PSD is the frequency response of periodic random signal. It gives information of average power distribution in frequency component. For classifying the experimental EEG signals,
Fuzzy entropy based seizure detection algorithms for EEG data analysis
the Quadratic SVM concept has been used. The computed normalized values are used by Quadratic SVM classifier to train and test the system. The performance of proposed methodology is evaluated in terms of parameters of confusion matrix & ROC Curve [2731] shown in Figs. 11.6 and 11.7.
Figure 11.6 Confusion matrix.
Figure 11.7 ROC curve.
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Table 11.4 Comparison with other application. Authors
Sensitivity
Specificity
Selectivity
Accuracy
N.kannathal et al. (2005) Rajesh et al. (2011) Zhen Zhang et al. (2013) Saman Saraf (2017) Our Findings
100%
91.3%
93.5%
90% 90.8% 93.33% 70.5% 96.2%
In this work, the quadratic SVM classifier had gained the sensitivity, specificity; selectivity and accuracy are 100%, 91.3%, 93.5% and 96.2% respectively. Table 11.4 shows comparison with other applications
11.4 Conclusion The main goal of this paper is to develop a technique for the fast analysis and classification of epileptic patients. This fast automated approach will help to reduce the sudden death rate due to delay in diagnosis. This paper uses a unique combined approach of PSD, Fuzzy Entropy and Quadratic SVM classifier for differentiating the Epileptic EEG signal from normal ones. The data set is taken from Natus NeuroWorks EEG Recording Machine from RML Institute of Medical Sciences, Lucknow (U.P.), India. Our classification result shows sensitivity, specificity, selectivity and accuracy are 100%, 91.3%, 93.5% and 96.2% respectively.
Acknowledgment The authors are very thankful to the valuable guidance and support of Dr. A. K. Thacker, Head of Department, Department of Neurology, RML Institute of Medical Sciences and Ms. Nidhi Singh, Technical Staff EEG Lab, RMLIMS, Lucknow (U.P.).
References [1] K. Brigham, B.V.K. Vijaya Kumar, Subject identification from electroencephalogram (EEG) signals during imagined speech. Biometrics: Theory Applications and Systems (BTAS), 2010 Fourth IEEE International Conference on. IEEE, 2010. [2] J. Wolpaw, N. Birbaumer, D.J. McFarland, G. Pfurtscheller, T.M. Vaughan, Brain-computer interfaces for communication and control, Clin. Neurophysiol. 113 (2002) 767791. [3] A.D. Boro, S. Haut, EEG Waveform, The Saul R Korey Department of Neurology, Albert Einstein College of Medicine; Physician, Department of Neurology, Montefiore Medical Center, 2017. [4] A.S. Al-Fahoum, A.A. Al-Fraihat, Methods of EEG signal features / extraction using linear analysis in frequency and time-frequency domains, ISRN Neurosci. 2014 (2014). [5] E.D. Ubeyli, Statistics over features: EEG signals analysis, Comput. Biol. Med. 39 (8) (2009) 733741. [6] R. Agarwal, J. Gotman, D. Flanagan, B. Rosenblatt, Automatic EEG analysis during long-term monitoring in the ICU, Electroencephalogr. Clin. Neurophysiol. 107 (1) (1998) 4458.
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[7] A. Meyer-Lindenberg, The evolution of complexity in human brain development: an EEG study, Electroencephalogr. Clin. Neurophysiol. 99 (5) (1996) 405411. [8] L. Orosco, A. Garcés Correa, E. Laciar, A survey of performance and techniques for automatic epilepsy detection, J. Med. Biol. Eng. 33 (6) (2013) 526537. [9] A. Subasi, E. Erçelebi, Classification of EEG signals using neural network and logistic regression, Comput. Methods Prog. Biomed. 78 (2) (2005) 8799. [10] A. Subasi, EEG signal classification using wavelet feature extraction and a mixture of expert model, Exp. Syst. Appl. 32 (4) (2007) 10841093. [11] H. Jasper and L.D. Proctor, Reticular Formation of the Brain, 1958. [12] M.R.N. Kousarrizi, A.A. Ghanbari, M. Teshnehlab, M. Aliyari, A. Gharaviri, Feature extraction and classification of EEG signals using wavelet transform, SVM and artificial neural networks for brain computer interfaces, in: Proceedings of the International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing (IJCBS ’09), 2009, pp. 352355. [13] A.M. Ivanitsky, EEG recording, Cognitive EEG Laboratory, Institute of Higher Nervous Activity and Neurophysiology, 2001. [14] H. Kordylewski, D. Graupe, K. Liu, A novel large-memory neural network as an aid in medical diagnosis applications, IEEE Trans. Inf. Technol. Biomed. 5 (3) (2001) 202209. [15] I. Guler, E.D. Ubeyli, Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients, J. Neurosci. Methods 148 (2) (2005) 113121. [16] E.D. Ubeyli, Wavelet/mixture of experts network structure for EEG signals classification, Exp. Syst. Appl. 34 (3) (2008) 19541962. [17] J. Xiang, et al., The detection of epileptic seizure signals based on fuzzy entropy, J. Neurosci. Methods 243 (2015) 1825. [18] W. Chen, et al., Characterization of surface EMG signal based on fuzzy entropy, IEEE Trans. Neural Syst. Rehabil. Eng. 15 (2) (2007) 266272. [19] A. Anwar, An entropy-based feature in epileptic seizure prediction algorithm, IOSR Journal of Computer Engineering (IOSR-JCE) e-ISSN: 2278-0661, p-ISSN: 2278-8727, vol. 17, issue 6, Ver. I (Nov Dec. 2015), pp. 4754. [20] Y. Yeniyayla, Fuzzy entropy and its application. Diss. DEÜ Fen Bilimleri Enstitüsü, 2011. [21] G. James, D. Witten, T. Hastie, R. Tibshirani, An Introduction to Statistical Learning, Springer New York, New York, 2013. [22] R. Singla, et al., Comparison of SVM and ANN for classification of eye events in EEG, J. Biomed. Sci. Eng. 4 (01) (2011) 62. [23] D. Gajic, Z. Djurovic, S. Di Gennaro, F. Gustafsson, Classification of EEG signals for detection of epileptic seizures based on wavelets and statistical pattern recognition, Biomed. Eng.: Appl., Basis Commun. 26 (2) (2014) 145. [24] http://www.medicine.mcgill.ca/physio/vlab/biomed_signals/EEG_n.htm, Bio Signal Asquisition, The McGill Physiology Virtual Laboratory, McGill University, 2018. [25] DataSet, Department of Neurology, RMLIMS, Lucknow (India), 2018. [26] M.M. Siddiqui, G. Srivastava, S.H. Saeed, Detection of rapid eye movement behaviour disorder using short time frequency analysis of PSD approach applied on EEg signal (ROC-LOC), Biomed. Res. 26 (3) (2015) 587593. [27] S. Saraswat, G. Srivastava, S. Shukla, Classification of ECG signals using cross-recurrence quantification analysis and probabilistic neural network classifier for ventricular tachycardia patients, Int. J. Biomed. Eng. Technol. 26 (2) (2018) 141156. [28] S.M. Usman, M. Usman, S. Fong, Epileptic Seizures Prediction Using Machine Learning Methods, Computational and Mathematical Methods in Medicine, 2017. [29] M. Yildiz, E. Bergil, C. Oral, Comparison of different classification methods for the preictal stage detection in EEG signals, Biomed. Res. 28 (2017) 2. [30] M. Bandarabadi, et al., Epileptic seizure prediction using relative spectral power features, Clin. Neurophysiol. 126 (2) (2015) 237248. [31] J.S. Borer, et al., Sensitivity, specificity and predictive accuracy of radionuclide cineangiography during exercise in patients with coronary artery disease. Comparison with exercise electrocardiography, Circulation 60 (3) (1979) 572580.
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