Phase and Amplitude Coupling Feature Extraction ...

4th International Conference on Knowledge-Based Engineering and Innovation (KBEI-2017) Dec. 22th, 2017 (Iran University of Science and Technology) – Tehran, Iran

Phase and Amplitude Coupling Feature Extraction and Recognition of Ictal EEG using VMD Amirmasoud Ahmadi, Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran

Mahsa Behroozi, Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology Tehran, Iran

Abstract— Automatic classification of epileptic signals plays a critical role in long term monitoring and diagnosis. This study provides a novel feature extraction and pattern recognition technique for classifying epilepsy using phase-phase, phaseamplitude and amplitude-amplitude coupling based on Variational Mode Decomposition (VMD). The EEG signal was decomposed to band limited intrinsic mode functions (BLIMFs) in the first place. Second, a coupling of BLIMFs was extracted as a feature and after determining an optimal feature two classifiers, strictly speaking, Random Forest and Extreme Learning Machine have classified the inputs. In order to evaluate and compare the performance of the new presented method Bonn University dataset of epilepsy have been used. Results indicate a more desirable performance than any other applied method suggested previously. Keywords— Epileptic, Variational Mode Decomposition (VMD), Phase-Phase Coupling (PPC), Amplitude-Amplitude Coupling (AAC), Phase-Amplitude Coupling (PAC), Extreme Learning Machine (ELM), Random Forest Classifier

I. INTRODUCTION The widely held belief that, seizures as frequent symptoms of brain disorders are restricted to epileptic brains, may not be true. Seizures can occur in brains under a variety of conditions, completely irrelevant to the host’s characteristics [1]. World health organization (WHO) reports stated epilepsy as the second probable neurological disease underneath stroke. Epilepsy afflicts an estimated 50 million people and this population increases 5% per year as time goes by. A considerable percent of epileptics are from countries with less than average incomes. [2]. Electroencephalogram signal (EEG), generally used to find problems related to electrical activity of brain, is done to diagnose and monitor seizure disorders based on collecting brain neuronal firings [3]. Although the typical standard procedure for diagnosing epilepsy which includes physician’s direct examination is an exhausting routine, not all neurologists’ diagnosis results are identical [4]. Consequently, promoting an automated computer aided diagnostic method to facilitate the physician’s diagnosis plays an important role in anticipation of

978-1-5386-2640-5/17/$31.00 ©2017 IEEE

Vahid Shalchyan, Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology Tehran, Iran

Mohammad Reza Daliri Biomedical Engineering Department School of Electrical Engineering, Iran University of Science and Technology Tehran, Iran

seizure in order to boost the analysis [5]. All things considered, EEG is a redundant discrete-time sequence. In order to develop an automated system for seizure detection, satisfying feature extraction and classification are needed to exclude redundancy. In the direction of finding the most advantageous feature extraction, frequency domain, time domain and time-frequency domain frameworks have been offered [6]. Techniques in time frequency analysis comprises the study of a signal in time and frequency domains, using time and frequency representations concurrently. Wavelet transform [8-10] and short Time Fourier Transform [7] offer calculable time frequency attributes of EEG signals. Since STFT as a consequence of using a fixed time window has its own disadvantages WT has been used to mask this disadvantage by a mother wavelet which is a template based appearance of measure, the swing template and inconclusive decomposition level. Empirical mode decomposition (EMD) a time frequency method which can decompound a signal into secondary components entitled intrinsic mode functions (IMFs) [11, 12]. Empirical mode decomposition has been largely used in several work bases, distinctively for nonlinear- nonstationary signal processing such as EEG [13-15]. An area measurement done by Pachori et al showed an acceptable inequity in performance with EMD by using the trace of analytic IMFs [13]. Li et al demonstrated that employing EMD to decompound the ictal and inter-ictal EEG into distinct IMFs, results in coefficient of variation and fluctuation index. Consequently parameters have been used for classification [14]. Alam et al showed that different kinds of EEG signals could be distinguish better as the order statistics of IMFs increased. Calculated IMFs can vary based on methods of extremal point finding, interpolation of extremal point finding and stopping criteria [15, 16]. EMD confronted several problems and limitations such as the mode mixing problem and bounded mathematical understanding, as a consequence complete ensemble empirical mode decomposition (CEEMD) and ensemble empirical mode decomposition (EEMD) [17-18] were suggested to compensate the imperfections and enhancements were accessible. Previously a combination scheme of k-means clustering with EEMD resulted in 98% accuracy for epileptic seizure discovery these techniques

could discriminate normal and seizure EEGs [19]. A new method based on CEEMD has been developed. Variational mode decomposition (VMD) [20, 16] a flexible decomposition technique decompounds a signal to a series of band limited intrinsic mode functions (BIMFs) concurrently and nonperiodically. VMD solves the Variational issue by focusing on augmented Lagrangian method as an approach. In comparison to other possible choices VMD is based on an improved mathematical theory, a proper derivation process and capable of dividing homogenous frequency harmonic signal [16]. Undeveloped EEG in accordance with special rules is decomposed to particular subcomponents, hence, considerable numbers of parameters are calculated as features, such as short term maximum Lyapunov exponents [22] dynamical similarity algorithm [23], effective correlation dimension [24], accumulated energy [25] and phase synchronization [26] to predict an epileptic seizure. Researches declared that multivariate calculations in predicting seizures are more influential on performance than univariates [5, 27-28]. The Phase Locking Value (PLV), a bivariate measure, which reflects the rate of phase and amplitude synchronization in a particular frequency band [29]. As the synchronization rate increases individual brain parts tend to work coherently [30-31]. Neural synchronization plays an impressive role in understanding cognitive tasks [32-33]. At the other hand epileptic EEGs revealed synchronizations which can be measured as parameters [28, 34-36]. Although different studies have been done on PLV [5, 26-27] their data application on general epileptic patients due to intracranial method of gathering is prohibited. According to PLV selecting a proper frequency band is indispensable. Intrinsic frequency bands for EEGs in human brain consists of theta (4-8 Hz), alpha (8-12 Hz), beta (12-30 Hz) and gamma (3060 Hz) [37]. In order to filter the frequency bands and decompounding signals filtering algorithms such as digital band-pass filtering (BPF) [38], empirical mode decomposition (EMD) [12], multivariate empirical mode decomposition (MEMD) [39] and noise-assisted multivariate empirical mode decomposition (NA-MEMD) [40] could be employed to optimize the results. The machine learning method have been used to recognize seizure in EEG signals, previously. Different approaches have been employed to classify EEG signals, [4150] such as artificial neural networks (ANN) [43]. Since back propagation (BP), a conventional learning algorithm for ANN is likely to drop in a local, it is tedious to adjust and slow in learning ANN can not be described as a particular method [44]. Support vector machine (SVM), could be considered as a favored approach in classification of epileptic EEG signals [4547].beyond everything training SVM carrying a quadratic programming (QP) problem, which brings complexity to computations , is quadratic regarding the number of training examples so far as possible[45]. Huang et al [48, 49] advised a derived machine learning method labeled as Extreme learning machine (ELM) for the generalized single hidden layer feedforward neural networks (SLFN). In this approach hidden node parameters are made disconnectedly and the output

weights are analytical [48, 49]. ELM was productively successful at seizure detection heretofore [42, 44]. The current study offers a novel VMD-based classification approach. Applying BLIMF coefficients on feature extraction in order to classify epileptic seizure with ELM and Random Forest have not been examined formerly. This attempt achieved 100 percent performance frequently. II. MATERIALS AND METHODS This study provides a VMD-based method. In the first place the EEG signal is segregated to 10 BLIMFs afterwards, phase and amplitude of BLIMFs were calculated with Hilbert transform after picking optimal features out with statistical tests and calculating the number of optimal features for each case with ELM and Random Forest the classification step was performed (Fig.1). Above mentioned cases are: Case I: D vs E Case II: AB vs E Case III: CD vs E Case IV: ABCD vs E A. Dataset EEG database is collected from Bonn University hospital of Freiburg, Germany Epilepsy center [21] which consists of five subsets from A to E. Each subset is a 100 single channel EEG record and each single segment is 23.6 seconds duration. Previously mentioned EEG database have been recorded in international 10-20 electrode placement scheme. A and B subsets was captured extracranially while C to E have been captured intracranially. After visual check ups for artifacts unbroken multichannel EEG was cut to segments. Further information about the database will be reported in Table I. TABLE I.

SUMMARY OF EEG DATASETS

A&B

C&D

E

Subject

5 Healthy

5 Epileptic

5 Epileptic

Electrode Type

Surface

Intracranial

Intracranial

State

Eye open and normal

Seizurefree(Interictal)

Seizure(ictal)

Epoch Duration

23.6 sec

23.6 sec

23.6 sec

Number of Epoch

100

100

100

Fig. 1. Block diagram of proposed algorithm

B. Variational Mode Decomposition Since the decompositions in EMD brings more exhaustive details than the primary time series [11] as reported in EMD a signal named x(t) is decompound into a summation of IMFs [12], which M is total number of IMFs, Cm(t) is the m-th IMF and r(t) is the residue. ( ) + ( ),

( ) =

= 1,2, … ,

(1)

Dragomiretskiy et al [16] offered VMD as an augmentation for EMD in order to decompound a real valued input signal f into a discrete number of modes uk which offers specific sparsity properties while propogating the signal. Wk is a central measure which BLIMF concentrates around it. The VMD is identical to a constrained Variational problem [16].

{

},{

[

}{

( )+ s.t. ∑

( )]

∗

}

(2)

=

Where k is number and uk is notation BLIMFs. In order to resolve the reconstruction constraint augmented Lagrangian L is proposed. L({

,{

‖ ( ) −∑

}, } = ∑

[

( )+

( )] + ( )‖ +〈 ( ), ( ) − ∑

(3) ( )〉

(

).

(1 + |

|)

(4)

( ) =

( )= ( )+

( ) .

( )

(5)

After calculating the Hilbert transform the amplitude of the signal in time step n and instantaneous phase are determined by the following formula. ( )=

( ) + ( )

(6)

( ) ) ( )

(7)

( ) = arctan (

1) Phase-Phase Coupling In the current study PLV was used to estimate phase phase coupling assuming that each x(n) and y(n) signals oscillating in a different BLIMF. After measuring instantaneous phase and amplitude by Hilbert transform phase locking value norm estimates the amount of phase coupling [51] if Δ ( ) = ( )− ( ) signals are said to be n:m synchronized PLV could be calculated as follows. =

∗

In this place k=10 is noticeable and BLIMFs number was fixed at 10. Unprocessed EEG is decomposed to 10 BLIMFs that for each one the amplitude oscillates intensely. In order to decrease the effect of unpredictability a base-a logarithmic operation is applied on BLIMFs and new BLIMFs named nBLIMFs. The logarithmic operation is as follows: =

C. Phase coupling based feature extraction To demonstrate the amplitude and instantaneous phase of signal Hilbert transform have been used. If the analytic signal X(n) is defined as:

1

( )

(8)

All the signals between the same reference, we can use n=m=1. PLV can fluctuate between 0 and 1. While zero means no coupling one indicates absolute coupling between frequency bands. 2) Phase-Amplitude Coupling In the direction of estimating phase and amplitude coupling both phase and amplitude of x(n) and y(n) must be calculated by Hilbert transform then a second Hilbert transform is done to ) for applying PLV as measure a phase of an amplitude ( below [52]

=

1

(

[ ]

[ ])

(9)

The amount of Pyx is understood as PLV for PPC 3) Amplitude-Amplitude Coupling Unlike two methods above the amplitude amplitude coupling is computed with Pearson correlation: ( , )

( , ) =

(10)

X and Y are the power densities of x(n) and y(n). Previously mentioned correlation can vary between -1 to 1. Correlation 1 presents full coupling and -1 reveals the fact that no linear correlation occurred between x and y. D. Feature Selection As a matter of fact redundancy of features generates a reduced classification accuracy and demands considerable computational preconditions. Accordingly excessive features should be discarded before giving the suitable individuals to the classifier. In feature selection procedure features are ranked with statistical based calculations. Features with higher discrimination and low correlations are ranked above others [53]. In case that attributes of classes distribute in a different form from standard nonparametric statistical significance test such as T-test and RF [53]. Here two feature ranking methods, strictly speaking RF and T-test are employed. 1) Relief (RF) Among supervised feature ranking methods RF can play an impressive role in detection of conditional dependencies within attributes before feature selection this algorithm is employed in order to preprocess since RF can deal with missing or noisy data. RF is able to afford a united vision on the attributes in classification. RF can be calculated in this way if the number of cases is N: ( ) =

1 2

(

,

−

( )

−

,

−

( )

)

(11)

Fdc and fsc which are mentioned above show the amount of ith feature closest to xi. Fdc and fsc are in association with dissimilar and similar class labels respectively. Ft,i represents the amount of sample xi on feature fi and P stands for interval computations. 2) T-Test Regarding feature selection for classification, ineffective features must be eliminated and valuable features that contain information should remain for calculation. T-test a statistical test which arbitrates among quantities to see if their means diverge crucially or not, have been used in this place[54].

E. Random forest (RF) Random forest, is a new, accelerated, rigorous, noise resistant classification method which has bagging and random feature selection collectively. RF includes enormous numbers of decision trees which choose the features based on a reboot training set Si (i is the ith internal node). RF trees expansion forms according to classification and regression trees (CART) method without cutbacks. Generalization error grows as the number of trees increases, until it converges to a borderline [55]. The number of trees was set to 100 in this place. F. Extreme Learning Machine (ELM) A single hidden layer feedforward neural networks (SLFN) with unpredictably chosen input weights and biases generates ELM which has shown a confirmed reduction by one hundred to thousand times in training [56]. If Xj=[ , … , ] and Yj= [ , … , ] are the input and output nodes in SLFN and g () is the activation function yi could be calculated as follows: =

(

.

+ )

(12)

Something to bear in mind is, the number of hidden nodes was titled L, hidden layer biases bi and input weights Wi=[ , , … , , ] are completely random. Bi represents the ith weights among the output and the hidden layer. As the matrix H is the quantity produced from the hidden layer aforementioned equation can be rewritten as: =

(13)

Obtaining a least square solution is the basis of ELM. As reported by Moore-Penrose in generalized inverse theory [49] the output layer weights could be measured as follows: =

(14)

should be considered the generalized Moore-Penrose pseudo-inverse of H. III. RESULTS AND DISCUSSION EEG signal is a nonstationary nonlinear natural signal which frequency domain based techniques such as FFT can only unveil comprehensive components during the time that the epileptic characteristic waves might occur spontaneously. Transient behaviors could be extracted from EEG signals by VMD. The EEG dataset from Bonn University was given to VMD on account of decomposing to a certain number of BLIMFs. By any means as noted in [15] while EMD was employed to EEG signal the IMFs are produced recursively. Meaning that the first IMF consists of the highest frequency and the second IMF contains a component with a lower frequency and so on. The amount chosen for BLIMFs is changeable based on the desired number that the supervisor demands but the IMFs amount can not be changed. From another point of view the most important

Fig. 2. rows represent the participant ratios for each pair of BLIMFs and features respectivively TABLE II.

ELM +ttest 100 100 100 100

Case I Case II Case III Case IV

CLASSIFICATION ACCURACY (MEAN%)

Elm+RF 100 100 100 100

differentiation between VMD and EMD is the arbitrary number of BLIMFs in VMD. After calculating BLIMFs PAC, AAC and PPC coupling features were extracted. The VMD coefficient number was set to 10 as mentioned in methods. The number of extracted PPC, AAC and PAC coupling features were 45,45 and 100. Firstly, each class of the aforementioned four cases were divided to 10 folds randomly. 9 folds were put aside for training and 1 fold remained for the test. These two steps were repeated TABLE III.

Reffrence

ABCD vs E

D vs E

Random Forest+ttest 99 100 99.25 99.87

Random Forest+RF 99.35 100 98.75 100

for each fold and the mean of the correct rate was accepted as the performance of the proposed method. Feature selection was executed on the train data and the classification was performed by ELM and Random Forest classifiers (notice Table II for results). Optimal features investigated in each case by t-test feature selection method and two BLIMFs which have the maximum selected features are established in Fig.2. This figure demonstrates the most effective coupling feature for

PERFORMANCE OF THE PROPOSED METHOD COMPARE WITH OTHER PREVIOUS WORKS.

Method

CR%

[19]

EEMD + Kmeans

98

[20]

CEEMDAN + ANN

98.87

[57]

Adaptive Wavelet Packets + SVM

97.85

Proposed Method

VMD + PPC & PAC & AAC + ELM

100

[42]

Sample entropy + ELM

99

[20]

CEEMDAN + ANN

97.15

[15]

EMD + HOS

100

Proposed Method

VMD + PPC & PAC & AAC + ELM

100

classification. As the results indicate, PAC was selected more frequently than any other feature. In comparison with the correct rates claimed before on this data (notice Table III), this new method manifests a better performance by means of innovating in feature extraction,by calculating phase and amplitude coupling using BLIMFs for the first time .This technique is a novel strategy in classifying biological signals. IV. CONCLUSION Above all it seems pertinent to remember that in the current study a novel VMD based method for feature extraction with coupling was introduced. In all studied instances investigated by ELM a persuasive correct rate of 100% occurred. Another key thing to remember is, this unique method of feature extraction can be an advantageous technique in classification of biological signals such as Alzheimer and MS disease. REFERENCES F. Mormann, T. Kreuz, C. Rieke, R. G. Andrzejak, A. Kraskov, P. David, et al., "On the predictability of epileptic seizures," Clinical neurophysiology, vol. 116, pp. 569-587, 2005. [2] W. H. Organization, "http://www. who. int/mediacentre/factsheets/fs340/en," url> http://www. who. int/mediacentre/factsheets/fs241/en/