Empirical wavelet transform based automated alcoholism detecting using EEG signal features

Biomedical Signal Processing and Control 57 (2020) 101777

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Empirical wavelet transform based automated alcoholism detecting using EEG signal features Arti Anuragi, Dilip Singh Singh Sisodia ∗ National Institute of Technology Raipur, GE Road, 492010, Raipur, India

a r t i c l e

i n f o

Article history: Received 21 November 2018 Received in revised form 25 October 2019 Accepted 16 November 2019 Keywords: Signal processing Electroencephalograms (EEGs) Alcoholism Empirical wavelet transform (EWT) Hilbert–Huang transform (HHT)

a b s t r a c t Electroencephalogram (EEG) signals are well used to characterize the brain states and actions. In this paper, a novel empirical wavelet transform (EWT) based machine learning framework is proposed for the classification of alcoholic and normal subjects using EEG signals. In the framework, the adaptive filtering is used to extract Time–Frequency-domain features from Hilbert–Huang Transform (HHT). The boundary detection method is used for segmenting the Fourier spectrum of the EEG signals to represent in scale-space. Hilbert–Huang Transform (HHT) examines time and frequency information in a single domain using instantaneous amplitude (IA) and instantaneous frequency (IF). The IA and IF are used to form intrinsic mode functions (IMF). The empirical wavelets transform (EWT) using Hilbert–Huang transforms (HHT) extract the statistical features such as mean, standard deviation, variance, skewness, kurtosis, Shannon entropy, and log entropy from each of the intrinsic mode functions (IMF). The extracted features are evaluated by t-test for finding the most significant features. The significant feature matrix is fed to various classification algorithms listed as least square-support vector machine (LS-SVM), support vector machine (SVM), Naïve Bayes (NB), and k-Nearest Neighbors (K-NN). The leave-one-out crossvalidation (LOOCV) is used for training and testing of used models to minimize the chance of overfitting. The results suggest that the highest numbers of the positive samples are obtained using LS-SVM classifier with the polynomial kernel. The LS-SVM also achieved an average accuracy of 98.75%, the sensitivity 98.35%, specificity 99.16%, the precision 99.17%, F-measure 98.76%, and Matthews Correlation Coefficient (MCC) 97.50%. © 2019 Elsevier Ltd. All rights reserved.

1. Introduction Excessive alcoholism leads to an alcohol use disorder (AUD) where the subject is unable to control his/her drinking desire. The AUD stages vary from subject to the subject according to the quantity and drinking pattern of alcohol. People affected by AUD continuously satisfy their cravings in spite of knowing all the physical, mental, and social-related problems. Alcoholism going to be a leading mortal abuse worldwide [1]. According to a world health organization (WHO) report, the intense use of alcohol raises the graph of death rate to 3 million, which is 5 % of the global disease burden. Approximately 2 billion people drink alcohol out of which 81.7 million people are seriously addicted [2–4]. The psychological and counseling-based screening of AUD often fails because sufferer subjects during the questionary method are reluctant to share habitual weaknesses due to social stigma.

∗ Corresponding author. E-mail address: [email protected] (D.S.S. Sisodia). https://doi.org/10.1016/j.bspc.2019.101777 1746-8094/© 2019 Elsevier Ltd. All rights reserved.

Nowadays, noninvasive computer-aided detection (CAD) techniques are used to detect many chronic disorders. A suitable combination of signal preprocessing techniques, pattern recognition and various machine learning approaches form an efficient CAD [5]. CAD acts as a decision-making system for doctors and physicians. Electroencephalogram (EEG) signals are used to capture all the neuropsychiatric stages of the brain [6,7]. The EEG signal based noninvasive test may be useful in screening of many nervous system disorders, including AUD [8]. EEG signals are recorded in a microvolt (␮v) and represented in time series. The EEG signals comprise different frequency components such as delta, theta, alpha, beta, and gamma. A brief description of the EEG signals subbands is presented in Table 1. The voltage fluctuations in EEG signals produced by electrodes is very small (in microvolt (␮v)) [4]. The manual screening of the EEG signal very difficult and tend to inaccurate results [9]. Theta and delta sub-bands are higher in the alcoholic subject as compared to normal was observed by Bauer [10] concluded that beta amplitude increases in the alcoholic subject ranging (19.5 Hz–39.8 Hz). Neurophysiological evaluation becomes more convenient if the power

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Table 1 Frequency bands in EEG signals. Frequency

Wave Band

More than 40 Hz 13-40 Hz 8–12 Hz 4–7 Hz 0.1–4 Hz

Gamma () Beta (ˇ) Alpha (˛) Theta () Delta (ı)

Frequency Band

Activity of brain Considering, coordinated idea Awake and excited, alertness Awake and Relaxed Light sleep Deep Sleep

spectrum of alcoholic and Non-alcoholic EEG signals are computed. However, due to the non-linear nature of EEG signals, the time domain or frequency, domain analysis is too difficult. Therefore, various Time-frequency domain signal processing techniques such as Fourier transform (FFT) [11], Short-Time Fourier Transform (STFT) [12,13], wavelet transform (WT), Wigner–Ville distribution (WVD) [14], and Hilbert–Huang transform (HHT) [15–17] time-frequency distributions (TFD), eigenvector methods (EM) and autoregressive method (ARM), are employed to interpret EEG signals [18–22]. In recent past various studies are carried out for automatic alcoholism detection using different wavelet transform including Narrow bandpass Butterworth filters [23], orthogonal wavelet filter bank [24], dual-tree complex [25], empirical mode decomposition (EMD) [26], tuned-Q wavelet (TQWT) [27,28] and flexible analytical wavelet transform (FAWT) [29]. The empirical wavelet transform (EWT) [30] uses an empirical method instead of a dilation factor for decomposing EEG recording into certain sub-bands using an adaptive bandpass filtering technique. The construction of filter banks is based on the Fourier spectrum of the signal. Therefore, in literature, many applications of EWT on EEG signals have been discussed including focal EEG signal [31], epileptic seizure [32,33], Parkinson [34], diagnosis for glaucoma [35], etc. However, to the best of our knowledge, the empirical wavelets transform (EWT) with Hilbert–Huang transforms (HHT) based machine learning framework is proposed first time in this paper for automated alcohol use disorder (AUD) detection. In this paper, Hilbert–Huang transform (HHT) based on EWT used to segment the time series EEG signal into intrinsic mode functions (IMF) (narrow sub-bands) for both alcoholic and non-alcoholic subjects. The combination of statistical and entropy-based features such as mean, standard deviation, variance, skewness, kurtosis Shannon entropy, and log energy entropy is extracted from each decomposed sub-bands (IMFs). The most significant features are selected using a t-test and further passed to various classifiers for training. The trained models are used for detecting alcoholic and non-alcoholic subjects. The remaining text of this paper is organized under the following sections. Section 2 discussed the different state of artwork done for automated alcoholism detection using various wavelet transform. In Section 3, the proposed methodology is discussed in detail. The experimental results are presented in Section 4. The discussions on the results are outlined in Section 5. In Section 6, the paper is concluded with some suggested limitations and future works.

In [36], the author has extracted two features, namely absolute power and relative power from quantitative Electroencephalography (QEEG). For selecting the most significant features t-test and PCA were utilized, which discriminate both subjects very well and used for classification. The authors concluded that out of all the classifier logistic model trees (LMT) using 10-fold cross-validation outperforms with an accuracy of 96 %. An experiment was performed on features like spectral powers and inter-hemispheric coherence, Area under the ROC curve (AUC) of each feature was computed which is to be considered as a feature selection method, selected features were passed to the classifiers for evaluating the performance of the system. Logistic regression (LR) classifier perform better with accuracy of 90 % [37]. The drawback of time and frequency domain technique is it losses spectral and temporal information which is taken over by recently used technology such as time-frequency domain by employing different wavelet transform [38]. Pachori et al. [27] implemented tuned-Q wavelet transform (TQWT) for the decomposition of the signal along with center correntropy (CC) as a feature. A dimension of the feature vector was reduced by PCA, trained and tested by least squaresupport vector machine (LS-SVM) classifier gain highest efficient with 97 % also formulated an index for monitoring the treatment of the patients. Dual-tree complex wavelet transform (DTCWT) using L2 Norms (L2N) and log-energy entropies (LEE) features from each sub-bands of the signal are evaluated by LS-SVM and sequential minimal optimization support vector machine (SMO-SVM) but the best outcome achieved by SMO-SVM with 97.917 % accuracy [25]. Log-energy feature is extracted employed three-band orthogonal wavelet filter bank (TBOWFB) [39], significant features are then selected based on the ranking method wherein they are fed to several supervised machine learning approaches. A prominent result is achieved by LS-SVM (RBF kernel) with 97.08 % accuracy. Empirical mode decomposition (EMD) wavelet transform [26] is utilized for the disintegration of the signal with various entropy-based features such as entropy, negentropy, kurtosis, skewness and mean are computed, and then evaluated by LS-SVM along with different kernel tricks, but RBF kernel gives the best result with 97.2 % accuracy. Empirical wavelets transform (EWT) is the extended version of EMD where the mode of the decomposed signal is selected adaptively depends upon the frequency spectrum of the EEG signal, which is suitable of non-stationarity signals. Table 2 summarizes the related works for automated alcoholic and Non- alcoholic subject detection based on EEG using different wavelet transforms.

2. Related work

3. Methods and material

In the past various studies have been carried out to ascertain alcoholic and non-alcoholic subjects making use of EEG signals.

The visual illustration of the proposed methodology for alcoholism detection using EEG signals is shown in Fig. 1. Fig.1 mention

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Table 2 The summary related work based on alcoholism detection using EEG signals. Reference

Feature /methods

Classifier

Result

S. Shah et al. [24] M. Sharma et al. [25]

Orthogonal wavelet filter bank (OWFB) Dual-tree complex wavelet transform (DTCWT), Feature - L2 Norms (L2N) and log-energy entropies (LEE)

94.20 % 97.91 %97.91 %95.83 %

A. Priya et al. [26]

Empirical mode decomposition (EMD)

S. Patidar et al. [27] M. Sharma et al. [39]

Tuned-Q wavelet transform (TQWT), centered correntropy (CC) Three-band orthogonal wavelet filter bank (TBOWFB) with log energy as a feature Approximate Entropy (Apen), Higher Order Spectra (Hos), Largest Lyapunov Exponent (Lle), Sample Entropy (Sampen), Haar Mother Wavelet, PCA

K nearest neighbor (KNN) Sequential Minimal Optimization Support Vector Machine (SMO-SVM) LS-SVM Fuzzy Sugeno classifier (FSC) LS-SVM (RBF), LS-SVM (polynomial), LS-SVM LS-SVM (RBF), LS-SVM (Quadratic), Support Vector Machine (SVM) SVM K-NN SVM NB Logistic Regression (LR)

94.67 %,

U. R. Acharya et al. [40] M. R. Nazari Kousarrizi et al. [41] W. Mumtaz et al. [42]

Synchronization likelihood

96.67 %

97.92 %

97.02 % 97.08 %

96.37 %

91.7 % 98.83 %

98 % 93.6 % 91.7 %

Fig. 1. Visual illustration of the proposed methodology used for alcoholism detection using EEG signals.

all important steps performed to carry out the proposed work and their description is given in the following sections. The first step of the framework starts with the preprocessing of EEG signal data; it includes segmentation of 32 s. Duration time-series recording into 8 s. Segmentation forms 120 trails having 2048 samples of each subject with a sampling frequency of 256 Hz. Artifacts present in the signal are removed using filters. Next step is to extract time-frequency information of the signal using Empirical wavelet transform (EWT), EWT work on adaptive filtering banks, which segments the signal using spectral information. Using the scale-space representation technique boundary detection of the frequency spectrum is done. Each segment undergoes through the bandpass filter, which is well defined by scaling and wavelet function. Sub-bands are extracted from each signal. Next, by using Hilbert transform IA and IF are computed for

time-frequency representation (TF representation). This representation demonstrates the oscillation level of the signal. It is followed by feature extraction methods where some of the common features are extracted from the TF representation of both classes. Selected features using a t-test are passed to different classifiers models for classification between alcoholic and non-alcoholic subjects. 3.1. EEG recordings The experiments are carried out on the publicly available dataset of the University of California, Irvine (UCI) Knowledge Discovery in Databases (https://kdd.ics.uci.edu/databases/eeg/eeg.data.html) [43]. The used dataset emerges after a long observation on the genetic predisposition of alcoholic EEG subjects and consists of two

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Fig. 2. (a) The region wise list of electrodes (b) The position of each electrode using standard10/ 20 scheme for recording EEG signals [65,66].

Table 3 The brief description of used dataset. Subjects

Normal

Alcoholic

Stimuli Present S1 obj S2 match S2 no match S1 obj S2 match S2 no match

3.2. Empirical wavelet transform (EWT)

Number of Segments

Sampled Frequency

No. of Samples

120

256 Hz

2048

120

256 Hz

2048

*During data collection, two types of stimuli are taken Single (S1) and dual stimuli (S1 match S2 and S1 do not match S2).

classes alcoholic and normal. This dataset is available in three versions. First is a small dataset that contains only 2 subjects one for each class with all different stimuli. During small dataset collection, two types of stimuli were used, such as single (S1) and dual stimuli (S1 match S2 and S1 do not match S2). The second dataset is large in which 10 subjects of each class with separate training and testing data set is used. The third dataset is a full data set having 122 subjects each with 120 trails. These datasets are recorded fro each subject using different stimuli having 90 pictures. In the present work, experiments are performed using a small dataset because publicly available full dataset (with 122 subjects) is incomplete with few trails are with empty files or labeled as “err”. The 64 number of electrodes (as shown in Fig. 2(a) along with their belonging regions) are used to obtained EEG recordings. The position of each electrode is fixed according to the 10/20 international montage [44] as shown in Fig. 2 (b). Cz was used as a reference, and grounding is done using a nose electrode with an impedance of less than 5 k ˝ [45,46]. The X and Y electrodes are respectively used for horizontal and vertical bipolar Electrooculography (EOG) signal recording. The dataset consists of 32-seconds (approximately 16,400 samples) EEG signals recording with 256 Hz sampling frequency and 12-bits resolution. In the present work, experiments are performed using small dataset where artifacts like eyes and muscular movements (>73.3v) are removed using the filter on the baseline itself. After performing preprocessing only 30 EEG recordings of both the classes are left. The large 32-seconds EEG recordings are segmented into an 8-seconds window size of 4 equal segments of 2048 samples. Summary of the used dataset is given in Table 3. The Fig. 3 gives a picture of one trail of alcoholic and normal subjects. It is evident from Fig.3 that the amplitude of normal subject is more as compared to an alcoholic subject because the normal subject’s mind is awakened and excited while alcoholic subjects are comparatively unconscious. However, the EEG signals are recorded in microvolt, and the manual screening of EEG signals in alcoholic and non-alcoholic is very difficult. Therefore, automated wavelet-based machining learning techniques may be very useful for this task.

In this section, the empirical wavelet transform used for rhythm separation is discussed in detail. The EWT is used to extract various modes of the Fourier spectrum of EEG signal by constructing adaptive wavelets filter bank. The different important steps used by this method are described as follows. Step  1) Apply FFT to the signal (t), where f (t) is a discrete signal, t = ti , i = 1, 2, 3, . . .. . ..n, n denotes the number of samples, the frequency   spectrum X (w) is computed, then find the set of maxima n = ni , i = 1, 2, 3, . . ...m in the Fourier spectrum and deduce

 

their corresponding frequencies w = wi , i = 1, 2. . .. . .m. Here, m denotes the number of maxima. Step 2) Fourier spectrum of the EEG signal is properly separated by the boundaries detection method. The border of each segment can be defined by taking the middle of two progressive local maxima expressed by Eq.1 Where Wi and Wi+1 are two frequency and the boundaries set is denoted by ˝ = {˝1, ˝2 , . . ...˝N−1 } ˝i =

Wi + Wi+1 2

(1)

Step 3) this step defines an adaptive filter bank of m wavelet filter, which is composed of one low-pass filter and m − 1 bandpass filter, based on boundaries. The expressions for the Fourier transform of a scaling function ϕ1 (w) and the empirical wavelets i (w) are given by Eq. 2 and 3.

ϕ1 =

⎧ 1, |w|  (1 − )˝i ⎪ ⎪ ⎪ ⎨ 

cos( ∝ ( ⎪ 2 ⎪ ⎪ ⎩

)˝1 ≺ |w|  (1 +

, ˝1 )) (1 −

)˝1

(2)

0, otherwise

i =

⎧ 1 (1 + )˝i ≺ |w| ≺ (1 − )˝i+1 ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎨ cos(  ˛( , ˝i+1 )) (1 − )˝i+1  |w|  (1 + )˝i+1 2

⎪  ⎪ ⎪ sin( ˛( ⎪ ⎪ 2 ⎪ ⎪ ⎪ ⎩

(3) , ˝i )) (1 −

)˝i  |w|  (1 +

)˝i

0, otherwise





Where ˛ , ˝i = ˇ



1 2˝i



|w| − (1 − ) ˝i



,  ensures that

there should no overlap between two successive transitions and also form the tight frame between empirical scaling function and empirical wavelets, expression of is formulated in Eq. 4 and ˇ (x) is a random function defined in Eq. 5. The equation of the tight frame can be defined as follows [30]:



 ≺ mini

˝i+1 − ˝i ˝i+1 + ˝i

(4)

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Fig. 3. The sample of EEG signal for (a) alcoholic (b) normal subjects.

ˇ (x) =

⎧ ⎨0x ≤ 0 ⎩

1x ≥ 1

(5)

Now finally the reconstruction of subbands signals can be defined in Eq. 8

ˇ (x) + ˇ (1 − x) = 1 , xD (0, 1) .

Step 4) to extract IA and IF from each IMF mode scaling, and wavelet functions are performed. In Eq. 6 approximate coefficients, which are the product of scaling function with analyzed signal f is defined.

f (t) = Wf (0, t) *˚i (t) +

N i=1

Wf (i, t) *i (t)

(8)

The above steps are summarized in the pseudo-code of EWT as algorithm 1 Algorithm 1.

Pseudo Code of EWT

 Wf (0, t) = f, i  =

f ( ) i ( − t) d .

(6)

Similarly, when analyzed signal f is multiplied by empirical wavelet, we get detailed coefficients of the signal defined in Eq. 7.



Wf (i, t) = f,

i

=

f ( )

i ( − t) d .

(7)

Here, Wf (i, t) denotes the detailed coefficients for the i th filter bank at the t th time point.

3.2.1. Time-frequency representation (TFR) Time-frequency representation (TFR) encapsulates temporal and spectral information in a single representation. The Hilbert transform method is used for the time-frequency representation of EEG signals. Let y(t) be the analyzed signal which is broken down into N number of sub-band signals yi (t) : i = 1, 2, 3. . .. . ..N. These sub-band signals are nothing but a narrow-band modulation [47]. The HHT is enforced to resolve IA and IF from each N no. of subband signals. Expression of the analytic signal representation of each sub-band signal in terms of the Hilbert transform function (H) [48] as shown in Eq. (9). The conjugate term in Eq. (9) defines the

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Fig. 4. Hilbert Transform method for time–Frequency representation.

Hilbert function. Fig. 4 shows the working function of the Hilbert transform. y+i (t) = yi (t) + jH [yi (t)]

(9)

Another form of Eq. (9) can be represented by Eq. (10). y+i (t) = Ai (t) ej˚i (t)

(10)

Ai (t) Instantaneous Amplitude (IA) is formulated by Eq. (11).



Ai (t) =

(H [yi (t)])2 + yi2 (t)

(11)

Instantaneous Phase ˚i (t) expressed in Eq. (12). ˚i (t) = arc tan

H

[yi (t)] yi (t)



(12)

The instantaneous frequency (IF) is expressed in terms of fi (t) in Eq. (13). fi (t) =

1 d [∅ (t)] 2 dt i

(13)

The coefficients of the time-frequency of each level of decomposition are expressed as follows [49]: Tfi (f, t) = Ai (t) ı [f − fi (t)]

(14)

3.3. Feature extraction Feature extraction is adopted to acquire hidden discriminative characteristics of EEG signals. The EEG signals are decomposed into 10 IMF (Sub-Bands) using an empirical wavelet transform. The extracted features include mean, standard deviation, variance, skewness, kurtosis, Shannon entropy, and log entropy, which used to characterize the location and variability of EEG signals precisely. The total seventy (7 × 10) time-frequency domain features from each sub-band are extracted for classification. The brief description and expressions used to compute all used features for experimentation are summarised in Table 4. 3.4. Machine learning techniques 3.4.1. Least square support vector machine (LS-SVM) LS-SVM is one of the most widely used classifiers in the biomedical field, which is an extended form of support vector machine (SVM). LS-SVM incorporate equality constraint solutions acquired by linear equation instead of quadratic programming (QP) used in standard SVM [50,51]. The objective function of LS-SVM is mathematically expressed as given in Eq. (22). y (t) = sign



N i=1

yi˛i K (t, ti ) + b

(22)

The detail description of LS-SVM is given in [52,53]. LS-SVM can be implemented using various kernels in Eq. (22) K (t,ti ) is the kernel function may be substituted by different kernel functions such as linear- k(x, y) = (xT y + c), polynomial- k(x, y) = (∝ xT y + c) and radial basis function (RBF) - k (x, y) =

n  k=1



− xk −y

e

d

2 d k

, ti

denotes feature vector which is fed to the various machine learning models for training and testing data, y is predicated class, N represents number of samples used for Lanagrange multipliers. 3.4.2. Support vector machine Support Vector Machine (SVM) [54] is a popularly used machine learning algorithm for non-linear classification. SVM works on the principle of minimizing structural risk in which two different classes are employed for the generation of the hyperplanes. SVM follows the principle of margin maximization [55]. Let (xb , yi ) is data, with i = l to M training points. Each sample input xi , has D features which below to either of two classes yl = −1 or + 1. After the removal of optimization problem hyper plane f (x) = 0 is achieved. This can be expressed as Eq. (23).

M 1 Minimize w2 + c i 2 i=1  

(23)



Subject to yi wt xi + b ≥ 1 − i Here, c referred to as an error penalty term. 3.4.3. Naive Bayes classifier Naive Bayes (NB) is one of the supervised machine learning algorithms which is used for classification. Naive Bayesian (NB) classifier uses Bayes theorem and conditional probability, which considered all features as an independent value. It uses the maximum likelihood method for parameter estimation. A brief discussion about SVM, KNN, and Naïve Bayes is given in [56–58]. 3.4.4. K-NN classifier KNN is a learner classifier, based on nearest neighbors. This algorithm is lazy-learner and non-parametric classifier. The nonparametric classifier is based on features similarity. The number of √ k is obtained using formula k = n Where n is a number of samples. Usually, K-NN depends on distance matrixes such as Euclidean distance, cosine, Minkowsky1, Correlation, Chi-Square, etc. [59]. 3.4.5. Classification assessment methods The performance of the classifier is manifested into scalar values in the form of accuracy, sensitivity, specificity, f-measure, and precision and Matthews Correlation Coefficient (MCC). The classifier performance is also measured using the area under receiver operating characteristics (AUC-ROC) curve plots. ROC curve plotted between sensitivity v/s specificity and used to visualize the performance of the models. All the performance measures are derived from the confusion matrix [60]. The confusion matrix is an n × n matrix that demonstrates the positioning of samples. The formula used for each evaluation measure of the classifier is shown in Fig. 5 4. Results Experiments are performed by implementing the proposed method in MATLAB for automated diagnosis of alcoholic and non-alcoholic subject classification. The MATLAB implementation of EWT for decomposing the EEG signal into the IMF is publicly available at (https://in.mathworks.com/matlabcentral/ fileexchange/42141-empirical-wavelet-transforms). Ten IMF subbands are extracted from each alcoholic and non-alcoholic signal

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Table 4 A brief description of the extracted features. Sr. No

Feature

Detail

Formula

1

Mean

=

2

Standard deviation (SD)

3

Variance

It is the average of the signal, it just adds all the samples in the signal and divides by the no of the total samples N. In the discrete set of the samples it is the central values. It measures the distribution of the dataset, if the value of Standard deviation is large, our data is spread over the wide range, and values show low if the distribution is much closer to the mean value. SD is based on the variance of the samples measures the dispersion of the data. Shows how our dataset is distributed over the wide range from the fixed mean value of the sample.

5

Skewness

Kurtosis

It measures the degree of asymmetry of a distribution around the mean of the signal. The values of the skewness are positive if the data is spread more toward the right side of the distribution. If the data set lies to the left of the distribution, skewness is negative.

N 

1 N

ai

(15)

i=1

=

 N   a − 2 1  N−1 i

(16)

i=1

V= 1 N−1

4

Eq. No

S=

N   i=1 R3 R2

√

Rk = 1 N

N 

R2

(17) ai

 − 2

, where

k (ai − )

i=1 R = R 4R 2 2

It defines the peaks of the data distribution in our data. If the value of K is higher means, the peak is very sharp. We get the smooth curve of the data point if the value of K is less.

K Rk =

It is the non-linear measure the degree of complexity in the EEG signal.

HShanEn (A) =

1 N

N 

(18)

Where

(19)

k (ai − )

i=1

6

Shannon entropy

−c

(20)

N−1 

p (ai ) ln p (ai ) i=0

7

Log energy entropy

It measures the relative information of the signal.

HLogEn (A) = N−1 

(log(p(ai )))

(21) 2

i=1

*Where A= {a1 , a2 , . . .. . ..an } is the EEG signal sample. N is the No of Samples of each trails Ai N = 2048.  Represent mean of Ai signal.  Is the standard deviation of the signal. Variance is denoted by V. S notation is used for the skewness of the signal. K is the Kurtosis. p (ai ) Is the probability of the sample, c= is the constant.

Fig. 5. The summary of classification model performance measures.

Fig. 6. Visual description of (a) Detected Boundaries (in red color) and FFT Spectrum (in blue color) (b) Filter-bank (FB1-FB10) generated from alcoholic subject’s EEG signal.

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Fig. 7. Visual description of (a) Detected Boundaries (in red color) and FFT Spectrum (in blue color) (b) Filter-bank (FB1-FB10) generated from normal subject’s EEG signal.

using a boundary detection method. The detected frequency boundaries of alcoholic and normal EEG signals of all modes are shown in Fig. 6(a) and 7(a) where red dotted lines denote boundaries detected (BD) and blue thick line denotes frequency spectrum (FT spe.) of the signal in legend. Whereas Figs. 6 (b) and 7 (b) shows the construction of filter bands (FB) based on Littlewood–Paley and Meyer’s wavelets of both EEG signal were FB1 to FB10 shows all the ten obtained EWT based filter banks. Ten IMF is obtained after signal decomposition for both the classes and depicted in Fig. 8 from high to low-level decomposition (bottom to up direction in the plot). The graph is plotted between frequency v/s amplitude in the time axis of the signal along with the residue of alcoholic as well as the normal subject These IMF contain IF information which is plotted into a timefrequency (T-F) plane, as shown in Fig. 9. It is observed that the frequency and time resolution of HHT is better than other conventional methods such as SFFT [61]. HHT also captures the oscillating nature of the signal at a very low frequency [62]. The instantaneous frequency (IF) components are clearly visible at low frequencies, but at high-frequency IF components are hard to identify due to poor visibility [63]. Seven different types of statistical and entropy-based features are extracted from EEG signals, as shown in Table 4. The seven features are extracted from each mode of instantaneous amplitude (IA) using signals of both the classes. After the extracting of features, a feature vector of dimension 240 × 70 is formed. If this high dimension feature vector is passed as it is to the classifier, then the performance of the learner may be degraded, so to optimize the performance of the classifier, only the most significant features are used. The t-test is used to determine the most significant features and to reduce the model complexity. In this experiment, statistical significance or alpha value is set to (˛ < 0.01) as a threshold to calculate the p-values of all features for all IMF. The features with lower pvalues are considered as the most significant features. Here, out of the seven features, only four features are selected as a significant and used for training the classifiers for optimized performance. The p-values of extracted features from each decomposed IMF is summarized in Table 5. This is evident from Table 5 that the statistical features such as mean, standard deviation, variance, and, skewness are the most significant features and very useful to differentiate the alcoholic and non-alcoholic classes. The box plot representation of the most significant features for different IMF sub-bands is shown in Fig. 10, where on X-axis A is denotes for alcoholic and N for normal class EEG signals. Fig. 10 also demonstrated how two classes are different with respect to all four significant features. The feature visualization through box-plots concluded that alcoholic and non-alcoholic

classes are well distinguished from each other by most significant features except skewness. The red ‘+’ shows the outliers present in the data. After performing the significance (t-test) test, the four most significant features (such as mean, standard deviation, variance, and skewness) are selected. These selected features are used to form a feature vector of 240 × 40 and provided to the classifier for training and testing. Different machine learning models such as the least square support vector machine (LS-SVM), SVM, Naïve Bayes, and k-NN are used for classification. The models training and testing are performed using leave one out cross-validation (LOOCV) for avoiding the overfitting problem. The performance of classification models is evaluated using accuracy, sensitivity, specificity, f-measure, and precision and Matthews Correlation Coefficient (MCC). All these measures are summarized in Fig. 5. The implementation of LS-SVM classifier is done using the open-source Matlab code available at www.esat.kuleuven.be/sista/lssvmlab/. Optimization is used for improving the performance of the classifiers. The cross-validation error is minimized using a hybrid optimization function, which is the combination of coupled simulated annealing (CSA) and a simplex method. The optimal values of kernel and regularization parameters are evaluated as described in [64]. The various kernels are used to map data into high dimension space where non-linear data is easily separated. The RBF, polynomial, and linear kernels are used for the evaluation of the LS-SVM. The parameters and their values used by different classifiers are summarized in Table 6. The training and testing of models are done using leave-one-out cross-validation (LOOCV), and as it runs a number of data sample (n) times, the evaluation of the model is done by considering an average of n number of iterations. Table 7 summarizes the performance of all models used for experimentation in the form of the confusion matrix. The column values of Table 7 are represented as true positive (TP), true negative (TN), false positive (FP), false negative (FN) as given in confusion matrix of Fig. 5. The TP, TN, FP, and FN values are used to compute the performance of evaluating parameters. It is observed from the confusion matrix given in Table 7 that true positive and true negative value of LS-SVM with polynomial kernel gives the highest number of positive and negative samples are classified correctly and produced the highest accuracy of 98.7 %. Similarly, the confusion matrix for Naïve Bayes classifier shows a higher misclassification of positive and negative samples, which degrade the performance of the model end up to the accuracy of 85.6 % only. The confusion matrix of Table 7 is used to compute all the classification assessment measures such as accuracy, sensitivity, specificity, precision, f-measure, and MCC. The mathematical expressions used to compute the evaluation measures are shown

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Fig. 8. IMF1-IMF10 subband signals extracted using EWT based time-scale decomposition of (a) Alcoholic (b) Normal EEG signals.

Fig. 9. Plot of EWT based time-frequency representation (TFR) of (a) alcoholic (b) normal subject EEG signal.

Table 5 p-values for all extracted features of alcoholic and normal EEG signals using a t-test. p-values IMF modes

Mean

STD

Variance

Skewness

Shannon

Log Entropy

kurtosis

IMF1 IMF2 IMF3 IMF4 IMF5 IMF6 IMF7 IMF8 IMF9 IMF10 Result

1.57e-23 2.33e-20 1.74e-20 2.88e-19 8.36e-15 1.04e-22 4.97e-18 9.45e-21 9.45e-21 1.69e-26 Significant

1.92e-10 2.90e-13 1.62e-11 5.02e-14 5.90e-13 1.58e-11 4.06e-12 2.39e-12 1.98e-09 4.95e-11 Significant

2.42e-22 1.31e-21 4.28e-22 9.01e-20 7.21e-15 1.64e-25 5.84e-21 1.02e-22 1.99e-22 1.08e-26 Significant

2.26e-06 4.04e-08 1.25e-09 2.22e-11 1.26e-10 1.09e-07 1.26e-08 4.34e-10 1.21e-08 2.75e-10 Significant

0.48 0.11 0.31 0.62 0.02 0.14 0.31 0.11 0.09 1.22e-09 Not Significant

0.67 0.16 0.35 0.68 0.05 0.008 0.39 0.01 0.004 1.37e-09 Not Significant

0.95 0.21 0.99 0.35 0.50 0.38 0.42 0.58 0.75 3.10e-09 Not Significant

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Fig. 10. Box-plot of significant features for alcoholic and normal class (a) Mean (b) Variance (c) Standard deviation (d) Skewness.

in Fig. 5. The summary of the evaluated performance measures of the classification models is shown below in Table 8. To ensure the robustness of binary classification models performance, the ROC curves are also plotted between true positive rate (TPR) and false-positive rate (FPR) as shown in Fig. 11. In literature, the ROC curve is also plotted between sensitivity v/s specificity. The best classification performance is demonstrated at the upper left corner of the ROC curve as it classifies all positive and negative samples correctly and area under curve values [60]. In our experiments, the LS-SVM with a polynomial kernel performs best, as shown in Fig. 11. However, only looking at the ROC curve and comparing various classifiers’ performance is very difficult due to more or less similar nature of ROC curves and absence of particular numeric values value to represent the performance of the classifier. Therefore, the area falls under the ROC curve is computed on a scale ranging from 0 to 1 as AUC values. The AUC values achieved by of LS-SVM with the polynomial kernel is 0.98, which is highest among all the classifiers. So, it is concluded that LS-SVM with the polynomial kernel is the best

classifier among all other classifiers used here for testing and training the alcoholic EEG dataset. The small values of AUC indicate the poor performance of the model, and here Naïve Byes shows the lowest values 0.87 is meant to be the poorest classifier in this experiment. 5. Discussion In this study, empirical wavelet transform (EWT) was used to decompose the alcoholic and normal EEG signals into IMF subbands. EWT is an adaptive method and used frequency information of the signal spectrum to decompose non-stationary and non-linear EEG signals. Fast Fourier transform (FFT) is used to compute frequency spectrum (FT spe.) of the signals as shown with blue color line in Figs. 6(a) and 7 (a) for alcoholic and normal EEG signals respectively. Boundary frequencies are detected from frequency spectrum and narrow non-overlapping wavelet filter banks (FB1FB10) as shown in Fig. 6(b) and 7 (b) are used to decompose signals into ten IMF modes (IMF1- IMF10). IMF sub-bands are shown in

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Fig. 11. AUC- ROC curve of all the classifier evaluated in the experiment (a) LS-SVM (RBF) (b) LS-SVM (Linear) (c) LS-SVM (Polynomial) (d) SVM (e) Naïve Byes (f) K-NN.

Fig. 8(a) and (b) for alcoholic and normal EEG signals respectively. The optimal set of boundary frequencies from all the detected boundary frequencies of trail 1 of alcoholic and normal EEG signals are found using scale-space boundary detection method. It is observed from Fig. 8 that each IMF mode are arranged in increasing order of frequency, in which first row of the figure is associated lowest frequency. Time-frequency domain-based features (a combination of statistical and entropy-based) such as mean, standard deviation, variance, skewness, Shannon, log entropy and kurtosis are extracted from obtained IMFs sub-bands which form a feature vector of 70 (10*7). Statistical t-test was performed to test the significance of extracted features as given in Table 5. The significant test is used to evaluate which features are most discriminant to tell whether signal is alcoholic or normal using p-values (␣ < 0.01). Significance test results suggest that entropy-based feature such as Shannon, log entropy and kurtosis are non-discriminant as most of the obtained p-values from each sub-band are greater than ␣ so we can leave them from further consideration. It is observed from the Box-plots of significant features (as shown in Fig. 10) that most of the features such as mean, variance, and standard deviation shows

higher values for normal EEG signals as compared to alcoholic EEG signals while for skewness feature alcoholic EEG signals shows higher values than normal EEG signals. Discriminatory significant features having feature matrix of (10*4) are fed to LS-SVM classifier with different kernels such as radial basis function (RBF), linear and polynomial. For better performance the hyper-parameters of classification models are tuned with couple simulated annealing (CSA) optimization method. The best-tuned values of parameters are shown in Table 6. The sample-set is small, so we use leave-one-out-cross validation (LOOCV) for training and testing of the models to minimize the chances of overfitting. LOOCV takes more time to compute model but it gives better performance as compared to other available cross-validation methods. The classification model performance using widely used assessment measures (Table 8) suggest that LS-SVM with polynomial kernel (t = 3.4696 and degree of polynomial (d) = 3) perform best with accuracy (98.75 %), sensitivity (98.35 %), specificity (99.16 %), precision (99.17 %), F-Measure (98.76 %) and MCC (97.50 %). If the degree of the polynomial increases beyond 3, then the chances of overfitting will also increase. Whereas the performance with

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Table 6 The summary of all parameters and values used by different classifiers. S.No.

1

2

3

Classifier

LS-SVM (RBF)

LS-SVM (Linear)

LS-SVM (polynomial)

4

SVM

5

Naïve Bayes

6

K-NN

Parameters

Values

Tuned regularization constant ( ) Squared tuned kernel bandwidth ( 2) Kernel function Method Biasing (b) cost function Tuned regularization constant ( ) Kernel function Method Tuned regularization constant ( ) t The degree of the polynomial (d) Kernel function Method Kernel Kernel Scale Box constraint level Method

2.832551

Distribution Prior Kernel estimator No of neighbor Distance metric Distance weight

192.1008 RBF Linear equation Lagrange multiplier Leave one out 0.021956 Linear Linear equation 0.040956 3.4696 3 polynomial Linear equation Gaussian 6.3 1 Quadratic programming problem Normal Uniform False 10 Cosine Equal

Table 8 The summary of performance of different classification models. Classifiers

ACC (%)

SEN (%)

SPE (%)

PRE (%)

FM (%)

MCC (%)

LS-SVM (RBF kernel) LS-SVM (linear kernel) LS-SVM (Polynomial kernel) SVM Naïve Bayes KNN

96.67

95.90

97.46

97.50

96.69

93.35

96.67

9.590

97.46

97.50

96.69

93.35

98.75

98.35

99.16

99.17

98.76

97.50

90.46 85.83 89.17

87.60 87.07 89.17

93.75 84.68 89.17

94.17 84.17 89.17

90.76 84.17 89.17

81.14 71.71 78.33

*Where ACC denotes accuracy, SEN denotes sensitivity, SPE denotes specificity, PRE denotes precision, FM denotes f-measure and, MCC denotes Matthews Correlation Coefficient.

shows that the EWT based classification outperformed others and achieved the highest accuracy of 98.75 % with LS-SVM. EWT analyses the multi-component of the signals through extracting more consistent decomposition sub-bands as compared to other wavelet methods. There is high variability of EEG signals among individuals. Therefore, we cannot claim that the proposed method is generalized to other EEG signals as it is. However, due to unavailability of other public dataset on this subject restrain us to demonstrate performance on single UCI dataset only. 6. Conclusion

Table 7 Confusion matrix of classification performed using different classifiers. Classification Models

Kernel Used

117 5 117 Linear 5 119 Polynomial 2 113 Gaussian 16 101 15 107 13 RBF

LS-SVM

SVM Naïve Bayes KNN

Alcoholic (Instances)

Normal (Instances)

Classified as

3 115 3 116 1 118 7 105 19 105 13 107

Alcoholic Normal Alcoholic Normal Alcoholic Normal Alcoholic Normal Alcoholic Normal Alcoholic Normal

RBF and Linear kernel is less because of the randomness or nonstationary nature EEG signal. The poorest performance (85.83 % accuracy) is reported by naïve Bayes classifiers because it assumes each feature independent and classifies each instance with a certain probability which increases the chance of misclassification rate. The result of the present study is also compared with already reported work in the literature as shown in Table 9. The comparison used same UCI- EEG dataset but different wavelet transforms, features sets, and classifiers for automated detection of alcoholic and non-alcoholic EEG subjects. This comparison

In this paper, an empirical wavelet transform (EWT) based machine learning framework is discussed for alcoholism detection using EEG signals. EEG signals are decomposed in sub-bands using EWT and IA and IF are computed using HHT to analyze the signal in the time-frequency domain. Statistical and entropybased features are extracted from IA and IF of each sub-bands. The extracted features are mean, standard deviation, skewness, variance, log energy, Shannon entropy, and kurtosis. The extracted features are evaluated using p-test, and statistical features are found more significant for further consideration. These significant features are used for preparing feature vectors for training the various machine learning models. Hyper-parameter tuning was done using a couple of simulated annealing (CSA) optimization techniques to improve the performance of models. The training and testing are performed using leave-one-out cross-validation (LOOCV) to avoid the over-fitting problem. The classification model’s performance was evaluated using various measures and also visualized by plotting ROC. The results suggest that the LSSVM with polynomial kernel performs best with an accuracy of 98.75 % while LS-SVM with linear and RBF kernel achieved 96.67 % accuracy. The other measures also demonstrated the same trend. The results suggested that the proposed model is very efficient for alcoholism detection. This non-invasive solution might be more useful in remote areas with non-availability of diagnosis facilities. However, before applying this model on the actual subjects, it is advised that the model should be rigorously tested under various conditions on more large new datasets. In the future, the proposed work may be used to detect other neurological disorders such as epilepsy seizure, sleep disorder, migraines, Parkinson, etc.

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Table 9 Summary of recent works done for automated alcoholism detection. Sr.No

Reference

Wavelet

Features Set

Classifier

Performance of the classifier

1

Sharma et al. [39]

Log energy

LS-SVM

97.08 %

2

Sharma et al. [25] Priya et al. [26]

three-band orthogonal wavelet filter bank (TBOWFB) Dual-Tree Complex Wavelet Transform empirical mode decomposition (EMD)

LEEs, L2Ns

SMO-SVM, LS-SVM, FSC LS-SVM (RBF) LS-SVM (polynomial) gradient boosting decision tree, KNN, SVM, and neural network

97.91 %

3

4

Jianfeng [59]

No wavelet is applied

5

Pachori et al. [27]

6

The proposed work

Tunable-Q Wavelet Transform (TQWT) Empirical wavelet decomposition

mean, kurtosis, skewness, entropy, and negentropy

sample entropy, fuzzy entropy, approximate entropy, and spectral entropy Centered Correntropy (CC) Mean, Standard deviation, Variance, and Skewness.

97.92 % with RBF kernel

94.0 %

LS-SVM

97.02 %

LS-SVM, SVM, NB, KNN

98.75% (LS-SVM Polynomial)

*The best performance of the system is shown in bold.

Declaration of Competing Interest None.

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