.

Abstract-This paper presents the VLSI architecture design for adaptive filtering of Electroencephalogram (EEG) signal and epileptic seizure

detection using Quantitative Recurrence Plot (QRP). The preprocessing of EEG is done by using notch, wavelet and adaptive filter. The

comparison of Signal to Noise Ratio (SNR) of the filter outputs proves that the adaptive filter provides better performance and VLSI

architecture for adaptive filter has been proposed in which optimal compressor based addition technique is used in order to reduce the power

consumption and increase the performance. The design is developed usingVerilog HDL and mapped to 65nm technological node. The power

results are compared with conventional architecture of adaptive filter. The major advantage of choosing QRP is that, it provides better

information even for short non-stationary and nonlinear signals where other methods fail to provide good results. And it requires no

conventions about data set size or dispersal of the data. The algorithm is applied on epileptic EEG signal from CHB MIT database. The QRP

measures are determined from the recurrence plot and its performance is measured in terms of Sensitivity and Specificity as 97.4% and 93.5%

respectively.

Keywords-Epileptic seizure, Adaptive filter, Quantitative Recurrence Plot, Compressor based addition

1. INTRODUCTION

Epilepsy is a disorder of brain function which can be characterized by the abnormal discharges in large number of

neurons in brain structures. The epilepsy disease affects approximately 1% of the world’s population. Each year 2.4

million new cases are estimated to occur globally. The abnormal discharges in brain structures will appear either in

the period of seizures or between seizures which are often known as ictal or inter ictal segments respectively. EEG is

used to measure brain’s electrical activity [5]. The epileptic seizures which are manifestations of epilepsy disease

caused due to sudden and synchronous neuron firing in cerebral cortex and are recorded by EEG. Theseizures

VLSI ALGORITHM AND ARCHITECTURE FOR SEIZURE DETECTION IN EEG USING QUANTITATIVE RECURRENCE PLOT

L.Murali1, D.Chitra2, T.Manigandan3

1Assistant Professor, Dept. of ECE, Hindusthan College of Engineering and Technology,Coimbatore,muralilakshmanan20apr86@gmail.com

2 Professor, Dept. of CSE, P.A.College of Engineering and Technology3Principal, P.A.College of Engineering and Technology

have twomajorcategories which includes partial and generalized seizures.The partial seizure will occur in a particular

part of brain whereas the generalized seizure involves the whole brain causing seizures.

The EEG recording will consists of both ictal and inter ictal periods. The most familiar forms of the inter ictal periods

are individual spikes or sharp waves. This condition occurs in most of the EEG signals obtained from epileptic

patients. During the ictal period a distinct pattern in EEG can be observed as sharp waves or low amplitude

waveforms. Although inter ictal periods prove the existence of epilepsy, the diagnosis of epilepsy usually be carried

out on observation of the seizures.

The visual inspection of long term EEG signal will be time consuming and accuracy will be less. Hence, it has not

been proven to be efficient for the diagnosis of epilepsy. So, automatic seizure detection algorithms and time

frequency analysis methods have been developed [3], [6]. The purpose of those algorithms are not to replace the

neurologist to detect epilepsy but to lessen the burden by reducing the time consumption, improving the efficiency

and to decrease the misinterpretation results in the epileptic seizure detection [2].

The parameters derived from analysis of EEG signals will be very useful in the epilepsy diagnosis. Usually the

EEG is a recording of electrical activities from brain. It consists of different frequency bands which includes delta

(0.4 to 4 Hz), theta (4 to 8 Hz), alpha (8 to 12 Hz), beta (12 to 30 Hz) and gamma (above 30 Hz). The conventional

frequency analysis method is not sufficient to analyze EEG signal to detect epilepsy as the signal is non-stationary

and non-linear time series. So, time frequency analysis methods are used to overcome the drawbacks in the frequency

analysis method including Fourier transforms and other parametric methods.

There are two major classifications of artifacts affecting EEG signal which includes physiological and non-

physiological artifacts. The physiological artifacts are patient related such as eye movements, eye blinking, cardiac or

muscle activity. The non-physiological artifacts include power line interference, cable movements, broken wire

contacts, electrode popping, etc. The acquired EEG signal from patients may be affected by above artifacts so that

preprocessing of EEG signal is a significant stage before applying any algorithm to detect epileptic seizures. The

general purpose digital signal micro-processors possess a large range of applications by implementing moderately

complex digital filters in the given frequency. And the digital signal microprocessor architectures are usually

optimized for the sum-of-products operation by fetching the data from the memory units [20]. These architectures are

not intended to design a specific Digital Signal Processing (DSP) function or for specific algorithm or to the specific

design quality metrics corner. But they are generally designed as the cores or the software packages.

In this paper, an effort has been made to develop the power aware architecture at the filter level which can be utilized

in digital signal processing applications. The concept of compressor is incorporated to perform the addition of the

parallel generated samples in the adaptive FIR filter implementation. This helps in reducing the area required,

further reducing the power consumption and it also reduces the interconnect delays between the gates as the lesser

compressor blocks are required. In this paper line frequency or power line artifact from EEG signal is removed using

various filters. The notch filter is used to remove artifacts from EEG signal but it provides poor performance. So

wavelet filter is used in which input signal is decomposed into sub bands and denoising is performed. But adaptive

filtering of EEG provides better performance in terms of Signal to Noise Ratio.

The related works are summarized in section II. The proposed algorithm and preprocessing are described in

section III. The simulation results have been shown in section IV. Section V deals with results and discussion. The

conclusions are made in section VI.

II. RELATED WORK

The time and frequency domain measures have been carried out on EEG signal for diagnosis of epilepsy over the past

two decades. The algorithms had been started working in the early 1970s. Some of the algorithms includes Artificial

Neural Networks (ANN), rule based approaches, Independent Component Analysis (ICA), template matching and

topographic classification. But recently time-frequency distributions are widely used in this field. When frequency

domain is alone considered for analysis of EEG signal, it does not provide good time resolution [7]. To overcome this

time scale transform which includes wavelet transform has been proposed. 2D Fast Fourier Transform (FFT) is also

used for epileptic seizure detection. Yule Walker and Burg’s method obtains power spectral density to identify the

seizures.

The non-stationary signal cannot be analyzed by the use of classical time domain representations or frequency

domain representations based on Fourier Transform. It requires those methods which do not assume condition of

stationary and hence Time Frequency Distribution (TFD) has been introduced as a tool for analysis of EEG.

Lyapunov exponent’s method, a quantitative measure for discriminating among the various types of orbits based on

their dependence of sensitivity was proposed by A. Wolf et al., (1985). The Lyapunov exponents are estimated from

the dynamic system equations [4], [17]. Then a theoretical breakthrough in the field of time frequency was proposed

by Wigner (1987) in the context of quantum mechanics. It provided an optimal spectral resolution. But, it also

possess cross term interferences and artifacts. So, wavelet analysis has been introduced which outperforms previous

methods [10]. Discrete Wavelet Transform (DWT) is a better signal processing tool to deal with nonlinear signal

analysis [18].

In [1] an adaptive non-linear measure has been proposed as Empirical Mode Decomposition (EMD) by

N.E.Huang et al., (1998) for EEG signal analysis. It decomposes EEG signal into number of Intrinsic Mode

Functions (IMFs) based on Hilbert transforms. Though EMD provides good performance, the interpretation of results

is difficult [8], [11].

III. PROPOSED ALGORITHM

The Recurrence Quantification Analysis (QRP) is a nonlinear method which was developed by Charles Webber and

Joseph Zbilut. This algorithm will quantify differently appearing recurrence plots [12]. The recurrence behavior of

phase space trajectories of dynamical systems can be visualized using QRP. It has its wide application in fields like

physiology, geology, finance, climatic observations, etc.

A. Flow of algorithm

Niknazar et al., [12] have proposed an algorithm for epileptic seizure detection based on Recurrence

Quantification Analysis. A modified algorithm in which preprocessing phase of EEG signal involves adaptive filter

to remove artifacts from raw EEG signal especially the line frequency / power line interference has been proposed in

this paper. The flow of proposed algorithm is shown in Fig. 1. The EEG signal is obtained from CHB MIT database

[19] which is given as the input to adaptive filter. EEG signal of different patients are taken as input and simulation is

done. The notch filter output is simulated and compared with the adaptive filer output.

Input EEG signal

Pre Processing

Recurrenc

e Plot

ParameterExtraction

Seizure Detection

QRP analysis

Fig. 1.Flow chart of proposed algorithm

B. Pre processing

1) Notch filter

Power line interference can be removed with the use of notch filter when the noise distribution is centered exactly

at the frequency for which the filter was designed. However the frequency of this artifact is not constant at 50Hz or

60Hz. But notch filter will remove the frequencies which includes the EEG information. Hence the performance will

be poor.

2) Wavelet filter

The power line artifact in EEG signal is removed using wavelet filter in which wavelet decomposition is performed

and then filtering is obtained. It shows nearly an equal performance of notch filter.

3) Adaptive filter

EEG signal possess overlapping spectra so that the conventional filters do not give optimized performance, hence

adaptive filtering technique is used. It adjusts its parameters according to the selected features of the signals being

analyzed. When the input signal is given into adaptive filter, the coefficients will adjust themselves to obtain desired

coefficients results of the linear filter and then its frequency response will generate a signal which is similar to noise

component present in the signal to be removed. Adaptive FIR filter using Recursive Least Square (RLS) algorithm is

used. FIR filters are simple and stable and RLS algorithm has less computational power.

AdaptiveFilter ∑

S(n)

r(n)

S(n)=x(n)+l(n)

Filter output+_

Fig. 2.Structure of Adaptive Filter

The signal is modeled as a combination of true EEG x(n) and a noise component l(n). r(n) is the reference input

(line frequency artifact). Then the output from noise canceller e(n) is EEG free from artifact which can be expressed

by

e(n) = S(n)-r(n)+[l(n)-r(n)] (1)

C. Architecture Design

Application for Biomedical Signal processing is the continuously monitoring system, where filtering process is

commonly required. FIR filters have finite impulse response and they are also known as non-recursive digital filters

due to the absence of feedback in its realization. A standard adaptive filter realization in such design needs the

transversal structure implementation. Adaptive FIR filter structure is depicted in Fig. 3. Relationship between the FIR

filter input x(n) and output y(n) is given by:

Y (n )=∑m=0

M−1

h (m ) x (n−m) (2 )

And the error signal e (n ) can be represented as the following.

e (n )=d (n )−x(n)(3)

e(n)

Adaptive controller is the function of error signal and it contains the algorithm that monitors e (n )and adjusts the filter

co-efficient until the e (n ) becomes zero. But in real time, it’s not possible to get 100% match for the response ofthe

system. Hence system is considered to have reached optimal adaptation when the error signal e (n )has minimal value

[24].

Fig. 3.Adaptive Filter FIR Architecture

In the insight of Fig.3, the Z−1 is the one sample/ unit of time delay and is implemented using shifter register

components. Output is the weighted sum of present input and previous samples. Each of the output samples needs

(M-1) registers to store (M -1) samples and ‘M’ registers to store the ‘M’ co-efficient. Hence the critical path of the

N-tap filter structure would be single multiplier and number of adder delays. In [25], the author has demonstrated a

filter architecture which has single multiplier and 4 adder delays in its critical path.

Multi-level addition in the filter implementation can be done in various methods and the conventional way is adding

in stages of two. This method involves the horizontal and vertical carry propagation in every addition stage.

Conventional Full Adder architecture is not suited in multi-level addition, which involves horizontal and vertical

carry propagation. Hence a Full adder architecture which generates the sum and carry logics parallel is suited, so that

the overall delay in multi-level addition is reduced. The use of compressed addition with standard compressor cell

saves the delay in terms of stages by generating the carries internally within the cell. For example to add 4 rows of

digits, conventional method requires 2 stage delays with horizontal and vertical carry propagation, whereas

compressed additions involves only one stage delay.

In this paper, the compressed addition concept is incorporated in the addition part of the adaptive filter

implementation. Among which the conventional compressor standard cell has four gate delays each for sum path &

vertical carry and two gate delays for horizontal carries. The proposed compressor component has two gate delays

each for sum path and vertical carry and one gate delay for horizontal carry. The conventional and proposed

compressor component is shown in Fig. 2. The proposed .compressor cell architecture needs lesser gates to

implement and it directly relates to the power consumption. i.e., lesser the area; less will be the power consumption.

This proposed architecture also reduces inter gate delays.

Fig. 4.Conventional compressor componentFig. 5. Proposed compressor component

In the proposed architecture the transistor stack of the gates are higher than the gates in conventional architecture.

The impact of transistor stacking on the power consumption is large due to the negative gate to source voltage (Vgs)

and increased threshold voltages. The transistor stacking can be analyzed using the sub-threshold and gate leakage

current. The transistor in the top of the stack with increased threshold voltage leaks less than the single transistor in

the stack during OFF condition. Hence the ON resistance of the transistor stack will be large and reduces the leakage

current flow between the supply rails (Vdd and Vss), when the transistors are in standby mode.

D. Recurrence Plot

The important measure in QRP is recurrence plot which is obtained for filtered EEG signal in this application. The

recurrence plot describes a plot which shows at a given moment of time, the time at which the phase space

trajectories approximately visits the same area in phase space [13].

~x ( i )≈ ~x ( j )(4)

Showing i on x-axis and j on y-axis, and ~x is the phase space trajectory. Phase space is nothing but a space in which

all the possible states of a dynamical system are represented with each possible states of the system which

corresponds to one unique point in the phase space. The evolving state of that particular system over time traces a

path known as phase space trajectory.

The recurrence plots describes the group of pairs of times at which the phase space trajectory will be at the same

place, that is set of (i, j) with~x (i )≈ ~x ( j). The recurrence can be represented as a binary function as,

R (i , j )={1 if ∨¿~x ( i )≈ ~x ( j)∨¿≤ €(5)

Where ||.|| represents the maximum norm and € is the cut off distance in recurrence plot. The recurrence plot have

black dots (points) at the coordinates (i, j) if R (i, j) = 1 [14].

E. QRP parameter extraction

Each dynamical system has recurrence plot with different topology. EEG is such a system in which the

recurrence plot can be quantified as QRP parameters based on the measures of diagonal and vertical lines [15].

Diagonal line measures include determinism, average diagonal line length and entropy. The vertical line measures

include Laminarity and Trapping time.

Determinism (predictability) of the system is the ratio of recurrence points that forms diagonal structures of

minimum length lmin to all recurrence points.

DET = ∑l=lmin

N

lP (l)

∑l=1

N

lP (l) (6)

The average diagonal line length describes the mean prediction time given as,

L= ∑l=lmin

N

lP (l)

∑l=lmin

N

P(l)(7)

The entropy measure in QRP corresponds to Shannon entropy which depicts the complexity of recurrence plot

with respect to diagonal lines.

ENTR = -∑l=lmin

N

p (l ) lnP (l)(8)

Where p (l )=P (l)N l

(9)

The Laminarity measure is the ratio of recurrence points forming the vertical structures to the entire set of

recurrence points.

LAM = ∑

v=vmin

N

vP (v )

∑v=1

N

vP(v)(10)

Trapping time represents the average length of vertical structures.

TT= ∑

v=vmin

N

vP (v )

∑v=vmin

N

P(v)(11)

It determines the mean time for which the specific state is trapped. In this paper lmin=2 and vmin=2 is adopted [16].

IV. SIMULATION

The adaptive filtering of EEG signal, EMD and QRP analysis were simulated in MATLAB tool. The EMD algorithm

is applied for the same EEG database and the parameters including sensitivity and specificity are compared with

those values obtained by QRP on EEG signal. The noise affected EEG was given as input to adaptive filter and

filtered output is obtained as shown in Fig.7. The performance of the adaptive filter used is compared with the notch

filter which is used to filter the input EEG signal. The adaptive filter is proven to be efficient in terms of Signal to

Noise Ratio.

Fig. 6.Empirical Mode Decomposition of EEG signalFig. 7. Adaptive Filter Output

The line frequency artifact which occurs at 50 to 60 Hz is eliminated by using adaptive filter. The following table

shows the comparison of SNR for notch and adaptive filter. The input EEG is also filtered using notch filter and the

results are shown in table and graphical representations as in TABLE I and Fig.7.respectively.

The recurrence plot for filtered EEG signal is obtained which can be then quantified to determine QRP measures

and it is shown in Fig. 8. The recurrence plot is simply a graphical depiction of the recurrence matrix of order NxN. It

is an autocorrelation plot of the given signal x (t) with x (i) along the horizontal axis and x (j) along the vertical axis.

Only those points which satisfies x (i) = x (j) are defined as the values of i and j. The vertical line and diagonal line

measures quantified from recurrence plot are plotted as shown in Fig. 9.

Fig.8.Recurrence plot for input EEG signalFig. 9.QRP extracted parameters for input EEG signal

V. RESULTS AND DISCUSSION

The adaptive filter used for filtering EEG is compared with notch filter, wavelet filter and their performance is

measured in terms of SNR.The SNRO represents the signal to noise ratio of the original EEG affected with line

frequency artifact which is given as the input to the filter. The SNRN, SNRW and SNRA represent the output signal

from notch filter, wavelet filter and adaptive filter respectively. The comparison graph has been plotted using the

values in TABLE I.

Digital Adaptive FIR Filter architectures are designed using Verilog HDL, verified the functionality using waveform

editor of Mentor Graphics Modelsim tool and then synthesized with the help of Synopsys Design compiler by

mapping the design to 65nm technological node. Standard ASIC Design methodology was applied to benchmark the

results of both existing and proposed FIR Filter architectures. Results are tabulated in Table II.

The compressed addition is incorporated in the addition of the output samples of the multiplier in the filter

architecture. Table II gives the synthesized results of conventional and proposed Adaptive FIR filter architectures,

from which we can observe that the significant amount (10%) of Leakage power of the proposed design is reduced.

As mentioned in section III, the results prove that the leakage power can be reduced by the use of transistor stacked

gates. It can also be observed that the delay has been matching in both existing and proposed architectures. This due

to the increased delay by transistor stacking is balanced by the reduction of inter gate delays.

A digital adaptive FIR filter architecture was implemented and the importance of Data path circuits is addressed.

Both the proposed and conventional architectures were implemented with the compressed addition technique to add

the output samples of the multiplier of the adaptive FIR filter architecture, to reduce the delay involved in the

addition process.

TABLE I

SNR comparison of Notch, Wavelet and Adaptive filter

Fig. 10. Comparison of Signal to Noise Ratio

Input EEG

Patient IDSNRO SNRN SNRW SNRA

Chb01_18 45.1899 62.0996 62.4578 69.2669

Chb02_26 58.9864 62.4830 62.7014 69.6056

Chb03_03 55.0055 68.9478 69.1596 75.6573

Chb05_06 53.6271 60.1685 60.1733 66.8727

Chb08_09 58.1092 61.7597 61.7211 68.3376

Chb09_15 59.4289 67.0238 67.0536 73.6336

Chb13_20 33.1207 60.6131 60.8659 67.4651

Chb17_12 46.1049 54.3497 54.5318 61.0446

Chb18_07 60.5250 63.4840 63.6207 70.3669

Chb21_05 49.3458 64.0489 64.1583 70.8031

TABLE II

Power results of Digital Adaptive FIR Filter architectures

Note: Area in Square microns

Timing in nano seconds

Dp = Dynamic power in micro watts

Lp = Cell Leakage power in micro watts

Tp = Total Power in micro watts

The performance of proposed algorithm is described in measures such as sensitivity (SEN) and specificity (SPE)

given as,

SEN = TP

TP+FN x 100% (12)

SPE = TN

TN+ FPx 100% (13)

where TP, TN, FP and FN are true positive, true negative, false positive and false negative values respectively. The

comparison of performance of EMD and QRP which are executed on 10 EEG input segments from CHB MIT

database are given in Table III. The input EEG signal contaminated with line frequency artifact is preprocessed using

Architecture Existing Proposed%

gain

Area 1250.64 1300.68 -4.00

Timing 9.86 9.86 0.00

Dp 47.00 47.51 -1.09

Lp 13.13 11.75 10.51

Tp 6013.70 5926.2 1.46

three different filters. The preprocessed output from various filters are analyzed with QRP algorithm and the

parameters measured including true positive, false positive, true negative and false negative are taken to obtain the

sensitivity and specificity which are then compared with the values obtained from EMD algorithm. The adaptive

filtered EEG analyzed using QRP algorithm shows better performance in terms of sensitivity and specificity.

TABLE III

Comparison of EMD and QRP algorithm

Method Pre processingT F T F

SE

N

(%)

SPE

(%)

EMDBand pass

filter40 2 21 4 90.9 91.3

QRP

Notch filter 39 2 24 3 92.8 92.3

Wavelet

filter40 2 26 3

93.

092.8

Proposed

Adaptive filter38 2 29 1 97.4 93.5

VI. CONCLUSION

The simulation of EMD and QRP algorithm is done on same EEG signals fromCHB MITPhysionet database. The

adaptive filter provides a better performance in terms of SNR which is proved from the simulation results shown in

TABLE I and VLSI architecture proposed for adaptive filter is compared with conventional architecture and

performance is proved to be better in terms of power and its performance is shown in TABLE II. The QRP applied

for filtered output from adaptive filter shows better Sensitivity and Specificity of 97.4% and 93.5% respectively. The

QRP measures are quantified from the recurrence plot of EEG signal and are compared with EMD algorithm

parameters. The advantage of using QRP is that it provides good results even for short and non-stationary data and it

can beaninspiring tool for nonlinear dynamical system like EEG signal analysis.

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