Online ISSN : 2249-4596 Print ISSN : 0975-5861 DOI : 10.17406/GJRE
Bite Controller for ESM ECG Arryhthmia Classifier
Modelling of Radar Antennas Empirical Mode Decomposition
VOLUME 16 ISSUE 3 VERSION 1.0
Global Journal of Researches in Engineering: F Electrical and Electronics Engineering
Global Journal of Researches in Engineering: F Electrical and Electronics Engineering Volume 16 Issue 3 (Ver. 1.0)
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Contents of the Issue
i. Copyright Notice ii. Editorial Board Members iii. Chief Author and Dean iv. Contents of the Issue
1. Imp lementation of Complete Ensemble Empirical Mode Decomposition to Analyze EOG Signals for Eye Blink Detection. 1-4 2. Microwave Principles in the Modelling of Radar Antennas for Automotive Applications. 5-7 3. ECG Arryhthmia Classifier. 9-16 4. The Charge is Not the Invariant of the Speed. 17-32 5. Bite Controller for ESM Systems. 33-37
v. Fellows vi. Auxiliary Memberships vii. Process of Submission of Research Paper viii. Preferred Author Guidelines ix. Index Global Journal of Researches in Engineering: F Electrical and Electronics Engineering Volume 16 Issue 3 Version 1.0 Year 2016 Type: Double Blind Peer Reviewed International Research Journal Publisher: Global Journals Inc. (USA) Online ISSN: 2249 -4596 & Print ISSN: 0975 -5861
Implementation of Complete Ensemble Empirical Mode Decomposition to Analyze EOG Signals for Eye Blink Detection By Md. Sakib Galib Sourav Khulna University of Engineering & Technology, Bangladesh Abstract- This paper reports on application of Complete Ensemble Empirical Mode Decomposition (CEEMD) technique to pre-process Electro-Oculogram (EOG) signals before eye blink detection technique is implemented. EOG is a non-stationary signal which is affected by different kinds of interferences. During the time of recording EOG signal gets contaminated by Electromyography (EMG) signal. In this paper CEEMD is used to decompose the EOG signal into several intrinsic mode functions (IMFs). After thresholding each IMF the signal is reconstructed using all of the IMFs. The resulting denoised signal is then used to detect eye blink. Keywords: EOG, complete ensemble empirical mode decomposition, eyelid movement. GJRE-F Classification : FOR Code: 090699
ImplementationofCompleteEnsembleEmpiricalModeDecompositiontoAnalyzeEOGSignalsforEyeBlinkDetection
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© 2016. Md. Sakib Galib Sourav. This is a research/review paper, distributed under the terms of the Creative Commons Attribution-Noncommercial 3.0 Unported License http://creativecommons.org/licenses/by-nc/3.0/), permitting all non commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Implementation of Complete Ensemble Empirical Mode Decomposition to Analyze EOG Signals for Eye Blink Detection
Md. Sakib Galib Sourav
Abstract- This paper reports on application of Complete been proposed [5] which is called Complete-EEMD Ensemble Empirical Mode Decomposition (CEEMD) technique (CEEMD). CEEMD algorithm provides an exact to pre-process Electro-Oculogram (EOG) signals before eye reconstruction of the original EOG signal which can be 201 blink detection technique is implemented. EOG is a non- used to detect eye blinks precisely. stationary signal which is affected by different kinds of Year
4 interferences. During the time of recording EOG signal gets x 10 1.5 contaminated by Electromyography (EMG) signal. In this 1 paper CEEMD is used to decompose the EOG signal into 1 several intrinsic mode functions (IMFs). After thresholding each IMF the signal is reconstructed using all of the IMFs. The 0.5 resulting denoised signal is then used to detect eye blink.
e w rd : EO itude 0 K y o s G, complete ensemble empirical mode pl decomposition, eyelid movement. Am -0.5 I. Introduction
-1 I III Version ssue he EOG signal is an electrical measurement of the
-1.5 potential difference between the front of the eye 0 1000 2000 3000 4000 5000 6000 7000 8000 Samples T(cornea) and the back of the eye (retina). This XVI I potential varies from 0.4 to 1.0 mV (Malmivuo & Plonsey, Figure 1 : Original EOG signal 1995). EOG signal can be measured in horizontal channel and vertical channel. The eye movements in II. Complete Ensemble Empirical Mode Volume
horizontal directions are recorded by horizontal channel
Decomposition () and vertical channel records the eye movements in vertical direction. In addition to eye movements vertical EMD [6] decomposes a signal into a number of channel also records eyelid movements i.e. eye blinks. IMFs. An IMF has two properties : (i) number of extrema An EOG signal is shown in Figure 1 which includes and number of zero crossing are equal or differ by one; eyeball rotation, movements and eyelid movements. The and (ii) at any point the average value of upper and muscles of the eye, eye blinks, electrode placement and lower envelop is zero. EEMD algorithm adds different head movements produce EMG signals [1]. To eliminate realizations of white noise to the original data x[n]. Thus unwanted signals from EOG signal Empirical Mode an ensemble of data sets is generated. To cancel out Decomposition (EMD) has been used [2]. EMD white noise ensemble average of different trials is algorithm is sensitive to noise. This can cause mode calculated. EEMD algorithms can be described as [4]: Researches in Engineering mixing. Mode mixing is defined as an IMF that includes 1. Add series of white Gaussian noise wi[n] oscillations of dramatically disparate scales or a (i=1,…….L) to the original signal x[n] and generate of nal component of similar scale residing in different IMFs [3]. xi[n]= x[n]+wi[n]; our To eliminate the mode mixing problem an extension to 2. Derive a set of IMFs d (j=1,…….,k) and residues r EMD algorithm was proposed [4] which is called i j i (i=1,…….L) by decomposing each of xi[n] applying
Ensemble EMD (EEMD).It performs EMD over an J Global EMD; where, d is the jth IMF of the ith trial. ensemble of Gaussian white noise assisted data. But i j the reconstructed signal contains residual noise and 3. Repeat the above steps until i>L. different realizations produce different modes. To 4. Average over the ensemble to obtain the final IMF. overcome these limitations variation of EEMD has The CEEMD algorithm can be described as [5]:
Author: Electrical and Electronic Engineering from Khulna University of 1. Add white noise to the original signal x[n] Engineering & Technology, Bangladesh department of EEE in the 2. Khulna University of Engineering & Technology as a lecturer. Obtain the first decomposed component applying e-mail: [email protected] EMD.
©2016 Global Journals Inc. (US) Implementation of Complete Ensemble Empirical Mode Decomposition to Analyze Eog Signals for Eye Blink Detection
3. Repeat the decomposition and add white noise of during data acquisition. In this paper CEEMD has been different realizations. used to eliminate the interferences from EOG signals 4. Average over the ensemble to obtain the IMF1: collected from Physionet database. The EOG signal is 1 L considered to be corrupted by additive white noise IMF1= E1[x[n] + wi[n]] L= number of L =1 during the process of signal acquisition. CEEMD realizations, δ= ratio coefficient, Ei computes ith �𝑖𝑖 δ decomposes the EOG signal into 11 IMFs shown in IMF. Figure. 2. It can be seen that see that most of the noise 5. Compute the residue, r1[n]=x[n]-IMF 1. information are distributed to the 1st intrinsic mode 6. Compute the second IMF component IMF : 2 functions [7]. Suppressing the insignificant components IMF2= E1[[x[n] + E1[wi[n]]] we can reconstruct the signal as follows: =1 7. Repeat𝟏𝟏 the𝐋𝐋 above steps to obtain the (m+1)th IMF 𝐋𝐋 𝑖𝑖 X = I component� IMF : δ T =1 n m+1 𝑁𝑁 where I is the set of𝑛𝑛 N IMFs. The noisy EOG IMFm+1= E 1[x[n] + Em [wi[n]]] ∑ 201 =1 𝟏𝟏 𝐋𝐋 signal and the corresponding denoised signal is shown 𝐋𝐋 �𝑖𝑖 δ in Figure. 3 Year III. Eog Signal Denoising
Generally EOG signals are affected by the
2 noises of power-line interference and EMG interference
4 x 10 1 0
EOG -1 2000 0 -2000 1000 0 -1000 2000 0 -2000 1000 ume XVI Issue III Version I III Version XVI Issue ume 0 ol
V -1000 -500 )