modified nonlinear statistic known as Multiscale Entropy which resulted to be very useful for this task. We want to contribute in the Big Data field by showing how ...
Abstract
Intelligent Transport Systems and Data Science , Guadalajara, Mexico, 30 - 31 May, 2016
Massive Data Analysis by Nonlinear Statistics in Electroencephalographic Signals (EEG) for Education Ricardo Zavala-Yoé 1, Ricardo Ramírez-Mendoza 2, Juan Manuel Sánchez3, Víctor C. Ramos-Aguilera 4, Roberto Ruíz Ricardo Ramírez-Mendoza Villalobos5 Ricardo Zavala-Yoé, (1), , Ricardo RamírezMendoza : Tecnológico de Monterrey, Calzada del Puente 222, Colonia Ejidos de Huipulco, 14380 Ciudad de México, Mexico. +52 555 454832020 E-mail : {rzavaly,ricardo.ramirez}@itesm.mx
Short Abstract: It is well known that EEG signals are complex patterns which are needed to analyse either affected or normal persons. Typically, the former requires several EEG studies which implies many printed papers which become unhandy for physicians and engineers. The latter, may imply as well long term records to study complexity of signals in nonaffected subjects. In this document, we show how to use a modified nonlinear statistic known as Multiscale Entropy which resulted to be very useful for this task. We want to contribute in the Big Data field by showing how to deal with massive EEG information. The EEG recording was done in a g.Tech EEG amplifier/acquisition set in our campus. This experience allowed our students to learn better difficult topics in Random Processes.
2- Complexity of TS.
Key words: Massive EEG, Entropy of Time Series. 1- Introduction.
Serious central nervous system affections as epilepsy needs to be diagnosed by a neurologist with the aid of an EEG record. Depending of the gravity of the illness, the number of EEG may be more than one by year. In addition, these EEG studies are long term records which last from 1-3 hours. Since the sample time is typically from 4-5 ms, the length of the resulting time series (TS) encompasses thousands or millions of samples. Analysing normal brain tasks may involve long period records as well. A statistical study of such TS can not be practiced by usual methods which generally assumed normal random variables, normal distributions and linear stochastic models. Instead of dealing with those limitations we propose to study these TS by means of entropy of TS. As a particular case, we will use one modified by ours [ZR1] which showed to be quite applicable in this kind of TS. Entropy of TS measures how regular or predictable a TS is. For instance, a periodic signal has complexity equal to zero and in contrast, an EEG signal or a white noise variation have high complexity or high entropy. In this work we will name entropy to the one we modified in [ZR1]. Such parameter is a nonlinear statistic because the way it is
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determined is by means of an algorithm rather than by a linear operator as an integral or a summation. The goal of researching complexity of brain signals is two folded: First, we offer here a way to encompass massive data in much simpler curves (complexity curves). Second, our students could learn how to tackle a problem of modelling complexity of massive long term signals using an easy-to-use device in situ. The resultant experience was quite interesting and useful because they could learn the basics of this topic plus how to acquire/record/analyse EEG signals. The device used was bought with a NOVUS fund received in 2015.
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The complexity or entropy of a TS is computed by an algorithm as it was mentioned above. There are several entropy parameters but all of them base its computation in the so called Aproximate Entropy (ApEn) [P1]. This parameter evolved to the Sample Entropy (SaEn) and later to the Multiscale Entropy (MSE). In [ZR1], we improved the computation of this statistic. In general, these algorithms produce low entropy values (about 0) if the signal is poor (periodic) and values bigger than 1 if the signal is rich (nonpredictable) as a white noise variable or a brain signal. For the time being we just will mention this. Please see details in [ZR1]. Our students recorded the EEG signals and programed our improved version of the MSE in order to analyse massive data contained in EEG databases recorded by them. The algorithm comes now and (as mentioned above) was improved with respect to the original version. Our students programed it in MATLAB in order to interact with this software and SIMULINK.
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Figure 1. G Nautilus LADYbird In figure 1, we can observe the way the device is used by a subject. The software which drives this equipment runs under MATLAB/Simulink environment and is quite easy to use. See figure 2.
Figure 2. Simulink –based program to record EEG signals in our g.Tech device. At the end of this, we take the average of all Si and name it Multiscale Entropy (MSE). From this algorithm it can be seen that a signal is complex if MSE1 because the logarithm is taken for different quantities (think for instance in a white noise signal or an EEG signal). But if this operation is taken in similar quantities (a periodic signal) then the resulting logarithm of this ratio will be close to zero.
The signals can be observed in Simlulink or in MATLAB. In figure 3, we give an example of the signals recorded.
3- Acquiring/Recording the EEG.
These operations were done in our g.Tech Nautilus EEG wireless device. Our version of this set permits to record 8 channels at 4ms of sample time. An additional advantage is that the software of this apparatus is MATLAB/Simulink based and so, the programs developed by the students matched well with all the equipment environment.
Figure 3. EEG signals recorded in a g.Tech equipment.
As an example, a set of four activities were recorded during 6
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minutes of EEG time. Normally, analyzing 6 minutes of a tasks. The utility of such statistic was evident and traditional printed EEG may take a long time by a physician and experimented by our students which programed the moreover a longer one. See table 1. computation of this parameter as well as the acquisition and recording of the EEG signals. They realized that it is more convenient to describe long term massive TS by means of this entropy measure than trying to find out stochastic models for these TS. Difficult concepts of time series, stochastic processes, stochastic control were easily than usual understood by students as a result of the activities linked with the EEG device. They could conceive in situ in vivo the meaning of all these themes. Table 1. Activities developed by a normal person. [ZR1] Zavala-Yoé R, Ramírez-Mendoza R. and Cordero LM (2015) Novel way to investigate evolution of children M1-M6 means minute 1 to minute 5. R means resting, MA refractory epilepsy by complexity metrics in massive means muscle activity, HM is hearing music (lying down), T is information. Springer Plus 4(437): 1–33. talking (lying down as well), LD means lying down (closed [P1] Pincus SM (1991) Approximate entropy as a measure of eyes), SD is soft dancing and W means walking slowly. system complexity. Proc. Nat. Acad. Sci. USA. 88(1): 2297– 2301.
Figure 4. Manipulation of electrodes by students. An example of an entropy curve is given below. This curve shows how behaves the complexity of these signals per channel but encompassing a long period in just a few points of the mentioned curve. See figure 5. Of course, much more longer periods than 6 minutes can be used to work with. Notice how the complexities are all above 1.
Figure 5. Complexity plots of 4 activities of an EEG. 6 minutes (60,000 samples) are encompassed here (8 channels). 4- Conclusions.
In this work, an entropy measure was used to describe the complexity of massive multiple EEG signals in high complex Paper Number
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