=Paper=
{{Paper
|id=Vol-2145/p07
|storemode=property
|title=Analysis of Electroencephalograms: Application of Artificial Neural Networks for Detection of Epileptic Discharges
|pdfUrl=https://ceur-ws.org/Vol-2145/p07.pdf
|volume=Vol-2145
|authors=Rokas Mykolas Deveikis,Tadas Meškauskas
}}
==Analysis of Electroencephalograms: Application of Artificial Neural Networks for Detection of Epileptic Discharges==
https://ceur-ws.org/Vol-2145/p07.pdf
Analysis of Electroencephalograms: Application of
Artificial Neural Networks for Detection of Epileptic
Discharges
Rokas Mykolas Deveikis Tadas Meškauskas
Faculty of Mathematics and Informatics, Vilnius University, Faculty of Mathematics and Informatics, Vilnius University,
Lithuania Lithuania
rokas.deveikis@mif.stud.vu.lt tadas.meskauskas@mif.vu.lt
Abstract — Algorithms based on multilayer feedforward and machine learning and in particular artificial neural networks
recurrent neural networks were employed to automatically detect can be employed for the automatic EEG analysis. The main
epileptic discharges (spikes) in unprocessed motivation behind selection of this method is an opportunity to
electroencephalograms (EEG). Results were compared to two develop an algorithm that can potentially solve problems
separate benchmarks: analysis provided by an expert and output mentioned earlier and improve identification of various EEG
of algorithm based on mathematical morphological filters [3]. elements. Nonetheless, it would be much easier to adjust such
Feedforward neural network was able to on average detect ~95% algorithm for diagnostic of other medical conditions. Case
of spikes detected by the expert and ~60% of spikes detected by study is done with spikes common to benign childhood
an aforementioned algorithm. Recurrent neural network was
epilepsy with centrotemporal spikes (referred as Rolandic
able to on average detect ~93% and 80% spikes respectively.
While in some cases artificial neural network was able to
epilepsy from now on), yet described methods can potentially
outperform algorithm based on morphological filter in terms of be applied for diagnostic purposes of other types of epilepsy
detected spikes, the main issue remains the high number of false and brain injuries alike.
positives and in particular low-amplitude spike-like waveforms The paper is organized into three main parts: first of all we
that cannot be utilised for diagnostic purposes. will discuss the current state of research in an automatic EEG
analysis. Secondly we will describe data collection and
Keywords— Electroencephalograms, Epileptiform discharge,
preparation procedures as well as main research methods.
Artificial neural network, Machine Learning, Epilepsy
Finally we will present how multilayer feedforward neural
network and recurrent neural network was able to deal with
I. INTRODUCTION aforementioned research goals.
Early and accurate diagnostics of epilepsy is one of the key
factors in order to prescribe proper treatment and improve the II. RELATED WORK
quality of life for patients and caretakers alike.
Idea to develop algorithm for automatic EEG analysis dates
Electroencephalograms (EEG) remain one of the main tools
back to 1970s. In 1972 Carrie JR. [5] published paper
used for diagnostic purposes and can be accurately analysed by
describing one of the first automatic algorithms aimed to detect
professionals; however, such process is time consuming and
epileptic discharges (also called spikes) visible in EEG.
inefficient. While many algorithms are employed to support
Algorithm was based on calculation of certain spike features,
manual EEG analysis, issues like classification quality or
namely specifications of spike peak and sharpness. Later
inflexibility still limit practical application.
Guedes de Oliveira, et al. [6] described method to identify EEG
Despite that currently there are many algorithms designed spikes based on spike curves and a certain threshold was
for automatic EEG analysis, in many cases it’s still done applied to separate spikes from non-spikes. One of more recent
manually by visually inspecting recording [2]. Given that EEG works was published by A.V. M. Misiūnas, et al. where
recordings can last from few minutes to several hours, mathematical morphological filters were employed to identify
nonetheless are composed of 21 or more channels and can be EEG spikes associated with Rolandic epilepsy [3]. Based on
affected by various noises [2], such analysis remains difficult expert evaluations – this algorithm is able to identify ~90%
and time consuming. While aforementioned algorithms EEG spikes. This algorithm was also used in this paper for
certainly help in EEG analysis, they are not always reliable to comparison purposes.
identify elements of interest and in most cases are hardcoded to
While aforementioned algorithms present good results in
detect specific elements thus making them non-reusable for
certain cases they all face several issues:
other diagnostic purposes (see [2], [3], [4]).
The main purpose of this research is to analyse how 1) Sensitivity to noise. In most cases results are highly
Copyright held by the author(s).
43
affected by noise, which highly depends not only on how distinct sharp waveforms also called spikes. A typical spike is
recording was performed but on device itself characterized by high amplitude (more than twice higher than
2) Being hardcoded for one specific problem. Therefore average amplitude of normal brain activity prior spike) and
application for other purposes remains limited and in many change of phase (see [13], [14]). There are also non-typical
EEG elements associated to epileptic discharges yet given that
cases – impossible.
those only constitute from 1% to 7% of all cases (see [15],
Machine learning algorithms (and especially artificial [16]); therefore, those are not further investigated. While
neural networks) are in many cases employed to address aforementioned spikes might slightly differ, common spike
similar problems. Main advantage of such method is that it’s characteristics remains similar among different patients.
not necessary to specify spike duration and morphology [8]
Spikes can be classified into two main categories based on
therefore neural network can be quickly adjusted (trained) to
duration [22]:
analyse signals recorded with different electroencephalographs
and employed to detect spikes with different durations and - Spikes – duration of 20 – 70 ms.
morphologies. Research related to application of neural
networks for EEG analysis can mostly fall within two distinct - Sharp waves – duration of 70 – 200 ms.
categories: pre-processed EEG data (see. Gabor and Seyal, Both can be described as short term elements, clearly
1992 [11]; Webber et al., 1996 [9]) and raw EEG data (see distinguishable from normal brain activity with the main
Ozdamar and Kalayci, 1998 [10]). Ozdamar and Kalayci was component having negative phase in most cases. Both (spike
able to design a neural network that can correctly classify EEG and sharp waves) have similar initial (or elevation) stage;
elements (with main purpose of spike-detection) with however, sharp waves have longer demotion stage. In this
sensitivity of ~94%, however Webber et al. [9] was not able to paper spikes are not distinguished from sharp waves and are
replicate such results (and found true positives to be ~76% and analysed together.
sensitivity ~40%).
Second distinct characteristic can be defined as a
Cheng-We and Hsiao-Wen [12] used feedforward neural combination of three key measurements: amplitude, duration
network composed of 3 layers (with 30, 6 and 1 neuron and sharpness. Frost J.D. [17] analysed aforementioned
accordingly). They also tried to replicate research of Ozdamar characteristics among spikes with a duration up to 70 ms and
and Kalayci [10] and concluded that initial results was possibly developed CPS (composite spike parameter) index that can
biased due to errorous data preparation. One of the main issues help to detect Rolandic EEG spikes. The author defined
authors encountered is overemphasis on 10th neuron. Authors sharpness as second derivative of voltage at spike peak and
argue that this happened due to fixed location of spike centres, normalized by amplitude. Among all spikes analysed by author
where 10th element of supplied time-series always aligned to mean values of amplitude, duration and sharpness were
spike centre, thus resulting I high numbers of false positives. 160,9μV, 74ms. and 0.022 respectively.
Authors tried to train neural network with varying coordinates
of spike centre relative to analysed section, yet were B. Data description and preparation
unsuccessful to achieve satisfactory results and concluded that
at that point in time computing technologies were not viable to For this research EEG readings of 20 patients was used.
apply neural networks for EEG analysis. Nonetheless this Among those data of 3 patients was analysed by expert. In each
emphasizes importance of data preparation and training of the files aforementioned expert (a paediatric neurologist)
strategy. marked spikes and elements that visually resemble spikes but
are definitely not. In total coordinates of 499 spikes were
While it‘s rather difficult to analyse raw EEG data, much collected.
better results were achieved while working with pre-processed
datasets. Nigam, and Graupe [21] employed feedforward Considering spike characteristics discussed previously, also
neural networks for spike detection, however signal was pre- given that sharp waves and spikes can be both associated with
processed with non-linear filters. Authors were able to achieve Rolandic epilepsy – a fixed spike length of 200 ms (maximum
~96% classification accuracy thus implying that data pre- length of sharp wave) will be used in this research. Nonetheless
processing is a good alternative. However, given that such sub-elements will not be separated in any way and will be
filters must be individually prepared for different EEG this treated as one typical epileptic discharge associated with
method has the same issues as discussed in previous chapters Rolandic epilepsy. EEG readings used in this paper was
therefore it’s not further analysed in this paper. presented in .edf (European data format) type files recorded in
256Hz sample rate (1 element in time series is equal to 3.90625
ms). Further data analysis was done in fixed-duration sections.
III. CHARACTERISTICS OF ROLANDIC EPILEPSY EEG In order to prepare such sections, 200 ms. (maximum spike
SPIKES AND DATA COLLECTED FOR THIS REASERCH duration used in this research) was approximated to 50
elements in time series or roughly 196 ms. Each section with
A. EEG spikes specific for Rolandic epilepsy spike was prepared by choosing 25 elements backwards from
Rolandic epilepsy is one of the most common types of spike centre and 25 elements forward, thus ensuring that spike
childhood epilepsy with ~23% of early school age (mean age centre (peak) and centre of section will align.
~7 years) children being affected [13]. In most cases, the
While expert tried to mark spikes at the exact centre it was
disease affects boys more than girls (with ration 5 to 1). EEG
proven to be difficult to point exact coordinates, therefore data
readings of patients diagnosed with this type of epilepsy have
44
was further post-processed by shifting positions of spike minimal adjustments as well as to potentially work with
centres to lowest voltage value in the section. While extraction of other EEG features. Nonetheless, working with
theoretically spike peaks can have positive phase it was not the raw EEG allows to evaluate neural network’s ability to deal
case for this data therefore no further adjustments was done. with variability in data. Considering aforementioned arguments
Example is presented in figure 1 only 50Hz notch filter was applied in order to reduce noise
created by power supply.
FIG 1. EXAMPLE OF INITIAL (SOLID VERTICAL LINE) AND
ADJUSTED (DASSHED VERTICAL LINE) SPIKE CENTERS
IV. NEURAL NETWORK DESIGN
In this paper we employ and compare two neural network
architectures: multi-layered feedforward neural network and
recurrent neural network based on LSTM model.
A. Training data
As discussed in paragraph III data from 3 patients and 4
EEG channels were used for initial training of neural networks.
Given that main purpose of this analysis is to be able to
identify spikes in full EEG channel data was prepared in
following manner. Each channel was divided into sections each
consisting of 50 elements based on rolling window i.e. each
section starts with element n and ends at n+50 while next
section starts at n+1 and ends at n+1+50. Under such rules each
channel is represented by number of sections equal to number
of elements in channel.
In the next step segments was classified into two main
categories with corresponding numeric values:
0. Non-spikes
Waveforms that morphologically resemble spikes, however
have lower amplitude, were not included to initial sample. 1. Spikes
Those can be considered a non-typical spikes. While those Data of full EEG channel was used for training. Each
sometimes can be associated with Rolandic epilepsy, however, section was classified as spike, if centre of that section falls
as discussed with expert, final diagnosis is never based on such within interval:
“non-typical“ elements. Summary of amplitude characteristics
of all spikes is presented in table I where it is evident that [v – Section size * 0,425; v + section size* 0, 425]
average amplitude is approximately 12% lower when
Where v – centre of actual spike, section size – section size
compared to research of Frost J.D. [17] with ~35% lower
or in this case 200 ms.
standard deviation. Both can be explained by usage of different
measuring device, however deviations remains insignificant. Coefficient 0,425 represents 42,5 % and is calculated under
main assumption that at least ¾ of spike must be visible in
TABLE I. AMPLITUDE CHARACTERISCITS OF EEG SPIKES
order to correctly classify segment it as spike. Therefore
USED IN THIS REASERCH (assuming that all elements of specific segment falls within
interval [0 ms.; 200 ms.] with spike centre being at 100 ms.)
Mean 142,8
Standard deviation 44,2
the earliest point when ¾ of shortest possible spike (20ms) can
Minimum 51 be visible in this segment is when x coordinate of spikes’
Maximum 325,7 centre is equal to 15 ms. (3/4 * 20 ms. = 15 ms.) and latest
Aforementioned data was used for all further researches point is 185 ms. (200 ms. – 15 ms.). This gives interval of [15
presented in this paper. ms.; 185 ms.] or ~21 element. Summary of data used for
training is presented in TABLE II.
As discussed in previous chapter data pre-processing can
potentially improve detection quality, however we decided to
keep it to a minimum, mostly because EEG data pre-processing TABLE II. SUMMARY OF TRAINNG DATA
methods used for feature extraction or denoising are in most Patient Chanel Spikes Segments Total
cases designed to work with specific data (eg. classified as spikes segments
Electrooculogram (EOG) induced noise can be successfully 1 T4 177 4248 47310
removed in pre-procesing stage by applying regression based 2 T3 144 3456 185550
methods [23] or filters based on independent component 2* T5 28 672 185550
analysis [24] however such approach requires to have data with 3 P4 150 3600 66510
separate EOG measurements which is not always available). * Due to low number of spikes, data from patient 2, cahnnel T5 was used only
for training purpose and not utilized for testing.
However, main purpose of this research is to develop algorithm
that can be employed to analyse data from various sources with
45
B. Measuring Classification quality was employed (with k = 3). Due to non-standard measurement,
At this point it‘s important to establish criteria to evaluate data was not shuffled but instead neural network was trained
algorithm performance. Typical binary classification accuracy with data from two patients, while tested on data from third
is not suitable here for several reasons: one. As in typical cross validation such technique was repeated
3 times. In order to have a starting point several researches
First of all, given that in typical 60 second EEG channel were taken into consideration:
(that is consisted of 15360 segments), only less than 1% of
those segments can be typically classified as spikes, simple Based on research by Cheng-Wen Ko and Hsiao-Wen Chu
binary accuracy will provide relatively good results despite that [12] a network architecture consisting of 3 layers with 30
spikes was not necessarily found. Nonetheless main purpose of (segment size), 6 and 1 neurons accordingly were chosen;
such algorithms is spike detection. Also when using binary Cheng-wen et. al. [18] used four layered neural network
classification accuracy, a case where multiple sections was with 4,5 and 1 neurons (input layer is excluded since it always
detected next a single spike, is treated in a same manner as holds number of neurons equals to segment size)
when multiple sections were detected next to different spikes.
Yet again – given that main purpose is to detect all spikes this Weng and Khorasani [19] used four layered neural network
would not represent desired performance. with 9, 90 and 1 neurons.
Therefore 3 main criteria was defined: Mirchandani and Cao [20] presented an idea that number of
neurons in hidden layer can be calculated using following
1) Ratio of all detected spikes with spikes detected by formula:
expert (true spikes)
H = log2 M
2) Elements (group of sections) falsely classified as spikes
3) Spikes falsely classified as non-spikes Where H – number of neurons in hidden layer, M – largest
number of linear separable regions in input data (in this M = 50
– 1 = 49).
Given that exact coordinates of spikes were well-known
following methodology was employed: Using aforementioned researches as a starting point and
employing k-fold cross validation optimal feedforward network
1) All algorithms were design in a way that only sections architecture was found to be 4 layers with following
that were classified as spikes will be outputted. If distribution of neurons in each corresponding layer: 50, 25, 90,
section was classified as spike it‘s coordinate is 1. Averaged results of cross validation is presented in table III
adjusted in following manner: (TP and FP refers to True Positives and False Positives
xn = xo + section size / 2 respectively).
where xn – new coordinate of segment;
TABLE III. CLASSIFICATION RESULTS OF FEEDFORWARD
xo – old coordinate of segment (number of first NEURAL NETOWRK WHEN COMPARING TO ANALYSIS PROVIDED
element); BY EXPERT
Average Δ found Average FP
section size – 200 ms detected spikes spikes sections Δ FP sections
2) Each xn is then compared to known spike coordinates. 95% 4% 30% 48%
Whenever it falls within aforementioned interval of:
[v – section size * 0,425; v + section size* 0, 425] While algorithm was able to detect ~95% spikes detected
(where v – centre of actual spike) by expert, there were ~30% of segments (not spikes) falsely
classified as spikes. Nonetheless ~48% difference of FP and TP
that section is considered correctly classified and spike segments in channels indicated that results are highly
is considered detected. dependent upon channel. Nonetheless at this point analysis was
According to our evaluation methodology - each spike can only performed on 3 EEG files therefore results are not good
be only detected once, therefore more sections (classified as representation of algorithm’s performance.
spikes) around single spike will not inflate result. All sections In order to expand analysis results from single channel
outside interval are classified as non-spikes. Those sections were further processed. In order to ensure that each false
were further grouped in order to assess number of false positive segment will represent a new spike results were
positives where each group was composed of up to 42 sections grouped. First of all true positives were properly identified and
starting from first false positive section, with last one being not matched to corresponding spikes. Then each false positive is
further than 42 elements while ignoring true positives. grouped so that each group was filled with segments that are no
further from the false positive (that is not part of any other
group) than 50 elements. E.g. whenever segment is classified
as false positive a new group is formed where that segment is a
C. Application of Multilayer Feedforward Neural Network
first group member and all other segments that are distanced no
In order to design optimal structure of neural network more than 50 elements from the first one are assigned to the
(number of neurons in hidden layers) a k-fold cross validation same group. One distant segment can have its’ own group.
46
T3 channel of patient 2 was further processed using max 99% 56% 100%
aforementioned technique thus resulting in 53 false positive
groups that will be referred as 53 false positives from now on.
Further analysis of false positives revealed that such elements On average feedforward neural network was able to detect
can be classified into 3 main categories: 60% of spikes, detected by algorithm based on mathematical
morphological filters, however a lot of elements were falsely
1) Segments that are close to spike, however falls outside identified as spikes (average of ~78%). Nonetheless in 11 out
pre-defined range. of 17 (with 3 channels removed from initial sample) cases
2) Spike-like waveforms with low amplitude neural network was able to detect more than 50% spikes with
3) Elements that are not related to spikes 86% detected spikes on average.
Technically 1st group is classified correctly, however given At this point false several examples of positives were sent
that spike peak is on the edge of the segment (at least 3/4 of to expert for detailed analysis. Due to limited time and
spike is not visible) it would be impossible to tell without any resources expert was able to analyse only 12 false positives
further references that this is actually a spike therefore such however this potentially reveals certain trend. Among 12
elements won‘t be included or reclassified. See fig.2 for results were distributed in following order:
example. 2 were identified as spikes
FIG 2. BARELY VISIBLE SPIKE CLASSIFIED AS FALSE POSITIVE
2 were identified as non-spikes
8 were identified as either spike-like waveforms
with low amplitude or true spikes, however expert
was not able to correctly classify those with
certainty.
FIG 2. EXAMPLE OF SPIKE (CICRLED ON THE LEFT SIDE) AND
LOW AMPLITUDE SPIKE-LIKE WAVEFORM (ON THE RIGHT)
Therefore we conclude that the main problem remains low
amplitude or low sharpness spike-like waveforms that are
constantly identified as spikes although are unwanted for
Despite all efforts – due to low amount of high quality data diagnostic purposes.
it is difficult to came up to conclusions therefore data sample To sum up we conclude that multilayer feedforward neural
(of 3 patients) was increased by 17 more EEG files without network can potentially be used for primary analysis of EEG
spike coordinates. To prepare more results for validation an with a purpose to identify spikes associated with Rolandic
algorithm based on morphological filters were employed [3]. epilepsy and can detect ~60% of spikes on average when
All 17 files were analysed and results were compared with ones comparing with algorithm based on morphological operations
produced by algorithm based on neural network. Comparison or ~95% when comparing to data prepared by expert. However
was done in the same manner as previously discussed. main problem remains low amplitude, spike-like waveforms
Summary of results are presented in table IV. All numbers identified as spikes that can potentially be removed by certain
(True positives or false positives) are in relation to results of filters. Finally, algorithm discussed in this paragraph did not
algorithm based on morphological filters. Also 3 channels, detected spikes in channels where there potentially were no
where aforementioned algorithm identified less than 10 spikes, spikes (or low number of spikes, eg. 1).
were removed from final results.
D. Recurrent Neural Network
TABLE IV. CLASSIFICATION RESULTS OF FEEDFORWARD
Given that too many low-amplitude spike-like waveforms
NEURAL NETOWRK COMPARED TO ALGORITHM BASED ON were falsely classified as spikes, a recurrent neural network
MORPHOLOGIC FILTERS was employed to address this issue. Main rationale for this type
Detected TP FP
of neural network is that it captures information from previous
spikes elements therefore it‘s actually possible to estimate spike
St. dev 37% 19% 19% amplitude relative to the background brain activity.
mean 60% 22% 78% Jezusefovich et. al [7] analysed more than 10000 recurrent
min neural network architectures and proven that there is no
0% 0% 44%
alternative that can consistently outperform LSTM model and
47
GRU (gated recurrent unit). While there are not many V. RESULTS AND DISCUSSION
researches where LSTM was applied for EEG analysis,
considering previous statement this model was selected as
potentially offering the best performance. Two algorithms aimed to detect spikes in unprocessed EEG
data were analysed in this research.
While working with the same data, first step was to identify
optimal network architecture. Same three-fold cross validation The algorithm based on recurrent neural network was able
was applied as previously discussed with feedforward neural to on average detect 78% of spikes (see table VI) when
network. Optimal network depth (layers) was proven to be 1 comparing results to an algorithm based on mathematical
input layer, 2 LSTM layers and 1 output layer (feedforward morphological filters. Also, it outperformed algorithm based on
neural network). Each LSTM layers had 90 units (also referred feedforward neural network by ~10% (see table V) on average
as cells). Results of cross validation (when algorithm output in terms of detected spikes, but increased number of false
was compared to analysis performed by expert) are presented positives by 3% on average. While neither algorithm developed
in Table V. Only results of network with aforementioned in this paper is sufficient on its’ own for diagnostic purposes,
optimal structure is included. It is evident that while recurrent neural networks can be used for data preparation or primary
neural network performed slightly worse in comparison to analysis.
feedforward neural network (eg.93% detected spikes versus
While neural networks can effectively classify EEG spikes
95%), deviation is minor and given low number of test and
in some cases, the main issue is false positives and in particular
training samples in each case we conclude that there are no
- low amplitude spike-like waveforms (see figure 2). While in
major differences in performance when testing on similar type
some cases such waveforms can be classified as spikes, those
of data.
are not used for diagnostic purposes thus are undesirable and
should be removed from the result set. It can possibly be
TABLE V. CLASSIFICATION RESULTS OF RECURRENT NEURAL achieved by applying amplitude based filters thus making such
NETOWRK WHEN COMPARING TO ANALYSIS PROVIDED BY combined algorithm an effective tool for EEG analysis,
EXPERT
however, this is beyond scope of this paper and is
Average Δ found Average FP recommended for future research.
detected spikes spikes sections Δ FP sections
93% 10% 30% 10% ACKNOWLEDGMENT
We deeply acknowledge paediatric neurologist MD Rūta
Furthermore, research was repeated in a same manner as Samaitienė for her input and expertise related to epilepsy and
previously (compared to results of algorithm based on data collection efforts. Also, I would like to thank MSc
morphological filters [3]). Data of same 20 patients were used. Andrius Vytautas Misiukas Misiūnas for support and
Results are presented in TABLE VI. Same as previously - all methodological guidance.
numbers (True positives or false positives) are in relation to
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