Automated Seizure and Burst-Suppression Pattern Detection … · Automated Seizure and Burst-Suppression Pattern Detection in the Neonatal EEG using Wavelet Analysis M.D. Punt, B.Sc

Automated Seizure and Burst-Suppression Pattern Detection in the

Neonatal EEG using Wavelet Analysis

M.D. Punt, B.Sc.Operations Research, Universiteit Maastricht

Clinical Neurophysiology, academisch ziekenhuis Maastricht

July 9, 2007

Abstract

The detection of seizures and burst-suppression patterns in a neonatal EEG is a complex process. The presentedresearch tries to obtain solutions to solve this complex process by using wavelet domain analysis. For the detectionof seizures, the interspike interval (ISI) method and the energy ratio-stability (ERS) method are used.These two methods are optimized for the used data sets, which consist of neonatal EEG signals of 28 seizures in6 hours and 36 minutes, 35 seizures in 3 hours and 10 minutes and 42 seizures in 6 hours and 20 minutes.The analysis focuses on possibilities for upgrading the current implementations of the algorithms for the detectionof seizures in such a manner that both sensitivity and specificity of the algorithms increase.The results show that a small increase in performance is possible, although most changes result in a decreaseof performance. Besides, the use of an independent component analyzer does not result in an increase of thedetection rate. By using the optimal settings, 76.5% of the seizures are detected and the number of false alarmsis 0.85 per hour.Considering the burst-suppression pattern detection, an algorithm which can detect the inter-burst intervals inan EEG is presented. Further research is required to optimize the parameters of the algorithm in order to have acorrect detection.

Keywords: Neonates, Seizure, Burst-suppression, EEG, Wavelet, ICA

1 IntroductionEpileptic seizures are among the main conditions that can occur to neonates. To ensure that the neonates donot obtain any, or as little as possible, damage to the brain, it is of great importance that epileptic seizures aredetected as soon as possible so a medical doctor can administer medication.

In this study, research is performed on the automatic detection of seizures in the electroencephalogram (EEG)of neonates. In contrast to previous work [6, 7, 8, 9, 10, 14, 21], this research does not only focus on the seizuresin the EEG, but it also focuses on burst-suppression patterns [12, 16]. All the algorithms implemented are basedon the wavelet transform [26] because this method performs well on phasic events such as EEG signals of bothneonates and adults [1, 3, 6, 14, 15, 19, 20, 23, 25]. Furthermore, an extension to the model of Coninx [6] is madeby implementing artifact filtering techniques that are used to preprocess the EEG signals [2, 3].

1.1 Research goalOne goal of this research is to find and validate improved methods for the detection of seizures and burst-suppression patterns in the neonatal EEG. On the one hand, the research is focused on detecting as manyseizures as possible (high detection rate or high sensitivity). Otherwise, it is important to have as few as possiblefalse alarms (low false alarm rate or high specificity). Summarizing these two criteria: the goal is to find methodsthat outperform the current techniques for detecting seizures in neonatal EEG signals on both the detection rateand the false alarm rate. Another goal of this research is to find a method that detects burst-suppression patterns.

This article is structured as follows: first, the data sets that are used in the different tests throughout thisresearch are described. Thereafter, a discussion about the algorithms used for the detection of seizures. Insection 4, previous research is discussed and aimed is at optimizing the algorithms presented in Section 3. In

M.D. Punt, B.Sc. 2

section 5, the wavelet enhanced independent component analysis method is discussed. This is a technique usedfor artifact removal on a wide variety of signals. Section 6 discusses the burst suppression detection. Besides,tests are performed to see how this algorithm performs and some results are discussed in this section. Finally,the conclusions and recommendations are given.

2 DataIn this section, data acquisition and the three data sets used are described.

2.1 Data acquisitionThe EEG data is acquired using the 10-20 electrode system [13] as illustrated in Figure 1. The 10-20 system is acommonly used system where the distances between the electrodes are 10 or 20 degrees. Next to the EEG signals,other signals are measured including electrocardiogram (ECG), electromyogram (EMG) from an arm and a leg,electrooculogram (EOG) and respiration.

Figure 1: A schematic view of the 10-20 system used for the recording of EEG signals [13]

EEG data can be acquired in two ways: either unipolar or bipolar. When EEG data is unipolar, this meansthat the values in the signals are calculated as the difference between the average voltage of all the measurementpoints together and the location of interest. Bipolar data is, in contrast to unipolar, the difference between twopoints. Here, the bipolar data is calculated by determining the difference between two points, located at oppositespots at the other side of the hemisphere, for instance the difference between C3 and C4 (see Figure 1). Todetermine the value of a electrode of interest, first its voltage is determined and also the voltage of the electrodeat the opposite position across the hemisphere is measured and these values are subtracted from each other. Thetype of recording has an influence on the quality of the presented methods as discussed in section 4.4.

2.2 Data setsIn this research, three different data sets are used which contain in total 28 EEG signals of neonates. Eachconsists of different files which are annotated by a neurophysiologist. From each file, the channel in which theseizure is best visible is used.

Training setThe training set consists of 7 bipolar recordings with a total recording time of 6 hours and 36 minutes, andcontains 28 seizures and has a sampling frequency of 200 Hz. This is the same data set as used by Coninx [6].

Test set #1Test set #1 is a data set that is also used by Coninx [6]. It consists of 10 multichannel unipolar recordingssampled at 250 Hz. The total recording time is 3 hours and 10 minutes and contains 35 seizures.

Test set #2The second test set consists of 11 multichannel unipolar recordings with a total recording time of 6 hours and 20minutes. The signals are sampled at 250 Hz. In this recording there are 42 seizures.

M.D. Punt, B.Sc. 3

3 Seizure detectionIn literature, several algorithms are found for detection of seizures. Among the first studies that were performedon the topic of detecting neonatal seizures were two studies of Gotman [10, 9]. Hereafter, several attemptsfollowed which tried to improve this algorithm, by, among others, Liu [17] and Celka and Colditz [4, 5]. This hasled to significant advances in this region. Nowadays, the approaches for the detection of (neonatal) seizures areshifting from the time and frequency domain to the wavelet domain. The wavelet domain combines the strongaspects of those two domains.

In this research, two wavelet-based seizure detection algorithms are used. The algorithms are the waveletInterspike Interval (ISI) method [6] and the wavelet Energy Ratio Stability (ERS) method [6]. Furthermore,when combining these two algorithms, this results in the Combined Union (CU) method and the CombinedIntersection (CI) method [6].

3.1 Background level detectionIn order to determine the background level of the EEG signal, a method is developed which takes the historyof the current signal into consideration. In this method, data from the last three minutes is used to determinea background level. From the first and second minute the mean value of the wavelet coefficients is determinedand hereafter the threshold (Spike detection threshold) (STH) is set at a multiple (α) of the minimum of themean of these two minutes. The third minute (last minute before new data) is a gap which makes sure that thebackground level does not interfere with the new data.The actual EEG data to be analyzed consists of a 10 seconds epoch. This epoch is tested for a seizure andhereafter the procedure is repeated 2.5 seconds later with a dynamically changing background threshold. For anillustration of this procedure, see Figure 2. W 2

bg1(a, b) are the wavelet coefficients over background 1 and and

Figure 2: Background detection

W 2bg2(a, b) are the wavelet coefficients over background 2 and they represent the powers of the wavelet transformed

signals, and W 2e (a, b) is the power of the transformed signal and is used to determine local maxima. a is defined

as the scale of the wavelet transformed signal which is used for seizure detection and b is defined as the translationparameter.

3.2 Wavelet Interspike Interval Method (ISI)In the wavelet transformed neonatal EEG, spikes may occur. Multiple spikes close to each other may indicate aseizure. In order to reduce the number of falsely detected seizures (false alarms), a maximum interval betweenthe spikes (Maximum Interspike Interval) (MISI) is chosen. To make sure that the selected spikes are differentspikes and are not subspikes of the same spike, an exclusion period (EP) is made. This period ensures thatevery spike is at least as distant from any other spike as indicated by the exclusion period (see Figure 3). In thefollowing, characteristics of the wavelet interspike interval method are described in more detail. This method isrepeated every 2.5 seconds until the end of the signal is reached.

1. Wavelet transformationThe current epoch (of 10 seconds) is transformed by the wavelet transform of, for instance, the Mexican

hat mother wavelet at the scale that covers the neonatal EEG range (a = 12 for the Mexican hat motherwavelet when the sampling frequency is 200 Hz and a = 15 for the 250 Hz signals). Hereafter, W 2

e (a, b) iscalculated as the power of the transformed signal and is used to determine local maxima.

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Figure 3: The ISI algorithm [6]

2. Peak detectionBy using a peak threshold (PTH), the local maxima are extracted. The PTH is defined as a fraction ofthe maximum value of the actual epoch. The PTH is used to distinguish between the actual peaks andnoise in the signal, and may vary between epochs. To exclude sub-peaks, another parameter used in peakdetection is EP. A new peak has to be at least 0.2 seconds away from any other peak [6]. An interpretationof this period is that seizures can have frequency components up to 7.5 Hz [24] and that every wave is thenapproximately 0.13 seconds apart. The 0.2 seconds gap results in that waves with frequency componentsabove the 5 Hz, are detected as separate peaks. Unfortunately, peaks belonging to parts of the signal withfrequency components from 5 to 7.5 Hz are partially ignored.

3. Spike detectionAfter peaks are detected in the peak detection part of the algorithm, a spike-detection threshold (STH) iscalculated from the backgrounds (see equation (1)).

STHupdate = α · min{µISIbg1 , µISI

bg2 } (1)

with α as a constant scaling factor and µISIbg1 and µISI

bg2 defined as the mean power of the background [11]:

µISIbg1 =

‖Wbg1(a, b)‖22

N= mean(W 2

bg1(a, b))

µISIbg2 =

‖Wbg2(a, b)‖22

N= mean(W 2

bg2(a, b)) (2)

where W 2bg1(a, b) and W 2

bg2(a, b) represent the power background wavelet coefficients and N is the numberof wavelet coefficients. The minimum is taken in order to reduce the influence in cases where high-powerartifacts occur.To ensure that no rapid changes occur in the STH values, the mean of the old and the updated STH aretaken every 2.5 seconds and this is considered the new STH (see equation (3)).

STHnew =STHold + STHupdate

2. (3)

Whenever the local maximum of a spike exceeds the STH, it is recorded as a spike.

4. Maximum interspike intervalThe maximum interspike interval (MISI) is the maximum allowed time between 2 spikes in one epoch. The

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boundaries restrict the interspike intervals to go outside the epoch. With this method artifacts and singlespikes can be eliminated from the seizure detection.

3.3 Wavelet Energy Ratio Stability (ERS)The wavelet energy ratio stability algorithm measures the relative difference between energy in the backgroundand energy in the epoch. In this algorithm two parts are important: energy ratio and stability.

The background level is different from the previous quantification presented. The new background level isdetermined as given in Figure 4.

Figure 4: Background detection

The algorithm can be split in three parts:

1. Wavelet transformationThe wavelet transformation of the current epoch is performed as described in Section 3.2 step 1.

2. Energy ratioFrom the 10 second epoch the mean value of the wavelet transformed signal is taken as the energy level.This value, Ee is compared with the minimum energy level of the two background levels (see equation (4)).The minimum of the two energy levels is chosen in order to obtain a suitable background level with thehigh-energy artifacts excluded.

Ebg = min{µERSbg1 , µERS

bg2 } (4)

Here, µERSbg1 and µERS

bg2 are defined as

µERSbg1 =

‖Wbg1(a, b)‖1

N= mean(|Wbg1(a, b)|)

µERSbg2 =

‖Wbg2(a, b)‖1

N= mean(|Wbg2(a, b)|) (5)

Wbg1(a, b) and Wbg2(a, b) represent the size of the background wavelet coefficients and N is the number ofwavelet coefficients.The energy ratio (ER) of the epoch is then determined by

ER =Ee

Ebg. (6)

When a seizure occurs in the current epoch, the ER increases and when the epoch that is checked has passed,the ER decreases.

3. StabilityTo find the stability of the epoch, each epoch is divided into five segments each of 2 seconds. Hereafter, byapplying equation (7) to the data, the relative difference (RD) between the maximum and minimum valueof these 5 segments is determined. The values V = {V1 . . . V5} are the sum of the absolute values of thesegments.

RD =max(V ) − min(V )

min(V )(7)

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Figure 5: The ERS algorithm [6]

When the relative difference decreases it means that the stability increases, this is caused by the fact thatthe minimum and maximum value of an epoch are closer to each other.

Figure 5 illustrates an epoch with a seizure in it. When the energy ratio is high in a signal and the stability ishigh (less difference between the peaks in a series of segments), a seizure is detected.

3.4 Combined Union (CU)In order to improve the quality of seizure detection, it is possible to combine both algorithms. When the unionof the detected seizures is taken of both the ISI and the ERS algorithm, the number of epochs that is detectedas seizure is at least the maximum of the two detection rates. Because both algorithms focus on different aspectsof the signal, it is likely that some extra epochs are detected as seizure, in comparison to the two algorithms bythemselves. Equation (8) gives the mathematical representation of the union of the results from the ISI (Rα,P T H

ISI )and the results from the ERS (RER,S

ERS ) methods.

CU = Rα,P T H

ISI

⋃RER,S

ERS (8)

3.5 Combined Intersection (CI)Another combination of the two algorithms for the detection of seizures is the intersection of the detected epochsfrom both the ISI and the ERS method. Only when in both algorithms a seizure is detected, the CI methoddetects a seizure. In mathematical notition this is represented by

CI = Rα,P T H

ISI

⋂RER,S

ERS . (9)

A drawback of this method is that a seizure is not detected when it’s only detected by one of the two algorithms.Especially when a seizure is better detectable by one of the two methods, the CI will have problems detectingthe seizure. A benefit of this approach is that false alarms which occur only in one of the two algorithms arecanceled out.

3.6 CriterionWhen one of the above mentioned algorithms label an epoch as a seizure epoch, this can be sufficient to designateit as a seizure. Another way to look at the detection is that, when there are multiple consecutive epochs detectedas a seizure, they are designated as a seizure, otherwise not.

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Single epochWhen the optimization criterion is that the number of true seizure detections has to be maximized, even if thishas as a drawback that more false alarms are reported, a method is used that directly classifies an epoch asseizure when a seizure is detected.

Three epochs in a rowWhen the number of false alarms has to be minimized, seizure are only reported when there are consecutiveepochs detected as a seizure. In this research, the minimum number of consecutive epochs is three.

4 AnalysisIn this section research is performed on the topic of validation and possible improvements on earlier research. Inthe cases where searched is for an optimal value for the detection of seizures, a grid computing model is used forthe approximation of a global optimal point. All the relevant parameters are tested within ranges that widelycover the area of interest. The standard problem with parameter estimation is that a balance between globaloptimality (all signals) and local optimality (training set) has to be found. When the focus is directed to thelocal optimality, the model can get over trained on that specific data set [22]. Moreover, because of the small sizeof the training set, the possibility to over train is large and therefore it is hard to overcome this problem easily.

The performance per test is indicated by two values, the percentage of detected seizures (DS(%)) (higher isbetter), which is called the sensitivity, and the false alarm rate per hour (Fa/H) (lower is better), which is calledthe specificity.

Note that the values that are defined as optimal are chosen because they have a good trade off betweensensitivity and specificity. If one finds it more interesting to have a higher detection rate or less false alarms perhour, these values are different.

4.1 Exploring rangesTo explore how the different parameters may influence the performance of the ERS and ISI algorithms, and tosee how comparable the data sets are, a grid is computed with values which cover the points of interest. Thewavelet scale (a) is set at 12 for the training set (with a sampling frequency of 200 Hz) and at 15 for the twotest sets (with a sampling frequency of 250 Hz). The mother wavelet that is used throughout this research is themexican hat wavelet, unless otherwise stated.

Figure 6 shows the performance in the grid for the ISI algorithm and Figure 7 gives the performances ofthe ERS algorithm both with mean background level. Subfigure (a) shows the performance of the percentageof detected seizures and subfigure (b) shows the false alarms per hour. In both figures, values lower than 60%detected seizures were set on 60% and false alarm per hour values exceeding 2 were set to 2. With this, therelevant values can be viewed more easily.

At the end of this section, Table 1 summarizes the results.

Training set - ISIWhen viewing Figure 6, the optimal range for α and PTH can be given. For the sensitivity, α should be between1 and 11 and greater than or equal to 7 for the specificity. This results in an α between 7 and 11. PTH shouldbe between 0.1 and 0.2 for both the sensitivity and specificity.

Training set - ERSFrom Figure 7, it can be noticed that both the sensitivity and the specificity have a range in which they performoptimal. For ER this lies between 1 and 2 for sensitivity and between 1.6 and 3 for specificity. Combined, ERshould be between 1.6 and 2. S is optimal in a range from 1 to 2 for sensitivity and between 0.7 and 2 forspecificity. Combined, S should be between 1 and 2. Further it can be noticed that the performance of the ERSalgorithm is more influenced by the S parameter than by the ER parameter.

Test set #1 - ISIIn the following figures, the percentage of detected seizures which are lower than 50% are set at 50% insteadof 60%. Figure 8 visualizes that the optimal range for α is smaller than 5 considering sensitivity and greaterthan 4 for specificity. For PTH, these are smaller than 0.20 for sensitivity and greater than 0.14 for specificity.Combining these results, a range for α is for 4 to 5 and for PTH this is between 0.14 and 0.20.

M.D. Punt, B.Sc. 8

(a) (b)

Figure 6: ISI algorithm surface plot: (a) The percentage of detected seizures (b) The false alarms per hour

(a) (b)

Figure 7: ERS algorithm surface plot: (a) The percentage of detected seizures (b) The false alarms per hour

M.D. Punt, B.Sc. 9

(a) (b)


(a) (b)


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Test set #1 - ERSFigure 9 shows that the optimal range for ER is smaller than 2 for sensitivity and greater than 2 for specificity.For S, the numbers are greater than 1 for sensitivity and smaller than 1 for specificity. Combining these results,S has an optimal value of 1, and ER has an optimal value of 2.

Test set #2 - ISI

(a) (b)


In Figure 10 can be seen that the optimal range for α is smaller than 5 considering sensitivity and greaterthan 9 for specificity. For PTH, these are smaller than 0.20 for sensitivity and greater than 0.22 for specificity.The problem with these parameter values is that they do not overlap. As a result of this, it is to be expectedthat the performance will not be very good.

Test set #2 - ERSFigure 11 shows that the optimal range for ER is smaller than 1.4 for sensitivity and is a special case for specificity.ER should be between 2 and 2.3 or greater than 2.6. For S, the numbers are greater than 0.6 for sensitivity andsmaller than 0.5 for specificity. As a result, it is likely that the performance of the optimal value is not good.

An overview of the before mentioned ranges is shown in Table 1.

4.2 Seizure files detectionBecause it is important to detect a seizure when it occurs, the methods presented in this article are developedto detect every seizure that occurs. When multiple seizures occur in a short time span, it is less critical to missone or more seizures, as long as at least one is detected. For the following tests, the parameter values are: ER =2, S = 1.5, α = 9 and PTH = 0.15, which were determined by Coninx [6].

In Table 2 and 3 the results are given. In these tables, the columns stands for:• NR Seizures - The number of seizures (N = 1. . . 9) in a data file.

• # Total files - The number of files in the data set with the number of seizures indicated at the begin of therow.

• # Detected files - The number of times that minimally 1 seizure is correctly detected in a file with N seizures.

• Total - The sum over the three data sets to get a more global view of this topic.

ERSIn the following test, ER = 2 and S = 1.5. As can be seen in Table 2, two 1-seizure files, two 3-seizure files andone 5-seizure file are not detected.

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(a) (b)


ER S α PTHData set Sensitivity

Training set 1.0-2.0 1.0-2.0 1-11 0.10-0.20Test set #1 1.0-2.0 1.0-2.0 1-5 0.10-0.20Test set #2 1.0-1.4 0.6-2.0 1-5 0.10-0.20

SpecificityTraining set 1.6-3.0 0.7-2.0 7-20 0.10-0.20Test set #1 2.0-3.0 0.1-1.0 4-20 0.14-0.30Test set #2 2.0-2.3 2.6-3.0 0.1-0.5 9-20 0.22-0.30

CombinedTraining set 1.6-2.0 1.0-2.0 7-11 0.10-0.20Test set #1 2 1 4-5 0.14-0.20Test set #2 - - - -

Table 1: The ranges per data set

Training set Test set #1 Test set #2 TotalNR # Total # Detected # Total # Detected # Total # Detected # Total # Detected

Seizures files files files files files files files files1 2 2 5 4 2 1 9 72 1 1 1 1 1 1 3 33 1 1 3 2 2 1 6 44 1 1 0 0 1 1 2 25 0 0 1 1 3 2 4 36 0 0 0 0 1 1 1 17 0 0 0 0 1 1 1 18 1 1 0 0 0 0 1 19 1 1 0 0 0 0 1 1

Table 2: Number of files in which at least one seizure is detected when using the ERS algorithm

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ISIThe ISI algorithm was tested with α = 9 and PTH = 0.15. As can be seen in Table 3, two 1-seizure files, one



Table 3: Number of files in which at least one seizure is detected when using the ISI algorithm

3-seizure files and one 5-seizure file are not detected.

CIIn the test ER = 2 and S = 1.5, α = 9 and PTH = 0.15. As can be seen in Table 4, four 1-seizure files, two



Table 4: Number of files in which at least one seizure is detected when using the CI algorithm

3-seizure files and one 5-seizure file are not detected.

CUIn the test ER = 2 and S = 1.5, α = 9 and PTH = 0.15. As can be seen in Table 5, two 1-seizure files, one3-seizure files and one 5-seizure file are not detected.

DiscussionFrom Table 2 it can be seen that for the ERS algorithm in total 23 out of 28 files are detected as a file with aseizure. For the ISI algorithm (see Table 3) these numbers are 24 out of 28. This is the same number as theCU algorithm (Table 5). The CI algorithm has the lowest number of files with a seizure detected in it. It onlyrecognizes 21 seizure files from the 28 seizure files. In total, this means that about 75% to 85% of the files witha seizure are detected and that 15% to 25% are missed. The numbers of missed seizure files are high and in therest of this report a performance increase is searched for.

When the results per test set are taken into consideration, it has to be noticed that Test set #2 misses moreseizure files in comparison to the other two data sets. After careful visual inspection of the data in this dataset, it is logically that some seizures are missed. In one case, 5 seizures are missed because the frequency inthe seizures was always above the 6 Hz (8-9 Hz) and the seizure has a low voltage. Because of the fact thatthis system is build for the detection of seizures between 0.5 Hz and 6 Hz, these are missed. In another case, 3seizures are missed because their duration is shorter than 10 seconds and the system is designed in such a way

M.D. Punt, B.Sc. 13



Table 5: Number of files in which at least one seizure is detected when using the CU algorithm

that a seizure should last for at least 10 seconds. The last case in this data set where the seizure could not bedetected by the ISI method, is where the peaks of the seizures are more than 0.6 seconds apart from each other(approximately 0.75 seconds). Hereby the distances are too large and the seizure is missed. This last argumentis also applicable for two cases in Test set #1. Here, the peaks of the seizures are also more than 0.6 secondsapart from each other.

When these five seizures files are excluded from Test set #2 and Test set # 1, the results look a lot morepromising. The ISI algorithm would get a detection rate of 23 out of 23, the ERS algorithm 23 out of 26 (threeextra because the ERS algorithm can detect one seizure more), the CU algorithm 23 out of 23 and the CIalgorithm 21 out of 23.

4.3 Mean versus MedianWhen the algorithm is changed in such a manner that it takes the median of the background level instead of themean background level (see Section 3.1), the background level becomes less sensitive to outliers. Therefore, µISI

bg1

from equation (2) is changed to

µISIbg1 = argmin

jε1..m

m∑i=1

|W 2bg1(a, b)(i) − W 2

bg1(a, b)(j)| (10)

and µISIbg2 to

µISIbg2 = argmin

jε1..m

m∑i=1

|W 2bg2(a, b)(i) − W 2

bg2(a, b)(j)|. (11)

µERSbg1 from equation (5) to

µERSbg1 = argmin

jε1..m

m∑i=1

|Wbg1(a, b)(i) − Wbg1(a, b)(j)| (12)

and µERSbg2 to

µERSbg2 = argmin

jε1..m

m∑i=1

|Wbg2(a, b)(i) − Wbg2(a, b)(j)|. (13)

Here, argmin means that searched is for the element in the data that minimizes the distance to the other elementsin the data. W 2

bg1(a, b)(i) and W 2bg2(a, b)(i) represent the power of the ith element of the background wavelet

coefficients, Wbg1(a, b)(i) and Wbg2(a, b)(i) represent the amplitude of the ith element of the background waveletcoefficients, W 2

bg1(a, b)(j) is the jth element of the background wavelet coefficients which should be chosen insuch a way that it minimizes the sum of all differences.

An example of the differences between mean and median is shown in the following figure.As can be seen in Figure 12, the median background level is never above the mean background level. This can

be explained by the fact that, because the wavelet transformed signal only consists of positive values, the medianvalue of the background is less influenced by higher values. This results in a lower amplitude for the background.When these two different background levels are used for the detection of seizures, it can happen that an epoch

M.D. Punt, B.Sc. 14

Figure 12: Example of the difference between the mean and median background level

is detected as a seizure epoch while the other algorithm does not when using the same parameters. Because ofthis, it is likely that these two methods have different values for their parameters. Although by using differentparameters both algorithms can maximize the number of seizures they detect, the performance can differ.

ResultsFor the following tests, the parameters of the ERS algorithm are: ER = 2 and S = 1.5, and for the ISI algorithm:α = 9 and PTH = 0.15. These optima are found by computing the performances of the algorithms in a grid. Allof these parameters are within the ranges specified in Table 1.

In Table 6 the differences in performance are given for the mean background level with single epoch detection.Table 7 shows the results for the criterion when there must be at least three epochs in a row before a seizure isdetected. The optimal scores for an algorithm per data set are given in bold.

Training set Test set #1 Test set #2 AverageAlgorithm DS(%) Fa/H DS(%) Fa/H DS(%) Fa/H DS(%) Fa/H

ISI 96.4 0.61 81.0 1.26 64.3 1.95 78.4 1.29ERS 96.4 0.30 76.2 3.47 57.1 1.35 74.0 1.34CI 89.3 0.15 71.4 1.26 57.1 0.90 70.5 0.67CU 100 0.76 81.0 3.79 64.3 2.10 79.1 1.89

Table 6: Mean background level and ’single epoch detection’ criterion


ISI 89.3 0.15 57.1 0.32 59.5 1.05 66.6 0.55ERS 92.9 0.15 66.7 0.95 52.4 1.05 67.9 0.67CI 85.7 0.15 57.1 0.32 50.0 0.60 61.9 0.37CU 96.4 0.15 81.0 0.95 59.5 1.50 76.5 0.85

Table 7: Mean background level and ’three detected epochs in a row’ criterion

When the background level is changed from mean to median, the values of the parameters of the ERSalgorithm are: ER = 2 and S = 0.44 for the single epoch criterion and ER = 1.6 and S = 1.4 for the three epochsin a row criterion, and for the ISI algorithm: α = 59 and PTH = 0.10. Tables 8 and 9 show the optimal valuesin this case.

DiscussionIn the tables given above (Tables 6- 9) it can be seen that when changing from mean to median background level,the number of detected seizures decreases although the performance considering the false alarm rate increases(the number of false alarms decrease). This can be explained by the fact that when there are less epochs detectedas a seizure, the number of detected epochs during a seizure is also equal or less. The number of false alarmsalso decreases (or remains equal) because of this.

M.D. Punt, B.Sc. 15


ISI 96.4 0.00 71.4 3.47 52.4 1.80 70.5 1.40ERS 100 0.15 76.2 1.26 47.6 1.35 71.3 0.85CI 85.7 0.00 52.4 0.63 45.3 0.90 58.5 0.49CU 100 0.15 81.0 4.43 52.4 2.10 74.6 1.77

Table 8: Median background level and ’single epoch detection’ criterion


ISI 92.9 0.00 66.7 0.95 47.6 0.79 66.0 0.49ERS 100 0.15 81.0 1.26 57.1 1.65 76.5 0.97CI 82.1 0.00 57.1 0.63 47.6 0.75 60.0 0.42CU 100 3.03 81.0 4.42 59.5 4.65 77.5 3.94

Table 9: Median background level and ’three detected epochs in a row’ criterion

When Tables 6- 9 are taken into consideration, the combined union algorithm with mean background leveland three epochs in a row criterion performs best with a result of 76.5% detected seizures and 0.85 falsealarms per hour.

Further can be noticed that the CI algorithm always has a lower percentage of detected seizures and a lowerfalse alarm rate than the CU. As expected, the restrictions set on the CI are much more strict than the ones onthe CU. Because of this, CI and CU always differ.

Another point of interest is that when considering the single epoch criterion and the three epochs in a rowcriterion that the single epoch criterion always has a higher detected seizure percentage, but also a higher falsealarm rate. This can be explained by the fact that when there are only one or two epochs in a row detected asa seizure epoch, these are rejected by the three epochs in a row criterion as a seizure. This results in that thenumber of detected seizure epochs can be maximally equal to the number of detected seizure epochs at the singleepoch criterion, but normally this is lower. Because of this, the number of detected seizures decreases and alsothe number of false alarms.

When taken into account the fact that 9 seizures from Test set #2 cannot be detected by the algorithms, theperformance of the optimal settings increases. The percentage of detected seizures goes from 76.5% to 83.7%.

4.4 Unipolar versus bipolar measurementIn order to find the optimal way of processing the EEG signals, research is performed on the difference in detectionquality when the signals are unipolar or bipolar. When the signals are unipolar, the values of the signal representthe difference between the power of a selected channel and the mean of the power of all the channels together,whereas bipolar signals are the difference between the power of a selected channel and its equivalent on the otherside of the hemisphere.

The unipolar and bipolar signals are compared on Test set #1 and Test set #2 (see Table 10 and Table 11).The parameters used are the optimal parameters, which are for the unipolar tests: for ISI, PTH = 0.15 and α= 9; for ERS, ER = 2.0 and S = 1.5. The median background level parameters are for ISI: PTH = 0.10 and α= 59; for ERS these are: ER = 2.0 and S = 0.44. When comparing the results between unipolar and bipolarsignals, Table 10 shows that all results become worse. Table 11 reveals comparable results, one seizure detectionrate increases whereas the rest of the results shows a decrease in quality.

Test set #1 Test set #2Unipolar Bipolar Unipolar Bipolar

Algorithm DS(%) Fa/H DS(%) Fa/H DS(%) Fa/H DS(%) Fa/HISI 81.0 1.26 81.0 1.58 64.3 1.95 54.7 1.80

ERS 76.2 3.47 57.1 4.11 57.1 1.35 50.0 1.80

Table 10: Unipolar - Bipolar comparison when the background is specified with the mean value

M.D. Punt, B.Sc. 16

Test set #1 Test set #2Unipolar Bipolar Unipolar Bipolar

Algorithm DS(%) Fa/H DS(%) Fa/H DS(%) Fa/H DS(%) Fa/HISI 71.4 3.47 76.2 4.74 52.4 1.80 57.1 2.10

ERS 76.2 1.26 52.4 0.32 47.6 1.35 45.3 1.50

Table 11: Unipolar - Bipolar comparison when the background is specified with the median value

DiscussionAlthough it seems logically to look at the bipolar signals from a neurophysiological point of view, both the ISIas the ERS method suffer from the translation of unipolar signals to bipolar signals. In most tests the detectedseizure percentage decreases and also the false alarm rate increases.

4.5 Parameter verification and optimizationIn order to find optimal values for the parameters of both the ISI and the ERS algorithm, research is performedon the optimal values per data set and tested those values on the two other data sets. For the tests both thesingle epoch seizure detection and the three epochs in a row seizure detection are used. The outcome of thesetests are given in Appendix A. Figure 13 visualizes how the differences are between the various outcomes.

Figure 13: False alarms per hour versus percentage of detected seizures

DiscussionWhen a maximum of one false alarm per hour is taken, the optimal performance is met when the ERS algorithmis taken and the background level is determined by using the median value. Further, the criterion that a seizurehas to consist of at least three consecutive epochs should be used. With these settings a performance is reachedof 76.5% seizure detection and 0.97 false alarms per hour. Note that the optimal score from section 4.3 has equalperformance on the seizure detection, but it performs better on the false alarm rate with 0.85 false alarms perhour.

When viewing Appendix A in more detail, it can be seen that when considering the ERS algorithm outcomes,the optimal values of both the mean and the median, and single epoch and three epochs in a row detection ERis everywhere (close to) two. S changes more between the different tests.

Taking the ISI algorithm into consideration, it can be noticed that all the optimal values of PTH are (closeto) 0.15. α on the other hand changes by the transformation from mean to median background level.

M.D. Punt, B.Sc. 17

4.6 Combination of mean and medianIn the CI and CU algorithms it is possible to use one mean algorithm and one median algorithm. The parametersused in these tests are the same as in Section 4.3. Figure 14 shows a scatter plot in which the percentage ofdetected seizures is plotted against the false alarm rate. The best results is from the CU with a mean backgroundlevel for both ISI and ERS. For more detailed information about the results, see Appendix B and Tables 6 - 9.

Figure 14: False alarms per hour versus percentage of detected seizures

DiscussionConsidering Figure 14, it can be concluded that there is a link between the percentage of seizure detections andthe false alarm rate. More specific, if one finds it important to have a lower false alarm rate, this can only beachieved by having a lower seizure detection rate. This also holds the other way around, when one wants to havea high seizure detection rate, the false alarm rate increases.

When considering only the results in which the false alarm rate is below one false alarm per hour, the bestcombination is when the Combined Union algorithm is used with both the ERS and ISI algorithm having a meanbackground level. Moreover, the criterion of three epochs in a row should be used. This results in a performanceof 75.8 percent seizure detection and a false alarm rate of 0.85 per hour. If one wants to have a higher seizuredetection rate, this results in a higher false alarm rate.

4.7 Exclusion of seizure periodIt is expected that a background signal with as few seizures as possible should perform better. The exclusion ofthe seizures is done as follows:

• From begin of epoch, go back one minute.

• From that point, add parts of 2.5 seconds that are not detected as (part of) a seizure epoch.

• When 24 of these periods are found, these 24 periods become background 2.

• Perform this procedure again for background 1 beginning at one 2.5 seconds period before the earliestdetected non-seizure 2.5 second period.

In the first period this algorithm will not work because there are not 48 periods that can be used. To compensatefor this, the method described in section 3.1 is used for the first two minutes and as soon as can distinguishedfrom it, by the method described here, this is done. This constructs two periods and the rest of the procedure isthe same as described in section 3.1. The differences in the performance are given in Table 12 where the test isperformed by using Test set #1. Further, the parameter settings are: ER = 2, S = 1.5, α = 9 and PTH = 0.15.Furthermore, the single epoch criterion is used.

M.D. Punt, B.Sc. 18

Seizures in background Seizure free backgroundMean Median Mean Median

DS(%) Fa/H DS(%) Fa/H DS(%) Fa/H DS(%) Fa/HISI 81.0 1.26 71.4 3.47 76.7 1.26 76.7 3.47

ERS 76.2 3.47 76.7 1.26 47.6 1.95 52.4 0.63

Table 12: The differences between seizures in the background and seizure-free backgrounds

DiscussionAs can be seen in Table 12, the performance decreases when changing the way the background level is determined.This can be explained by the fact that the parameters are optimized for the other background strategy. Additionalresearch has to be performed on this topic to find the best settings of background determination and parametersettings.

4.8 Mother waveletIn order to search for an optimal mother wavelet, five wavelet types are tested; these are the mexican hat wavelet,meyer wavelet, morlet wavelet, biorthogonal wavelet and the coifman wavelet [18]. The different wavelets aretested on both test sets after the optimal parameters are found for that wavelet on the training set. Table 13and Table 14 shows the results from the tests.

Training set Test set #1 Test set #2 AverageMother wavelet Scale DS(%) Fa/H DS(%) Fa/H DS(%) Fa/H DS(%) Fa/HBiorthogonal 2.4 44 96.4 0.15 76.2 2.21 47.4 0.90 70.2 0.97

Coifman 2 39 96.4 0.15 76.2 1.89 47.6 1.35 70.2 0.85Mexican hat 15 96.4 0.30 76.2 3.47 57.1 1.35 74.0 1.34

Meyer 18 96.4 0.30 76.2 2.84 45.3 1.05 69.3 1.09Morlet 20 100 0.15 66.7 3.16 45.3 1.05 67.0 1.10

Table 13: The performance of the differences wavelet types in the ERS algorithm

Training set Test set #1 Test set #2 AverageMother wavelet Scale DS(%) Fa/H DS(%) Fa/H DS(%) Fa/H DS(%) Fa/HBiorthogonal 2.4 34 96.4 0.15 66.7 0.63 64.3 1.95 73.7 0.97

Coifman 2 35 96.4 0.15 66.7 0.63 57.1 1.95 70.8 0.97Mexican hat 15 96.4 0.61 81.0 1.26 64.3 1.95 78.4 1.29

Meyer 26 89.3 0.00 66.7 0.63 45.3 1.50 64.1 0.73Morlet 28 92.9 0.00 61.9 0.94 50.0 0.90 65.4 0.53

Table 14: The performance of the differences wavelet types in the ISI algorithm

DiscussionThe test results shows that on both the ERS and the ISI algorithm the mexican hat wavelet performs best on thepercentage of detected seizures. Considering the false alarm rate, when the ERS algorithm is used, the Coifman2 wavelet performs best. At the ISI algorithm, the biorthogonal 2.4 wavelet and the Morlet wavelet perform best(taking also into consideration the detected seizure percentage). From this, it is hard to say which wavelet typeis the best, for both ERS and ISI it depends on if sensitivity or specificity found more important. Depending onthe preferences, mexican hat, Coifman 2, biorthogonal 2.4 and the morlet wavelets can be good choices.

5 Wavelet enhanced Independent Component AnalysisBecause the EEG signal is sensitive to artifacts from the body and other noise factors, it is to be expected thatartifact removal is beneficial for the quality of the detection. More specific, the ECG, EOG, EMG and respirationartifacts can be quite large in comparison to the original signal and therefore disturb the correct detection of theseizures (or falsely detect a seizure). To overcome this problem, an artifact suppression method was developed

M.D. Punt, B.Sc. 19

by Castellanos and Makarov [3]. This method, the wavelet enhanced Independent Component Analysis (wICA)is an extension to the Independent Component Analysis (ICA) as proposed by Bell and Sejnowski [2] in theinfomax algorithm. Castellanos and Makarov claim that wICA preserves both the spectral and the coherencecharacteristics of the real EEG signal when removing the artifacts which is not (always) the case when usingICA.

5.1 Independent Component AnalysisThree assumptions are used for the ICA, these are:• Experimental data is a spatially stable mixture of the activities of temporarily independent cerebral and

artifactual sources.

• The superposition of potentials arising from different parts of the brain, scalp, and body is linear at theelectrodes, and propagation delays from the sources to the electrodes are negligible (K simultaneous record-ings).

• The number of sources N is no bigger than the number of electrodes (N ≤ K).In the ICA, the goal is to remove the influences of the sources on each other. The recorded data X(t) is acombination of an artifact A(t) and the original source data S(t).

X(t) = A(t) + S(t) (14)

To reconstruct the source data, the ICA algorithm tries to construct both a mixing matrix M and the sourcesS(t) to construct the recorded signals X(t).

S(t) = M−1X(t) (15)

Once the algorithm is applied, the source data S is analysed to find components that result in artifacts.Hereafter, the artifactual parts are set to 0 (Sartf (t) = 0) and a new matrix S(t) is created where the artifactsare suppressed. The final signals are constructed as:

X(t) = MS(t). (16)

From this transformation an artifact free signal can be obtained.

5.2 Wavelet enhanced Independent Component AnalysisThe wICA is constructed as follows:

1. Perform ICA on original EEG data

2. Do wavelet transform on ICA components

3. Threshold wavelet coefficients

4. Inverse wavelet transform

5. Compose wICA-corrected EEGThe ICA is performed as mentioned in the previous section with as outcome the X(t). The threshold value usedin step three is determined by

K =√

2 log Nσ, (17)

where N is the number of elements in the data segment and σ is√

median(|W (a, b)|)/0.6745, which is anestimator of the magnitude of the neural wide band signal part [3].

To illustrate the differences between the signal before and after applying the wICA algorithm, Figure 15shows a signal that has a ECG artifact. After the wICA was applied on the signal, the ECG artifact is removedcompletely. Nevetheless, the resulting signal has lost most of its power.

5.3 AnalysisBoth the ICA and the wICA are tested independently to find the differences between these two as well as thedifferences between these data sets and the original data set. After applying the ICA and wICA algorithm toboth the test sets, the channel in which the EEG signal is best preserved is selected. The training set cannot betaken into account for these tests because only one channel is available. Because of this, Test set #1 is used asa training set and Test set #2 as the test set.

The parameters used in the tests are calculated by grid computing when using the mean background level.The values of the parameters can be found at the end of each of the following tables (Tables 15-18).

M.D. Punt, B.Sc. 20

Figure 15: Signal before and after the removal of artifacts

Test set #1 Test set #2 Total ParametersDS(%) Fa/H DS(%) Fa/H DS(%) Fa/H ER S

Single epoch 42.9 1.26 40.0 9.16 41.3 6.62 1.4 0.4Three epochs in a row 57.1 1.26 37.9 3.16 46.6 2.55 1.6 1.4

Table 15: ERS with ICA corrected signals

Test set #1 Test set #2 Total ParametersDS(%) Fa/H DS(%) Fa/H DS(%) Fa/H α PTH

Single epoch 52.4 0.95 40.0 5.68 45.9 4.16 13 0.06Three epochs in a row 61.9 0.95 33.4 8.21 46.3 5.87 6 0.12

Table 16: ISI with ICA corrected signals

Test set #1 Test set #2 Total ParametersDS(%) Fa/H DS(%) Fa/H DS(%) Fa/H ER S


Table 17: ERS with wICA corrected signals

Test set #1 Test set #2 Total ParametersDS(%) Fa/H DS(%) Fa/H DS(%) Fa/H α PTH

Single epoch 16.1 1.89 39.1 1.42 28.6 1.58 9 0.20Three epochs in a row 19.4 0.63 32.6 0.79 26.6 0.74 8 0.08

Table 18: ISI with wICA corrected signals

M.D. Punt, B.Sc. 21

5.4 DiscussionWhen viewing the results of the ICA tests and comparing these with the results presented in the previous section,a decrease in performance is seen. Although it was expected to have at least the same detection rate (and hopingfor a higher sensitivity and higher specificity) it must be concluded that ICA does not make a useful contributionto the detection of seizures in this setting.

An explanation for the decrease in performance can be that not only the artifacts are filtered away, but alsothe seizures. The seizures are different from the rest of the signal, and because of this, the method can move theseizures to another channel. An illustration of this is given in Figure 16. Seen can be that the spikes from theoriginal signal (red) are suppressed to only small spikes in the ICA and wICA corrected signals (blue and green).As a result, the detection of this seizure is becoming a problem.

Figure 16: Differences in signal after applying ICA and wICA

6 Burst-suppression pattern detectionBurst-suppression is a pattern that can occur in the neonatal EEG. As can be seen in Figure 17, the burst-suppression pattern has a very characteristic form, it consists of bursts of activity interrupted by signal suppres-sion.

Figure 17: A Burst-Suppression pattern

6.1 Previous researchIn previous research by Leistritz et al [16], the inter-burst intervals were detected and also a distinction betweenthe bursts and suppressions could be made. They made use of neural networks in order to recognize automaticallywhere the bursts and suppressions are in the signal. Another research by Hansen [12] used the wavelet transformin order to detect the burst and suppression periods of a burst-suppression pattern. In the research the focus ison where the power is in the spectrum to detect whether a peak is or is not part of a burst-suppression pattern.

M.D. Punt, B.Sc. 22

6.2 Detection algorithmFor the detection of a burst-suppression pattern, two cases must be considered. First of all, the burst, whichindicates a beginning, should be detected. Hereafter is checked if the burst is followed by a suppression period.

To determine if a part of the signal has a burst-suppression in it, two parts are considered. On the one handthere is the background level that has to be determined and on the other hand there is the part of the signal thathas to be analyzed. The background level is determined in the same manner as in section 3.1, but now instead oftaking the minimum of two mean or median values, the background level is determined by taking the minimumof the two maximum values of the background. Thereafter, an epoch of 60 seconds is analyzed in four steps:

1. The epoch is transformed from a time-domain signal into a wavelet-domain signal by the mexican hat wavelettransformation. From this, scale 12 is used for the 200 Hz signals and scale 15 is used for the 250 Hz scale.Figure 18 gives an example of how original signal and its wavelet transformed signal looks like.

Figure 18: A signal and a scalogram of the wavelet transformed signal

2. With an, adaptive to the background level, threshold value that is determined beforehand by grid computing,the peaks that are in the signal are searched (see Figure 19).

Figure 19: The wavelet transformed signal and a threshold value

3. Searched is for the peaks in the transformed signal where a peak is wide enough, or different peaks are closeenough to each other such that they can be considered as a larger peak. For this, four parameters should beset. The first one is a threshold level which selects the peaks. This value is set at 0.2 times the backgroundlevel. A second parameter states that a burst has to have a duration of at least 0.5 second. The thirdparameter has the value 0.1 and is set such that in a burst at least 10 percent should be above the threshold

M.D. Punt, B.Sc. 23

level. This 10 percent has to be between innermost and outermost peak of a burst. The fourth parameter isthat two peaks should be at least 2 seconds apart from each other.If such a spot is found, all the non-peaks in between are set as a peak. If there are not enough spots closeto each other, the peak is considered an artifact and is set to a non-peak. In Figure 20 this procedure ispresented. First, the blue parts are the peaks detected because they passed the threshold. Hereafter, thegreen parts are blue parts which are grouped because they are close enough to each other. At last, the redparts are the peaks that are selected because they last long enough. Note that in Figure 20 one peak (atapproximately 243 seconds) is filtered away by this method.

Figure 20: Blue is where the peaks have passed the threshold. Red are the parts where the peaks are linkedtogether. Green are the parts that are long enough to be considered a burst

4. From each peak-period its begin point is determined. Hereafter is checked whether consecutive peak-periodsare far enough from each other. If they are not, the second peak-period is removed. Figure 21 is an exampleof the end result.

Figure 21: A wavelet transformed EEG signal with burst suppression pattern and the detection of the bursts

Hereafter the time is shifted 10 seconds, and the above mentioned background level and peak detection is repeateduntil the end of the file is reached.

6.3 Future researchFuture research should aim at testing how well the inter-burst intervals (IBIs) are detected. First, an annotateddata set is needed and hereafter can be tested how correctly the algorithm works. A way to visualize theperformance is to plot the inter-burst intervals against the time. An inter-burst interval is the time between twoconsecutive bursts in the signal. When the IBI’s are plotted against the time, Figure 22 can be constructed.

A problem that can occur is that one burst is (or multiple bursts are) missed in the signal which results ina large inter-burst interval time. A method should be developed which detects this problem en searches for themissing burst, if this burst exists in the signal.

M.D. Punt, B.Sc. 24

Figure 22: Example of inter-burst-intervals during time

7 Conclusions and Recommendations7.1 ConclusionsWhen considering the first part of the research, the optimization of the seizure detection algorithms, it hasbecome clear that only little improvement can be obtained. When considering a maximum false alarm rate ofone per hour, it is best to use a Combined Union algorithm where the ERS and the ISI algorithm both use themean background level. This results in a detected seizure percentage of 76.5 percent and a false alarm rate of0.85 false alarms per hour.

From the test results it can be concluded that the optimal parameters differ from data set to data set, butthat these values are in general close to each other. Furthermore, the improvement obtained by changing theparameters to the optimal ones for that data set is small. Although there is a chance that the parameters getoverfitted to one particular data set [22], it appears that, with the current settings, this effect can be ignoredwith these data sets. The make sure that all the effects disappear, a larger training set is needed, or more errorsduring the determination of the optimal parameter values should be allowed.

When considering the algorithmic changes, the values can change drastically. Although this was not expected,the performance tends to decrease by the adjustments, although in some situations it was possible to increasethe performance of one data set. In general however, this leads to a decrease of quality at the other two datasets.

Preprocessing by ICA or wICA does has a great influence on the performance of the algorithms. Althoughartifacts are removed from the EEG signal, also many seizures are (partly) removed. Logically, this leads to asignificant decrease in performance.

Considering the burst-suppression pattern recognition, it is possible to determine interburst intervals. Never-theless, for a better detection of these interburst intervals, the parameters of the algorithm have to be optimizedand a larger training set with burst-suppression patterns is needed.

7.2 RecommendationsIn future research two directions can be taken. First, the quality of detection can be improved. On the otherhand can be tried to generalize the methods build in this research in such a way that these can be implementeddirectly into hardware.

To start with the first group, a point of interest is the personal information of a neonate. Simple informationlike the age, or more complex information like the principal frequency of the EEG can give more insight in thedifferences between seizures that are detected and the ones which are missed. Further research can also focus atmultiple wavelet scales. These can contain more information than a single scale and also seizures that are notin the standard frequency domain can be detected. It should be analyzed what the influences are when usingdifferent scales in one method. A change can be made to the CI algorithm, when two as seizure detected epochsare close to each other, but not exact on the same spot, this can be seen as the same seizure. This will resultin a higher sensitivity, but a lower specificity. Also research can be done on an extension of the CI and CUalgorithms. It is possible to make combinations of, for instance, a CU method with mean background level anda CI method with median background. This could lead to an increase in performance. Determination of thebackground level can be optimized. When the artifacts are removed from the background level, the parameterscan be optimized further. It is also possible to make a cofmbination of the mean and median background level,

M.D. Punt, B.Sc. 25

for instance the average between these two. This may result in a better performance. Some seizures are missedbecause they cannot be found by the current algorithms (higher frequency, shorter duration or longer interspikeinterval times). It should be analyzed how the algorithms have to be adapted, such that they can detect thoseseizures. Furthermore, it is possible to develop a system which searches for both seizures and burst-suppressionpatterns. The information from both algorithms may be used to increase the sensitivity and specificity of both.Artifact removal is beneficial for the signal when only the artifacts are removed. A method has to be developedwhich only deletes the artifacts and not the seizures from the EEG. Also other preprocessing algorithms can bebeneficial for the detection of seizures.

When one wants to implement the algorithms into hardware, several adjustments have to be made before itcan work. One of the most important ones is that the EEG-channel (or channels), which is now selected manuallyhas to be detected automatically. Next to this, in this research the artifact removal is completed beforehand.When applying artifact removal in a real case situation, this has to be done together with the seizure detection.Research has to be performed how this can be done efficiently.

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[12] Hansen, HHG (2005). Quantification of the EEG of full-term newborns. M.Sc. thesis, Eindhoven Universityof Technology.

[13] Kind, T and Visser, FE (2003). Automatische detectie en classificatie van epileptiforme afwijkingen.Technical report, Delft University of Technology.

[14] Kitayama, M, Otsubo, H, Parvez, S, Lodha, A, Ying, E, Parvez, B, Ishii, R, Mizuno/Matsumoto, Y,Zoroofi, RA, and Snead, OC (2003). Wavelet analysis for neonatal electroencephalographic seizures.Pediatric Neurology, Vol. 29, pp. 326–333.

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[16] Leistritz, L, Jger, H, Schelenz, C, Witte, H, Putsche, P, Specht, M, and Reihar, K (1999). New approachesfor the detection and analysis of electroencephalographic burst-suppression patterns in patients undersedation. Journal of Clinical Monitoring and Computing, Vol. 15, pp. 357–367.

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A Parameter optimizationMean - single epochTable 19 - 20

Training set Test set #1 Test set #2 Total ParametersDS(%) Fa/H DS(%) Fa/H DS(%) Fa/H DS(%) Fa/H ER S

Training set optimal 96.4 0.30 76.2 3.47 57.1 1.35 74.0 1.34 2.0 1.5Test set #1 optimal 96.4 0.30 76.2 1.26 54.8 1.20 73.0 0.85 2.0 1.0Test set #2 optimal 96.4 0.91 76.2 4.42 57.1 1.35 74.0 1.76 2.0 1.7

Table 19: Comparison of ERS algorithm performance per test for optimal values

Training set Test set #1 Test set #2 Total ParametersDS(%) Fa/H DS(%) Fa/H DS(%) Fa/H DS(%) Fa/H α PTH

Training set optimal 96.4 0.61 81.0 1.26 64.3 1.95 78.4 1.29 9 0.15Test set #1 optimal 96.4 0.61 81.0 0.95 64.3 2.25 78.4 1.34 10 0.10Test set #2 optimal 96.4 0.61 81.0 1.26 64.3 1.50 78.4 1.10 10 0.20

Table 20: Comparison of ISI algorithm performance per test for optimal values

Mean - three epochs in a rowTable 21 - 22


Training set optimal 92.9 0.15 66.7 0.95 52.4 1.05 67.9 0.67 2.0 1.5Test set #1 optimal 100 1.52 81.0 1.58 57.1 3.15 76.5 2.19 1.5 1.2Test set #2 optimal 92.9 0.15 66.7 1.26 52.4 1.05 67.9 0.73 2.0 1.7





Median - single epochTable 23 - 24


Training set optimal 100 0.15 76.2 1.26 47.6 1.35 71.1 0.85 2.0 0.44Test set #1 optimal 92.9 0.30 76.2 1.26 50.0 1.35 70.2 0.91 2.3 0.50Test set #2 optimal 96.4 0.15 76.2 4.11 54.8 1.05 73.0 1.28 2.3 1.00


M.D. Punt, B.Sc. 28


Training set optimal 96.4 0.00 71.4 3.47 52.4 1.80 70.5 1.40 59 0.10Test set #1 optimal 100 1.21 76.2 2.53 61.9 3.45 76.8 2.37 30 0.25Test set #2 optimal 96.4 0.45 76.2 4.11 64.3 2.10 76.9 1.82 40 0.15


Median - three epochs in a rowTable 25 - 26


Training set optimal 100 0.15 81.0 1.26 57.1 1.65 76.5 0.97 1.6 1.3Test set #1 optimal 100 0.15 81.0 1.26 57.1 1.65 76.5 0.97 1.6 1.3Test set #2 optimal 96.4 0.15 71.4 0.60 54.8 1.20 71.4 0.66 1.8 1.5





M.D. Punt, B.Sc. 29

B Combination of mean and median

Training set Test set #1 Test set #2 AverageDS(%) Fa/H DS(%) Fa/H DS(%) Fa/H DS(%) Fa/H

Single epoch 100 2.73 81.0 0.32 69.0 2.85 81.3 2.31Three epochs in a row 89.3 0.15 57.1 0.32 59.5 1.20 66.6 0.61

Table 27: Combined Union algorithm with median background level in the ERS algorithm and the mean back-ground level in the ISI algorithm


Single epoch 100 0.30 81.0 5.05 54.8 2.10 75.6 1.95Three epochs in a row 96.4 0.15 76.2 0.95 54.8 1.20 73.0 0.73

Table 28: Combined Union algorithm with mean background level in the ERS algorithm and the median back-ground level in the ISI algorithm



Table 29: Combined Intersection algorithm with median background level in the ERS algorithm and the meanbackground level in the ISI algorithm



Table 30: Combined Intersection algorithm with mean background level in the ERS algorithm and the medianbackground level in the ISI algorithm

Documents

Automated Seizure and Burst-Suppression Pattern Detection … · Automated Seizure and Burst-Suppression Pattern Detection in the Neonatal EEG using Wavelet Analysis M.D. Punt, B.Sc