Time frequency based newborn EEG seizure detection using low and high frequency

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1 Home Search Collections Journals About Contact us My IOPscience Time frequency based newborn EEG seizure detection using low and high frequency signatures This article has been downloaded from IOPscience. Please scroll down to see the full text article. 24 Physiol. Meas ( View the table of contents for this issue, or go to the journal homepage for more Download details: IP Address: The article was downloaded on 4/1/211 at 1:18 Please note that terms and conditions apply.

2 INSTITUTE OF PHYSICS PUBLISHING Physiol. Meas. 25 (24) PHYSIOLOGICAL MEASUREMENT PII: S (4) Time frequency based newborn EEG seizure detection using low and high frequency signatures Hamid Hassanpour, Mostefa Mesbah and Boualem Boashash Laboratory of Signal Processing Research, Queensland University of Technology, GPO Box 2434, Brisbane, QLD 41, Australia Received 8 January 24, accepted for publication 28 June 24 Published 22 July 24 Online at stacks.iop.org/pm/25/935 doi:1.188/ /25/4/12 Abstract The nonstationary and multicomponent nature of newborn EEG seizures tend to increase the complexity of the seizure detection problem. In dealing with this type of problem, time frequency based techniques were shown to outperform classical techniques. Neonatal EEG seizures have signatures in both low frequency (lower than 1 Hz) and high frequency (higher than 7 Hz) areas. Seizure detection techniques have been proposed that concentrate on either low frequency or high frequency signatures of seizures. They, however, tend to miss seizures that reveal themselves only in one of the frequency areas. To overcome this problem, we propose a detection method that uses time frequency seizure features extracted from both low and high frequency areas. Results of applying the proposed method on five newborn EEGs are very encouraging. Keywords: vector EEG seizure detection, spike detection, time frequency, singular 1. Introduction Clinical signs of central nervous system dysfunctions in the neonate are often revealed by seizures which are the results of synchronous discharge of a large number of neurons. Seizures tend to increase the risk of impaired neurological and developmental functioning in neonatal period if not kept under control (Paige and Carney 22). Monitoring brain activity through the electroencephalogram (EEG) is an important tool in the diagnosis of neurological disorders in newborns. The onset of an EEG seizure is identified by transient sharp waves and repetitive rhythmic patterns (Ktonas 1987). The detection of these waveforms is, however, complicated by the presence of artefacts and the fact that the /4/4935+1$3. 24 IOP Publishing Ltd Printed in the UK 935

3 936 H Hassanpour et al brain of a normal neonate may produce spurious waveforms and sharp spikes which are the result of extra electrical activities associated with the maturing brain (Coppola et al 1995). The problem is then to differentiate between the waveforms related to seizure and those due to normal brain activities. There are a number of techniques for detecting seizures in neonatal EEG signals in the time (Celka and Colditz 22), frequency (Gotman et al 1997) and time frequency (Hassanpour and Mesbah 23) domains. Nonstationary and multicomponent behaviour of the EEG signal prompted many researchers to use the time frequency (TF)/time-scale domain in their analysis. The time-scale methods are often based on orthogonality principles (Hardin and Hong 23). This orthogonality imposes the restriction that the resulting decomposition produces a series of components in different frequency bands which are not correlated with each other. Since EEG data across frequency bands are correlated (Breakspear and Terry 22), there is a need for analysis techniques that take into account this property. Time frequency signal analysis (TFSA) is a powerful tool for analysing nonstationary signals such as newborn EEG (Boashash et al 23). The TF distribution (TFD) of a signal can be used to show the energy distribution and frequency variation of the signal over time. It can also be used to localize individual components of a multicomponent signal. The analysis of EEG data recorded by a digital system with a high sampling rate (for example 256 Hz) shows that EEG seizure signatures may exist in a wide frequency range, from as low as.5 Hz to higher than 7 Hz. Consequently, some researchers attempted to detect seizure activity using low frequency signatures (lower than 1 Hz) (Celka and Colditz 22, Gotman et al 1997, Liu et al 1992) while others concentrated on higher frequency signatures (higher than 7 Hz) (Hassanpour and Mesbah 23, Sun et al 1997). In this paper, the experiments performed using existing techniques on EEG of five newborns show that the seizure detection techniques based on either low or high frequency signatures may miss the seizures that appear in any one of the two frequency areas. This led us to propose using both low and high frequency signatures in the seizure detection process. The authors recently developed two TF-based techniques for detecting seizures in low and high frequency areas (Hassanpour and Mesbah 23, Hassanpour et al 23b). Performance comparison of these techniques with other existing techniques in this paper, using five newborn EEGs, shows the superiority of the TF-based methods. Hence, the two TF-based methods are selected to be used in detecting EEG seizures in the proposed combined technique. 2. Materials and methods 2.1. Data acquisition EEG data acquisition was performed on newborns, whose ages range between two days and two weeks, at the Royal Women s Hospital, Brisbane, Australia. The electrodes were placed on the scalp according to the 1 2 International System of Electrode Placement. The data were recorded on 2 channels using Medelec (Oxford Instruments, UK) software/hardware environment. The signals were low-pass filtered with a cut-off frequency of 7 Hz and then were sampled with the sampling rate of 256 Hz. A 5 Hz notch filter was applied on the signals. The seizure activities on the recordings were visually labelled by a neurologist from the Neurosciences Department at the Royal Children s Hospital, Brisbane, Australia. Artefact free EEG of the five newborns, selected by the neurologist, have been used in this research.

4 Time frequency based newborn EEG seizure detection using low and high frequency signatures 937 Fs=2Hz N=6 Time res=5 3 Fs=2Hz N=6 Time res= Time (seconds) 2 15 Time (seconds) (a) LFM behaviour (b) piecewise LFM behaviour Figure 1. TFDs of two different EEG seizures (the z-dimension gives the amplitude of the energy density). The figures show that EEG seizures can be approximated by LFM (a), or piecewise LFM (b) Time frequency analysis of EEG seizure The TFD of a signal is a joint representation in both time and frequency domains. For a given signal, x(t), the TFD that belongs to the quadratic class can be expressed as (Boashash 23) ( ρ z (t, f ) = e j2πv(u t) g(v, τ)z u + τ ) ( z u τ ) e j2πf τ dv du dτ (1) 2 2 where z(t) is the analytic signal associated with x(t) and g(v, τ) is a two-dimensional kernel that determines the characteristics of the TFD. For example, by setting g(v, τ) = 1 we get the Wigner Ville distribution (WVD), and with g(v, τ) = Ɣ(β+jπv) 2 the equation represents Ɣ 2 (β) the modified B-distribution (MBD) (Boashash and Barkat 2). In the MBD case, Ɣ(.) represents the Gamma function and β is a smoothing parameter ( β 1). The bilinear operation on the signal in equation (1) may produce spurious components, called cross-terms, in the TFD when the signal is multicomponent or nonlinear FM (Williams 23). The reduced interference distributions (RIDs), such as the MBD, have been introduced to reduce the effect of cross-terms on the TFD of a signal (Boashash and Barkat 2). Different TF kernels are valuable under certain conditions; hence, their suitability is application dependent. The visual analysis of EEG signals in the TF domain indicates that EEG seizures in the form of rhythmic low frequency patterns can be approximated by either a linear frequency modulation (LFM) law or a piecewise LFM (see figure 1) (Boashash and Mesbah 21). EEG seizures may also appear in the form of spike events (Hassanpour and Mesbah 23, Hassanpour et al 23a). Spikes are defined as short-time broadband events with high instantaneous energy. The high instantaneous energy of spikes is reflected as a localized energy pattern in the TF domain (Hassanpour et al 23c). The width of the localized energy patterns becomes narrower in higher frequency areas. Consequently, a spike can be seen as a line or ridge at high frequencies in the TF domain (see figure 2) Existing methods for newborn seizure detection Liu s method. Liu et al developed an autocorrelation based method to detect EEG seizures in newborns (Liu et al 1992). The technique relies on the assumption that the essential

5 938 H Hassanpour et al Amplitude (µv) Time (second) (a) time domain Time (second) (b) TF domain Figure 2. EEG seizure containing spike events. characteristic in newborn EEG seizures is periodicity. To asses the amount of periodicity, the EEG data is segmented into 3 s epochs and each epoch is divided into five windows. Depending on the autocorrelation function of a window, up to four primary periods are calculated for each window in an epoch. The windows are then scored whereby more evenly spaced primary periods are allocated larger scores. After each window in an epoch is scored, a rule based detection scheme is applied to classify each epoch as seizure positive or negative. If two or more channels of EEG data in the same epoch are seizure positive, the epoch is then classified as containing seizure activity Gotman s method. The method introduced by Gotman et al is mainly based on the spectrum analysis of short epochs of EEG data (Gotman et al 1997). In this technique, to detect seizure activities, the EEG data are segmented into 1 s epochs using a sliding window. The window is moved along the EEG in 2.5 s steps. The algorithm was designed to extract features from each epoch and compare them with those of the background. The background is defined as a 2 s segment of EEG finishing 6 s before the start of the current epoch. The frequency spectrum of the individual epochs is calculated and the following features are extracted and used for seizure detection: (1) the frequency of the dominant spectral peak, (2) the width of the dominant spectral peak and (3) the ratio of the power in the dominant spectral peak to that of the background spectrum in the same frequency band Celka s method. Celka et al (22) proposed a method for newborn EEG seizure detection using singular spectrum analysis (SSA). The SSA method is suited for extracting information from stationary or quasi-stationary signals cluttered with noise. In this method to detect seizure activity in EEG data, the signal is preprocessed. The preprocessing is based on a nonlinear whitening filter that spreads the spectrum of the background while keeping rhythmical features of the seizure activities. The filtered signal is then segmented into 1 s epochs using sliding window with a 1.25 s steps. The individual epochs are converted into a matrix for separating the noise subspace from the signal subspace. The signal subspace is obtained by using n singular vectors (SVs) related to the n largest singular values of the matrix using the SVD technique. To find n, as a criterion for space division, the authors used the Rissanen minimum description length (MDL) method. In this

6 Time frequency based newborn EEG seizure detection using low and high frequency signatures 939 technique if n is equal to 1 the related epoch is considered as background otherwise it is a seizure The TF technique (using the low frequency signature). Authors of this paper recently developed a newborn seizure detection technique using the characteristic of rhythmic low frequency patterns of EEG in the TF domain (Hassanpour et al 23b). This technique uses the distribution function of the SVs (DFSVs) associated with the TFD of the signal. In this approach, the EEG signal is initially filtered by a band-pass filter (.5 Hz 15 Hz). The filtered signal is then segmented into 3 s epochs. Choosing 3 s for the duration of epochs is found to be adequate for the feature extraction process. Once the EEG was segmented, the epochs are mapped onto the TF domain using the MBD. Different TFDs were tested and MBD was found to be the most suitable one in characterizing low frequency EEG patterns. It has been shown that the low frequency signature of seizures can be characterized using the SVs associated with the TFD of the signal (Hassanpour et al 23b). To extract the features, the SVD technique is applied to the TFD of the EEG epochs. Let X tf represent a TFD matrix of signal x. Using SVD, this matrix can be represented as X tf = U f Vt T where U f (M M), (M N) and V t (N N) are matrices of left SVs, singular values and right SVs, respectively. Note that the time and frequency dependence of the TFD are included in the orthonormal bases, V t and U f respectively. Since the SVs are orthonormal (Nakos and Joyner 1998), their squared elements can be treated as probability density functions (PDFs) (Groutage and Bennink 2). The PDFs are then used in the process of seizure feature extraction technique. The PDFs are formed from individual columns of matrices associated with the left and right SVs. For example, the PDF related to the first column of matrix U f,f U1, is given by f U1 = { u 2 } 11,u2 12,...,u2 1M (2) where u 1i represents the ith element of U 1 (the first column of U f ) and M i=1 u2 1i = 1. The related probability distribution function can be obtained by where F U1 ={υ 1,υ 2,...,υ M } (3) υ j = j u 2 1i for j = 1toM i=1 Figure 3 shows the TFD of two EEG epochs containing seizure and nonseizure activities. The distribution functions extracted from the first left SVs of the TFDs are shown in figure 4. Due to the fact that there is no significant changes in a large interval in the distribution functions, histograms (approximation of distribution functions) related to these distribution functions are used to reduce the dimension of feature space and hence the computational load. To discriminate between seizure and nonseizure activities in EEG signals using the TFD, the technique uses two left and two right SVs related to the higher singular values as they have contributed more to the signal and are usually part of the signal space (Hassanpour et al 23b). The features extracted through the histograms of the four SVs are reorganized into a feature vector to be fed to a neural network. A two-layer feed-forward neural network, with eight and two neurons in the hidden and output layers respectively, was used to classify the input data into seizure and nonseizure activities. More details can be found in (Hassanpour et al 23b).

7 94 H Hassanpour et al Fs=2Hz N=6 Time res=5 3 Fs=2Hz N=6 Time res= Time (seconds) 2 15 Time (seconds) (a) seizure (b) nonseizure Figure 3. TFD of EEG signals. As can be seen, TFD of a nonseizure signal does not have a specific and consistent pattern. 1 Distribution functions 1 Distribution functions The first left singular vector.6 The first left singular vector Histograms Bin number Histograms Bin number (a) (b) Figure 4. The probability distribution functions and their histograms associated with the left SV of seizure (a), the left SV of nonseizure (b) The TF technique (using the high frequency signature). A TF-based technique for seizure detection has been recently developed in (Hassanpour and Mesbah 23) that uses the high frequency signature of newborn EEG seizures. The technique is composed of two stages: (1) spike detection, (2) seizure detection using the detected spikes. To detect spikes in the EEG signal, the signal is first mapped into the TF domain. In this domain, a spike can be seen as a line or ridge at high frequencies (see figure 2). To detect EEG spikes, we use the TF approach introduced in (Hassanpour et al 23c). Two frequency slices extracted around 6 Hz and 65 Hz of the TFD are utilized to find the signatures of spikes (see figure 5). A spike is considered to exist if its signature is detected at the same position in both frequency slices. This requirement is used to limit the number of false alarms.

8 Time frequency based newborn EEG seizure detection using low and high frequency signatures 941 2nd Frequency Slice 1st Frequency Slice 15 x x 18 Time (second) Time (second) 1.5 Output Time (second) Figure 5. The results of applying the TF spike detection algorithm on the EEG signal in figure 2. The dashed lines on the frequency slices represent threshold values. Ones on the output represent positions of the detected spikes. The 1st and 2nd frequency slices were extracted near to 6 Hz and 65 Hz, respectively Successive spikes intervals (a) seizure activity Successive spikes intervals (b) nonseizure activity Figure 6. Histogram of successive spike intervals. The distribution of spikes in EEG signals can be used to characterize EEG seizures (Hassanpour and Mesbah 23, Hassanpour et al 23a). The EEG signal is encoded into zeros and ones, where ones represent the positions of the detected spikes in the original signal. Analysing successive spike intervals (SSIs), in the TF domain allows one to distinguish the nature of spike firing patterns (Hassanpour and Mesbah 23, Hassanpour et al 23a). This can be done by constructing a histogram of those intervals. Figure 6 shows the first ten bins of the histograms of the SSIs (HSSIs) related to the seizure and nonseizure epochs. It can be seen that there is a significant difference between the HSSIs of seizure and nonseizure activities. In the detection process of this technique, individual channels of EEG data are segmented into 4 s epochs. The duration of seizures varies widely. However, the results of this research show that the HSSIs for any duration of seizures are mostly similar. The results also show that by increasing the length of epochs the good detection rate (GDR) as well as

9 942 H Hassanpour et al Table 1. Performance results of different seizure detection methods. Autocorrelation (%) Spectral (%) SSA (%) HSSI (%) DFSV (%) Patients GDR FDR GDR FDR GDR FDR GDR FDR GDR FDR Baby Baby Baby Baby Baby Average the false detection rate (FDR) are increased. However, by choosing 4 s EEG epochs the best trade-off was achieved. In this technique, an EEG epoch is considered as seizure if the related HSSI is similar to the HSSI of any of different seizure classes. These classes are unsupervisely constructed based on the amount of similarity between the HSSIs by applying the k-nearest neighbour algorithm on a database of seizure epochs. For more information, refer to (Hassanpour and Mesbah 23). 3. Results and discussion In this section, the above-mentioned techniques are applied on the five newborn EEGs (1 min recording length in average) to assess their performance in detecting newborn EEG seizures. The performance results were summarized in table 1. In this table, the GDR and FDR are defined as GDR = 1 GD R %, FDR = 1 FD GD + FD % where GD and FD are the total number of good and false detections respectively and R represents the total number of seizures recognized by the neurologist. A good detection occurs if the epoch detected as a seizure by the detector matches the epoch labelled as a seizure by the neurologist. Note that the five newborn EEGs contain, on average, nine epochs of seizure, and the data set used to train the HSSI and DFSV has not been taken from these babies. Table 1 shows that the TF-based methods, the DFSV and HSSI, have better average detection rates than the other techniques. However, the low GDR of the DFSV and HSSI techniques on baby 1 and baby 3, respectively, is considerable. To better analyse the performance results of different techniques provided in the table, the techniques can be classified into two main groups: low frequency based methods (the Autocorrelation, SSA, Spectral and DFSV) and high frequency based method (HSSI). On baby 1, the low frequency based methods have a lower GDR than the high frequency based technique. These results convey the existence of some seizure activities on the EEG of baby 1 lacking low frequency seizure signature. On the other hand, a lower GDR of the HSSI on baby 3 compared to those of the low frequency based methods conveys the existence of seizure activities lacking the high frequency signature. Thus, seizure detection techniques based on either low or high frequency signatures may miss EEG seizures whose signatures are latent in the frequency area under analysis. This conclusion suggests that a more efficient EEG seizure detection technique can be obtained by combining the low frequency and high frequency based detectors.

10 Time frequency based newborn EEG seizure detection using low and high frequency signatures 943 Table 2. Comparison between HSSI, DFSV and the HSSI+DFSV techniques in seizure and nonseizure detection rates. Detection HSSI DFSV HSSI + DFSV technique (%) (%) (%) GDR FDR Combining low frequency and high frequency detectors and future directions Combining the output of several classifiers may lead to an improved classification result as the sets of patterns misclassified by the different classifiers would not necessarily overlap. Thus, this paper proposes combining EEG seizure detection techniques to improve the detection results. In choosing different classifiers to construct a combined classifier, it is preferred to select classifiers which work in different feature spaces as suggested in (Kittler et al 1998). Hence, among the studied seizure detection techniques the HSSI and DFSV are selected for the combined classifier. As the DFSV and HSSI employ the low and high frequency signatures respectively, they potentially offer complementary information about the EEG patterns to be classified. In addition, they have better performance compared to the other techniques. In the combined detection technique, both the HSSI and DFSV techniques are applied on EEG epochs to detect seizure activities. In the combined method, a signal is considered as seizure if any of the two techniques recognizes the signal as seizure; and a signal is considered as nonseizure if none of them recognizes the signal as seizure. Table 2 shows the results of applying the HSSI, DFSV and HSSI + DFSV (using combined technique) on the EEG data of the first two babies in table 1. The results show that the combined method successfully detects all the seizure epochs. However, this result was accompanied by an increase in FDR. Thus, to further improve the accuracy of the detection algorithm, an optimal approach is needed to combine the detectors. For this reason, the authors are in the process of trying different methods of combining classifiers such as the one based on the Dempster Shafer theory of evidence (Al-Ani and Deriche 22) and ensemble learning algorithms for combining the results of several classifiers (Pennock et al 2). 4. Conclusion It has been shown that the time frequency approaches, developed by the authors, have superior performance than the other approaches in detecting newborn EEG seizures. The results of this paper, obtained on the EEG of five newborns, show that using both low and high frequency signatures is potentially more efficient in detecting EEG seizures than using high frequency or low frequency signatures separately. The high and low frequency based seizure detection techniques, introduced in this paper, need to be optimally merged to further improve the seizure detection performance. Acknowledgments This research is funded by the Australian Research Council (ARC). The authors wish to thank Professor Paul Colditz of the Royal Women s Hospital in Brisbane for providing access to the Perinatal Research Centre and Dr Chris Burke of the Royal Children s Hospital in Brisbane for his assistance with the interpretation of the EEG data.

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