An efficient, robust and fast method for the offline detection of epileptic seizures in long-term scalp EEG recordings

Clinical Neurophysiology 118 (2007) 2332–2343 www.elsevier.com/locate/clinph

An efficient, robust and fast method for the offline detection of epileptic seizures in long-term scalp EEG recordings R. Hopfenga¨rtner *, F. Kerling, V. Bauer, H. Stefan Epilepsy Center, Department of Neurology, University Hospital Erlangen, Schwabachanlage 6, 91054 Erlangen, Germany Accepted 28 July 2007 Available online 21 September 2007

Abstract Objective: A robust and fast algorithm for the offline detection of epileptic seizures in scalp EEG is described. It is aimed for seizure detection with high sensitivity and low number of false detections in long-term EEG data without a priori information. Methods: To capture the characteristic electrographic changes of seizures, we developed an efficient method based on power spectral analysis techniques. The integrated power is calculated in two frequency bands for three multi-channel seizure detection montages (referenced against the average of Fz–Cz–Pz, common average, bipolar) using the same parameters for all montages and all patients taking into account an appropriate artifact rejection. Results: A total of 3248 h of scalp recordings containing 148 seizures from 19 patients were examined. The averaged sensitivity was 90.9% and selectivity (false-positive errors/h, FPH) was 0.29/h of the Fz–Cz–Pz montage; the other montages yielded lower sensitivities but even better selectivity values. Conclusions: Taking into account that the method has been performed in a standardized way with fixed parameters for all patients and montages the obtained values for sensitivity are quite high while the selectivity is acceptably low. The parameters can additionally be tuned to patient specific seizures. It is assumed that this may further improve the seizure detection performance. Significance: The proposed method may enhance the clinical use for the detection of seizures in scalp EEG long-term monitoring during presurgical evaluation.  2007 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved. Keywords: EEG; Epilepsy; Presurgical evaluation; Seizure detection; Time frequency analysis

1. Introduction Long-term video–EEG monitoring is a well-established procedure in the presurgical evaluation of patients suffering from medically intractable epileptic seizures (Gotman, 1985; Gumnit, 1987). The efficiency of this method depends on the ability to detect seizures. Usually seizures are detected by direct observation of the medical staff or by patients pressing a push-button alarm. However, observers are sometimes not present and patients may be unaware of * Corresponding author. Tel.: +49 9131 85 32313; fax: +49 9131 85 36469. E-mail address: [email protected] (R. Hopfenga¨rtner).

their seizures (Kerling et al., 2006). In general, these unnoticed seizures can be detected in principle by reviewing the continuously recorded video–EEG data. Still, in most centers this time consuming reviewing has to be performed by trained EEG technologists in order to achieve high accuracy and sensitivity. Therefore attempts have been made early to develop computerized algorithms for offline detection systems and online warning systems for scalp EEG. In general terms one has to divide proposed algorithms into two categories: One for invasive EEG recordings, and one for scalp EEG. In the following, we restrict our discussion mainly to seizure detection methods for scalp EEG. For details on algorithms used for seizure detection in invasive recordings the reader is referred to Harding (1993), Osorio et al. (2002), Khan and Gotman (2003) or Gardner

1388-2457/$32.00  2007 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.clinph.2007.07.017

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et al. (2006). These approaches are essentially based on the electrographic changes of seizures which manifest themselves most commonly by a sequential change in frequency and amplitude which are distinct from non-seizure activity. However, an important problem in the use of automatic seizure detection systems in scalp EEG is that false detections due to artifacts (e.g., caused by movement, eye blinking, chewing, etc.) can be very frequent and consequently the practical clinical value is reduced. One of the first approaches for seizure detection was based on mimetic methods where individual EEG waves are broken into half-waves and different measures are calculated (Gotman, 1982). Subsequently improvements to this seizure detection method have been described by Gotman (1990) and Qu and Gotman (1993). Its clinical capability has been tested in several studies (e.g., Pauri et al., 1992; Salinsky, 1997). Alternative approaches for computerized seizure detection have been proposed (Panych et al., 1988; Murro et al., 1991). Recently the wavelet-transform for time series analysis has become popular in different scientific fields (Daubechies, 1992). For the automated seizure detection wavelet-based artificial neural networks have been proposed (Webber et al., 1996; Gabor et al., 1996). Here, the neural network is trained by being presented with many types of seizure and non-seizure patterns; after training the neural network is applied to test data.

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intractable seizures were analysed. All data used in this study were collected from patients hospitalised at the Epilepsy Center long term video–EEG monitoring unit at the University of Erlangen, Germany. EEG recordings were obtained using a 64 channel IT-med system (Usingen, Germany). Data were digitized at 256 Hz, the resolution of the ADC is 16 bit. For raw data acquisition a hardware analogue high-pass filter (cutoff 0.08 Hz) and a low-pass filter (cutoff 86 Hz) have been applied. No additional digital filter has been used for pre-processing. For recording the EEG signals have been referenced against CPz; the montages used are based on the extended 10–20 system (containing electrodes of the 10–20 system and Fp1, SO1, F11, TP9, sphenoidal electrode SP1 and their corresponding homologous electrodes on the right side) as well as 2 ECG channels. In our study all EEG data recorded from the patient during presurgical evaluation were analysed. No record was excluded because of the presence of artifacts or poor recording quality due to deteriorating electrodes. Patients were not preselected avoiding an over-representation of a specific seizure pattern. The only criterion for selection was that patients should have at least two seizures with ictal EEG patterns, seizures not accompanied by epileptiform EEG changes were not considered. Video–EEG-reviewing and analysis of the seizures have been done independently by two experts. Table 1 provides clinical information about the patients.

The goals of this study were as follows: • To develop a fast and robust offline algorithm for seizure detection in scalp EEG recordings that would detect on average at least 80% of seizures and with an acceptable average false positive rate 61.0/h using the same parameters for all patients and all recordings. • The method should provide a fast screening of longterm EEG recordings for interesting paroxysms (e.g., subclinical events, spike-wave bursts) without having any a priori information about the patient’s EEG. • To study the influence of different seizure detection montages (SDM) on the sensitivity and selectivity (false detection rate). • The approach allows an easy user-tuneability to exploit the trade-off between sensitivity, selectivity and detection delay. Note that, despite the fact that our algorithm allows user-tuneability, it was not the aim to tune the parameters for each patient and seizure type individually in order to obtain the best results. 2. Methods 2.1. Data selection A total of 3248 h of scalp EEG recordings containing 148 seizures from 19 patients suffering from medically

Table 1 Age, sex, clinical syndrome, type of seizures during presurgical evaluation Patient

Age (years)

Sex

Clinical syndrome/ focus area

Type of seizures

1 2 3 4 5 6 7 8 9 10 (+) 11 12 13 (+) 14 15 16 (+) 17 18 19

19 29 37 29 25 21 37 43 18 23 38 51 30 51 40 26 26 17 26

F M F F M F M M M M M F M F F F M M F

FT T P T T T T T T P T T FT T T F FT F T

CP CP CP SP, CP, CP with SGTC CP with SGTC SP (epigastric Aura) CP, CP with SGTC CP CP, CP with SGTC SP, CP CP CP CP SP, CP, CP with SGTC CP CP with SGTC CP CP with SGTC SP, CP, CP with SGTC

CP, complex partial; SP, simple partial; SGTC, secondarily generalized tonic–clonic; F, frontal, T, temporal, P, parietal. (+) patients had a closely spaced montage based on the 10–10 system, the numbers of EEG channels were 49, 47, and 45, respectively.

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2.2. Data analysis 2.2.1. Definition of multi-channel seizure detection montages Generally speaking electrographic seizure activity manifests itself by a sequential change in frequency and amplitude that is distinct from non-seizure or background activity. Depending on the individual patients these characteristic changes in the EEG can occur at different locations in the brain and sometimes only at one or two scalp electrodes. Furthermore, the detection of seizures might be difficult in multi-channel EEG recordings, e.g., for closelyspaced montages based on the 10–10-system, where focal seizure activity with only moderate changes in amplitude and frequency is in general difficult to recognize. It might be of advantage to have a global overview on seizure activity on both hemispheres instead of looking at single EEG channels. Therefore for our analysis we chose the following strategy: (1) we defined special subsets of electrodes which might be representative for the brain activity of the left and right hemispheres, respectively. Subset S1 for the right side: F12, FT10, TP10, F8, T8, P8, O2, F4, C4, P4; Subset S2 for the left side consists of the homologous electrodes: F11, FT9, TP9, F7, T7, P7, O1, F3, C3, P3. The electrodes at the frontopolar region have been omitted, because they are often contaminated by eye artifacts. (2) In order to study the influence of the chosen montage on the seizure detection performance we have defined standardized multi-channel seizure detection montages SDM(i) (i = 1-6) for the right and left hemispheres. For details see Table 2. From the viewpoint of time series analysis it is obvious that the same EEG channels used in different montages represent different signals with inherent characteristics, irrespective of the fact, which montage is preferable for reviewing by the electroencephalographers. 2.2.2. Seizure detection algorithm Almost all methods used for computerized seizure detection rely on the calculation of certain frequency-amplitude features which change during seizures. In this study, we used a similar approach based on Fourier transformation techniques which is a traditional method in time series analysis for stationary signals (Niedermeyer and Lopes Da Silva, 1999; Priestley, 1981). Because EEG is a non-stationary signal a standard procedure is to segment it into

smaller epochs, assuming stationarity in each epoch (Barlow, 1985). On these epochs Fourier spectral analysis might be applied. Here, we used non-overlapping epochs of width DT = 2 s. The corresponding frequency resolution Df = 1/ DT = 0.5 Hz provides a good compromise between time and frequency resolution. It is assumed that scalp EEG of most seizures contains frequencies in the range 3–30 Hz (Gotman, 1982). The predominant seizure activity of many seizures might be accounted for by restricting the power spectral analysis to two frequency bands: band 1 ranges from 3 to 12 Hz, band 2 from 12 to 18 Hz. The basic strategy for the calculations is as follows: First for each montage SDM(i) defined in Table 2 the referenced channels j will be divided into k equal length epochs of duration DT = 2 s, where k denotes the discrete time index of the epoch. For each channel j and epoch k the ‘‘normalized energy’’ E[j,k] (Eq. (A1)) and the integrated power IP[j,k,b] (Eq. (A3)) in both frequency bands b have been calculated (for details see Appendix A). Due to the fact that during long-term monitoring a variety of artifacts can contaminate the scalp EEG, we have implemented a simple artifact rejection method before calculating the characteristic features E[j,k] and IP[j,k,b]. This rejection of high amplitude EEG sections will be accomplished by calculation of d½j; k ¼ ~xmax ½j; k  ~xmin ½j; k where ~xmax;min ½j; k ¼ max; min fxn ½j; k  x½j; kg are the n maximum and minimum detrended amplitudes of the signal in channel j and epoch k, respectively, x½j; k is the corresponding mean (Eq. (A2)), omitting those epochs where d[j,k] is greater than a fixed threshold dthreshold. For scalp EEG we have used a reasonable value for dthreshold = 600lV, background EEG signals usually do not exceed this value. Denote Nchi[k] the number of channels in epoch k of the seizure detection montage SDM(i) for which the inequality d[j,k] 6 dthreshold holds. Finally, we have calculated for each epoch k and SDM(i) the averaged energy E½k (Eq. (A4)) and averaged power in band IP ½k; b (Eq. (A5)). The quantities EðkÞ and IP ½k; b can be interpreted as an estimate for characteristic frequency and amplitude changes of the multi-channel EEG in montage SDM(i). For epochs k where Nchi[k] = 0 the values for E½kand IP ½k; b are set to predefined values, which are significantly smaller than typical background values.

Table 2 Definition of the standardized seizure detection montages SDM(i) (i = 1–6) used for all calculations SDM(i)

Used electrodes

Type of montage

1 2 3 4 5

S1 = F12, FT10, TP10, F8, T8, P8, O2, F4, C4, P4 S2 = F11, FT9, TP9, F7, T7, P7, O1, F3, C3, P3 S1 S2 S1

6

S2

Referenced against the average of Fz–Cz–Pz Referenced against the average of Fz–Cz–Pz Common average = referenced against the average of S1 and S2 Common average = referenced against the average of S1 and S2 Bipolar-longitudinal: F12–FT10, FT10–TP10, F8–T8, T8–P8, P8–O2, F4–C4, C4–P4, P4–O2 Bipolar-longitudinal: F11–FT9, FT9–TP9, F7–T7, T7–P7, P7–O1, F3–C3, C3–P3, P3–O1

The same electrodes have been used in subsets S1 and S2 for the three different types of montages.

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2.2.3. Thresholding techniques for seizure detection During the evolution of a seizure often a gradual increase of the amplitude of the EEG in certain frequency ranges can be seen, which should lead to an increase of IP ½k; b ( A2, where A is the amplitude of the signal). This characteristic increase will be detected by a thresholding technique and has been achieved as follows: denote t0 = k0DT the discrete time where IP ½k 0 ; b is larger than or equal than a predefined fixed value IP[b]threshold. A seizure will be marked at time t1 (t1 = k1DT, t1 > t0) and a predefined seizure decision value SDV[t1,b] = SDV0[b] will be set, when the following criteria hold for t0 < tk < t1 and both frequency bands: (a) IP ½k; b P IP ½bthreshold , (b) P k¼k 1 k¼k 0 IP ½k; b P IP SUM ½bthreshold , (c) EðkÞ 6 E threshold , and (d) t1-t0 P DTthreshold (see Fig. 1). For times tk (tk = kDT, t1 < tk 6 t2) the seizure decision value increases linearly and for times tk > t2, where IP ½k; b < IP ½bthreshold , the value SDV[k,b] = 0. Of course, SDV[k,b] = 0 holds also for tk < t1. One advantage of the linear mapping is that prolonged seizures can be seen immediately. The parameters for the calculations have been fixed in all seizure detection montages SDM(i) to DTthreshold = 10s, Ethreshold = (150 lV)2 for both frequency bands, while the frequency specific parameters are for (a) band 1 (3–12 Hz): IP[b]thresh2 2 old = 200 lV , IP_SUM[b]threshold = 4000 lV and (b) band 2 2 (12–18 Hz): IP[b]threshold = 40 lV , IP_SUM[b]threshold = 800 lV2. For adjusting these parameters we have used training data sets from more than 10 patients (not considered in this study) whose records contained at least one seizure and have provided a high sensitivity for seizure detection while leading to an acceptable low rate of falsepositive events. One comment on the choice of these parameters: typical values for background EEG activity for awake conditions without any artifacts and pathologi-

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cal changes are in the ranges: IP ½k; b ¼ 1 10–100 lV2, 2 IP ½k; b ¼ 2 1–15 lV2, and EðkÞ  ð5–40lVÞ for the referenced montage Fz–Cz–Pz. The corresponding values for IP_SUM[b]threshold are 20 times larger than IP[b]threshold and ensure that typical seizure patterns are recognized. The parameter Ethreshold assures that many artifacts with high amplitude will not be counted as false positive events. The choice of parameters for the linear mapping of SDV[k,b] is not important and have been chosen for visualization purposes. 2.3. Performance measures 2.3.1. Sensitivity The sensitivity is defined as the ratio of the number of detected seizures found by the algorithm to the number of seizures marked by the EEG expert. Because we are interested in global conclusions for the sensitivity of each type of seizure detection montage the individual sensitivities of the left and right hemispheres for each frequency band have been combined. That is, we have defined the combined seizure detection montage (denoted hereafter as CSDM(i), i = 1,2,3). CSDM(1) combines SDM(1) and SDM(2), CSDM(2) combines SDM(3) and SDM(4), and CSDM(3) combines the montages SDM(5) and SDM(6). Each independent detection made by the algorithm was counted separately and was not grouped as a single detection even if they were e.g., within 10–20 s of one another. 2.3.2. Selectivity Selectivity is defined as the number of false-positive events per hour (FPH) detected by the algorithm and has been determined for both frequency bands and the com-

Fig. 1. Time dependence of the averaged power IP ½k; b ¼ 1 for referenced seizure detection montage SDM(1) in band 1 (a) and the corresponding seizure decision value SDV[k,b = 1] (b) for the pre-ictal, ictal and post-ictal phase of a typical complex partial seizure. The characteristic times t0,t1,t2 and the parameters IPthreshold, SDV0 are explained in the text.

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In this section, we present the results for 19 patients. The ages ranged from 17 to 51 years. The total recording time was 3248 h with an average duration of 171 h (range 90– 332 h), no record was excluded because of the presence of artifacts (e.g., during eating and strong movements) or poor recording quality due to technical problems. A total of 148 seizures with different types of seizures and focus areas were examined (Table 1).

38.5% vs. 45.2%). This finding is related to the fact that almost all seizures investigated in this study started with a predominant rhythmic activity in the lower band 1 (3– 12 Hz). During the evolution of seizures also higher frequencies were commonly seen. These characteristic high frequency activities are often obscured by strong EMG artifacts in the scalp EEG. The seizures of patient 3 differ from the above-mentioned characteristics in that his seizures started with rhythmic activity in the beta band as can be seen in Fig. 2. This patient had 16 CP seizures exhibiting more or less the same EEG activity. Interestingly, for this patient all seizures have been found by the seizure detection algorithm in band 2, while for band 1 a slightly reduced number of 14 events has been detected. The time course of the integrated power IP ½k; b and the seizure decision value SDV[k,b] for both frequency bands 1 and 2 of a long term EEG record of this patient are shown in Fig. 3. For the sake of clarity only the results for the montage SDM(1) are displayed. It can be seen that the detection algorithm for band 2 detects 4 seizures correctly, while for band 1 only two seizures are found. In addition, the detection delay for band 2 is significantly shorter than that of band 1 (insets of Figs. 3b and d). The sensitivity for CSDM(1) for band 2 of patients 5, 16, and 18 (who had CP with SGTC) was also 100%. The sensitivity values CSDM(1) of the other patients varied between 0% and 83.3%. The sensitivities of the common average montage CSDM(2) and bipolar montage CSDM(3) for band 2 are comparable to each other.

3.1. Sensitivity

3.2. Selectivity

First, results on the sensitivity measure are presented. To avoid biasing by patients having many seizures sensitivities are presented on a per-patient basis and as averages for all patients. The calculations for both frequency bands and the different combined seizure detection montages CSDM(i) have been performed for fixed parameters as described in Section 2. The results for the frequency band 1 (3–12 Hz) and band 2 (12–18 Hz) are summarized in Table 3. For band 1, the highest (averaged) sensitivity has been found for CSDM(1) = 90.9%, followed by CSDM(2) = 59.5% and CSDM(3) = 45.2%. A closer look at the individual sensitivities of CSDM(1) in Table 3 reveals that some seizures, e.g., of patient 2 (sensitivity = 20%) and 9 (sensitivity = 62.5%), are missed by the detection algorithm. In comparison to the montage CSDM(1) the averaged sensitivities for the common average montage CSDM(2) and the bipolar montage CSDM(3) are significantly lower. Only for patient 5 and 18 sensitivity values for CSDM(3) were 100%. Both patients showed secondarily generalized tonic–clonic seizures. The results for the sensitivity of band 2 (12–18 Hz) are presented next. In comparison to band 1 the corresponding sensitivities for CSDM(1), CSDM(2) and CSDM(3) are clearly lower (58.4% vs. 90.9%, 36.1% vs. 59.5%, and

The results for the selectivity (false-positive errors/h, FPH) of the 19 patients are summarized in Table 3. To avoid biasing by patients having many false-positive events results are presented on a per-patient basis and as averages for all patients. First, we present results for the selectivity obtained for band 1 (3–12 Hz). The averaged value FPH = 0.29/h for CSDM(1) (montage with the highest sensitivity) is quite low. This value would be even lower (FPH = 0.11/h) if the large value FPH = 3.39/h of patient 4 would not have been considered. The averaged values for FPH of the seizure detection montages CSDM(2) and CSDM(3) are comparable to each other. Interestingly, the rate of false-positive events in band 1 is hardly influenced by chewing. The reason is that there is no significant overlap between characteristic frequencies of chewing artifacts and the frequency band 1 used for detection. In addition, the applied artifact rejection method further decreases the influence of chewing and a variety of other artifacts on IP ½k; b and SDV[k,b]. This is demonstrated in Fig. 4, where an EEG segment of a complex partial seizure during eating is shown. This seizure has been successfully detected by the three combined seizure detection montages CSDM(i) for band 1 and, moreover, neither a significant increase of the power

bined seizure detection montages CSDM(i) also avoiding double counting of events. 2.3.3. Detection delay For any seizure detection algorithm it would be preferable to have short detection delays (Qu and Gotman, 1995; Saab and Gotman, 2005). For offline detection of seizures this is not important. Detection delay was defined as the time elapsed between the occurrence of first clear changes in the electrographic seizure pattern and the first epoch detected by the detection algorithm. The same fixed parameters have been used for all patients and all recordings. A typical calculation for 24 h scalp EEG recording, which includes all three types of seizure detection montages in both frequency bands, takes approximately 15 min on a standard PC with 3 GHz CPU and 1 GB RAM. 3. Results

Patients

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

Seizures

5 5 16 5 2 2 2 5 8 22 4 5 14 28 7 3 6 2 7 148

Hours

139 170 113 140 90 263 193 186 158 183 260 108 216 139 113 118 139 188 332 3248

CSDM(1): band 1

CSDM(2): band 1

CSDM(3): band 1

CSDM(1): band 2

CSDM(2): band 2

CDSM(3): band 2

Sensitivity (%)

FPH

Median delay (s)

Sensitivity (%)

FPH

Sensitivity (%)

FPH

Sensitivity (%)

FPH

Sensitivity (%)

FPH

Sensitivity (%)

FPH

100 20 87.5 100 100 100 100 100 62.5 86.4 100 100 100 85.7 100 100 100 100 85.7 90.9

0.01 0.04 0.20 3.39 0.01 0.02 0.01 0.17 0.03 0.04 0.01 0.69 0.14 0.54 0.02 0.01 0.02 0.04 0.03 0.29

19 19 39 18 19 18 44 23 22 18 24 10 13 19 22 22 11 10 17

100 0 12.5 80 100 50 0 40 62.5 40.9 75 100 64.3 53.6 28.6 66.7 100 100 57.1 59.5

0.01 0.01 0.01 0.05 0.01 0.01 0.01 0.02 0.01 0.01 0.06 0.01 0.15 0.07 0.01 0.01 0.01 0.03 0.01 0.03

0 0 6.25 80 100 50 0 0 62.5 41 75 80 57.1 14.3 0 66.7 83.3 100 42.9 45.2

0.01 0.02 0.01 0.01 0.01 0.01 0.01 0.05 0.01 0.06 0.02 0.01 0.01 0.54 0.01 0.01 0.01 0.01 0.02 0.04

60 20 100 80 100 50 50 20 62.5 0 75 80 64.3 21.4 14.3 100 83.3 100 28.6 58.4

0.01 0.04 0.08 0.03 0.01 0.01 0.21 0.06 0.09 0.01 0.36 0.10 0.28 0.20 0.01 0.01 0.71 0.15 0.16 0.13

0 0 87.5 80 100 0 0 0 62.5 0 75 40 35.7 10.7 0 66.7 0 100 28.6 36.1

0.01 0.01 0.02 0.01 0.01 0.01 0.07 0.01 0.01 0.01 0.28 0.01 0.17 0.02 0.01 0.01 0.38 0.05 0.07 0.06

0 20 87.5 80 100 0 0 0 62.5 0 75 40 57.1 14.3 0 66.7 0 100 28.6 38.5

0.01 0.01 0.03 0.01 0.01 0.01 0.02 0.02 0.04 0.04 0.44 0.93 0.17 0.36 0.01 0.01 0.43 0.22 0.21 0.16

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Table 3 Results for frequency band 1 (3–12 Hz) and band 2 (12–18 Hz) for different combined seizure detection montages CSDM(i) (i = 1,2,3) for the parameters DTthreshold = 10 s, IP[b = 1]threshold = 200 lV2, IP_SUM[b = 1]threshold = 4000 lV2, IP[b = 2]threshold = 40 lV2, IP_SUM[b = 2]threshold = 800 lV2

The results for sensitivity and FPH in the last row are averaged values. For band 1 median delay times for CDSM(1) are presented.

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Fig. 2. Detected seizure from patient 3. The electrographic seizure activity is characterized by an activity in the b-band. The electrodes are from subset S1 for the referenced montage (Ref = Fz–Cz–Pz). The first seizure detection by the montage SDM(1) for band 2 is marked by the arrow.

IP ½k; b has been observed nor a false-positive event has been generated during eating before the seizure occurred. Concerning the results of the selectivity of band 2 (12– 18 Hz) see Table 3. For this frequency band all three montages CSDM(i) show comparable values for FPH, i.e., 0.13/h, 0.06/h, and 0.16/h. In contrast to the selectivity of band 1, here more false-positive events have been detected due to chewing or other EMG-artifacts. The patient 12 had a rather high FPH value (0.93/h) in CSDM(3), which was mainly caused by technical problems of two electrodes during recording and therefore especially pronounced in the bipolar montage.

3.3. Detection delay Here, we present typical results for the median detection delays obtained for the combined seizure detection montage CSDM(1) for band 1 (3–12 Hz), which yielded the highest sensitivity (see Table 3). As can be seen, the delay times varied in the range of 10 s up to 44 s and depend strongly on the individual seizure pattern of the patient. 3.4. User tuneability The design of our seizure detection algorithm was certainly not patient-specific. In this section, we present results

Fig. 3. Time course of the averaged power IP ½k; b and the corresponding seizure decision value SDV[k,b] for band 1 and band 2 for the seizure detection montage SDM(1) of a long-term EEG record (duration  8 h) from patient 3. The patient showed four complex partial seizures during this record. (a) and (b) show IP ½k; b and SDV[k,b] for band 1. (c) and (d) show IP ½k; b and SDV[k,b] for band 2. Details of SDV[k,b] for the first and fourth seizure are displayed in the insets (b) and (d). The dashed lines mark the times of first seizure detection.

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Fig. 4. Detected complex partial seizure from patient 12 during eating. The electrographic seizure activity is contaminated by chewing artifacts. (a) shows the EEG (20 s) for the referenced montage (Ref = Fz–Cz–Pz) of the electrodes on the left hemisphere, (b) shows the bipolar montage of the same channels. The first seizure detection by SDM(2) for band 1 is marked by the arrow.

on performance measures for two cases: (1) the predefined seizure detection montages are slightly extended and (2) the two parameters IP[b]threshold and IP_SUM[b]threshold of the detection algorithm are moderately varied. The first tuning method can be applied e.g., when the first seizure of a patient has been recorded and information on those EEG channels is provided, which are involved in the electrographic seizure pattern. We have applied this approach to patient 2. This patient showed 5 CP seizures (only one has been detected with the standardized montages) with a predominant rhythmic activity on both sphenoidal electrodes SP1 and SP2. Taking this observation into account we have re-examined the calculations for all records of this patient using the same parameters for the single-channel seizure detection montage SP1–SP2 (bipolar). Interestingly, all seizures have been found (sensitivity = 100% vs. 20% before) in band 1, the corresponding value for the selectivity being FPH = 0.57/h. Similarly one can try to reduce the number of false-positive errors (FPH) by a slight modification of the seizure detection montage SDM(i). This has been accomplished for patient 4, who has a sensitivity of 100% but a high error rate FPH = 3.39/h for the referenced montage CSDM(1) in band 1 (see Table 3). The corresponding values of FPH for

CSDM(2) and CSDM(3) are quite low (0.05/h and 0.01/h, respectively). This patient showed a large number of segments in the EEG (duration varied between 10 and 20 s) characterized by high amplitude, rhythmic activity in the delta band represented bilaterally and frontotemporal. By consideration of the two additional electrodes F11 and F12 in the montage CSDM(1) leading to the referenced Cz–Fz–Pz–F11–F12 montage, we have re-examined the calculations for all EEG data sets in band 1 using the same threshold parameters. Quite surprisingly, the value found for FPH = 0.66/h is now significantly lower while the sensitivity of the algorithm was still 100%. In order to improve the rather low values for the sensitivities in band 1 obtained for the montages CSDM(2) and CSDM(3) one may argue that the chosen threshold parameters for seizure detection IP[b]threshold = 200 lV2, IP_SUM[b]threshold = 4000 lV2 are too high. We adjusted both parameters to IP[b]threshold = 150 lV2, IP_ SUM[b]threshold = 3000 lV2 and re-examined the calculations of all EEG data for the patients 1, 3, and 14. The results are summarized in Table 4. For patient 1 the sensitivity obtained for CSDM(3) has been significantly improved from 0% to 80%. The values for FPH for all montages have not changed significantly. For patient 3

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Table 4 Re-examined results for three patients of frequency band 1 (3–12 Hz) for different combined seizure detection montages CSDM(i) (i = 1,2,3) and the parameters DTthreshold = 10 s, IP[b = 1]threshold = 150 lV2, IP_SUM[b = 1]threshold = 3000 lV2 Patients Seizures Hours CSDM(1): referenced Fz–Cz–Pz

1 3 14

5 16 28

139 113 139

CSDM(2): common average

Seizures detected

Sensitivity (%)

FPH

Seizures detected

5 (5) 16 (14) 25 (24)

100 (100) 100 (87.5) 89.3 (85.7)

0.02 (0.01) 5 (5) 1.38 (0.20) 11 (2) 2.01 (0.54) 22 (15)

CSDM(3): bipolar

Sensitivity (%)

FPH

Seizures detected

100 (100) 68.8 (12.5) 78.6 (53.6)

0.01 (0.01) 4 (0) 0.12 (0.01) 1 (1) 0.35 (0.07) 15 (4)

Sensitivity (%)

FPH

80 (0) 6.25 (6.25) 53.6 (14.3)

0.04 (0.01) 0.17 (0.01) 1.54 (0.54)

The values in brackets (..) are taken from Table 3 for comparison.

the sensitivity of CSDM(1) has also improved from 87.5% to 100%, but on the other side FPH has increased significantly from 0.20/h up to 1.38/h. The best improvement for this patient has been obtained for CSDM(2), here the sensitivity increased from 12.5% to 68.8% while the value FPH = 0.12/h is rather low. The sensitivity for CSDM(3) has not been changed at all. The variation of parameters for patient 14 has led to an increase of the sensitivity from 14.3% to 53.6% for CSDM(3), but at the same time FPH increased approximately by a factor of three from 0.54/h to 1.54/h. 4. Discussion The goal of this study was to develop an efficient and robust algorithm for the offline detection of epileptic seizures in long-term scalp EEG providing good results for the performance measures sensitivity and selectivity. The algorithm should be applicable to a variety of different seizure types without the necessity to have any a priori information on the electrographic seizure pattern of the patient. Furthermore, we were interested in the applicability of the algorithm in typical ‘real-world-situations’ in the monitoring unit, i.e., all EEG data recorded of the patient during the presurgical evaluation have been investigated. The algorithm detects characteristic changes of the EEG activity in frequency and amplitude during the evolution of seizures using power spectral analysis techniques. We calculated the integrated power in two frequency bands (3–12 and 12–18 Hz) for three different multi-channel seizure detection montages using identical parameters for all EEG-data. The calculations have been performed by taking into account dynamical artifact rejection. The parameters have been determined from training data not included in the study. 4.1. Sensitivity and selectivity The first observation was that the chosen seizure detection montage has a significant influence on sensitivity and selectivity. In almost all studies reported so far, the bipolar montage is employed for seizure detection analysis. Second, the averaged sensitivity values obtained for the two frequency bands are in general higher for band 1. The highest averaged sensitivity was 90.9% for the referenced mon-

tage Fz–Cz–Pz in band 1. For patient 2 the sensitivity was only 20%. The reason was due to the fact that this patient typically showed a low amplitude, rhythmic EEG activity on the scalp during seizures which was comparable to the normal background activity of the brain. Furthermore, these seizures showed a dominant activity on the sphenoidal electrodes SP1, SP2, but these electrodes have not been considered for the standardized evaluations. The reason that these focal seizures have not been detected is essentially due to the fact that the power IP ½k; b (Eq. (A5)) for band 1 is significantly reduced by averaging and therefore does not reach the predefined threshold values for seizure detection. Generally speaking, for focal seizures, whose activity is restricted to few EEG channels (even in the referenced montage Fz–Cz–Pz), it might be more promising to define a special seizure detection montage SDM and apply the algorithm for the predefined threshold values. This possibility has been demonstrated in Section 3.4. For patient 9 three complex partial seizures were missed by the algorithm in the referenced montage. Visual inspection revealed that these seizures were characterized by an irregular, diffuse EEG activity in the delta band and were of rather short duration (68 s, the threshold parameter for time DTthreshold = 10 s). Only clinical manifestations have been observed in the video. The great majority of seizures have been correctly detected by the reference montage. Concerning the choice of the combined reference Fz–Cz–Pz one remark should be stated here: For the data also the individual electrodes CPz, Cz, and Fz have been investigated for referencing. We obtained similar results for the sensitivities, comparable to the averaged one of Fz–Cz–Pz, but the corresponding values for the selectivity were worse. The sensitivities for the common average (59.5%) and the bipolar montage (45.2%) for band 1 are significantly lower compared to the results of the referenced montage Fz–Cz–Pz. The main reason for this observation is probably due to the fact that the chosen parameters for these two montages are too high. For a typical seizure pattern more channels of the referenced montage show a higher amplitude, rhythmic activity than the corresponding bipolar montage (Fig. 4). Because the spectral power depends essentially on the square of the amplitude, the values for the integrated power are in general smaller. Furthermore, also the results presented in Section 3.4 support this argu-

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ment. Here, a re-examination for three patients with moderately changed threshold parameters yielded better results for the common average and bipolar montage. It is assumed that a more detailed study on the latter montages, where the parameters are adjusted individually but fixed for all EEG data, would also yield higher sensitivities. Whether these re-examinations would provide sensitivities as high as those obtained for the referenced montage Fz–Cz–Pz, yielding at the same time reasonable low values for selectivity, is a quite different debate (Table 4). Our main results clearly indicate that for fixed parameters the referenced montage Fz–Cz–Pz is superior to the other two montages when the trade-off between sensitivity and selectivity is considered. Similar arguments for the sensitivity were given by Gabor et al. (1996). The authors performed automated seizure detection using a self-organizing neural network for the bipolar montage. They stated that due to phase cancellation in bipolar montages the ability to detect seizures in averaged time-series could be decreased. Concerning the possible application of our method in clinical routine, the second important seizure performance parameter selectivity for the montage Fz–Cz–Pz is acceptably low 0.29/h (averaged) for those parameters providing also the highest sensitivity in band 1. As can be seen in Table 3, the selectivity values for the common average montage and the bipolar montage are even lower (0.03/h and 0.04/h) for band 1. This observation is not surprising because it is well known in routine EEG that these montages are in general more robust against artifacts (Niedermeyer and Lopes Da Silva, 1999). In comparison to the sensitivity of band 1 the corresponding values for band 2 for the three seizure detection montages are significantly lower (Table 3). The reason for this observation could be essentially attributed to the fact that almost all seizure patterns investigated in this study showed sequential frequency changes in the lower band 1. At later times during the evolution of seizures also frequencies in band 2 are commonly seen. However, this EEG activity is often obscured by strong EMG artifacts, which are partly recognized as such by the algorithm and not counted as true seizure events. Interestingly, one patient 3 showed seizures whose characteristic frequency changes at the beginning had a noticeable overlap with band 2. This interesting finding might justify to perform seizure detection analysis for band 2 for those patients whose electrographic seizure activity is similar to that of patient 3. It can be seen in Table 3 that the overall averaged FPH for all patients and all montages yielded acceptably low values of 0.06–0.16/h. 4.2. User tuneability Our primary goal was to develop an automated seizure detector, which functions without any user input and is capable to provide good results for a large group of patients. This goal has been met by using parameters

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adjusted from training data and standardized seizure detection montages. However, it can be assumed that certain patients have seizures whose EEG activity will not be captured appropriately either by the defined montages or by the fixed parameters. For such cases it would be desirable if the method can provide an easy-to-use tuneability for the detection of patient specific seizures. This secondary goal has also been accomplished as has been demonstrated in Section 3.4. Here, it was shown that already a slight modification of the seizure detection montage yielded a significant increase of the sensitivity (from 20% to 100%) for patient 2, while for patient 4 a much lower false positive rate (from 3.39/h to 0.66/h) has been achieved. Of course, the method presented can also be applied to patients whose recording montages are based on the 10–20 and 10–10 system. In case of the 10–20 system one simply has to remove some electrodes from the standardized montages in Table 2. 4.3. Comparison with other seizure detection methods Now, we compare our results for sensitivity and selectivity to those of other studies reported on the detection of epileptic seizures in scalp EEG. One of the first seizure detection methods used clinically was that of Gotman (1982, 1990). Pauri et al. (1992) used the mimetic method on 12 patients containing 253 focal seizures, at a total of 461 h EEG. The sensitivity of the bipolar records was 81.4%, a false positive result of 5.38/h has been found. Gabor (1998) described an automated seizure detection using a self-organizing neural network (CNET). The extensive evaluation was performed on 65 patients using 4554 h of scalp EEG containing 181 seizures. This large amount of data corresponds to approximately 70 h EEG per patient (mean). For analysis a bipolar montage has been used for all patients. An average sensitivity of 92.8% and a mean FPH of 1.35/h have been found. The sensitivity is slightly better compared to the one obtained in our study (90.9% for the referenced montage Fz–Cz–Pz and band 1), while FPH is significantly higher (vs. FPH = 0.29/h). Very recently, Wilson et al. (2004) examined with the (new) Reveal algorithm, which utilizes 3 methods (Matching Pursuit, small neural network rules and a connected-object hierarchical clustering algorithm), 672 seizures from 426 patients representing a total of 1049 h EEG (approximately 2.5 h of EEG per patient, mean). For false positive testing a total of 465 h of non-seizure EEG from other 33 patients, deemed to not have epilepsy, has been used. The sensitivity of this approach was 76% with a false positive rate of 0.11/ h. For comparison in the same study two other methods (Stellate Sensa v. 4.0 and CNET) were tested. The corresponding sensitivities were 35.4% and 48.2% and false positive rates of 0.11/h and 0.75/h, respectively. Saab and Gotman (2005) presented results of a new method (based on Wavelet decomposition, feature extraction and data segmentation) applied to 360 h scalp EEG (testing data) including 69 seizures in 16 patients (mean of 22.5 h of

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EEG per patient). For analysis a bipolar electrode montage and separate training data sets have been used. The adjustment of parameters on training data, which has also been done in our study, is important for the applicability of seizure detection methods in clinical routine. The results after use of tuning mechanisms showed a false positive detection rate FPH of 0.34/h and a sensitivity of 76%. In this study also the detection delay (10 s, median) has been considered, which is an important measure for seizure warning systems. It should be noted that a detailed and fair comparison of the different studies, including the present one, for the seizure performance measures sensitivity and selectivity can hardly be achieved because in general different EEG data sets with different lengths (e.g., ranging from approximately 2.5 to 170 h per patient, the latter value being the mean of the present study) and electrographic seizure patterns are used. Furthermore, in several studies preselection of data is used for analysis, which actually does not meet the real world situation in long-term video–EEG recordings. Here, it would be preferable to use a common database of scalp EEG for epileptic seizures. 5. Conclusion The proposed method for the offline detection of epileptic seizures in scalp EEG provides high values for sensitivity and acceptably low values for selectivity for the referenced montage Fz–Cz–Pz. In addition, the algorithm is robust and very fast and provides an easy user tuneability of parameters and montages for the detection of individual patient specific seizure patterns. Therefore, the seizure detection method, combined with the results of the integrated power spectrum, might be capable to make a significant contribution to the diagnostic gain of longterm scalp EEG monitoring during presurgical evaluation. We are aware that a rather small number of patients (n = 19) were investigated. For the applicability of an automated seizure detection method in clinical routine, certainly more patients have to be investigated. This will be done in a forthcoming study. Appendix A This appendix provides some definitions and formulae used in the algorithm for seizure detection. Given the time series x½jðtm Þ ¼ x½jðmDtÞ of channel j, where tm ¼ mDt denotes the discrete time, Dt is the sampling time, m = 0,1, . . . L1, the number of samples in the data file is denoted by L. The signal x½jðtm Þ is divided into epochs k of equal length DT, which will hereafter be denoted as xn[j,k] (0 6 k < M,0 6 n < N), where M is the number of epochs in the data file, N denotes the number of samples in each epoch. Here, the length of each epoch DT = 2 s corresponds to N = 512 samples, and L = 921,600, M = 1800 for 1 h of EEG.

For each channel j and epoch k the ‘‘normalized energy’’ 1 X ðxn ½j; k  x½j; kÞ2 ðA1Þ E½j; k ¼ N n with mean x½j; k ¼

1 X xn ½j; k N n

ðA2Þ

and the integrated power IP[j,k,b] in both frequency bands b (b = 1,2) have been calculated. The integrated power IP[j,k,b] in band b is given by: b

IP ½j; k; b ¼

f2 X

P n ½j; kðfl Þ:

ðA3Þ

fl ¼f1b

with Pn[j,k](fl) denoting the power spectrum or periodogram, and f1b , f2b being the predefined lower and upper cutoff edges of both frequency bands. The power spectrum in each channel and epoch has been calculated using FFT. To avoid leakage in the FFT the Welch windowing function wn has been applied (Dumermuth and Molinari, 1987; Press et al., 1992). For details of the FFT calculation used in this study see Press et al. (1992). Finally, for given E[j,k] and IP[j,k,b] we have calculated for each epoch k and each seizure detection montage SMD(i) the (artifact corrected) averaged quantities: X 1 E½k ¼ E½j; k ðA4Þ Nchi ½k j and IP ½k; b ¼

X 1 IP ½j; k; b Nchi ½k j

ðA5Þ

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