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Quantitative movement analysis differentiates focal seizures characterized by automatisms
Epilepsy & Behavior 20 (2011) 642–647
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Epilepsy & Behavior j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / ye b e h
Quantitative movement analysis differentiates focal seizures characterized by automatisms Jan Rémi a, João P. Silva Cunha b, Christian Vollmar a, Özgür Bilgin Topçuoğlu a,1, Alexander Meier a, Steffen Ulowetz a, Pedro Beleza a, Soheyl Noachtar a,⁎ a b
Epilepsy Center, Department of Neurology, University of Munich, Munich, Germany IEETA/Department of Electronics, Telecommunications and Informatics, University of Aveiro, Aveiro, Portugal
a r t i c l e
i n f o
Article history: Received 22 December 2010 Revised 17 January 2011 Accepted 20 January 2011 Available online 31 March 2011 Keywords: Epileptic automatism Seizure semiology Temporal lobe epilepsy Frontal lobe epilepsy
a b s t r a c t The analysis of epileptic seizures is typically performed by visual inspection, limited by interrater variation. Our aim was to differentiate seizures characterized by automatisms with an objective, quantitative movement analysis. In part 1 of this study we found parameters (extent and speed of movement of the wrist and trunk) separating seizures with predominant proximal (hyperkinetic, n = 10) and distal (automotor, n =10) limb automatisms (P b 0.002). For each movement parameter we used the lowest value recorded for a hyperkinetic seizure in part 1 as the cutoff parameter in part 2 on a consecutive sample of 100 motor seizures. As in part 1, the difference between hyperkinetic and non-hyperkinetic seizures was highly significant (b 0.001). When all movement parameters were above the threshold, a hyperkinetic seizure was identified with a probability of 80.8%, but the probability for a non-hyperkinetic seizure to have all parameters above the threshold was only 0.02%. © 2011 Elsevier Inc. All rights reserved.
1. Introduction The analysis of seizure symptoms is well established in the diagnostic evaluation of patients considered for resective epilepsy surgery [1,2]. However, the localizing value of seizure semiology has been debated [3], as currently seizure analysis is qualitative and based on the visual investigation of seizure semiology [4], which has poor interobserver reliability for most semiological features studied so far [5,6]. Ictal automatisms are complex behaviors that resemble normal body movements. In focal epilepsies, they occur during seizures, most commonly arising from temporal and less commonly from extratemporal regions [3,7,8]. The proposed Diagnostic Scheme for People with Epileptic Seizures and with Epilepsy of the ILAE Task Force on Classification and Terminology distinguishes focal motor seizures with typical (temporal lobe) automatisms and focal motor seizures with hyperkinetic automatisms [9]. The former are characterized by distal manual or oral automatisms, the latter by predominantly proximal limb or axial muscles producing irregular sequential ballistic movements, such as pedaling, pelvic thrusting, and thrashing [10]. Differentiation of the two is important for patients being considered for epilepsy surgery because hyperkinetic seizures occur more commonly in frontal lobe epilepsy (FLE) and automotor seizures in temporal lobe epilepsy (TLE),
⁎ Corresponding author at: Epilepsy Center, Department of Neurology, University of Munich, Marchioninistrasse 15, 81377 Munich, Germany. Fax: +49 89 7095 6691. E-mail address: [email protected] (S. Noachtar). 1 Current address: Department of Neurology, Erenköy Psychiatry and Neurology Education and Research Hospital, Istanbul, Turkey. 1525-5050/$ – see front matter © 2011 Elsevier Inc. All rights reserved. doi:10.1016/j.yebeh.2011.01.020
and TLE and FLE are the most common epilepsy syndromes in patients considered for resective epilepsy surgery [11]. We introduced an observer-independent technical setup that enables us to quantify movements in video-recorded seizures of patients with epilepsy without introducing major changes in the routine of our epilepsy monitoring unit [12,13]. Here, we applied this quantitative analysis of movements to determine whether there are objective movement parameters that can differentiate hyperkinetic from automotor and other non-hyperkinetic movements. 2. Methods This study had two parts. First, we analyzed the movement characteristics of 10 hyperkinetic and 10 automotor seizures. Several movement parameters could be identified that clearly differentiated these seizure types. To test the sensitivity and specificity of these parameters, we applied them as cutoffs on a sample of 100 consecutive EEG/video-recorded seizures with any motor activity in part 2 of our study. The study complied with the ethical guidelines of our institution, and the depicted patients gave written informed consent. 2.1. Part 1 A database search of the archives of the University of Munich Epilepsy Monitoring Unit was run using the terms automotor seizures and hyperkinetic seizures. Twenty seizures in 17 patients were included according to seizure video criteria described below. In epileptic automatisms, the single motion may appear natural, but its
J. Rémi et al. / Epilepsy & Behavior 20 (2011) 642–647
repetition and its inappropriate occurrence in a particular situation separate it from physiological movements. When only proximal limb or axial movements occur and large movements result (hyperkinetic seizure), their distinction from distal manual or oral automatisms (automotor seizure) typically poses no problem, but the continuum between those two poles is evident in the possibility of categorizing complex motor seizures as well, which contain both proximal and distal automatisms [9]. For the purpose of this study, to yield distinct cutoff parameters, we chose 10 hyperkinetic and 10 automotor seizures that two experienced epileptologists had independently classified concordantly (interobserver reliability κ = 1). The patient data are summarized in Table S1 (Supplementary Material—see Appendix). Epilepsy syndromes included TLE (n = 6), parieto-occipital lobe epilepsy (n = 3), FLE (n = 1), paracentral epilepsy (n = 1), and focal epilepsies (n = 6) that could not be localized further. 2.2. Part 2 In a second step, 100 consecutive motor seizures retrieved from the video archives of the University of Munich Epilepsy Monitoring Unit were included in the evaluation according to the criteria described below. The only restriction was that the selected seizures had to include motor symptoms. The data on these patients are summarized in Table S4 (see Appendix). Seizures of 65 patients were included. Epilepsy syndromes included TLE (n = 16), FLE (n = 12), paracentral epilepsy (n = 5), parietal lobe epilepsy (n = 2), occipital lobe epilepsy (n = 2), parieto-occipital lobe epilepsy (n = 1), and focal epilepsies (n = 27) that could not be further localized. 2.3. Patients All patients (parts 1 and 2, n = 82) had refractory focal epilepsies and underwent a standardized presurgical evaluation including video/EEG monitoring, MRI, and neuropsychological testing. Noninvasive video/EEG monitoring with closely spaced surface electrodes (10–10 system) and sphenoidal electrodes using 32- to 64-channel EEG machines (Vangard, Cleveland, OH, USA, and XLTEK, London, ON, Canada) was performed in all patients. Selected patients underwent additional invasive video/EEG monitoring with stereotactically implanted depth electrodes or subdural electrodes (n = 7). MRI included proton density-weighted, T1-weighted, and T2-weighted images in the axial, coronal, and sagittal planes (5-mm slices) (1.0-T Impact/Siemens). In cases in which routine MRI was normal, highresolution MRI with three-dimensional FLASH images, fluid-attenuated inversion–recovery (FLAIR), and magnetization prepared rapid attenuated gradient echo (MPRAGE) were performed (1.5-T Vision/ Siemens). Further imaging studies in selected patients included interictal FDG-PET and ictal and interictal subtraction ECD-SPECT [14,15]. The localization of the epileptogenic zone (epilepsy syndrome) was defined in a patient management meeting attended by epileptologists, neuroradiologists, neurosurgeons, and neuropsychologists. We did not restrict our sample to postoperatively seizure-free patients [3], but deliberately included patients for whom the syndrome diagnosis was established by aforementioned criteria to represent a typical epilepsy monitoring unit patient sample. 2.4. Seizure semiology and quantitative video analysis All seizures had been analyzed visually by at least two senior epileptologists and classified according to a semiological seizure classification [16,17]. Automotor seizures were characterized by prominent oral and manual automatisms. Hyperkinetic seizures were defined as prominent movements of the trunk and proximal limbs. Complex motor seizures were defined as seizures in which characteristics of both automotor and hyperkinetic seizures occurred
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[17]. The seizure evolutions of the hyperkinetic and automotor seizures in part 1 of the study are summarized in Table S1 (see Appendix). Wrist and trunk movements had to be continuously visible throughout the course of the seizures on the video recordings to include the seizure video for further analysis. Because of the methodological limitations of the two-dimensional video analysis, only those seizure videos in which the camera position was perpendicular to the trunk facing the camera in an upright position were included [13]. The camera angle was also the reason we could not include leg movements, because they were typically out of the perpendicular plane. 2.5. Movement quantification Movements were quantified from the videos by analyzing all video frames during the entire seizure (25/second) according to a published setting [12,13]. Using this approach, we tracked the two-dimensional coordinates (x, y) of three points of the patient's body, trunk center (T) and left and right wrists, as depicted in Fig. 1A. For further analysis we evaluated only the most active wrist (W). Trunk and wrist movement extent and speed were analyzed and compared between automotor and hyperkinetic seizures in part 1 of the study. Fig. 1 illustrates movement tracings and respective movement extent areas for an automotor seizure (Fig. 1A) and a hyperkinetic seizure (Fig. 1B). In part 2 of the study, we analyzed the same movement parameters as in part 1 of this study on a sample of 100 consecutive motor seizures of 65 patients. The cutoff values for the movement parameters that separated hyperkinetic seizures from automotor seizures in part 1 were applied to all 100 motor seizures in part 2. The sensitivity and specificity of the movement parameters for the differentiation of hyperkinetic and non-hyperkinetic seizures were evaluated on the basis of these cutoff parameters. For the purpose of part 2 of this study, complex motor seizures were included in the hyperkinetic group as the characteristics of complex motor seizures include also prominent trunk and proximal limb movements, which are typical features of hyperkinetic seizures [17] and the aim of our study was to separate hyperkinetic from non-hyperkinetic movements by means of quantitative analysis. 2.6. Movement extent A graphic representation of the wrist and trunk movements of patients with hyperkinetic and automotor seizures is provided by Fig. 1. The movement extent measure was defined by selecting the twodimensional extreme points of the movement trajectories (p1 … p4) and computing the corresponding area of the rectangle formed, as indicated by the dotted lines in Fig. 1. Given the structure of human arm joints, any trunk movement will influence wrist movement. So, for example, if trunk movements occur in the direction opposite wrist movements, the raw analysis would reduce the extent of wrist movement and the corrected analysis would result in a larger movement extent. Thus, to separately quantify trunk and wrist movements, one has to subtract any influence of trunk movements from the resulting wrist movement. Therefore, we subtracted these movements with a frame-by-frame correction algorithm that could be performed in Matlab software (Mathworks, Natick, MA, USA), which was used for all further analyses. (For further details and illustrations please refer to the Supplementary Material—see Appendix.) 2.7. Movement speed Trunk and wrist speed was computed by dividing the covered distance by the time of movement which was readily available as a function of the elapsed video frames (25 frames = 1 second). The maximum (Smax) and mean (Smean) speeds for the trunk (Ti) and for
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Fig. 1. Movement analysis of an automotor seizure (A) and a hyperkinetic seizure (B). The right wrist (label 1) movements of patients were tracked (gray tracking line) and overlain on the schematic outline. The dotted line represents movement extent, calculated as the maximum area traveled by the wrist and limited by the two-dimensional maxima p1, p2, p3, and p4. Note the larger extent of the right wrist movement in the hyperkinetic seizure (B).
the corrected (see above) wrist movements (Wci) were calculated. (For further details and illustrations please refer to the Supplementary Material.) 2.8. Seizure duration The duration of the seizures was measured based on onset and end of EEG seizure patterns. Scalp EEG recordings may miss the seizure onset, for example, in insular epilepsy. Therefore, the clinical onset was taken as seizure onset when it clearly preceded the EEG seizure onset. 2.9. Statistical analysis Quantitative parameters for the different groups were compared using the Mann–Whitney U test. Differences were considered significant at P b 0.05. Multiple testing was accounted for by Bonferroni–Holm review of the resulting P values. No statistically significant test result had to be rejected because of the Bonferroni–Holm review. Sensitivity and specificity of the quantitative movement parameters were calculated for the identification of hyperkinetic and nonhyperkinetic seizures for part 2 of the study. To estimate the classification power of a combination of all parameters, the empirical Bayes method was used [18], which allows us to approximate the probability of an original distribution (hyperkinetic vs nonhyperkinetic) from the dependent data (parameters above or below threshold). 3. Results 3.1. Part 1 The patient data from part 1 of the study on hyperkinetic and automotor seizures are summarized in Table S1 (see Appendix). Results of the quantitative analysis of movements during the 10 hyperkinetic and 10 automotor seizures are provided in Table 1; for details, please refer to Tables S2 and S3 (see Appendix).
Fig. 2. Extent of wrist movement in 10 hyperkinetic and 10 automotor seizures (part 1). Movement extent: maximum area traveled by the wrist expressed in (image pixels2). The hyperkinetic seizure with the lowest movement extent (12,954 pixels2) served as the lower cutoff (line) for part 2 of the study.
The analysis of wrist movement extent separated all hyperkinetic from automotor seizure types, as it was greater in all hyperkinetic (median = 47,179 pixels2) seizures than in automotor seizures (3102 pixels2) (P b 0.001). The hyperkinetic seizure with the lowest movement extent (12,954 pixels2) was clearly separated from the automotor seizure with the highest movement extent (6758 pixel2) (Fig. 2). The extent of trunk movement was also highly significantly greater in hyperkinetic seizures (4459 pixels2) than in automotor seizures (271 pixels2) (P b 0.001), but two automotor seizures had a larger extent of trunk movement than the hyperkinetic seizure with the smallest extent of trunk movement (Table S2—see Appendix). Maximum speed (median = 1104 pixels/second vs 210 pixels/ second, P b 0.001) and mean speed (69 pixels/second vs 28 pixels/ second, P = 0.002) (Table S3) of wrist movements were significantly faster in hyperkinetic seizures than in automotor seizures. Analysis of maximum speed of wrist movement also showed that only one
Table 1 Movement parameters from ten hyperkinetic and ten automotor seizures. Seizure type
Hyperkinetic Automotor P
Movement extent (pixels2)
Movement speed (pixels/s)
Trunk
Wrist
Trunk maximum
Trunk mean
Wrist maximum
Wrist mean
4,459 ± 20,060 271 ± 1207 b0.001
47,179 ± 60,276 3,102 ± 1,981 b0.001
221 ± 134 52 ± 13 b 0.001
25 ± 10 9±3 b0.001
1104 ± 1978 210 ± 146 b0.001
69 ± 34 28 ± 16 0.002
Note. Values are given as medians ± SD. n.s., not significant.
Seizure duration (s) 34 ± 54 81 ± 18 n.s.
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Table 2 Movement parameters from 100 consecutive motor seizures. Seizure type
Hyperkinetic Non-hyperkinetic P Sensitivity (%) Specificity (%)
Movement extent (pixels2)
Movement speed (pixels/s)
Trunk
Wrist
Trunk maximum
Trunk mean
Wrist maximum
Wrist mean
7747 ± 8580 399 ± 1339 b 0.001 98.5 54.3
43,524 ± 24,068 5,809 ± 9,820 b0.001 100 54.1
210 ± 95 88 ± 49 b 0.001 100 28.2
26 ± 15 7±7 b 0.001 100 55.6
688 ± 249 198 ± 192 b 0.001 95.9 63.0
56 ± 35 25 ± 19 b0.001 100 52.6
Seizure duration (s) 36 ± 20 69 ± 24 b0.001 n.a. n.a.
Note. Movement parameter values are given as medians ± SD. n.a., not applicable.
automotor seizure (530 pixels/second) was above the lowest maximum speed of wrist movements of hyperkinetic seizures (477 pixels/second). Maximum speed (median= 221 pixels/second vs 52 pixels/second, P b 0.001) and mean speed (25 pixels/second vs 9 pixels/second, P b 0.001) (Table S3—see Appendix) of trunk movements were significantly faster in hyperkinetic seizures than in automotor seizures. Again, no automotor seizure had faster trunk movements than any hyperkinetic seizure. The duration of the automotor seizures (78 ± 18 seconds, median= 81 seconds) showed a trend of being longer than that of the hyperkinetic seizures (58 ± 54 seconds, median = 34 seconds) (P = 0.06).
3.2. Part 2 For the patients in part 2, basic characteristics and seizure semiologies are summarized in Table S4 (see Appendix). Hyperkinetic and non-hyperkinetic seizures differed highly significantly with respect to all movement parameters studied (P b 0.001) (Table 2). For the purpose of this study, we also evaluated the ability of these parameters to differentiate hyperkinetic and non-hyperkinetic seizures. Therefore, specificity and sensitivity were calculated for all parameters. Please refer to Table S5 (see Appendix) for the correctly and incorrectly identified individual seizures. As cutoff parameters we chose the lowest recorded value for the hyperkinetic seizures in all categories in part 1 of this study. Extent of wrist movement (lowest movement extent = 12,954 pixels2) (Fig. 2) was able to identify all hyperkinetic seizures (sensitivity = 100%) (Fig. 3, Table S5—see Appendix). Specificity of extent of wrist movement was at 54.1%, with 63 of 80 non-hyperkinetic seizures being correctly classified based on this criterion. Extent of trunk movement (cutoff parameter = 1240 pixels2) misclassified only one hyperkinetic seizure (sensitivity = 98.5%, specificity = 54.3%) (Fig. 3). Trunk movement mean speed (cutoff: 15 pixels/frame) identified all hyperkinetic seizures (sensitivity = 100%, specificity = 55.6%) (Fig. 4). Maximum speed of trunk movements (cutoff = 81 pixels/ frame) identified all hyperkinetic seizures (sensitivity = 100%), but
Fig. 3. Extent of trunk and wrist movement in hyperkinetic and non-hyperkinetic seizures. Movement extent: maximum area traveled by the trunk or wrist (image pixels2). Data from part 2 of the study; limit bar (cutoff parameter) as described in text. Please note the logarithmic y axis.
had a low specificity (28.2%), as only 28 non-hyperkinetic seizures were correctly identified. Maximum speed of wrist movements (cutoff = 477 pixels/frame) identified 17 of 20 hyperkinetic seizures (sensitivity = 95.9%) and 70 of 80 non-hyperkinetic seizures (specificity = 63.0%) (Fig. 5). Mean wrist movement speed (cutoff = 40 pixels/frame) identified all hyperkinetic seizures (sensitivity = 100%) and identified 61 of 80 non-hyperkinetic seizures (specificity = 52.6%). The separation power of the movement parameters can be combined by the empirical Bayes method [18]. When all movement parameters in our study were above the threshold, a hyperkinetic seizure was identified with a probability of 80.8%, but the probability that a non-hyperkinetic seizure has all parameters above the threshold is only 0.02%. When choosing only four of the six parameters (movement extent of the wrist, mean speed of wrist movement, mean and maximum speeds of trunk movement), all hyperkinetic seizures are correctly identified (100% probability) and the probability of a non-hyperkinetic seizure to be above these thresholds is only 0.06%. In addition, we calculated that the average duration of the hyperkinetic seizures was 36 ± 20 seconds (range = 18–80, median = 28), which were significantly shorter than the non-hyperkinetic seizures, 71 ± 24 seconds (range = 7–122, median = 69) (P b 0.001). 4. Discussion 4.1. Quantitative movement analysis Our study demonstrates that the quantitative analysis of movements during epileptic seizures is able to identify objective movement parameters characteristic of hyperkinetic seizures that differentiate them well from other motor (especially automotor) seizures. In part 1 of the study, analysis of 10 hyperkinetic and 10 automotor seizures yielded quantitative movement parameters that clearly separated hyperkinetic from automotor seizures. For the purpose of this study, we had selected hyperkinetic and automotor seizures classified
Fig. 4. Speed of trunk movement in hyperkinetic and non-hyperkinetic seizures. The mean and maximum speeds are expressed as pixels per second. Data from part 2 of the study; limit bar (cutoff parameter) as described in text. Please note the logarithmic y axis.
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Fig. 5. Speed of wrist movement in hyperkinetic and non-hyperkinetic seizures. The mean and maximum speeds are expressed as pixels per second. Data from part 2 of the study; limit bar (cutoff parameter) as described in text. Please note the logarithmic y axis.
concordantly by two experienced epileptologists; therefore, we feel confident that these seizures yielded selective movement parameters. We then evaluated these movement cutoff parameters by using them on a consecutive, unselected sample of another 100 motor seizures, and the parameters remained reliable for the identification of hyperkinetic seizures. The sensitivity of the quantitative movement parameters for identification of hyperkinetic seizures was excellent. The specificity of some of the parameters was not as good, for example, trunk maximum speed, most likely because of the deliberate heterogeneity of the unselected non-hyperkinetic seizure group. The classification power could be improved even further by combining the results from all parameters. Using the Bayes method [18], we were able to show that the likeliness of a hyperkinetic seizure was above 80% when all parameters were above the threshold and the likelihood of a nonhyperkinetic seizure was below 0.1%. We, therefore, believe that the movement parameters from this study, namely, speed and extent of wrist and trunk movement, may be very useful in identifying hyperkinetic seizures for future research. Future research could relate movement patterns to the seizure onset zone. In our relatively small and, for part 2, deliberately heterogeneous patient sample, the movement parameters differed clearly between seizure types (hyperkinetic vs non-hyperkinetic), but did not reach statistical significance between epilepsy syndromes. Hyperkinetic seizures are typical of FLE, but also occur in other syndromes like TLE [19] and parietal lobe epilepsy [20]. Analysis of seizure evolution provides more localizing information than single seizure types [21]. A quantitative approach to seizure semiology analysis may improve the definition of the epileptogenic zone, for example, in presurgical patients. Here, in particular, the correlation of invasive EEG with video-recorded seizure semiology may provide helpful insight into the localization of the epileptogenic zone and spread pattern of epileptic activity [2,22]. Hyperkinetic seizures may be mistaken as nonepileptic events like parasomnic sleep disturbances and psychogenic seizures because clinically they may appear similar [23,24]. Here, our quantitative interobserver-independent analysis was applied to determine objective parameters that could differentiate epileptic from, for example, psychogenic seizures.
recorded seizures had to be excluded from analysis because either the movements were not fully visible in the camera or the majority of the patients’ movements did not occur in the plane perpendicular to the view of the camera system. These movements were missed by the two-dimensional analysis and could therefore not be quantified. Furthermore, distal automatisms are hard to quantify because of the limitation of the video resolution which may have reduced the significance of lateralization of ipsilateral and contralateral hand automatisms in another quantitative study [25]. We currently use infrared reflectors mounted on the patient, which allow the automated tracking and subsequent analysis of the movements based on fixed high-luminescence markers [13]. However, there are still limitations to this approach which can only be overcome with a three-dimensional analysis of the movements. Other investigators recently published two studies with similar data that follow closely our previous work [26,27]. They used a similar two-dimensional method [12,13] to analyze automatisms in patients with TLE and FLE. The limitations of the two-dimensional movement analysis we describe in detail under Methods were not addressed adequately, though. For example, the geometric camera-bed setup is not well defined. No data are given on the constancy of the geometry of the scene nor on error estimation studies in contrast to our methodology [13]. Previously we reported an error below 8% given certain constraints that we follow in our procedure [13]. Furthermore, the correction algorithm for passive limb movements as mentioned under Methods lacked evaluation of maximum and average speed. These considerations illustrate that the techniques and analysis algorithms used in quantitative seizure analysis should be well chosen. Nevertheless, these studies found similar, but less robust, results regarding repetition rate of the automatisms and the discrepancy between trunk and limb movements in hyperkinetic seizures, supporting a quantitative approach to movement analysis. 5. Conclusion In summary, quantitative analysis of ictal movements provides excellent objective differentiation with high sensitivity and specificity for hyperkinetic seizures. The visual inspection of seizures already results in important localizing information, but may be enhanced by the use of these quantitative methods. Further studies should use three-dimensional techniques, possibly even systems that do not require reflective markers, to overcome the methodological limitations of the two-dimensional approach and, for example, compare movements elicited by electrical stimulation of the cortex with movements occurring during habitual epileptic seizures. Acknowledgments We thank Zhanjian Li and José Fernandes for their help in programming some of the tools used for the quantitative motor analysis. This research was partly supported by CRUP-DAAD integrated actions and FCT (Portuguese Science and Technology Agency) Grants POSI/CPS/ 33802/2000, GRID/GRI/81833/2006, SFRH/BSAB/910/2009, and PTDC/ SAU-BEB/72305/2006. Appendix A. Supplementary data
4.2. Limitations of quantitative movement analysis and future improvements
Supplementary materials related to this article can be found online at doi:10.1016/j.yebeh.2011.01.020.
The two-dimensional movement analysis described in this study can use the setting of video recordings established in most of the epilepsy monitoring units around the world. Thus, videos that were already recorded could be used for analysis. However, because of the limitations of the two-dimensional analysis, many of the video-
References [1] Noachtar S, Borggraefe I. Epilepsy surgery: a critical review. Epilepsy Behav 2009;15:66–72. [2] Rosenow F, Luders H. Presurgical evaluation of epilepsy. Brain 2001;124: 1683–700.
J. Rémi et al. / Epilepsy & Behavior 20 (2011) 642–647 [3] Manford M, Fish DR, Shorvon SD. An analysis of clinical seizure patterns and their localizing value in frontal and temporal lobe epilepsies. Brain 1996;119:17–40. [4] Noachtar S, Peters AS. Semiology of epileptic seizures: a critical review. Epilepsy Behav 2009;15:2–9. [5] Bleasel A, Kotagal P, Kankirawatana P, Rybicki L. Lateralizing value and semiology of ictal limb posturing and version in temporal lobe and extratemporal epilepsy. Epilepsia 1997;38:168–74. [6] Vignatelli L, Bisulli F, Provini F, et al. Interobserver reliability of video recording in the diagnosis of nocturnal frontal lobe seizures. Epilepsia 2007;48:1506–11. [7] Kotagal P. Seizure symptomatology of temporal lobe epilepsy. In: Lüders HO, editor. Epilepsy surgery. New York: Raven Press; 1991. p. 143–56. [8] Kotagal P, Arunkumar G, Hammel J, Mascha E. Complex partial seizures of frontal lobe onset: statistical analysis of ictal semiology. Seizure 2003;12:268–81. [9] Engel Jr J. A proposed diagnostic scheme for people with epileptic seizures and with epilepsy: report of the ILAE Task Force on Classification and Terminology. Epilepsia 2001;42:796–803. [10] Blume WT, Luders HO, Mizrahi E, Tassinari C, van Emde Boas W, Engel Jr J. Glossary of descriptive terminology for ictal semiology: report of the ILAE Task Force on Classification and Terminology. Epilepsia 2001;42:1212–8. [11] Noachtar S, Winkler PA, Lüders HO. Surgical therapy of epilepsy. In: Brandt T, Caplan C, Dichgans J, Diener J, Kennard C, editors. Neurological disorders: course and treatment. 2nd ed. San Diego: Academic Press; 2003. p. 235–44. [12] Li Z, Martins da Silva A, Cunha JP. Movement quantification in epileptic seizures: a new approach to video-EEG analysis. IEEE Trans Biomed Eng 2002;49:565–73. [13] Silva Cunha JP, Vollmar C, Li Z, Fernandes J, Feddersen B, Noachtar S. Movement quantification during epileptic seizures: a new technical contribution to the evaluation of seizure semiology. Proceedings of the 25th Annual International Conference of the IEEE EMBS. Cancun, Mexico; 2003. p. 671–3. [14] Arnold S, Berthele A, Drzezga A, et al. Reduction of benzodiazepine receptor binding is related to the seizure onset zone in extratemporal focal cortical dysplasia. Epilepsia 2000;41:818–24. [15] Noachtar S, Arnold S, Yousry TA, Bartenstein P, Werhahn KJ, Tatsch K. Ictal technetium-99 m ethyl cysteinate dimer single-photon emission tomographic
[16]
[17] [18]
[19] [20]
[21]
[22]
[23] [24] [25]
[26]
[27]
647
findings and propagation of epileptic seizure activity in patients with extratemporal epilepsies. Eur J Nucl Med 1998;25:166–72. Noachtar S, Lüders HO. Classification of epileptic seizures and epileptic syndromes. In: Gildenberg P, Tasker R, editors. Textbook of stereotaxic and functional neursurgery. New York: McGraw–Hill; 1998. p. 1763–74. Lüders H, Acharya J, Baumgartner C, et al. Semiological seizure classification. Epilepsia 1998;39:1006–13. Robbins H. An empirical Bayes approach to statistics. Proceedings, Third Berkeley Symposium on Mathematical Statistics and Probability; Berkeley, CA, USA; 1956. p. 157–63. Vaugier L, Aubert S, McGonigal A, et al. Neural networks underlying hyperkinetic seizures of "temporal lobe" origin. Epilepsy Res 2009;86:200–8. Nishibayashi H, Ogura M, Taguchi M, Miki J, Uematsu Y, Itakura T. Nondominant parietotemporal cortical dysplasia manifesting as hypermotor seizures. Epilepsy Behav 2009;14:691–5. Henkel A, Noachtar S, Pfander M, Luders HO. The localizing value of the abdominal aura and its evolution: a study in focal epilepsies. Neurology 2002;58: 271–6. Picard F, Megevand P, Minotti L, et al. Intracerebral recordings of nocturnal hyperkinetic seizures: demonstration of a longer duration of the pre-seizure sleep spindle. Clin Neurophysiol 2007;118:928–39. Weinstock A, Giglio P, Kerr SL, Duffner PK, Cohen ME. Hyperkinetic seizures in children. J Child Neurol 2003;18:517–24. Kanner AM, Morris HH, Luders H, et al. Supplementary motor seizures mimicking pseudoseizures: some clinical differences. Neurology 1990;40:1404–7. Mirzadjanova Z, Peters AS, Remi J, Bilgin C, Silva Cunha JP, Noachtar S. Significance of lateralization of upper limb automatisms in temporal lobe epilepsy: a quantitative movement analysis. Epilepsia 2010;51:2140–6. Chen L, Yang X, Liu Y, et al. Quantitative and trajectory analysis of movement trajectories in supplementary motor area seizures of frontal lobe epilepsy. Epilepsy Behav 2009;14:344–53. Yang X, Chen L, Liu Y, et al. Motor trajectories in automatisms and their quantitative analysis. Epilepsy Res 2009;83:97–102.
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Epilepsy & Behavior j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / ye b e h
Quantitative movement analysis differentiates focal seizures characterized by automatisms Jan Rémi a, João P. Silva Cunha b, Christian Vollmar a, Özgür Bilgin Topçuoğlu a,1, Alexander Meier a, Steffen Ulowetz a, Pedro Beleza a, Soheyl Noachtar a,⁎ a b
Epilepsy Center, Department of Neurology, University of Munich, Munich, Germany IEETA/Department of Electronics, Telecommunications and Informatics, University of Aveiro, Aveiro, Portugal
a r t i c l e
i n f o
Article history: Received 22 December 2010 Revised 17 January 2011 Accepted 20 January 2011 Available online 31 March 2011 Keywords: Epileptic automatism Seizure semiology Temporal lobe epilepsy Frontal lobe epilepsy
a b s t r a c t The analysis of epileptic seizures is typically performed by visual inspection, limited by interrater variation. Our aim was to differentiate seizures characterized by automatisms with an objective, quantitative movement analysis. In part 1 of this study we found parameters (extent and speed of movement of the wrist and trunk) separating seizures with predominant proximal (hyperkinetic, n = 10) and distal (automotor, n =10) limb automatisms (P b 0.002). For each movement parameter we used the lowest value recorded for a hyperkinetic seizure in part 1 as the cutoff parameter in part 2 on a consecutive sample of 100 motor seizures. As in part 1, the difference between hyperkinetic and non-hyperkinetic seizures was highly significant (b 0.001). When all movement parameters were above the threshold, a hyperkinetic seizure was identified with a probability of 80.8%, but the probability for a non-hyperkinetic seizure to have all parameters above the threshold was only 0.02%. © 2011 Elsevier Inc. All rights reserved.
1. Introduction The analysis of seizure symptoms is well established in the diagnostic evaluation of patients considered for resective epilepsy surgery [1,2]. However, the localizing value of seizure semiology has been debated [3], as currently seizure analysis is qualitative and based on the visual investigation of seizure semiology [4], which has poor interobserver reliability for most semiological features studied so far [5,6]. Ictal automatisms are complex behaviors that resemble normal body movements. In focal epilepsies, they occur during seizures, most commonly arising from temporal and less commonly from extratemporal regions [3,7,8]. The proposed Diagnostic Scheme for People with Epileptic Seizures and with Epilepsy of the ILAE Task Force on Classification and Terminology distinguishes focal motor seizures with typical (temporal lobe) automatisms and focal motor seizures with hyperkinetic automatisms [9]. The former are characterized by distal manual or oral automatisms, the latter by predominantly proximal limb or axial muscles producing irregular sequential ballistic movements, such as pedaling, pelvic thrusting, and thrashing [10]. Differentiation of the two is important for patients being considered for epilepsy surgery because hyperkinetic seizures occur more commonly in frontal lobe epilepsy (FLE) and automotor seizures in temporal lobe epilepsy (TLE),
⁎ Corresponding author at: Epilepsy Center, Department of Neurology, University of Munich, Marchioninistrasse 15, 81377 Munich, Germany. Fax: +49 89 7095 6691. E-mail address: [email protected] (S. Noachtar). 1 Current address: Department of Neurology, Erenköy Psychiatry and Neurology Education and Research Hospital, Istanbul, Turkey. 1525-5050/$ – see front matter © 2011 Elsevier Inc. All rights reserved. doi:10.1016/j.yebeh.2011.01.020
and TLE and FLE are the most common epilepsy syndromes in patients considered for resective epilepsy surgery [11]. We introduced an observer-independent technical setup that enables us to quantify movements in video-recorded seizures of patients with epilepsy without introducing major changes in the routine of our epilepsy monitoring unit [12,13]. Here, we applied this quantitative analysis of movements to determine whether there are objective movement parameters that can differentiate hyperkinetic from automotor and other non-hyperkinetic movements. 2. Methods This study had two parts. First, we analyzed the movement characteristics of 10 hyperkinetic and 10 automotor seizures. Several movement parameters could be identified that clearly differentiated these seizure types. To test the sensitivity and specificity of these parameters, we applied them as cutoffs on a sample of 100 consecutive EEG/video-recorded seizures with any motor activity in part 2 of our study. The study complied with the ethical guidelines of our institution, and the depicted patients gave written informed consent. 2.1. Part 1 A database search of the archives of the University of Munich Epilepsy Monitoring Unit was run using the terms automotor seizures and hyperkinetic seizures. Twenty seizures in 17 patients were included according to seizure video criteria described below. In epileptic automatisms, the single motion may appear natural, but its
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repetition and its inappropriate occurrence in a particular situation separate it from physiological movements. When only proximal limb or axial movements occur and large movements result (hyperkinetic seizure), their distinction from distal manual or oral automatisms (automotor seizure) typically poses no problem, but the continuum between those two poles is evident in the possibility of categorizing complex motor seizures as well, which contain both proximal and distal automatisms [9]. For the purpose of this study, to yield distinct cutoff parameters, we chose 10 hyperkinetic and 10 automotor seizures that two experienced epileptologists had independently classified concordantly (interobserver reliability κ = 1). The patient data are summarized in Table S1 (Supplementary Material—see Appendix). Epilepsy syndromes included TLE (n = 6), parieto-occipital lobe epilepsy (n = 3), FLE (n = 1), paracentral epilepsy (n = 1), and focal epilepsies (n = 6) that could not be localized further. 2.2. Part 2 In a second step, 100 consecutive motor seizures retrieved from the video archives of the University of Munich Epilepsy Monitoring Unit were included in the evaluation according to the criteria described below. The only restriction was that the selected seizures had to include motor symptoms. The data on these patients are summarized in Table S4 (see Appendix). Seizures of 65 patients were included. Epilepsy syndromes included TLE (n = 16), FLE (n = 12), paracentral epilepsy (n = 5), parietal lobe epilepsy (n = 2), occipital lobe epilepsy (n = 2), parieto-occipital lobe epilepsy (n = 1), and focal epilepsies (n = 27) that could not be further localized. 2.3. Patients All patients (parts 1 and 2, n = 82) had refractory focal epilepsies and underwent a standardized presurgical evaluation including video/EEG monitoring, MRI, and neuropsychological testing. Noninvasive video/EEG monitoring with closely spaced surface electrodes (10–10 system) and sphenoidal electrodes using 32- to 64-channel EEG machines (Vangard, Cleveland, OH, USA, and XLTEK, London, ON, Canada) was performed in all patients. Selected patients underwent additional invasive video/EEG monitoring with stereotactically implanted depth electrodes or subdural electrodes (n = 7). MRI included proton density-weighted, T1-weighted, and T2-weighted images in the axial, coronal, and sagittal planes (5-mm slices) (1.0-T Impact/Siemens). In cases in which routine MRI was normal, highresolution MRI with three-dimensional FLASH images, fluid-attenuated inversion–recovery (FLAIR), and magnetization prepared rapid attenuated gradient echo (MPRAGE) were performed (1.5-T Vision/ Siemens). Further imaging studies in selected patients included interictal FDG-PET and ictal and interictal subtraction ECD-SPECT [14,15]. The localization of the epileptogenic zone (epilepsy syndrome) was defined in a patient management meeting attended by epileptologists, neuroradiologists, neurosurgeons, and neuropsychologists. We did not restrict our sample to postoperatively seizure-free patients [3], but deliberately included patients for whom the syndrome diagnosis was established by aforementioned criteria to represent a typical epilepsy monitoring unit patient sample. 2.4. Seizure semiology and quantitative video analysis All seizures had been analyzed visually by at least two senior epileptologists and classified according to a semiological seizure classification [16,17]. Automotor seizures were characterized by prominent oral and manual automatisms. Hyperkinetic seizures were defined as prominent movements of the trunk and proximal limbs. Complex motor seizures were defined as seizures in which characteristics of both automotor and hyperkinetic seizures occurred
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[17]. The seizure evolutions of the hyperkinetic and automotor seizures in part 1 of the study are summarized in Table S1 (see Appendix). Wrist and trunk movements had to be continuously visible throughout the course of the seizures on the video recordings to include the seizure video for further analysis. Because of the methodological limitations of the two-dimensional video analysis, only those seizure videos in which the camera position was perpendicular to the trunk facing the camera in an upright position were included [13]. The camera angle was also the reason we could not include leg movements, because they were typically out of the perpendicular plane. 2.5. Movement quantification Movements were quantified from the videos by analyzing all video frames during the entire seizure (25/second) according to a published setting [12,13]. Using this approach, we tracked the two-dimensional coordinates (x, y) of three points of the patient's body, trunk center (T) and left and right wrists, as depicted in Fig. 1A. For further analysis we evaluated only the most active wrist (W). Trunk and wrist movement extent and speed were analyzed and compared between automotor and hyperkinetic seizures in part 1 of the study. Fig. 1 illustrates movement tracings and respective movement extent areas for an automotor seizure (Fig. 1A) and a hyperkinetic seizure (Fig. 1B). In part 2 of the study, we analyzed the same movement parameters as in part 1 of this study on a sample of 100 consecutive motor seizures of 65 patients. The cutoff values for the movement parameters that separated hyperkinetic seizures from automotor seizures in part 1 were applied to all 100 motor seizures in part 2. The sensitivity and specificity of the movement parameters for the differentiation of hyperkinetic and non-hyperkinetic seizures were evaluated on the basis of these cutoff parameters. For the purpose of part 2 of this study, complex motor seizures were included in the hyperkinetic group as the characteristics of complex motor seizures include also prominent trunk and proximal limb movements, which are typical features of hyperkinetic seizures [17] and the aim of our study was to separate hyperkinetic from non-hyperkinetic movements by means of quantitative analysis. 2.6. Movement extent A graphic representation of the wrist and trunk movements of patients with hyperkinetic and automotor seizures is provided by Fig. 1. The movement extent measure was defined by selecting the twodimensional extreme points of the movement trajectories (p1 … p4) and computing the corresponding area of the rectangle formed, as indicated by the dotted lines in Fig. 1. Given the structure of human arm joints, any trunk movement will influence wrist movement. So, for example, if trunk movements occur in the direction opposite wrist movements, the raw analysis would reduce the extent of wrist movement and the corrected analysis would result in a larger movement extent. Thus, to separately quantify trunk and wrist movements, one has to subtract any influence of trunk movements from the resulting wrist movement. Therefore, we subtracted these movements with a frame-by-frame correction algorithm that could be performed in Matlab software (Mathworks, Natick, MA, USA), which was used for all further analyses. (For further details and illustrations please refer to the Supplementary Material—see Appendix.) 2.7. Movement speed Trunk and wrist speed was computed by dividing the covered distance by the time of movement which was readily available as a function of the elapsed video frames (25 frames = 1 second). The maximum (Smax) and mean (Smean) speeds for the trunk (Ti) and for
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Fig. 1. Movement analysis of an automotor seizure (A) and a hyperkinetic seizure (B). The right wrist (label 1) movements of patients were tracked (gray tracking line) and overlain on the schematic outline. The dotted line represents movement extent, calculated as the maximum area traveled by the wrist and limited by the two-dimensional maxima p1, p2, p3, and p4. Note the larger extent of the right wrist movement in the hyperkinetic seizure (B).
the corrected (see above) wrist movements (Wci) were calculated. (For further details and illustrations please refer to the Supplementary Material.) 2.8. Seizure duration The duration of the seizures was measured based on onset and end of EEG seizure patterns. Scalp EEG recordings may miss the seizure onset, for example, in insular epilepsy. Therefore, the clinical onset was taken as seizure onset when it clearly preceded the EEG seizure onset. 2.9. Statistical analysis Quantitative parameters for the different groups were compared using the Mann–Whitney U test. Differences were considered significant at P b 0.05. Multiple testing was accounted for by Bonferroni–Holm review of the resulting P values. No statistically significant test result had to be rejected because of the Bonferroni–Holm review. Sensitivity and specificity of the quantitative movement parameters were calculated for the identification of hyperkinetic and nonhyperkinetic seizures for part 2 of the study. To estimate the classification power of a combination of all parameters, the empirical Bayes method was used [18], which allows us to approximate the probability of an original distribution (hyperkinetic vs nonhyperkinetic) from the dependent data (parameters above or below threshold). 3. Results 3.1. Part 1 The patient data from part 1 of the study on hyperkinetic and automotor seizures are summarized in Table S1 (see Appendix). Results of the quantitative analysis of movements during the 10 hyperkinetic and 10 automotor seizures are provided in Table 1; for details, please refer to Tables S2 and S3 (see Appendix).
Fig. 2. Extent of wrist movement in 10 hyperkinetic and 10 automotor seizures (part 1). Movement extent: maximum area traveled by the wrist expressed in (image pixels2). The hyperkinetic seizure with the lowest movement extent (12,954 pixels2) served as the lower cutoff (line) for part 2 of the study.
The analysis of wrist movement extent separated all hyperkinetic from automotor seizure types, as it was greater in all hyperkinetic (median = 47,179 pixels2) seizures than in automotor seizures (3102 pixels2) (P b 0.001). The hyperkinetic seizure with the lowest movement extent (12,954 pixels2) was clearly separated from the automotor seizure with the highest movement extent (6758 pixel2) (Fig. 2). The extent of trunk movement was also highly significantly greater in hyperkinetic seizures (4459 pixels2) than in automotor seizures (271 pixels2) (P b 0.001), but two automotor seizures had a larger extent of trunk movement than the hyperkinetic seizure with the smallest extent of trunk movement (Table S2—see Appendix). Maximum speed (median = 1104 pixels/second vs 210 pixels/ second, P b 0.001) and mean speed (69 pixels/second vs 28 pixels/ second, P = 0.002) (Table S3) of wrist movements were significantly faster in hyperkinetic seizures than in automotor seizures. Analysis of maximum speed of wrist movement also showed that only one
Table 1 Movement parameters from ten hyperkinetic and ten automotor seizures. Seizure type
Hyperkinetic Automotor P
Movement extent (pixels2)
Movement speed (pixels/s)
Trunk
Wrist
Trunk maximum
Trunk mean
Wrist maximum
Wrist mean
4,459 ± 20,060 271 ± 1207 b0.001
47,179 ± 60,276 3,102 ± 1,981 b0.001
221 ± 134 52 ± 13 b 0.001
25 ± 10 9±3 b0.001
1104 ± 1978 210 ± 146 b0.001
69 ± 34 28 ± 16 0.002
Note. Values are given as medians ± SD. n.s., not significant.
Seizure duration (s) 34 ± 54 81 ± 18 n.s.
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Table 2 Movement parameters from 100 consecutive motor seizures. Seizure type
Hyperkinetic Non-hyperkinetic P Sensitivity (%) Specificity (%)
Movement extent (pixels2)
Movement speed (pixels/s)
Trunk
Wrist
Trunk maximum
Trunk mean
Wrist maximum
Wrist mean
7747 ± 8580 399 ± 1339 b 0.001 98.5 54.3
43,524 ± 24,068 5,809 ± 9,820 b0.001 100 54.1
210 ± 95 88 ± 49 b 0.001 100 28.2
26 ± 15 7±7 b 0.001 100 55.6
688 ± 249 198 ± 192 b 0.001 95.9 63.0
56 ± 35 25 ± 19 b0.001 100 52.6
Seizure duration (s) 36 ± 20 69 ± 24 b0.001 n.a. n.a.
Note. Movement parameter values are given as medians ± SD. n.a., not applicable.
automotor seizure (530 pixels/second) was above the lowest maximum speed of wrist movements of hyperkinetic seizures (477 pixels/second). Maximum speed (median= 221 pixels/second vs 52 pixels/second, P b 0.001) and mean speed (25 pixels/second vs 9 pixels/second, P b 0.001) (Table S3—see Appendix) of trunk movements were significantly faster in hyperkinetic seizures than in automotor seizures. Again, no automotor seizure had faster trunk movements than any hyperkinetic seizure. The duration of the automotor seizures (78 ± 18 seconds, median= 81 seconds) showed a trend of being longer than that of the hyperkinetic seizures (58 ± 54 seconds, median = 34 seconds) (P = 0.06).
3.2. Part 2 For the patients in part 2, basic characteristics and seizure semiologies are summarized in Table S4 (see Appendix). Hyperkinetic and non-hyperkinetic seizures differed highly significantly with respect to all movement parameters studied (P b 0.001) (Table 2). For the purpose of this study, we also evaluated the ability of these parameters to differentiate hyperkinetic and non-hyperkinetic seizures. Therefore, specificity and sensitivity were calculated for all parameters. Please refer to Table S5 (see Appendix) for the correctly and incorrectly identified individual seizures. As cutoff parameters we chose the lowest recorded value for the hyperkinetic seizures in all categories in part 1 of this study. Extent of wrist movement (lowest movement extent = 12,954 pixels2) (Fig. 2) was able to identify all hyperkinetic seizures (sensitivity = 100%) (Fig. 3, Table S5—see Appendix). Specificity of extent of wrist movement was at 54.1%, with 63 of 80 non-hyperkinetic seizures being correctly classified based on this criterion. Extent of trunk movement (cutoff parameter = 1240 pixels2) misclassified only one hyperkinetic seizure (sensitivity = 98.5%, specificity = 54.3%) (Fig. 3). Trunk movement mean speed (cutoff: 15 pixels/frame) identified all hyperkinetic seizures (sensitivity = 100%, specificity = 55.6%) (Fig. 4). Maximum speed of trunk movements (cutoff = 81 pixels/ frame) identified all hyperkinetic seizures (sensitivity = 100%), but
Fig. 3. Extent of trunk and wrist movement in hyperkinetic and non-hyperkinetic seizures. Movement extent: maximum area traveled by the trunk or wrist (image pixels2). Data from part 2 of the study; limit bar (cutoff parameter) as described in text. Please note the logarithmic y axis.
had a low specificity (28.2%), as only 28 non-hyperkinetic seizures were correctly identified. Maximum speed of wrist movements (cutoff = 477 pixels/frame) identified 17 of 20 hyperkinetic seizures (sensitivity = 95.9%) and 70 of 80 non-hyperkinetic seizures (specificity = 63.0%) (Fig. 5). Mean wrist movement speed (cutoff = 40 pixels/frame) identified all hyperkinetic seizures (sensitivity = 100%) and identified 61 of 80 non-hyperkinetic seizures (specificity = 52.6%). The separation power of the movement parameters can be combined by the empirical Bayes method [18]. When all movement parameters in our study were above the threshold, a hyperkinetic seizure was identified with a probability of 80.8%, but the probability that a non-hyperkinetic seizure has all parameters above the threshold is only 0.02%. When choosing only four of the six parameters (movement extent of the wrist, mean speed of wrist movement, mean and maximum speeds of trunk movement), all hyperkinetic seizures are correctly identified (100% probability) and the probability of a non-hyperkinetic seizure to be above these thresholds is only 0.06%. In addition, we calculated that the average duration of the hyperkinetic seizures was 36 ± 20 seconds (range = 18–80, median = 28), which were significantly shorter than the non-hyperkinetic seizures, 71 ± 24 seconds (range = 7–122, median = 69) (P b 0.001). 4. Discussion 4.1. Quantitative movement analysis Our study demonstrates that the quantitative analysis of movements during epileptic seizures is able to identify objective movement parameters characteristic of hyperkinetic seizures that differentiate them well from other motor (especially automotor) seizures. In part 1 of the study, analysis of 10 hyperkinetic and 10 automotor seizures yielded quantitative movement parameters that clearly separated hyperkinetic from automotor seizures. For the purpose of this study, we had selected hyperkinetic and automotor seizures classified
Fig. 4. Speed of trunk movement in hyperkinetic and non-hyperkinetic seizures. The mean and maximum speeds are expressed as pixels per second. Data from part 2 of the study; limit bar (cutoff parameter) as described in text. Please note the logarithmic y axis.
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Fig. 5. Speed of wrist movement in hyperkinetic and non-hyperkinetic seizures. The mean and maximum speeds are expressed as pixels per second. Data from part 2 of the study; limit bar (cutoff parameter) as described in text. Please note the logarithmic y axis.
concordantly by two experienced epileptologists; therefore, we feel confident that these seizures yielded selective movement parameters. We then evaluated these movement cutoff parameters by using them on a consecutive, unselected sample of another 100 motor seizures, and the parameters remained reliable for the identification of hyperkinetic seizures. The sensitivity of the quantitative movement parameters for identification of hyperkinetic seizures was excellent. The specificity of some of the parameters was not as good, for example, trunk maximum speed, most likely because of the deliberate heterogeneity of the unselected non-hyperkinetic seizure group. The classification power could be improved even further by combining the results from all parameters. Using the Bayes method [18], we were able to show that the likeliness of a hyperkinetic seizure was above 80% when all parameters were above the threshold and the likelihood of a nonhyperkinetic seizure was below 0.1%. We, therefore, believe that the movement parameters from this study, namely, speed and extent of wrist and trunk movement, may be very useful in identifying hyperkinetic seizures for future research. Future research could relate movement patterns to the seizure onset zone. In our relatively small and, for part 2, deliberately heterogeneous patient sample, the movement parameters differed clearly between seizure types (hyperkinetic vs non-hyperkinetic), but did not reach statistical significance between epilepsy syndromes. Hyperkinetic seizures are typical of FLE, but also occur in other syndromes like TLE [19] and parietal lobe epilepsy [20]. Analysis of seizure evolution provides more localizing information than single seizure types [21]. A quantitative approach to seizure semiology analysis may improve the definition of the epileptogenic zone, for example, in presurgical patients. Here, in particular, the correlation of invasive EEG with video-recorded seizure semiology may provide helpful insight into the localization of the epileptogenic zone and spread pattern of epileptic activity [2,22]. Hyperkinetic seizures may be mistaken as nonepileptic events like parasomnic sleep disturbances and psychogenic seizures because clinically they may appear similar [23,24]. Here, our quantitative interobserver-independent analysis was applied to determine objective parameters that could differentiate epileptic from, for example, psychogenic seizures.
recorded seizures had to be excluded from analysis because either the movements were not fully visible in the camera or the majority of the patients’ movements did not occur in the plane perpendicular to the view of the camera system. These movements were missed by the two-dimensional analysis and could therefore not be quantified. Furthermore, distal automatisms are hard to quantify because of the limitation of the video resolution which may have reduced the significance of lateralization of ipsilateral and contralateral hand automatisms in another quantitative study [25]. We currently use infrared reflectors mounted on the patient, which allow the automated tracking and subsequent analysis of the movements based on fixed high-luminescence markers [13]. However, there are still limitations to this approach which can only be overcome with a three-dimensional analysis of the movements. Other investigators recently published two studies with similar data that follow closely our previous work [26,27]. They used a similar two-dimensional method [12,13] to analyze automatisms in patients with TLE and FLE. The limitations of the two-dimensional movement analysis we describe in detail under Methods were not addressed adequately, though. For example, the geometric camera-bed setup is not well defined. No data are given on the constancy of the geometry of the scene nor on error estimation studies in contrast to our methodology [13]. Previously we reported an error below 8% given certain constraints that we follow in our procedure [13]. Furthermore, the correction algorithm for passive limb movements as mentioned under Methods lacked evaluation of maximum and average speed. These considerations illustrate that the techniques and analysis algorithms used in quantitative seizure analysis should be well chosen. Nevertheless, these studies found similar, but less robust, results regarding repetition rate of the automatisms and the discrepancy between trunk and limb movements in hyperkinetic seizures, supporting a quantitative approach to movement analysis. 5. Conclusion In summary, quantitative analysis of ictal movements provides excellent objective differentiation with high sensitivity and specificity for hyperkinetic seizures. The visual inspection of seizures already results in important localizing information, but may be enhanced by the use of these quantitative methods. Further studies should use three-dimensional techniques, possibly even systems that do not require reflective markers, to overcome the methodological limitations of the two-dimensional approach and, for example, compare movements elicited by electrical stimulation of the cortex with movements occurring during habitual epileptic seizures. Acknowledgments We thank Zhanjian Li and José Fernandes for their help in programming some of the tools used for the quantitative motor analysis. This research was partly supported by CRUP-DAAD integrated actions and FCT (Portuguese Science and Technology Agency) Grants POSI/CPS/ 33802/2000, GRID/GRI/81833/2006, SFRH/BSAB/910/2009, and PTDC/ SAU-BEB/72305/2006. Appendix A. Supplementary data
4.2. Limitations of quantitative movement analysis and future improvements
Supplementary materials related to this article can be found online at doi:10.1016/j.yebeh.2011.01.020.
The two-dimensional movement analysis described in this study can use the setting of video recordings established in most of the epilepsy monitoring units around the world. Thus, videos that were already recorded could be used for analysis. However, because of the limitations of the two-dimensional analysis, many of the video-
References [1] Noachtar S, Borggraefe I. Epilepsy surgery: a critical review. Epilepsy Behav 2009;15:66–72. [2] Rosenow F, Luders H. Presurgical evaluation of epilepsy. Brain 2001;124: 1683–700.
J. Rémi et al. / Epilepsy & Behavior 20 (2011) 642–647 [3] Manford M, Fish DR, Shorvon SD. An analysis of clinical seizure patterns and their localizing value in frontal and temporal lobe epilepsies. Brain 1996;119:17–40. [4] Noachtar S, Peters AS. Semiology of epileptic seizures: a critical review. Epilepsy Behav 2009;15:2–9. [5] Bleasel A, Kotagal P, Kankirawatana P, Rybicki L. Lateralizing value and semiology of ictal limb posturing and version in temporal lobe and extratemporal epilepsy. Epilepsia 1997;38:168–74. [6] Vignatelli L, Bisulli F, Provini F, et al. Interobserver reliability of video recording in the diagnosis of nocturnal frontal lobe seizures. Epilepsia 2007;48:1506–11. [7] Kotagal P. Seizure symptomatology of temporal lobe epilepsy. In: Lüders HO, editor. Epilepsy surgery. New York: Raven Press; 1991. p. 143–56. [8] Kotagal P, Arunkumar G, Hammel J, Mascha E. Complex partial seizures of frontal lobe onset: statistical analysis of ictal semiology. Seizure 2003;12:268–81. [9] Engel Jr J. A proposed diagnostic scheme for people with epileptic seizures and with epilepsy: report of the ILAE Task Force on Classification and Terminology. Epilepsia 2001;42:796–803. [10] Blume WT, Luders HO, Mizrahi E, Tassinari C, van Emde Boas W, Engel Jr J. Glossary of descriptive terminology for ictal semiology: report of the ILAE Task Force on Classification and Terminology. Epilepsia 2001;42:1212–8. [11] Noachtar S, Winkler PA, Lüders HO. Surgical therapy of epilepsy. In: Brandt T, Caplan C, Dichgans J, Diener J, Kennard C, editors. Neurological disorders: course and treatment. 2nd ed. San Diego: Academic Press; 2003. p. 235–44. [12] Li Z, Martins da Silva A, Cunha JP. Movement quantification in epileptic seizures: a new approach to video-EEG analysis. IEEE Trans Biomed Eng 2002;49:565–73. [13] Silva Cunha JP, Vollmar C, Li Z, Fernandes J, Feddersen B, Noachtar S. Movement quantification during epileptic seizures: a new technical contribution to the evaluation of seizure semiology. Proceedings of the 25th Annual International Conference of the IEEE EMBS. Cancun, Mexico; 2003. p. 671–3. [14] Arnold S, Berthele A, Drzezga A, et al. Reduction of benzodiazepine receptor binding is related to the seizure onset zone in extratemporal focal cortical dysplasia. Epilepsia 2000;41:818–24. [15] Noachtar S, Arnold S, Yousry TA, Bartenstein P, Werhahn KJ, Tatsch K. Ictal technetium-99 m ethyl cysteinate dimer single-photon emission tomographic
[16]
[17] [18]
[19] [20]
[21]
[22]
[23] [24] [25]
[26]
[27]
647
findings and propagation of epileptic seizure activity in patients with extratemporal epilepsies. Eur J Nucl Med 1998;25:166–72. Noachtar S, Lüders HO. Classification of epileptic seizures and epileptic syndromes. In: Gildenberg P, Tasker R, editors. Textbook of stereotaxic and functional neursurgery. New York: McGraw–Hill; 1998. p. 1763–74. Lüders H, Acharya J, Baumgartner C, et al. Semiological seizure classification. Epilepsia 1998;39:1006–13. Robbins H. An empirical Bayes approach to statistics. Proceedings, Third Berkeley Symposium on Mathematical Statistics and Probability; Berkeley, CA, USA; 1956. p. 157–63. Vaugier L, Aubert S, McGonigal A, et al. Neural networks underlying hyperkinetic seizures of "temporal lobe" origin. Epilepsy Res 2009;86:200–8. Nishibayashi H, Ogura M, Taguchi M, Miki J, Uematsu Y, Itakura T. Nondominant parietotemporal cortical dysplasia manifesting as hypermotor seizures. Epilepsy Behav 2009;14:691–5. Henkel A, Noachtar S, Pfander M, Luders HO. The localizing value of the abdominal aura and its evolution: a study in focal epilepsies. Neurology 2002;58: 271–6. Picard F, Megevand P, Minotti L, et al. Intracerebral recordings of nocturnal hyperkinetic seizures: demonstration of a longer duration of the pre-seizure sleep spindle. Clin Neurophysiol 2007;118:928–39. Weinstock A, Giglio P, Kerr SL, Duffner PK, Cohen ME. Hyperkinetic seizures in children. J Child Neurol 2003;18:517–24. Kanner AM, Morris HH, Luders H, et al. Supplementary motor seizures mimicking pseudoseizures: some clinical differences. Neurology 1990;40:1404–7. Mirzadjanova Z, Peters AS, Remi J, Bilgin C, Silva Cunha JP, Noachtar S. Significance of lateralization of upper limb automatisms in temporal lobe epilepsy: a quantitative movement analysis. Epilepsia 2010;51:2140–6. Chen L, Yang X, Liu Y, et al. Quantitative and trajectory analysis of movement trajectories in supplementary motor area seizures of frontal lobe epilepsy. Epilepsy Behav 2009;14:344–53. Yang X, Chen L, Liu Y, et al. Motor trajectories in automatisms and their quantitative analysis. Epilepsy Res 2009;83:97–102.