- Email: [email protected]
Seizure detection: Reaching through the looking glass
Clinical Neurophysiology 119 (2008) 2667–2668
Contents lists available at ScienceDirect
Clinical Neurophysiology journal homepage: www.elsevier.com/locate/clinph
Editorial
Seizure detection: Reaching through the looking glass See Article, pages 2687–2696
Seizure detection algorithms have been used for several decades (Saab and Gotman, 2005; Osorio et al., 2002; Wilson et al., 2004; Tzallas et al., 2007; Meier et al., 2008; Casdagli et al., 1997; Iasemidis, 2003; Litt and Lehnertz, 2002; Suffczynski et al., 2006; Lehnertz et al., 2007; Talathi et al., 2008), and a new one appears in this issue (Chan et al., 2008). Despite these, seizure detection often seems easy for an EEGer, but hard for a machine (Suffczynski et al., 2006; Lehnertz et al., 2007). The problem is, we do not know what to tell the machine to find, at least not with the precision needed by a computer. Also, maybe it is not so easy for people either. Give several EEGers a set of records to interpret, and they agree often, but not always (Gotman et al., 1978; Webber et al., 1993; Gotman, 1999; Wilson et al., 2003). Can one be consistent with oneself? As part of a study, one of us recently identified all afterdischarges in records of 13 patients, did this twice, some weeks apart, and found 11,944 (Lesser et al., 2008). There were 257 events marked once but not twice. A computer might have done this more consistently, but not necessarily more correctly. If the goal is to find every ‘‘true” event, and only those, which of the 257 should be found, and which not? With seizure detection algorithms, seizures might be missed, or non-seizures might be identified to be seizures, and, in some applications, erroneously treated. This is not unique to EEG: mistakes in recognition probably occur in every field of medicine (Williams et al., 1985; Williams et al., 1990; Webber et al., 1993; Eddy, 1988; Revesz and Kundel, 1977; Beam et al., 2003; Groopman, 2007). It has been surprisingly hard to tell the computer what to find, or to define all the elements that indicate an epileptiform discharge, whether ictal or interictal, and all the elements that do not (Ferri et al., 1989; Webber et al., 1994). Methods based on feature extraction may fail to be exact for this reason. What about raw data? There is a story, perhaps true, of a Pentagon algorithm developed to find tanks in a forest (Hanne, 1997; Priddy and Keller, 2005; Cohen et al., 2008). Initial tests were very encouraging. However, later tests with a different forest failed. Why? Unintentionally, the training set comprised images with tanks taken on cloudy days, while the images without tanks were taken on sunny days. The artificial neural network had learnt the difference between cloudy and sunny days, but not between tanks and no tanks. Something like that may occur with seizure detection. Do methods based on the raw data notice obvious features but miss subtle, but more important, ones? Do we do the same thing when we select parameters to use in seizure recognition algorithms? The second problem is degree. We know that a very dark cloud may mean lightning, and a white cloud usually does not.
What about a gray cloud? . . .And why doesn’t every dark cloud produce lightning? Just as we, standing on the ground, can’t see inside the cloud, so we, recording the EEG, can not see inside every cell that may, or may not, be contributing to whether a seizure will strike this time. More than this, lightning can stay within the cloud or spread, and just as a strike in part reflects an interaction between the cloud and the ground, where we may be standing, so seizure occurrence may reflect an interaction between what we used to think of as the seizure focus and ‘‘the rest” of the brain (Mormann et al., 2003; Mormann et al., 2007; D’Alessandro et al., 2005; Esteller et al., 2005; Mormann et al., 2005; Kalitzin et al., 2005; Le Van Quyen et al., 2005; Navarro et al., 2005). Elements that eventually produce a seizure can be thought of as parts of an n-dimensional data cloud. Maybe, if we understood more about the elements within the clouds, the dark ones would completely separate from the light ones, but it does not seem possible now. Does it matter if we find every event? It depends on what we want to do: study seizures with certain formal characteristics (say a certain range of voltages, durations, slopes, frequencies, or Lyapunov exponents), or study all seizures. What we can do with these tools is adequate for the former, but not for the latter (Suffczynski et al., 2006; Lehnertz et al., 2007). This can matter if we are using detections to decide whether a drug or other treatment is working, or to decide when (and whether) to treat acutely using methods such as brain stimulation, or something else. Seizures missed could mean treatments missed. False detections could mean unneeded treatments given, and we do not know what the long-term effects of these might be on epilepsy, or the brain. We have greatly advanced our understanding of seizures using detection and prediction algorithms, but for now, when we look at these on our computer screens, we are looking through a glass, darkly.
References Beam CA, Conant EF, Sickles EA. Association of volume and volume-independent factors with accuracy in screening mammogram interpretation. J Natl Cancer Inst 2003;95:282–90. Casdagli MC, Iasemidis LD, Savit RS, Gilmore RL, Roper SN, Sackellares JC. Nonlinearity in invasive EEG recordings from patients with temporal lobe epilepsy. Electroencephalogr Clin Neurophysiol 1997;102:98–105. Chan AM, Sun FT, Boto EH, Wingeier BM. Automated seizure onset detection for accurate onset time determination in intracranial EEG. Clin Neurophysiol 2008;119:2687–96. Cohen MX, Ridderinkhof KR, Haupt S, Elger CE, Fell J. Medial frontal cortex and response conflict: evidence from human intracranial EEG and medial frontal cortex lesion. Brain Res, in press. online 7 August 2008.
1388-2457/$34.00 Ó 2008 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.clinph.2008.09.011
2668
Editorial / Clinical Neurophysiology 119 (2008) 2667–2668
D’Alessandro M, Vachtsevanos G, Esteller R, et al. A multi-feature and multichannel univariate selection process for seizure prediction. Clin Neurophysiol 2005;116:506–16. Eddy D. Variations in physician practice: the role of uncertainty. In: Dowie J, Elstein A, editors. Professional judgment: a reader in clinical decision making. Cambridge, New York: Cambridge University Press; 1988. p. 45–59. Esteller R, Echauz J, D’Alessandro M, et al. Continuous energy variation during the seizure cycle: towards an on-line accumulated energy. Clin Neurophysiol 2005;116:517–26. Ferri R, Ferri P, Colognola RM, Petrella MA, Musumeci SA, Bergonzi P. Comparison between the results of an automatic and a visual scoring of sleep EEG recordings. Sleep 1989;12:354–62. Gotman J. Automatic detection of seizures and spikes. J Clin Neurophysiol 1999;16:130–40. Gotman J, Gloor P, Schaul N. Comparison of traditional reading of the EEG and automatic recognition of interictal epileptic activity. Electroencephalogr Clin Neurophysiol 1978;44:48–60. Groopman J. The eye of the beholder. In: How doctors think. Boston, New York: Houghton Mifflin Company; 2007. p. 177–202. Hanne T. Concepts of a learning object-oriented problem solver (LOOPS). In: Fandel G, Gal T, Hanne T, editors. Multiple criteria decision making: proceedings of the 12th international conference. Berlin: Springer; 1997. p. 338. Iasemidis LD. Epileptic seizure prediction and control. IEEE Trans Biomed Eng 2003;50:549–58. Kalitzin S, Velis D, Suffczynski P, Parra J, Da Silva FL. Electrical brain-stimulation paradigm for estimating the seizure onset site and the time to ictal transition in temporal lobe epilepsy. Clin Neurophysiol 2005;116:718–28. Le Van Quyen M, Soss J, Navarro V, et al. Preictal state identification by synchronization changes in long-term intracranial EEG recordings. Clin Neurophysiol 2005;116:559–68. Lehnertz K, Mormann F, Osterhage H, et al. State-of-the-art of seizure prediction. J Clin Neurophysiol 2007;24:147–53. Lesser RP, Lee HW, Webber WR, Prince B, Crone NE, Miglioretti DL. Short-term variations in response distribution to cortical stimulation. Brain 2008;131:1528–39. Litt B, Lehnertz K. Seizure prediction and the preseizure period. Curr Opin Neurol 2002;15:173–7. Meier R, Dittrich H, Schulze-Bonhage A, Aertsen A. Detecting epileptic seizures in long-term human eeg: a new approach to automatic online and real-time detection and classification of polymorphic seizure patterns. J Clin Neurophysiol 2008. Mormann F, Andrzejak RG, Elger CE, Lehnertz K. Seizure prediction: the long and winding road. Brain 2007;130:314–33. Mormann F, Andrzejak RG, Kreuz T, et al. Automated detection of a preseizure state based on a decrease in synchronization in intracranial electroencephalogram recordings from epilepsy patients. Phys Rev E Stat Nonlin Soft Matter Phys 2003;67:021912. Mormann F, Kreuz T, Rieke C, et al. On the predictability of epileptic seizures. Clin Neurophysiol 2005;116:569–87.
Navarro V, Martinerie J, Le Van QM, Baulac M, Dubeau F, Gotman J. Seizure anticipation: do mathematical measures correlate with video-eeg evaluation? Epilepsia 2005;46:385–96. Osorio I, Frei MG, Giftakis J, et al. Performance reassessment of a real-time seizuredetection algorithm on long ECoG series. Epilepsia 2002;43:1522–35. Priddy KL, Keller PE. Artificial neural networks: an introduction. Bellingham, WA: SPIE Press; 2005. p. 24. Revesz G, Kundel HL. Psychophysical studies of detection errors in chest radiology. Radiology 1977;123:559–62. Saab ME, Gotman J. A system to detect the onset of epileptic seizures in scalp EEG. Clin Neurophysiol 2005;116:427–42. Suffczynski P, Lopes da Silva FH, Parra J, et al. Dynamics of epileptic phenomena determined from statistics of ictal transitions. IEEE Trans Biomed Eng 2006;53:524–32. Talathi SS, Hwang DU, Spano ML, et al. Non-parametric early seizure detection in an animal model of temporal lobe epilepsy. J Neural Eng 2008;5:85–98. Tzallas AT, Tsipouras MG, Fotiadis DI. Automatic seizure detection based on timefrequency analysis and artificial neural networks. Comput Intell Neurosci 2007:80510. Webber WR, Litt B, Lesser RP, Fisher RS, Bankman I. Automatic EEG spike detection: what should the computer imitate? Electroencephalogr Clin Neurophysiol 1993;87:364–73. Webber WR, Litt B, Wilson K, Lesser RP. Practical detection of epileptiform discharges (EDs) in the EEG using an artificial neural network: a comparison of raw and parameterized EEG data. Electroencephalogr Clin Neurophysiol 1994;91:194–204. Williams GW, Lesser RP, Silvers JB, et al. Clinical diagnoses and EEG interpretation. Cleve Clin J Med 1990;57:437–40. Williams GW, Lüders H, Brickner A, Goormastic M, Klass DW. Interobserver variability in EEG interpretation. Neurology 1985;35:1714–9. Wilson SB, Scheuer ML, Emerson RG, Gabor AJ. Seizure detection: evaluation of the reveal algorithm. Clin Neurophysiol 2004;115:2280–91. Wilson SB, Scheuer ML, Plummer C, Young B, Pacia S. Seizure detection: correlation of human experts. Clin Neurophysiol 2003;114:2156–64.
Ronald P. Lesser Departments of Neurology and Neurosurgery, The Johns Hopkins Medical Institutions, The Johns Hopkins University, 2-147 Meyer Building, Baltimore, MD 21287, USA * Tel.: +1 410 955 1270; fax: +1 410 955 0751. E-mail address: [email protected] W.R.S. Webber Department of Neurology, The Johns Hopkins Medical Institutions, Baltimore, MD 21287, USA
Contents lists available at ScienceDirect
Clinical Neurophysiology journal homepage: www.elsevier.com/locate/clinph
Editorial
Seizure detection: Reaching through the looking glass See Article, pages 2687–2696
Seizure detection algorithms have been used for several decades (Saab and Gotman, 2005; Osorio et al., 2002; Wilson et al., 2004; Tzallas et al., 2007; Meier et al., 2008; Casdagli et al., 1997; Iasemidis, 2003; Litt and Lehnertz, 2002; Suffczynski et al., 2006; Lehnertz et al., 2007; Talathi et al., 2008), and a new one appears in this issue (Chan et al., 2008). Despite these, seizure detection often seems easy for an EEGer, but hard for a machine (Suffczynski et al., 2006; Lehnertz et al., 2007). The problem is, we do not know what to tell the machine to find, at least not with the precision needed by a computer. Also, maybe it is not so easy for people either. Give several EEGers a set of records to interpret, and they agree often, but not always (Gotman et al., 1978; Webber et al., 1993; Gotman, 1999; Wilson et al., 2003). Can one be consistent with oneself? As part of a study, one of us recently identified all afterdischarges in records of 13 patients, did this twice, some weeks apart, and found 11,944 (Lesser et al., 2008). There were 257 events marked once but not twice. A computer might have done this more consistently, but not necessarily more correctly. If the goal is to find every ‘‘true” event, and only those, which of the 257 should be found, and which not? With seizure detection algorithms, seizures might be missed, or non-seizures might be identified to be seizures, and, in some applications, erroneously treated. This is not unique to EEG: mistakes in recognition probably occur in every field of medicine (Williams et al., 1985; Williams et al., 1990; Webber et al., 1993; Eddy, 1988; Revesz and Kundel, 1977; Beam et al., 2003; Groopman, 2007). It has been surprisingly hard to tell the computer what to find, or to define all the elements that indicate an epileptiform discharge, whether ictal or interictal, and all the elements that do not (Ferri et al., 1989; Webber et al., 1994). Methods based on feature extraction may fail to be exact for this reason. What about raw data? There is a story, perhaps true, of a Pentagon algorithm developed to find tanks in a forest (Hanne, 1997; Priddy and Keller, 2005; Cohen et al., 2008). Initial tests were very encouraging. However, later tests with a different forest failed. Why? Unintentionally, the training set comprised images with tanks taken on cloudy days, while the images without tanks were taken on sunny days. The artificial neural network had learnt the difference between cloudy and sunny days, but not between tanks and no tanks. Something like that may occur with seizure detection. Do methods based on the raw data notice obvious features but miss subtle, but more important, ones? Do we do the same thing when we select parameters to use in seizure recognition algorithms? The second problem is degree. We know that a very dark cloud may mean lightning, and a white cloud usually does not.
What about a gray cloud? . . .And why doesn’t every dark cloud produce lightning? Just as we, standing on the ground, can’t see inside the cloud, so we, recording the EEG, can not see inside every cell that may, or may not, be contributing to whether a seizure will strike this time. More than this, lightning can stay within the cloud or spread, and just as a strike in part reflects an interaction between the cloud and the ground, where we may be standing, so seizure occurrence may reflect an interaction between what we used to think of as the seizure focus and ‘‘the rest” of the brain (Mormann et al., 2003; Mormann et al., 2007; D’Alessandro et al., 2005; Esteller et al., 2005; Mormann et al., 2005; Kalitzin et al., 2005; Le Van Quyen et al., 2005; Navarro et al., 2005). Elements that eventually produce a seizure can be thought of as parts of an n-dimensional data cloud. Maybe, if we understood more about the elements within the clouds, the dark ones would completely separate from the light ones, but it does not seem possible now. Does it matter if we find every event? It depends on what we want to do: study seizures with certain formal characteristics (say a certain range of voltages, durations, slopes, frequencies, or Lyapunov exponents), or study all seizures. What we can do with these tools is adequate for the former, but not for the latter (Suffczynski et al., 2006; Lehnertz et al., 2007). This can matter if we are using detections to decide whether a drug or other treatment is working, or to decide when (and whether) to treat acutely using methods such as brain stimulation, or something else. Seizures missed could mean treatments missed. False detections could mean unneeded treatments given, and we do not know what the long-term effects of these might be on epilepsy, or the brain. We have greatly advanced our understanding of seizures using detection and prediction algorithms, but for now, when we look at these on our computer screens, we are looking through a glass, darkly.
References Beam CA, Conant EF, Sickles EA. Association of volume and volume-independent factors with accuracy in screening mammogram interpretation. J Natl Cancer Inst 2003;95:282–90. Casdagli MC, Iasemidis LD, Savit RS, Gilmore RL, Roper SN, Sackellares JC. Nonlinearity in invasive EEG recordings from patients with temporal lobe epilepsy. Electroencephalogr Clin Neurophysiol 1997;102:98–105. Chan AM, Sun FT, Boto EH, Wingeier BM. Automated seizure onset detection for accurate onset time determination in intracranial EEG. Clin Neurophysiol 2008;119:2687–96. Cohen MX, Ridderinkhof KR, Haupt S, Elger CE, Fell J. Medial frontal cortex and response conflict: evidence from human intracranial EEG and medial frontal cortex lesion. Brain Res, in press. online 7 August 2008.
1388-2457/$34.00 Ó 2008 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.clinph.2008.09.011
2668
Editorial / Clinical Neurophysiology 119 (2008) 2667–2668
D’Alessandro M, Vachtsevanos G, Esteller R, et al. A multi-feature and multichannel univariate selection process for seizure prediction. Clin Neurophysiol 2005;116:506–16. Eddy D. Variations in physician practice: the role of uncertainty. In: Dowie J, Elstein A, editors. Professional judgment: a reader in clinical decision making. Cambridge, New York: Cambridge University Press; 1988. p. 45–59. Esteller R, Echauz J, D’Alessandro M, et al. Continuous energy variation during the seizure cycle: towards an on-line accumulated energy. Clin Neurophysiol 2005;116:517–26. Ferri R, Ferri P, Colognola RM, Petrella MA, Musumeci SA, Bergonzi P. Comparison between the results of an automatic and a visual scoring of sleep EEG recordings. Sleep 1989;12:354–62. Gotman J. Automatic detection of seizures and spikes. J Clin Neurophysiol 1999;16:130–40. Gotman J, Gloor P, Schaul N. Comparison of traditional reading of the EEG and automatic recognition of interictal epileptic activity. Electroencephalogr Clin Neurophysiol 1978;44:48–60. Groopman J. The eye of the beholder. In: How doctors think. Boston, New York: Houghton Mifflin Company; 2007. p. 177–202. Hanne T. Concepts of a learning object-oriented problem solver (LOOPS). In: Fandel G, Gal T, Hanne T, editors. Multiple criteria decision making: proceedings of the 12th international conference. Berlin: Springer; 1997. p. 338. Iasemidis LD. Epileptic seizure prediction and control. IEEE Trans Biomed Eng 2003;50:549–58. Kalitzin S, Velis D, Suffczynski P, Parra J, Da Silva FL. Electrical brain-stimulation paradigm for estimating the seizure onset site and the time to ictal transition in temporal lobe epilepsy. Clin Neurophysiol 2005;116:718–28. Le Van Quyen M, Soss J, Navarro V, et al. Preictal state identification by synchronization changes in long-term intracranial EEG recordings. Clin Neurophysiol 2005;116:559–68. Lehnertz K, Mormann F, Osterhage H, et al. State-of-the-art of seizure prediction. J Clin Neurophysiol 2007;24:147–53. Lesser RP, Lee HW, Webber WR, Prince B, Crone NE, Miglioretti DL. Short-term variations in response distribution to cortical stimulation. Brain 2008;131:1528–39. Litt B, Lehnertz K. Seizure prediction and the preseizure period. Curr Opin Neurol 2002;15:173–7. Meier R, Dittrich H, Schulze-Bonhage A, Aertsen A. Detecting epileptic seizures in long-term human eeg: a new approach to automatic online and real-time detection and classification of polymorphic seizure patterns. J Clin Neurophysiol 2008. Mormann F, Andrzejak RG, Elger CE, Lehnertz K. Seizure prediction: the long and winding road. Brain 2007;130:314–33. Mormann F, Andrzejak RG, Kreuz T, et al. Automated detection of a preseizure state based on a decrease in synchronization in intracranial electroencephalogram recordings from epilepsy patients. Phys Rev E Stat Nonlin Soft Matter Phys 2003;67:021912. Mormann F, Kreuz T, Rieke C, et al. On the predictability of epileptic seizures. Clin Neurophysiol 2005;116:569–87.
Navarro V, Martinerie J, Le Van QM, Baulac M, Dubeau F, Gotman J. Seizure anticipation: do mathematical measures correlate with video-eeg evaluation? Epilepsia 2005;46:385–96. Osorio I, Frei MG, Giftakis J, et al. Performance reassessment of a real-time seizuredetection algorithm on long ECoG series. Epilepsia 2002;43:1522–35. Priddy KL, Keller PE. Artificial neural networks: an introduction. Bellingham, WA: SPIE Press; 2005. p. 24. Revesz G, Kundel HL. Psychophysical studies of detection errors in chest radiology. Radiology 1977;123:559–62. Saab ME, Gotman J. A system to detect the onset of epileptic seizures in scalp EEG. Clin Neurophysiol 2005;116:427–42. Suffczynski P, Lopes da Silva FH, Parra J, et al. Dynamics of epileptic phenomena determined from statistics of ictal transitions. IEEE Trans Biomed Eng 2006;53:524–32. Talathi SS, Hwang DU, Spano ML, et al. Non-parametric early seizure detection in an animal model of temporal lobe epilepsy. J Neural Eng 2008;5:85–98. Tzallas AT, Tsipouras MG, Fotiadis DI. Automatic seizure detection based on timefrequency analysis and artificial neural networks. Comput Intell Neurosci 2007:80510. Webber WR, Litt B, Lesser RP, Fisher RS, Bankman I. Automatic EEG spike detection: what should the computer imitate? Electroencephalogr Clin Neurophysiol 1993;87:364–73. Webber WR, Litt B, Wilson K, Lesser RP. Practical detection of epileptiform discharges (EDs) in the EEG using an artificial neural network: a comparison of raw and parameterized EEG data. Electroencephalogr Clin Neurophysiol 1994;91:194–204. Williams GW, Lesser RP, Silvers JB, et al. Clinical diagnoses and EEG interpretation. Cleve Clin J Med 1990;57:437–40. Williams GW, Lüders H, Brickner A, Goormastic M, Klass DW. Interobserver variability in EEG interpretation. Neurology 1985;35:1714–9. Wilson SB, Scheuer ML, Emerson RG, Gabor AJ. Seizure detection: evaluation of the reveal algorithm. Clin Neurophysiol 2004;115:2280–91. Wilson SB, Scheuer ML, Plummer C, Young B, Pacia S. Seizure detection: correlation of human experts. Clin Neurophysiol 2003;114:2156–64.
Ronald P. Lesser Departments of Neurology and Neurosurgery, The Johns Hopkins Medical Institutions, The Johns Hopkins University, 2-147 Meyer Building, Baltimore, MD 21287, USA * Tel.: +1 410 955 1270; fax: +1 410 955 0751. E-mail address: [email protected] W.R.S. Webber Department of Neurology, The Johns Hopkins Medical Institutions, Baltimore, MD 21287, USA