Realtime Detection, Quantification, Warning, and Control of Epileptic Seizures— The Foundations of a New Epileptology
Realtime Detection, Quantification, Warning, and Control of Epileptic Seizures— The Foundations of a New Epileptology
The aim of abating seizures in a spatio-temporally selective manner is not a modern one. As early as 150 AD, Pelops from Alexandria, the teacher of Galen, was reported to have aborted what would likely be referred to today as ‘a simple partial seizure with sensory-motor manifestations’ by tying a ligature around the affected limb at the time of the appearance of the first paroxysmal manifestation.1 Brown-Sequard,2 Jackson,3 and, later, Gowers,4 who coined the term ‘counter-irritation’ to denote a strategy to abate seizures, reintroduced the notion of contingent therapy in the 19th century, an objective that since then and until recently was ignored. After nearly two millennia, the seminal tasks of automated seizure detection and contingent warning and delivery of therapy endowed with realtime feedback capabilities are finally not only feasible but also possibly safe and efficacious.5 This substantive advance, which at this juncture is in an ‘embryonic’ developmental stage, attempts to address the lack of efficacy of systemically administered pharmacological agents in a large group of patients with epilepsy,6 together with the relatively high incidence of serious idiosyncratic or intolerable dose-dependent adverse effects, including exacerbation of seizures,7 and to decrease the risk of injury and the psychosocial burden resulting from their unpredictability. The medical, psychosocial, and economic benefits that will be derived from achieving these objectives are salutary and self-evident to patients, their care-givers, and healthcare providers.
Realtime automated seizure detection, the obligatory condition for implementation of contingent warning and therapies, has been attempted with various degrees of success since the mid-1970s.8 The rationale for the method/algorithm,9,10 developed by the group of which these authors are members, and its architecture will be described in some detail for the purpose of shedding light on the process and on the value of signal analyses for the implementation of novel clinical epilepsy therapies. This algorithm is discussed herein simply because it is well understood by these authors. References to and comparisons with other algorithms are not germane, because, to date, a formal evaluation of their performance on a common data set has not been carried out.
Simply put, and making allowances for lack of mathematical rigor, this modular, adaptable algorithm separates and quantifies (measuring intensity, duration, and extent of spread) in realtime (as ‘things’ are happening) the seizure, from the non-seizure content in cortical electrical signals by comparing signal features on two timescales, one short (two seconds), containing ongoing (‘foreground’) activity, and the other long (30 minutes), containing recent past (‘background’) activity, which is used as a reference against which the current activity is weighed/quantified. It undertakes two different filtering steps: one discards the non-seizure activity and the other the epileptiform activity that does not qualify as a ‘seizure.’ There is also a division (yielding a ratio) of the doubly filtered seizure content in the short ‘foreground’ window by that in the ‘background’ window. All of the above is carried out with a worthwhile degree of accuracy and speed. The algorithm’s design/architecture was guided by three central concepts.
- The raw cortical signals (digitized voltages recorded directly by electrocorticogram (ECoG) from the brains of subjects with pharmacoresistant localization-related (‘focal’) seizures undergoing invasive surgical evaluation) have time-varying seizure (ictal) and non-seizure (inter-ictal) ‘components’ that are separable or decomposable (see Figure 1) and also quantifiable.
- The separation of seizure from non-seizure activity must be as complete (see Figure 2) as is practicable with existing analysis tools (and battery power available to implantable medical devices) to maximize sensitivity, accuracy, and speed of detection of relevant changes.
- Constraints must be imposed on the duration and intensity of changes in the ECoG’s seizure content that would be selectable for triggering warnings and delivery of therapy.
- Siegel RE (translator), Galen on the affected parts (de locis affectis). Book III, Basel, Switzerland: S Karger, 1976:94–7.
- Brown-Sequard CE, Researches on epilepsy: its artificial production in animals, and its etiology, nature and treatment in man, Boston Medical and Surgical Journal, 1856–1857;55–57.
- Jackson JH, Case of convulsive attacks arrested by stopping the aura, Lancet, 1868;1:618–19.
- Gowers WR, Epilepsy and other chronic convulsive diseases: their causes, symptoms and treatment, New York: William Wood, 1885:235–6.
- Osorio I, Frei M, Sunderam S, et al., Automated seizure abatement in humans using electrical stimulation, Ann Neurol, 2005;57: 258–68.
- Regesta G, Tanganelli P, Clinical aspects and biological bases of drug-resistant epilepsies, Epilepsy Res, 1999;34:109–22.
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- Osorio I, Frei MG, Giftakis J, et al., Performance reassessment of real-time seizure-detection algorithm on long ECoG series, Epilepsia, 2002;43:1522–35.
- Daubechies I, Ten lectures on wavelets, Philadelphia: SIAM, 1999.
- Sunderam S, Osorio I, Frei MG, Epileptic seizures are temporally interdependent under certain conditions, Epilepsy Res, 2007;76: 77–84.
- Osorio I, Frei MG, Manly BF, et al., An introduction to contingent (closed-loop) brain electrical stimulation for seizure blockage, to ultra-short-term clinical trials, and to multidimensional statistical analysis of therapeutic efficacy, J Clin Neurophysiol, 2001;18: 533–44.
- Durazzo TS, Spencer SS, Duckrow RB, et al., Temporal distributions of seizure occurrence from various epileptogenic regions, Neurology, 2008;70:1265–71.
- Di Gennaro G, Quarato PP, Sebastiano F, et al., Ictal heart rate increase precedes EEG discharge in drug-resistant mesial temporal lobe seizures, Clin Neurophysiol, 2004;115;1169–77.
- Blum DE, Eskola J, Bortz JJ, et al., Patient awareness of seizures, Neurology, 1996;47:260–64.
- Hoppe C, Poepel A, Elger CE, Epilepsy: accuracy of patient seizure counts, Arch Neurol, 2007;64:1595–9.
