Seizure Onset Determination

Rodin, Ervin Y.; Constantino, Tawnya; Rampp, Stefan; Modur, Pradeep N.

doi:10.1097/wnp.0b013e3181969017

Cited by 31 publications

(38 citation statements)

References 36 publications

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“…There are increasing numbers of studies pointing to the importance of high frequency activity - HFA (Jiruska and Bragin, 2011; Jiruska et al, 2010; Jouny et al, 2007b; Rodin et al, 2009), or high frequency oscillations (HFOs) (Burns et al, 2012; Firpi et al, 2007; Jacobs et al, 2008; Jacobs et al, 2010; Jirsch et al, 2006; Nariai et al, 2011a; Ochi et al, 2007; Rampp and Stefan, 2006; Worrell et al, 2008; Worrell et al, 2004), for localizing the seizure onset zone. Recent studies have reported increased postsurgical seizure-free outcomes after resection of brain regions revealing ictal HFOs (Fujiwara et al, 2012; Jacobs et al, 2012; Jacobs et al, 2010; Modur et al, 2011; Nariai et al, 2011b; Ochi et al, 2007; Zijlmans et al, 2012).…”

Section: Discussionmentioning

confidence: 99%

“…The role of high frequency activity - HFA (Jiruska and Bragin, 2011; Jiruska et al, 2010; Jouny et al, 2007b; Rodin et al, 2009) or high frequency oscillations - HFOs (Worrell et al, 2008; Worrell et al, 2004) in epilepsy has been extensively investigated recently, and has been reported to be temporally and spatially correlated to the seizure onset zone. During ictal recordings, HFOs were recently observed mostly in the region of seizure onset, and less frequently in areas of secondary spread (Jirsch et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

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Ictal propagation of high frequency activity is recapitulated in interictal recordings: Effective connectivity of epileptogenic networks recorded with intracranial EEG

et al. 2014

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Seizures are increasingly understood to arise from epileptogenic networks across which ictal activity is propagated and sustained. In patients undergoing invasive monitoring for epilepsy surgery, high frequency oscillations have been observed within the seizure onset zone during both ictal and interictal intervals. We hypothesized that the patterns by which high frequency activity is propagated would help elucidate epileptogenic networks and thereby identify network nodes relevant for surgical planning. Intracranial EEG recordings were analyzed with a multivariate autoregressive modeling technique (short-time direct directed transfer function - SdDTF), based on the concept of Granger causality, to estimate the directionality and intensity of propagation of high frequency activity (70–175 Hz) during ictal and interictal recordings. These analyses revealed prominent divergence and convergence of high frequency activity propagation at sites identified by epileptologists as part of the ictal onset zone. In contrast, relatively little propagation of this activity was observed among the other analyzed sites. This pattern was observed in both subdural and depth electrode recordings of patients with focal ictal onset, but not in patients with a widely distributed ictal onset. In patients with focal ictal onsets, the patterns of propagation recorded during pre-ictal (up to 5 minutes immediately preceding ictal onset) and interictal (more than 24 hours before and after seizures) intervals were very similar to those recorded during seizures. The ability to characterize epileptogenic networks from interictal recordings could have important clinical implications for epilepsy surgery planning by reducing the need for prolonged invasive monitoring to record spontaneous seizures.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ictal propagation of high frequency activity is recapitulated in interictal recordings: Effective connectivity of epileptogenic networks recorded with intracranial EEG

et al. 2014

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“…[46] In cats with penicillin-induced seizure foci, similar DC shifts were noted to be characterized by a horizontal dipole, having maximum amplitude at the focus with the field extending to about 10 mm. [47] In the early studies, DC shifts were not seen on conventional EEG recordings obtained from the AC amplifiers, possibly because of the short time constant of 0.3 s (~0.53 Hz low frequency filter) used during that time,[1] giving rise to the notion that dedicated DC amplifiers were needed to record such activity. However, Ikeda et al .…”

Section: Ibss and Isamentioning

confidence: 99%

“…[1] This mainly stems from technological advances that have allowed us to record, store and analyze large amounts of human EEG data with relative ease using sophisticated hardware and commercially available software packages. The progress in data acquisition and analysis has occurred in conjunction with several groundbreaking studies demonstrating the clinical utility of EEG activity >70 Hz, termed high frequency oscillations (HFOs).…”

Section: Introductionmentioning

confidence: 99%

High frequency oscillations and infraslow activity in epilepsy

Modur

2014

Ann Indian Acad Neurol

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In pre-surgical evaluation of epilepsy, there has been an increased interest in the study of electroencephalogram (EEG) activity outside the 1-70 Hz band of conventional frequency activity (CFA). Research over the last couple of decades has shown that EEG activity in the 70-600 Hz range, termed high frequency oscillations (HFOs), can be recorded intracranially from all brain regions both interictally and at seizure onset. In patients with epilepsy, HFOs are now considered as pathologic regardless of their frequency band although it may be difficult to distinguish them from the physiologic HFOs, which occur in a similar frequency range. Interictal HFOs are likely to be confined mostly to the seizure onset zone, thus providing a new measure for localizing it. More importantly, several studies have linked HFOs to underlying epileptogenicity, suggesting that HFOs can serve as potential biomarkers for the illness. Along with HFOs, analysis of ictal baseline shifts (IBS; or direct current shifts) and infraslow activity (ISA) (ISA: <0.1 Hz) has also attracted attention. Studies have shown that: IBSs can be recorded using the routine AC amplifiers with long time constants; IBSs occur at the time of conventional EEG onset, but in a restricted spatial distribution compared with conventional frequencies; and inclusion of IBS contacts in the resection can be associated with favorable seizure outcome. Only a handful of studies have evaluated all the EEG frequencies together in the same patient group. The latter studies suggest that the seizure onset is best localized by the ictal HFOs, the IBSs tend to provide a broader localization and the conventional frequencies could be non-localizing. However, small number of patients included in these studies precludes definitive conclusions regarding post-operative seizure outcome based on selective or combined resection of HFO, IBS and CFA contacts. Large, preferably prospective, studies are needed to further evaluate the implications of different EEG frequencies in epilepsy.

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“…Some assess the performance based on a heuristically derived static data division to training and testing datasets (Mitra et al, 2009; Navakatikyan et al, 2006; Aarabi et al, 2007), others perform statistical tests by dividing the data repeatedly to training and testing (Temko et al, 2011; Greene et al, 2008), while others do not have separate testing data at all and report results over the data on which the algorithm was developed (Deburchgraeve et al, 2008). Last but not least, not all studies (Mitra et al, 2009) clearly report the complete etiology and gestational age (GA) of the babies used in the dataset and do not include seizure etiology e.g. hypoxic ischemic encephalopathy or whether the dataset consists of full-term or pre-term babies – this complicates the selection of the best existing algorithm for a designated group of patients in NICU.…”

Section: Introductionmentioning

confidence: 99%

Performance assessment for EEG-based neonatal seizure detectors

Temko

Thomas

Marnane

et al. 2011

Clinical Neurophysiology

103

110

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ObjectiveThis study discusses an appropriate framework to measure system performance for the task of neonatal seizure detection using EEG. The framework is used to present an extended overview of a multi-channel patient-independent neonatal seizure detection system based on the Support Vector Machine (SVM) classifier.MethodsThe appropriate framework for performance assessment of neonatal seizure detectors is discussed in terms of metrics, experimental setups, and testing protocols. The neonatal seizure detection system is evaluated in this framework. Several epoch-based and event-based metrics are calculated and curves of performance are reported. A new metric to measure the average duration of a false detection is proposed to accompany the event-based metrics. A machine learning algorithm (SVM) is used as a classifier to discriminate between seizure and non-seizure EEG epochs. Two post-processing steps proposed to increase temporal precision and robustness of the system are investigated and their influence on various metrics is shown. The resulting system is validated on a large clinical dataset of 267 h.ResultsIn this paper, it is shown how a complete set of metrics and a specific testing protocol are necessary to extensively describe neonatal seizure detection systems, objectively assess their performance and enable comparison with existing alternatives. The developed system currently represents the best published performance to date with an ROC area of 96.3%. The sensitivity and specificity were ∼90% at the equal error rate point. The system was able to achieve an average good detection rate of ∼89% at a cost of 1 false detection per hour with an average false detection duration of 2.7 min.ConclusionsIt is shown that to accurately assess the performance of EEG-based neonatal seizure detectors and to facilitate comparison with existing alternatives, several metrics should be reported and a specific testing protocol should be followed. It is also shown that reporting only event-based metrics can be misleading as they do not always reflect the true performance of the system.SignificanceThis is the first study to present a thorough method for performance assessment of EEG-based seizure detection systems. The evaluated SVM-based seizure detection system can greatly assist clinical staff, in a neonatal intensive care unit, to interpret the EEG.

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Seizure Onset Determination

Cited by 31 publications

References 36 publications

Ictal propagation of high frequency activity is recapitulated in interictal recordings: Effective connectivity of epileptogenic networks recorded with intracranial EEG

Ictal propagation of high frequency activity is recapitulated in interictal recordings: Effective connectivity of epileptogenic networks recorded with intracranial EEG

High frequency oscillations and infraslow activity in epilepsy

Performance assessment for EEG-based neonatal seizure detectors

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