Yield of conventional and automated seizure detection methods in the epilepsy monitoring unit

Kamitaki, Brad K.; Yum, Alma; Lee, James; Rishty, Shelly; Sivaraaman, Kartik; Esfahanizadeh, Abdolreza; Mani, Ram S.; Wong, S.S.

doi:10.1016/j.seizure.2019.05.019

Cited by 19 publications

(16 citation statements)

References 26 publications

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“…Therefore, such a warning system must rely on an algorithm that is computationally efficient and can be easily implemented in real-time. Additionally, it must be sensitive to a wide range of seizure-onset patterns and still maintain high specificity to avoid alarm fatigue (Fürbass et al 2015; Kamitaki et al 2019; Bi et al 2020).…”

Section: Discussionmentioning

confidence: 99%

“…Modern algorithms and current technological systems allow for the development of practical and efficient warning system, however, this need remains unmet. Many of the recently developed algorithms are not practical for implementation as an alarm system that is sensitive to early detection of seizures in the EMU (Stacey 2018; Kamitaki et al 2019). Several algorithms have been developed for early-seizure-onset detection with real-time capabilities for scalp-EEG (Meier et al 2008a; Chisci et al 2010; Minasyan et al 2010; Fürbass et al 2015; Bogaarts et al 2016; Sridevi et al 2019).…”

Section: Discussionmentioning

confidence: 99%

“…Failure to detect electrographic seizures can increase risk of physical and neuronal injury, Atkinson et al reported in a 2008 study of 170 events in a single hospital, 44 clinical seizures and 57 electrographic seizures did not trigger a staff response (Atkinson et al 2012). While many centers have a higher response rate, it remains likely that the implementation of automatized early seizure detection algorithms, would unequivocally improve patient safety and efficiency in the EMU (Noe and Drazkowski 2009; Jouny et al 2011; Lee and Shah 2013; Kamitaki et al 2019). Reliable early seizure-onset detection in real-time would provide a trustworthy warning system in the EMU to alert personnel and allow assessment of the patient during an ongoing seizure.…”

Section: Introductionmentioning

confidence: 99%

“…The large number of channels to analyze reduces the signal to noise ratio, making it more challenging for seizure detection algorithms to achieve high specificity which is vital for clinical use. An algorithm with a high false positive rate does not provide a reliable alarm, and is typically ignored by clinical staff (Lee and Shah 2013; Kamitaki et al 2019; Bi et al 2020).…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Training of a Novelty Classifier Algorithm for Real-Time Early Seizure Onset Detection

Ehrens

Cervenka

et al. 2021

Preprint

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The objective of this study was to develop an adaptive framework for seizure detection in real-time that is practical to use in the Epilepsy Monitoring Unit (EMU) as a warning signal, and whose output helps characterize epileptiform activity. Our framework uses a one-class Support Vector Machine (SVM) that is being trained dynamically according to past activity in all available channels. This is done to evaluate the novelty of the current instance according to previous activity. Our algorithm was tested on intracranial EEG from human epilepsy patients that are admitted to the EMU for presurgical evaluation. In this study, we compared multiple configurations for using a one-class SVM to assess if there is significance over specific neural features or electrode locations. Our results show our algorithm is capable of running in real-time and achieving a high performance for early seizure-onset detection with a low false-positive rate and robustness to different types of seizure-onset patterns as well as to the number of channels used. This algorithm offers a solution to warning systems in the EMU as well as a tool for seizure characterization during post-hoc analysis of intracranial EEG data for surgical resection of the epileptogenic network.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Dynamic Training of a Novelty Classifier Algorithm for Real-Time Early Seizure Onset Detection

Ehrens

Cervenka

et al. 2021

Preprint

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“…Les crises survenues au chevet en la présence de personnel ont fait l'objet d'une analyse distincte. Résultats : Le TI médian était de 21,0 secondes (11,8). L'USE, les crises tonico-cloniques bilatérales (CTCB) et la notion d'alerte en début de crise ont été associées à une augmentation des probabilités d'intervention.…”

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Intervention Time and Adverse Events in a Canadian Epilepsy Monitoring Unit

Toffa

Assi

et al. 2020

Can. J. Neurol. Sci.

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Background: Intervention time (IT) in response to seizures and adverse events (AEs) have emerged as key elements in epilepsy monitoring unit (EMU) management. We performed an audit of our EMU, focusing on IT and AEs. Methods: We performed a retrospective study on all clinical seizures of admissions over a 1-year period at our Canadian academic tertiary care center’s EMU. This EMU was divided in two subunits: a daytime three-bed epilepsy department subunit (EDU) supervised by EEG technicians and a three-bed neurology ward subunit (NWU) equipped with video-EEG where patients were transferred to for nights and weekends, under nursing supervision. Among 124 admissions, 58 were analyzed. A total of 1293 seizures were reviewed to determine intervention occurrence, IT, and AE occurrence. Seizures occurring when the staff was present at bedside at seizure onset were analyzed separately. Results: Median IT was 21.0 (11.0–40.8) s. The EDU, bilateral tonic–clonic seizures (BTCS), and the presence of a warning signal were associated with increased odds of an intervention taking place. The NWU, BTCS, and seizure rank (seizures were chronologically ordered by the patient for each subunit) were associated with longer ITs. Bedside staff presence rate was higher in the EDU than in the NWU (p < 0.001). AEs occurred in 19% of admissions, with no difference between subunits. AEs were more frequent in BTCS than in other seizure types (p = 0.001). Conclusion: This study suggests that close monitoring by trained staff members dedicated to EMU patients is key to optimize safety. AE rate was high, warranting corrective measures.

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Ex‐SPECTing success: Predictors of successful SPECT radiotracer injection during presurgical video‐EEG admissions

et al. 2024

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ObjectivesTo determine predictors of successful ictal Single Photon Emission Computed Tomography (SPECT) injections during Epilepsy Monitoring Unit (EMU) admissions for patients undergoing presurgical evaluation for drug resistant focal epilepsy.MethodsIn this retrospective study, consecutive EMU admissions were analysed at a single centre between 2019‐2021. All seizures that occurred during the admission were reviewed. ‘Injectable seizures’ occurred during hours when the radiotracer was available. EMU‐level data were analysed to identify factors predictive of an EMU admission with a successful SPECT injection (successful admission). Seizure‐level data were analysed to identify factors predictive of an ‘injectable seizure’ receiving a SPECT injection during the ictal phase (successful injection). A multivariate generalised linear model was used to identify predictive variables.Results125 EMU admissions involving 103 patients (median 37 years, IQR27.0‐45.5) were analysed. 38.8% of seizures that were eligible for SPECT (n=134) were successfully injected; this represented 17.4% of all seizures (n=298) that occurred during admission. Unsuccessful admissions were most commonly due to a lack of seizures during EMU‐SPECT (19.3%) or no 'injectable seizures’ (62.3%). Successful EMU‐SPECT was associated with baseline seizure frequency >1 per week (95%CI 2.1‐3.0, p <0.001) and focal PET hypometabolism (95%CI 2.0‐3.7, p <0.001). On multivariate analysis, the only factor associated with successful injection was patients being able to indicate they were having a seizure to staff (95%CI 1.0‐4.4, p=0.038).SignificanceCompleting a successful ictal SPECT study remains challenging. Baseline seizure frequency of >1 per‐week, a PET hypometabolic focus and a patient's ability to indicate seizure onset were identified as predictors of success. These findings may assist EMUs in optimising their SPECT protocols, patient selection, and resource allocation.

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Yield of conventional and automated seizure detection methods in the epilepsy monitoring unit

Cited by 19 publications

References 26 publications

Dynamic Training of a Novelty Classifier Algorithm for Real-Time Early Seizure Onset Detection

Dynamic Training of a Novelty Classifier Algorithm for Real-Time Early Seizure Onset Detection

Intervention Time and Adverse Events in a Canadian Epilepsy Monitoring Unit

Ex‐SPECTing success: Predictors of successful SPECT radiotracer injection during presurgical video‐EEG admissions

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