Subcutaneous EEG Monitoring Reveals AED Response and Breakthrough Seizures

Weisdorf, Sigge; Zibrandtsen, Ivan C; Kjaer, Troels W

doi:10.1155/2020/8756917

Cited by 7 publications

(5 citation statements)

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“…Out-of-hospital monitoring can only document electrographic seizures but not whether these represent clinical seizures as that would require documentation of the ictal symptoms. We relied on within-patient seizure similarity of ictal EEG patterns (ie, seizure signature) (figure 2) as a method for acknowledgement of electrographic seizures without video documentation 16 22 25. However, this method may miss clinically relevant seizures that diverge from the stereotyped in-patient seizures or misidentify subclinical electrographic seizures as clinical seizures.…”

Section: Discussionmentioning

confidence: 99%

Out-of-hospital multimodal seizure detection: a pilot study

Nielsen,

Kristinsdóttir,

Zibrandtsen

et al. 2023

BMJ Neurol Open

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BackgroundOut-of-hospital seizure detection aims to provide clinicians and patients with objective seizure documentation in efforts to improve the clinical management of epilepsy. In-patient studies have found that combining different modalities helps improve the seizure detection accuracy. In this study, the objective was to evaluate the viability of out-of-hospital seizure detection using wearable ECG, accelerometry and behind-the-ear electroencephalography (EEG). Furthermore, we examined the signal quality of out-of-hospital EEG recordings.MethodsSeventeen patients were monitored for up to 5 days. A support vector machine based seizure detection algorithm was applied using both in-patient seizures and out-of-hospital electrographic seizures in one patient. To assess the content of noise in the EEG signal, we compared the root-mean-square (RMS) of the recordings to a reference threshold derived from manually categorised segments of EEG recordings.ResultsIn total 1427 hours of continuous EEG was recorded. In one patient, we identified 15 electrographic focal impaired awareness seizures with a motor component. After training our algorithm on in-patient data, we found a sensitivity of 91% and a false alarm rate (FAR) of 18/24 hours for the detection of out-of-hospital seizures using a combination of EEG and ECG recordings. We estimated that 30.1% of the recorded EEG signal was physiological EEG, with an RMS value within the reference threshold.ConclusionWe found that detection of out-of-hospital focal impaired awareness seizures with a motor component is possible and that applying multiple modalities improves the diagnostic accuracy compared with unimodal EEG. However, significant challenges remain regarding a high FAR and that only 30.1% of the EEG data represented usable signal.

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Section: Discussionmentioning

confidence: 99%

Out-of-hospital multimodal seizure detection: a pilot study

Nielsen,

Kristinsdóttir,

Zibrandtsen

et al. 2023

BMJ Neurol Open

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“…1,2,55,56 Although wearables for the detection of tonic-clonic seizures in particular are gaining approval, the ability to automatically detect focal seizures, particularly with impaired awareness or without major motor features, remains unmet. 57 Objective seizure counts may inform clinical decisions to avoid that an effective treatment is abandoned because no discernible effect in self-reported measures was apparent 58 or that an inefficacious treatment is maintained or initiated on the basis of nonepileptic events, because nonseizure events (eg, antiepileptic drug [AED] side effects, nonepileptic seizures) are incorrectly classified as seizures by the patient.…”

Section: Objective Seizure Countingmentioning

confidence: 99%

A new era in electroencephalographic monitoring? Subscalp devices for ultra–long‐term recordings

et al. 2020

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“…Dispositivos de uso en casa/ambulatorios: El ML es util para analizar bases de datos grandes y complejas y se aplica en detección automática de crisis (DAC) en EEG (25). Se utilizan diversas técnicas para lograr la lectura y clasificación con DL.…”

Section: Detección De Crisisunclassified

Inteligencia artificial en la evaluación y manejo de pacientes con epilepsia.

Paredes-Aragón

Burneo²

2022

Rev Neuropsiquiatr

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La epilepsia es una enfermedad que frecuentemente conlleva significativos niveles de morbi-mortalidad, afecta seriamente la calidad de vida y, en cerca de un tercio de los pacientes, es refractaria a diversos tratamientos. La inteligencia artificial (IA) ha beneficiado el estudio, tratamiento y pronóstico de los pacientes con epilepsia a través de los años. Estos logros abarcan diagnóstico, predicción de crisis automatizada, monitoreo avanzado de crisis epilépticas y electroencefalograma, uso de recursos genéticos en manejo y diagnóstico, algoritmos en imagen y tratamiento, neuromodulación y cirugía robótica. La presente revisión explica de forma práctica los avances actuales y futuros de la inteligencia artificial, rama de la ciencia que ha mostrado resultados prometedores en el diagnóstico y tratamiento de pacientes con epilepsia.

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Subcutaneous EEG Monitoring Reveals AED Response and Breakthrough Seizures

Cited by 7 publications

References 10 publications

Out-of-hospital multimodal seizure detection: a pilot study

Out-of-hospital multimodal seizure detection: a pilot study

A new era in electroencephalographic monitoring? Subscalp devices for ultra–long‐term recordings

Inteligencia artificial en la evaluación y manejo de pacientes con epilepsia.

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