Accidental injuries in patients with generalized tonic–clonic seizures. A multicenter, observational, cross-sectional study (QUIN-GTC study)

Salas‐Puig, Xavier; Iniesta, Marc; Abraira, Laura; Puig, Josep

doi:10.1016/j.yebeh.2018.10.043

Cited by 25 publications

(20 citation statements)

References 18 publications

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“…8 Patients with five or more GTCSs per year had an odds ratio for injuries 3.5 times higher than patients with only one seizure per year. 8 GTCS is the main risk factor of SUDEP. All video-EEG documented cases of SUDEP occurred after a GTCS.…”

Section: Characterization Of Seizure T Ype and Frequencymentioning

confidence: 89%

“…The most severe seizure types are the generalized tonic‐clonic seizures (GTCSs), including the focal to bilateral tonic‐clonic seizures (formerly known as secondarily generalized tonic‐clonic seizures), with the highest morbidity and mortality associated with seizures. Each year, 60% of the patients with GTCSs experience some accidental injury related to a GTCS and 25% experience at least one serious injury, causing incapacitation or requiring hospitalization or surgical intervention 8 . Patients with five or more GTCSs per year had an odds ratio for injuries 3.5 times higher than patients with only one seizure per year 8 .…”

Section: Automated Characterization Of Seizure Type and Frequencymentioning

confidence: 99%

“…Each year, 60% of the patients with GTCSs experience some accidental injury related to a GTCS and 25% experience at least one serious injury, causing incapacitation or requiring hospitalization or surgical intervention 8 . Patients with five or more GTCSs per year had an odds ratio for injuries 3.5 times higher than patients with only one seizure per year 8 . GTCS is the main risk factor of SUDEP.…”

Section: Automated Characterization Of Seizure Type and Frequencymentioning

confidence: 99%

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Biomarkers of seizure severity derived from wearable devices

et al. 2020

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Besides triggering alarms, wearable seizure detection devices record a variety of biosignals that represent biomarkers of seizure severity. There is a need for automated seizure characterization, to identify high‐risk seizures. Wearable devices can automatically identify seizure types with the highest associated morbidity and mortality (generalized tonic‐clonic seizures), quantify their duration and frequency, and provide data on postictal position and immobility, autonomic changes derived from electrocardiography/heart rate variability, electrodermal activity, respiration, and oxygen saturation. In this review, we summarize how these biosignals reflect seizure severity, and how they can be monitored in the ambulatory outpatient setting using wearable devices. Multimodal recording of these biosignals will provide valuable information for individual risk assessment, as well as insights into the mechanisms and prevention of sudden unexpected death in epilepsy.

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Section: Characterization Of Seizure T Ype and Frequencymentioning

confidence: 89%

Section: Automated Characterization Of Seizure Type and Frequencymentioning

confidence: 99%

Section: Automated Characterization Of Seizure Type and Frequencymentioning

confidence: 99%

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Biomarkers of seizure severity derived from wearable devices

et al. 2020

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“…Automated seizure alarms calling for help are especially important for generalized tonic‐clonic seizures (GTCS) including focal‐to‐bilateral tonic‐clonic seizures, since these seizure types are associated with the highest morbidity and mortality. Each year 25% of the patients with GTCS experience serious accidental injury related to the seizure 9 . GTCS during the preceding year was associated with a 27‐fold increased risk of sudden unexpected death in epilepsy (SUDEP), and the combination of not sharing a bedroom and having at least one GTCS per year had a 67‐fold increased risk of SUDEP 10 .…”

Section: Wearable Devicesmentioning

confidence: 99%

Machine learning and wearable devices of the future

et al. 2020

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Machine learning (ML) is increasingly recognized as a useful tool in healthcare applications, including epilepsy. One of the most important applications of ML in epilepsy is seizure detection and prediction, using wearable devices (WDs). However, not all currently available algorithms implemented in WDs are using ML. In this review, we summarize the state of the art of using WDs and ML in epilepsy, and we outline future development in these domains. There is published evidence for reliable detection of epileptic seizures using implanted electroencephalography (EEG) electrodes and wearable, non‐EEG devices. Application of ML using the data recorded with WDs from a large number of patients could change radically the way we diagnose and manage patients with epilepsy.

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“…Актуальность исследования прогноза, исходов и, в части случаев, эволюции ГЭ обусловлена пожизненным течением большинства синдромов с влиянием на качество жизни пациента, непосредственно приступов, когнитивных нарушений в структуре синдрома или социальных факторов, обусловленных стигматизацией, коморбидными тревожным и депрессивным расстройствами, ограничением в учебе, вождении транспорта, трудоустройстве [18][19][20][21][22][23]. Данные аспекты остаются малоизученными в настоящий момент именно с точки зрения эволюции, в т. ч. в развитых странах.…”

Section: клинические случаи / Case Studiesunclassified