title: Automated seizure detection systems and their effectiveness for each type of seizure.
abstract: Epilepsy affects almost 1% of the population and most of the approximately 20-30% of patients with refractory epilepsy have one or more seizures per month. Seizure detection devices allow an objective assessment of seizure frequency and a treatment tailored to the individual patient. A rapid recognition and treatment of seizures through closed-loop systems could potentially decrease morbidity and mortality in epilepsy. However, no single detection device can detect all seizure types. Therefore, the choice of a seizure detection device should consider the patient-specific seizure semiologies. This review of the literature evaluates seizure detection devices and their effectiveness for different seizure types. Our aim is to summarize current evidence, offer suggestions on how to select the most suitable seizure detection device for each patient and provide guidance to physicians, families and researchers when choosing or designing seizure detection devices. Further, this review will guide future prospective validation studies.
authors.affiliation: Department of Neurology, National Children's Hospital "Dr. Carlos Saenz Herrera", San José, Costa Rica. Electronic address: adrianaulate@hotmail.com.
authors.forename: A
authors.initials: A
authors.lastname: Ulate-Campos
authors.affiliation: Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA. Electronic address: francesca.coughlin@childrens.harvard.edu.
authors.forename: F
authors.initials: F
authors.lastname: Coughlin
authors.affiliation: Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Universidad Austral de Chile, Valdivia, Chile. Electronic address: marina.gainzalein@childrens.harvard.edu.
authors.forename: M
authors.initials: M
authors.lastname: Gaínza-Lein
authors.affiliation: Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Department of Child Neurology, Hospital Sant Joan de Déu, Universidad de Barcelona, Spain. Electronic address: ivan.fernandez@childrens.harvard.edu.
authors.forename: I Sánchez
authors.initials: IS
authors.lastname: Fernández
authors.affiliation: Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA. Electronic address: phillip.pearl@childrens.harvard.edu.
authors.forename: P L
authors.initials: PL
authors.lastname: Pearl
authors.affiliation: Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA. Electronic address: tobias.loddenkemper@childrens.harvard.edu.
authors.forename: T
authors.initials: T
authors.lastname: Loddenkemper
mesh_terms: D006801:Humans; D064926:Neurophysiological Monitoring / Q000295:instrumentation* / Q000379:methods*; D012640:Seizures / Q000145:classification / Q000175:diagnosis*
keywords: Automated seizure detection; Epilepsy; Intractable epilepsy; SUDEP; Seizure alarms; Seizure prediction sensors