Seizure forecasting based on AI-supported analysis of multidien and circadian cycles in EEG and non-EEG long-term datasets DOI Creative Commons
Gadi Miron, Christian Meisel

Deleted Journal, Год журнала: 2024, Номер unknown

Опубликована: Окт. 4, 2024

Abstract Long-term datasets in epilepsy encompassing weeks to months of continuous physiological signal recordings along with novel data analysis techniques have recently advanced the understanding several aspects. Patterns seizures, interictal discharges, and autonomous nervous system activity were observed often exhibit long, multidien cycles that are correlated each other. These observations provided basis for new approaches forecast seizure risk from electroencephalographic (EEG) non-EEG data.

Язык: Английский

Lethal Interactions of neuronal networks in epilepsy mediated by both synaptic and volume transmission indicate approaches to prevention DOI
Carl L. Faingold

Progress in Neurobiology, Год журнала: 2025, Номер 249, С. 102770 - 102770

Опубликована: Апрель 19, 2025

Язык: Английский

Процитировано

0

Artificial intelligence, digital technology, and mobile health in epilepsy DOI
Sándor Beniczky, Samden Lhatoo, Michael R. Sperling

и другие.

Epilepsia, Год журнала: 2025, Номер unknown

Опубликована: Апрель 26, 2025

Язык: Английский

Процитировано

0

Circadian and sleep–wake homeostatic modulation of EEG activity during sleep and wakefulness DOI Creative Commons
Christian Cajochen

Deleted Journal, Год журнала: 2024, Номер 37(4), С. 259 - 265

Опубликована: Окт. 25, 2024

Abstract The human sleep–wake cycle is regulated by two distinct processes: the circadian timing system (CTS) and homeostatic (SWH) process. CTS driven a small region in anterior hypothalamus of brain, which known as “circadian clock.” By contrast, SWH can be conceptualized an hourglass, whereby sleep pressure builds up during waking hours released sleep. In contrast to CTS, there no specific brain that controls this hourglass A complex modulation these processes affects electroencephalographic (EEG) activity throughout 24‑h day, resulting emergence EEG features. These features classified into three categories: those show clear patterns, are predominantly influenced process, combination both. This review describes quantified spectral analysis wakefulness derived from study protocols, enable separation influence clock Second, potential for interictal seizure occurrence will discussed, along with its implications diagnosis, treatment, prediction prevention.

Язык: Английский

Процитировано

0

Seizure forecasting based on AI-supported analysis of multidien and circadian cycles in EEG and non-EEG long-term datasets DOI Creative Commons
Gadi Miron, Christian Meisel

Deleted Journal, Год журнала: 2024, Номер unknown

Опубликована: Окт. 4, 2024

Abstract Long-term datasets in epilepsy encompassing weeks to months of continuous physiological signal recordings along with novel data analysis techniques have recently advanced the understanding several aspects. Patterns seizures, interictal discharges, and autonomous nervous system activity were observed often exhibit long, multidien cycles that are correlated each other. These observations provided basis for new approaches forecast seizure risk from electroencephalographic (EEG) non-EEG data.

Язык: Английский

Процитировано

0