We have developed a three-stage seizure detection methodology based on 339 h of data (26 seizures) collected from 10 patients in an epilepsy monitoring unit.

We have developed a three-stage seizure detection methodology based on 339 h of data (26 seizures) collected from 10 patients in an epilepsy monitoring unit.

Feb 1, 2017 · Stage I looks for concurrent activity in heart rate, arterial oxygenation and electrodermal activity, all of which can be monitored by a wrist- ...

A self-aware wearable system for real-time detection of epileptic seizures on a long-term basis using a multi-parametric machine learning technique.

Nov 13, 2015 · In many patients, additional extracerebral biosignals change in response to seizures and can be used to improve HR based algorithm accuracy.

This review aims to provide a sketchy overview of the methods derived from different disciplines lucubrating to the complexity of bio-signals in the field of ...

Sep 5, 2022 · This study aims to validate seizure-related individual differences in 24-h modulation patterns in autonomic recordings, including EDA, TEMP, and HR.

We present an unsupervised learning method for seizure detection. The method constructs a dynamic graph for multiple channel EEG by discovering the spurious ...

Nov 19, 2021 · However, monitoring is also needed in the day-to-day lives of people with epilepsy, where video electroencephalography is not feasible.

May 6, 2016 · The background objective of this work is to analyze electroencephalographic (EEG) signals recorded with depth electrodes during seizures in ...