Multimodal nocturnal seizure detection in a residential care setting: A long-term prospective trial.

Objective: To develop and prospectively evaluate a method of epileptic seizure detection combining heart rate and movement.

Methods: In this multicenter, in-home, prospective, video-controlled cohort study, nocturnal seizures were detected by heart rate (photoplethysmography) or movement (3-D accelerometry) in persons with epilepsy and intellectual disability. Participants with >1 monthly major seizure wore a bracelet (Nightwatch) on the upper arm at night for 2 to 3 months.

Automated video-based detection of nocturnal convulsive seizures in a residential care setting.

People with epilepsy need assistance and are at risk of sudden death when having convulsive seizures (CS). Automated real-time seizure detection systems can help alert caregivers, but wearable sensors are not always tolerated. We determined algorithm settings and investigated detection performance of a video algorithm to detect CS in a residential care setting.

Multimodal, automated detection of nocturnal motor seizures at home: Is a reliable seizure detector feasible?

Objective: Automated seizure detection and alarming could improve quality of life and potentially prevent sudden, unexpected death in patients with severe epilepsy. As currently available systems focus on tonic-clonic seizures, we want to detect a broader range of seizure types, including tonic, hypermotor, and clusters of seizures.

Methods: In this multicenter, prospective cohort study, the nonelectroencephalographic (non-EEG) signals heart rate and accelerometry were measured during the night in patients undergoing a diagnostic video-EEG examination.

Electroencephalographic events prior to epileptic major motor seizures.

Rationale: The goal of this study is to evaluate the electroencephalographic (EEG) events, prior to clonic phases of epileptic motor seizures. Analyzing video sequences we were able to detect these special phases of motor seizures, by image features. This can be used for an early detection and alerting for these events.

Automatic segmentation of episodes containing epileptic clonic seizures in video sequences.

Epilepsy is a neurological disorder characterized by sudden, often unexpected transitions from normal to pathological behavioral states called epileptic seizures. Some of these seizures are accompanied by uncontrolled, often rhythmic movements of body parts when seizure activity propagates to brain areas responsible for the initiation and control of movement. The dynamics of these transitions is, in general, unknown.