Back

Event Capture Rates of Ambulatory Video EEG

Nurse, E. S.; Hannon, T.; Wong, V.; Fernandes, K. M.; Cook, M. J.

2022-11-14 neurology
10.1101/2022.11.13.22282197 medRxiv
Show abstract

ObjectivesRecording electrographic and behavioral information during epileptic and other paroxysmal events is important during video EEG monitoring. This study was undertaken to measure the event capture rate of an ambulatory service operating across Australia using a shoulder-worn EEG device and telescopic pole-mounted camera. MethodsNeurologist reports were accessed retrospectively. Studies with confirmed events were identified and assessed for event capture by recording modality, whether events were reported or discovered, and wakefulness. Results6,265 studies were identified, of which 2,788 (44.50%) had events. A total of 15,691 events were captured, of which 77.89% were reported. The EEG-ECG amplifier was active for 99.83% of events. The patient was in view of the camera for 94.90% of events. 84.89% of studies had all events on camera, and 2.65% had zero events on camera (mean=93.66%, median=100.00%). 84.42% of events from wakefulness were reported, compared to 54.27% from sleep. ConclusionEvent capture was similar to previously reported rates from ambulatory studies, with higher capture rates on video. Most patients have all events captured on camera. SignificanceAmbulatory monitoring is capable of high rates of event capture, and the use of wide-angle cameras allows for all events to be captured in the majority of studies. HighlightsO_LIA review was undertaken of an Australia-wide ambulatory video-EEG monitoring service C_LIO_LIPatients were in view of camera for 94.90% of events, and 84.89% of studies had all events on camera C_LIO_LI84.42% of events from wakefulness were reported, compared to 54.27% from sleep C_LI

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.