Epilepsy Clinical Trial
Official title:
Validation of an Algorithm for Identification and Notification of Generalized Major Motor Seizures
The specificity and sensitivity of a novel seizure-detection mobile software application with a generalized tonic/clonic seizure detection algorithm (Motor Seizure Detection Algorithm [mSDA]) installed on a wearable device to be worn by the subject. The software will be tested using subjects from a patient population in an epilepsy monitoring unit (EMU) undergoing video and electroencephalograph (VEEG) observation. The number of generalized major motor seizures detected by the mSDA will be compared with those detected by VEEG.
Seizures are paroxysmal, abnormal behaviors which usually are associated with altered
awareness and amnesia. The frequency of seizures is not easily documented. The individual who
suffers from seizures may be unaware that a seizure is occurring. Many seizures, including
generalized major motor seizures, have stereotyped, vigorous motor activity associated with
the events.
Currently, accurate seizure detection relies on EEG and video which are limited by time, size
and mobility. Seizure detection can also use biomarkers such as movement patterns described
by gyroscopes. These devices can monitor patterns of movement which correspond to the
activity during seizures and kept in a log of seizures without patient input. The log can be
used to notify patients or caregivers of seizures.
This study is to determine the accuracy of a system using a commercial, wearable device
linked to a computer algorithm based in the cloud which stores the movement pattern and
notifies the patient and others of a generalized major motor seizure. The accuracy will be
determined by a comparison of the system detections to simultaneously recorded video
electroencephalogram, considered the "gold standard" of seizure detection.
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