Clinical Validation of a Video-based Epilepsy Examination Service
Clinical Validation of a Video-based Epilepsy Examination Service
The main purpose of this study is to validate the ability of the Nelli system (video-based
 epilepsy examination system) to detect epileptic motor seizure behaviors in patients at rest.
Proper seizure documentation is an important tool to evaluate treatment outcomes and risks
 associated with epileptic seizures. Apart from a costly in-hospital video
 electro-encephalogram (VEEG) monitoring, most patients with epilepsy log and track their
 seizures with the help of the seizure diaries only. However, the reliability of the diaries
 is heavily dependent on an accurate recognition and recording of the seizures. There is an
 obvious need for a more objective and reliable seizure detection method. The investigators
 propose a low-cost video-based examination system, designed to detect motor seizure behaviors
 in patients at rest.

 The main aim of this study is to clinically validate the ability of the Nelli system
 (video-based epilepsy examination system) to detect epileptic motor seizure behaviors in
 patients at rest, using simultaneous VEEG as the ground truth with the reference to all motor
 seizures and individual motor seizure types.

 Study design established as follows:

 1. Study population is represented by 90 epilepsy patients with suspected history of motor
 seizures undergoing a standard VEEG examination for clinical evaluation of epilepsy.

 2. Co-registration of standard VEEG with the Nelli video- and audio-based system is
 performed, duration of the registration is 1-4 days.

 3. The inclusion criteria for seizures:

 1. Epileptic motor seizures appearing from rest while patients are in supine position
 in their monitoring bed.

 2. The seizures are determined to be epileptic based on electroclinical
 characteristics by the responsible neurophysiologist / epileptologist.

 3. The seizures can be classified into motor seizure types (both focal and
 generalized) using the 2017 International League Against Epilepsy (ILAE) seizure
 classification.

 4. The registrations are analyzed by the Nelli system blinded to any clinical information.
 Nelli system consists of two evaluation components:

 1. Algorithmic component (pre-trained machine learning model based on video and audio
 signals)

 2. Supervisory component (performed by experienced VEEG technicians)

 5. Reports containing the seizure type classification (time-stamped) are provided by the
 VEEG laboratory, the performance of the Nelli system is assessed using these reports as
 the ground truth.

 a. Specificity and sensitivity of the Nelli system are evaluated with reference to both
 all motor seizures and individual motor seizure types.

 6. The secondary outcome measures are established as follows:

 1. To assess the latency from the onset of the seizure detection by the Nelli system
 in comparison to VEEG registration.

 2. To determine an inter-rater agreement for the supervisory component of the Nelli
 system.

 3. To investigate the performance of the algorithmic component of the Nelli system
 alone.
Inclusion Criteria:

 Patients with epilepsy undergoing a standard VEEG examination for clinical evaluation
 consenting to participate in the present study

 Exclusion Criteria:

 Patients not consenting to participate in the study