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.

Eligibility Criteria:
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