gotman.yaml

---
# Description of a seizure detection algorithm
title: "Gotman - Automatic recognition of epileptic seizures in the EEG (1982)" 
image: "ghcr.io/esl-epfl/gotman_1982:latest"
authors:
  - given-names: Jean
    family-names: Gotman
    affiliation: Montreal Neurological Institute
  - given-names: Clément
    family-names: Samanos
    affiliation: EPFL-ESL
  - given-names: Jonathan
    family-names: Dan
    email: jonathan.dan@epfl.ch
    affiliation: EPFL-ESL
    orcid: 'https://orcid.org/0000-0002-2338-572X'
version: 0.1
date-released: "1982-01-01"
abstract: >
  During prolonged EEG monitoring of epileptic patients, the continuous EEG 
  tracing may be replaced by a selective recording of ictal and interictal 
  epileptic activity. We have previously described methods for the EEG 
  recording of seizures with overt clinical manifestations and for the automatic 
  detection of spikes. This paper describes a method for the automatic detection 
  of seizures in the EEG, independently of the presence of clinical signs; it is 
  based on the decomposition of the EEG into elementary waves and the detection 
  of paroxysmal bursts of rhythmic activity having a frequency between 3 and 20 
  c/sec. Simple procedures are used to measure the amplitude of waves relative 
  to the background, their duration and rhythmicity. The evaluation of the 
  method on 24 surface recordings (average duration 12.4 h) and 44 recordings 
  from intracerebral electrodes (average duration 18.7 h) indicated that it was 
  capable of recognizing numerous types of seizures. False detections due to 
  non-epileptiform rhythmic EEG bursts and to artefacts were quite frequent but 
  were not a serious problem because they did not unduly lengthen the EEG 
  tracing and they could be easily identified by the electroencephalographer. 
  The program can perform on-line and simultaneously the automatic recognition 
  of spikes and of seizures in 16 channels."
license: GPL-3.0
repository: https://github.com/esl-epfl/gotman_1982

# List all datasets that were used to train this algorithm
Dataset:
    - title: "Gotman 1982"
      license: "https://doi.org/10.1016/0013-4694(82)90038-4"
      identifiers:
        - description: >
            Private dataset of 24 scalp-EEG recordings with an average duration 
            of 12.4 h and 44 intracerebral recordings with an average duration of 18.7h.
          type: doi
          value: "10.5281/zenodo.123456"