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Visual and automatic classification of the cyclic alternating pattern in electroencephalography during sleep.

R LargoM C LopesKaren SpruytC GuilleminaultYuan-Pang WangAgostinho C Rosa
Published in: Brazilian journal of medical and biological research = Revista brasileira de pesquisas medicas e biologicas (2019)
Cyclic alternating pattern (CAP) is a neurophysiological pattern that can be visually scored by international criteria. The aim of this study was to verify the feasibility of visual CAP scoring using only one channel of sleep electroencephalogram (EEG) to evaluate the inter-scorer agreement in a variety of recordings, and to compare agreement between visual scoring and automatic scoring systems. Sixteen hours of single-channel European data format recordings from four different sleep laboratories with either C4-A1 or C3-A2 channels and with different sampling frequencies were used in this study. Seven independent scorers applied visual scoring according to international criteria. Two automatic blind scorings were also evaluated. Event-based inter-scorer agreement analysis was performed. The pairwise inter-scorer agreement (PWISA) was between 55.5 and 84.3%. The average PWISA was above 60% for all scorers and the global average was 69.9%. Automatic scoring systems showed similar results to those of visual scoring. The study showed that CAP could be scored using only one EEG channel. Therefore, CAP scoring might also be integrated in sleep scoring features and automatic scoring systems having similar performances to visual sleep scoring systems.
Keyphrases
  • deep learning
  • machine learning
  • sleep quality
  • working memory
  • artificial intelligence
  • neural network
  • electronic health record