Signal regularity-based automated seizure detection system for scalp EEG monitoring

  • Authors:

  • Deng-Shan Shiau;J. J. Halford;K. M. Kelly;R. T. Kern;M. Inman;Jui-Hong Chien;P. M. Pardalos;M. C. Yang;J. Ch. Sackellares

  • Affiliations:

  • Optima Neuroscience, Inc., Gainesville, USA;Medical University of South Carolina, Charleston, USA;Drexel University College of Medicine, Philadelphia, USA and Allegheny General Hospital, Pittsburgh, USA and Allegheny-Singer Research Institute, Pittsburgh, USA;Optima Neuroscience, Inc., Gainesville, USA;Optima Neuroscience, Inc., Gainesville, USA;Optima Neuroscience, Inc., Gainesville, USA and Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, USA;Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, USA and Department of Industrial and Systems Engineering, University of Florida, Gainesville, USA and Depart ...;Department of Statistics, University of Florida, Gainesville, USA;Optima Neuroscience, Inc., Gainesville, USA

  • Venue:
  • Cybernetics and Systems Analysis
  • Year:
  • 2010

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Abstract

The purpose of the present study was to build a clinically useful automated seizure detection system for scalp EEG recordings. To achieve this, a computer algorithm was designed to translate complex multichannel scalp EEG signals into several dynamical descriptors, followed by the investigations of their spatiotemporal properties that relate to the ictal (seizure) EEG patterns as well as to normal physiologic and artifact signals. This paper describes in detail this novel seizure detection algorithm and reports its performance in a large clinical dataset.