Sleep spindle detection using artificial neural networks trained with filtered time-domain EEG: A feasibility study

  • Authors:

  • Errikos M. Ventouras;Efstratia A. Monoyiou;Periklis Y. Ktonas;Thomas Paparrigopoulos;Dimitris G. Dikeos;Nikos K. Uzunoglu;Constantin R. Soldatos

  • Affiliations:

  • Department of Medical Instrumentation Technology, Technological Educational Institution of Athens, Ag. Spyridonos Str., Egaleo, Athens 12210, Greece;Department of Electrical and Computer Engineering, National Technical University of Athens, Athens 15773, Greece;Sleep Research Unit, Department of Psychiatry, University of Athens, Athens 11528, Greece;Sleep Research Unit, Department of Psychiatry, University of Athens, Athens 11528, Greece;Sleep Research Unit, Department of Psychiatry, University of Athens, Athens 11528, Greece;Department of Electrical and Computer Engineering, National Technical University of Athens, Athens 15773, Greece;Sleep Research Unit, Department of Psychiatry, University of Athens, Athens 11528, Greece

  • Venue:
  • Computer Methods and Programs in Biomedicine
  • Year:
  • 2005

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Abstract

An artificial neural network (ANN) based on the Multi-Layer Perceptron (MLP) architecture is used for detecting sleep spindles in band-pass filtered electroencephalograms (EEG), without feature extraction. Following optimum classification schemes, the sensitivity of the network ranges from 79.2% to 87.5%, while the false positive rate ranges from 3.8% to 15.5%. Furthermore, due to the operation of the ANN on time-domain EEG data, there is agreement with visual assessment concerning temporal resolution. Specifically, the total inter-spindle interval duration and the total duration of spindles are calculated with 99% and 92% accuracy, respectively. Therefore, the present method may be suitable for investigations of the dynamics among successive inter-spindle intervals, which could provide information on the role of spindles in the sleep process, and for studies of pharmacological effects on sleep structure, as revealed by the modification of total spindle duration.