Fuzzy detection of EEG alpha without amplitude thresholding

  • Authors:

  • Eero Huupponen;Sari-Leena Himanen;Alpo Värri;Joel Hasan;Antti Saastamoinen;Mikko Lehtokangas;Jukka Saarinen

  • Affiliations:

  • Digital and Computer Systems Laboratory, Tampere University of Technology, Hermiankatu 12 C, P.O. Box 553, FIN-33101, Tampere, Finland and Signal Processing Laboratory, Tampere University of Techn ...;Department of Clinical Neurophysiology, Tampere University Hospital, P.O. Box 2000, FIN-33521, Tampere, Finland;Signal Processing Laboratory, Tampere University of Technology, Hermiankatu 12 C, P.O. Box 553, FIN-33101, Tampere, Finland;Department of Clinical Neurophysiology, Tampere University Hospital, P.O. Box 2000, FIN-33521, Tampere, Finland;Signal Processing Laboratory, Tampere University of Technology, Hermiankatu 12 C, P.O. Box 553, FIN-33101, Tampere, Finland;Digital and Computer Systems Laboratory, Tampere University of Technology, Hermiankatu 12 C, P.O. Box 553, FIN-33101, Tampere, Finland;Digital and Computer Systems Laboratory, Tampere University of Technology, Hermiankatu 12 C, P.O. Box 553, FIN-33101, Tampere, Finland

  • Venue:
  • Artificial Intelligence in Medicine
  • Year:
  • 2002

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Abstract

Intelligent automated systems are needed to assist the tedious visual analysis of polygraphic recordings. Most systems need detection of different electroencephalogram (EEG) waveforms. The problem in automated detection of alpha activity is the large inter-individual variability of its amplitude and duration. In this work, a fuzzy reasoning based method for the detection of alpha activity was designed and tested using a total of 32 recordings from seven different subjects. Intelligence of the method was distributed to features extracted and the way they were combined. The ranges of the fuzzy rules were determined based on feature statistics. The advantage of the detector is that no alpha amplitude threshold needs to be selected. The performance of the alpha detector was assessed with receiver operating characteristic (ROC) curves. When the true positive rate was 94.2%, the false positive rate was 9.2%, which indicates good performance in sleep EEG analysis.