Medical & Biological Engineering & Computing

, Volume 46, Issue 3, pp 263–272

A novel method for automated classification of epileptiform activity in the human electroencephalogram-based on independent component analysis

  • Marzia De Lucia
  • Juan Fritschy
  • Peter Dayan
  • David S. Holder
Original Article

Abstract

Diagnosis of several neurological disorders is based on the detection of typical pathological patterns in the electroencephalogram (EEG). This is a time-consuming task requiring significant training and experience. Automatic detection of these EEG patterns would greatly assist in quantitative analysis and interpretation. We present a method, which allows automatic detection of epileptiform events and discrimination of them from eye blinks, and is based on features derived using a novel application of independent component analysis. The algorithm was trained and cross validated using seven EEGs with epileptiform activity. For epileptiform events with compensation for eyeblinks, the sensitivity was 65 ± 22% at a specificity of 86 ± 7% (mean ± SD). With feature extraction by PCA or classification of raw data, specificity reduced to 76 and 74%, respectively, for the same sensitivity. On exactly the same data, the commercially available software Reveal had a maximum sensitivity of 30% and concurrent specificity of 77%. Our algorithm performed well at detecting epileptiform events in this preliminary test and offers a flexible tool that is intended to be generalized to the simultaneous classification of many waveforms in the EEG.

Keywords

Electroencephalogram Independent component analysis Automatic classification Epileptiform events Eye-blinks artefacts 

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Copyright information

© International Federation for Medical and Biological Engineering 2007

Authors and Affiliations

  • Marzia De Lucia
    • 1
  • Juan Fritschy
    • 1
  • Peter Dayan
    • 2
  • David S. Holder
    • 1
  1. 1.Medical Physics and Clinical NeurophysiologyUniversity College LondonLondonUK
  2. 2.Gatsby Unit of Computational NeuroscienceUniversity College LondonLondonUK

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