A novel method for automated classification of epileptiform activity in the human electroencephalogram-based on independent component analysis.
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.
De Lucia
Marzia
M
Medical Physics and Clinical Neurophysiology, University College London, London, UK. marzia.delucia@ucl.ac.uk
Fritschy
Juan
J
Dayan
Peter
P
Holder
David S
DS
D000465:Algorithms; D016477:Artifacts; D001767:Blinking; D001921:Brain / Q000503:physiopathology; D003627:Data Interpretation, Statistical; D004569:Electroencephalography / Q000379:methods*; D004827:Epilepsy / Q000175:diagnosis*; D006801:Humans; D025341:Principal Component Analysis; D012815:Signal Processing, Computer-Assisted