Fuzzy detection of EEG alpha without amplitude thresholding.
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.
Huupponen
Eero
E
Digital and Computer Systems Laboratory, Tampere University of Technology, Hermiankatu 12 C, P.O. Box 553, FIN-33101, Tampere, Finland. eeroh@cs.tut.fi
Himanen
Sari Leena
SL
Värri
Alpo
A
Hasan
Joel
J
Saastamoinen
Antti
A
Lehtokangas
Mikko
M
Saarinen
Jukka
J
D000328:Adult; D000368:Aged; D001185:Artificial Intelligence; D001331:Automation; D004569:Electroencephalography; D005189:False Positive Reactions; D005260:Female; D017143:Fuzzy Logic; D006801:Humans; D008297:Male; D008875:Middle Aged