Paper Info
Title
Fuzzy detection of EEG alpha without amplitude thresholding.
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.
Year
DOI
Venue
2002
10.1016/S0933-3657(01)00098-7
Artificial Intelligence In Medicine
Keywords
Field
DocType
eeg alpha,fuzzy reasoning,alpha detector,fuzzy rule,fuzzy detection,eeg analysis,amplitude thresholding,alpha activity,automatic detection,automated detection,alpha amplitude threshold,different electroencephalogram,different subject,false positive rate,roc curve,feature extraction,visual analysis,receiver operator characteristic
Alpha (ethology),False positive rate,Receiver operating characteristic,Pattern recognition,Computer science,Fuzzy logic,Artificial intelligence,Thresholding,Detector,Amplitude,Electroencephalography,Machine learning
Journal
Volume
Issue
ISSN
24
2
0933-3657
Citations 
PageRank 
References 
7
0.87
3
Authors
7
Name
Order
Citations
PageRank
E Huupponen1467.20
S-L Himanen2497.66
Alpo Värri35413.93
Joel Hasan4233.05
A Saastamoinen5527.14
M Lehtokangas615821.87
Jukka Saarinen726446.21