An artificial intelligence approach to classify and analyse EEG traces.
We present a fully automatic system for the classification and analysis of adult electroencephalograms (EEGs). The system is based on an artificial neural network which classifies the single epochs of trace, and on an Expert System (ES) which studies the time and space correlation among the outputs of the neural network; compiling a final report. On the last 2000 EEGs representing different kinds of alterations according to clinical occurrences, the system was able to produce 80% good or very good final comments and 18% sufficient comments, which represent the documents delivered to the patient. In the remaining 2% the automatic comment needed some modifications prior to be presented to the patient. No clinical false-negative classifications did arise, i.e. no altered traces were classified as 'normal' by the neural network. The analysis method we describe is based on the interpretation of objective measures performed on the trace. It can improve the quality and reliability of the EEG exam and appears useful for the EEG medical reports although it cannot totally substitute the medical doctor who should now read the automatic EEG analysis in light of the patient's history and age.
Castellaro
C
C
Micromed s.r.l. via Giotto 4, 31021 Mogliano Veneto, Treviso, Italy. cipriano.castellaro@micromed-it.com
Favaro
G
G
Castellaro
A
A
Casagrande
A
A
Castellaro
S
S
Puthenparampil
D V
DV
Salimbeni
C Fattorello
CF
D000328:Adult; D000465:Algorithms; D001185:Artificial Intelligence; D004569:Electroencephalography / Q000706:statistics & numerical data*; D004827:Epilepsy / Q000145:classification / Q000175:diagnosis / Q000503:physiopathology; D006801:Humans; D008959:Models, Neurological; D016571:Neural Networks, Computer; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation*