Adaptive EEG spike detection: determination of threshold values based on conditional probability.
Determination of the threshold value for automatic EEG spike detection was investigated adopting conditional probability. An adaptive spike detection method was constructed and evaluated. A discriminant function for detecting spikes was obtained by conditional probability calculated from the EEG spike data. The relationship among false-negatives, false-positives and threshold values for the discriminant function was investigated. An adaptive detection algorithm was developed by combining different threshold values. False-negative and false-positive rates for spike detection depended on the threshold values. The adaptive spike detection algorithm achieved a high detection rate and accuracy. The advantage of the proposed method is to construct an adaptive detection algorithm by combining the threshold values according to the purpose of spike detection. Since the threshold can be easily changed in the proposed method, it is practically effective for clinical use.
Department of Electrical and Electronic Engineering, Faculty of Science and Engineering, Saga University, Honjomachi, Saga 840-8502, Japan. sugi@cntl.ee.saga-u.ac.jp
Takenao
T
Sugi
Masatoshi
M
Nakamura
Akio
A
Ikeda
Hiroshi
H
Shibasaki
D000328:Adult; D000368:Aged; D000465:Algorithms; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D004569:Electroencephalography / Q000379:methods*; D004827:Epilepsy / Q000175:diagnosis* / Q000503:physiopathology; D005188:False Negative Reactions; D005189:False Positive Reactions; D005246:Feedback; D006801:Humans; D008875:Middle Aged; D008959:Models, Neurological; D015233:Models, Statistical; D011786:Quality Control; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013223:Statistics as Topic