Low complexity algorithm for seizure prediction using Adaboost.
This paper presents a novel low-complexity patient-specific algorithm for seizure prediction. Adaboost algorithm is used in two stages of the algorithm: feature selection and classification. The algorithm extracts spectral power features in 9 different sub-bands from the electroencephalogram (EEG) recordings. We have proposed a new feature ranking method to rank the features. The key (top ranked) features are used to make a prediction on the seizure event. Further, to reduce the complexity of classification stage, a non-linear classifier is built based on the Adaboost algorithm using decision stumps (linear classifier) as the base classifier. The proposed algorithm achieves a sensitivity of 94.375% for a total of 71 seizure events with a low false alarm rate of 0.13 per hour and 6.5% of time spent in false alarms using an average of 5 features for the Freiburg database. The low computational complexity of the proposed algorithm makes it suitable for an implantable device.
Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN 55455, USA. ayina004@umn.edu
Manohar
M
Ayinala
Keshab K
KK
Parhi
D000465:Algorithms; D004567:Electrodes, Implanted; D004569:Electroencephalography / Q000295:instrumentation / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D011237:Predictive Value of Tests; D012640:Seizures / Q000503:physiopathology*; D012815:Signal Processing, Computer-Assisted