title: A low computation cost method for seizure prediction.
abstract: The dynamic changes of electroencephalograph (EEG) signals in the period prior to epileptic seizures play a major role in the seizure prediction. This paper proposes a low computation seizure prediction algorithm that combines a fractal dimension with a machine learning algorithm. The presented seizure prediction algorithm extracts the Higuchi fractal dimension (HFD) of EEG signals as features to classify the patient's preictal or interictal state with Bayesian linear discriminant analysis (BLDA) as a classifier. The outputs of BLDA are smoothed by a Kalman filter for reducing possible sporadic and isolated false alarms and then the final prediction results are produced using a thresholding procedure. The algorithm was evaluated on the intracranial EEG recordings of 21 patients in the Freiburg EEG database. For seizure occurrence period of 30 min and 50 min, our algorithm obtained an average sensitivity of 86.95% and 89.33%, an average false prediction rate of 0.20/h, and an average prediction time of 24.47 min and 39.39 min, respectively. The results confirm that the changes of HFD can serve as a precursor of ictal activities and be used for distinguishing between interictal and preictal epochs. Both HFD and BLDA classifier have a low computational complexity. All of these make the proposed algorithm suitable for real-time seizure prediction.
authors: School of Information Science and Engineering, Shandong University, Jinan 250100, China; School of Information and Electronics Engineering, Shandong Institute of Business and Technology, Yantai 264005, China; Suzhou Institute, Shandong University, Suzhou 215123, China.
authors: Yanli
authors: Y
authors: Zhang
authors: School of Information Science and Engineering, Shandong University, Jinan 250100, China; Suzhou Institute, Shandong University, Suzhou 215123, China. Electronic address: wdzhou@sdu.edu.cn.
authors: Weidong
authors: W
authors: Zhou
authors: School of Information Science and Engineering, Shandong University, Jinan 250100, China; Suzhou Institute, Shandong University, Suzhou 215123, China.
authors: Qi
authors: Q
authors: Yuan
authors: School of Information Science and Engineering, Shandong University, Jinan 250100, China; Suzhou Institute, Shandong University, Suzhou 215123, China.
authors: Qi
authors: Q
authors: Wu
mesh_terms: D001185:Artificial Intelligence / Q000191:economics*; D001499:Bayes Theorem; D016208:Databases, Factual / Q000191:economics; D004569:Electroencephalography / Q000191:economics* / Q000379:methods*; D006801:Humans; D011237:Predictive Value of Tests; D012640:Seizures / Q000175:diagnosis* / Q000191:economics* / Q000503:physiopathology
keywords: Bayesian linear discriminant analysis; EEG; Higuchi fractal dimension; Kalman filtering; Seizure prediction