An epileptic seizure prediction algorithm based on second-order complexity measure. Export

@article{Jia2005, abstract = {The quality of life of many epilepsy patients may be improved significantly if the occurrence of epileptic seizures can be successfully forecasted and clinical intervention, such as electrical stimulation or drug delivery, can then be used to suppress their emergence, or warn the patient of the forthcoming events. In this paper, a prediction algorithm based on the second-order complexity measure was proposed to predict the impending seizures. Through the analysis of long-term intracranial EEG recordings from two frontal lobe epilepsy patients, the results indicated that the sensitivity of prediction was 77.8\% (14/18) and 66.7\% (4/6) and the number of false warnings was 3 and 2 for the two patients, respectively. Because only the information of past seizures was utilized to predict the current seizure and the computation load was low, the prediction algorithm could possibly be applied to clinical practice.}, address = {Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, People's Republic of China.}, author = {Jia, W. and Kong, N. and Li, F. and Gao, X. and Gao, S. and Zhang, G. and Wang, Y. and Yang, F.}, citeulike-article-id = {2943129}, citeulike-linkout-0 = {http://view.ncbi.nlm.nih.gov/pubmed/16088056}, citeulike-linkout-1 = {http://www.hubmed.org/display.cgi?uids=16088056}, issn = {0967-3334}, journal = {Physiological measurement}, keywords = {neuroscience, nonlinear}, month = {October}, number = {5}, pages = {609--625}, posted-at = {2008-06-30 06:50:31}, priority = {2}, title = {An epileptic seizure prediction algorithm based on second-order complexity measure.}, url = {http://view.ncbi.nlm.nih.gov/pubmed/16088056}, volume = {26}, year = {2005} }

TY - JOUR ID - Jia2005 L3 - citeulike-article-id:2943129 N2 - The quality of life of many epilepsy patients may be improved significantly if the occurrence of epileptic seizures can be successfully forecasted and clinical intervention, such as electrical stimulation or drug delivery, can then be used to suppress their emergence, or warn the patient of the forthcoming events. In this paper, a prediction algorithm based on the second-order complexity measure was proposed to predict the impending seizures. Through the analysis of long-term intracranial EEG recordings from two frontal lobe epilepsy patients, the results indicated that the sensitivity of prediction was 77.8% (14/18) and 66.7% (4/6) and the number of false warnings was 3 and 2 for the two patients, respectively. Because only the information of past seizures was utilized to predict the current seizure and the computation load was low, the prediction algorithm could possibly be applied to clinical practice. IS - 5 JF - Physiological measurement SN - 0967-3334 AD - Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, People's Republic of China. EP - 625 TI - An epileptic seizure prediction algorithm based on second-order complexity measure. VL - 26 SP - 609 KW - neuroscience KW - nonlinear AU - Jia, W AU - Kong, N AU - Li, F AU - Gao, X AU - Gao, S AU - Zhang, G AU - Wang, Y AU - Yang, F PY - 2005/10// UR - http://view.ncbi.nlm.nih.gov/pubmed/16088056 ER -