https://scholars.lib.ntu.edu.tw/handle/123456789/362371
標題: | Seizure prediction based on classification of EEG synchronization patterns with on-line retraining and post-processing scheme | 作者: | Chiang, C.-Y. Chang, N.-F. Chen, T.-C. Chen, H.-H. LIANG-GEE CHEN |
公開日期: | 2011 | 起(迄)頁: | 7564-7569 | 來源出版物: | Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS | 會議論文: | 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011 | 摘要: | Epilepsy is one of the most common brain disorders in the world. The spontaneous seizure onset influences the daily life of epilepsy patients. The studies on feature extraction and feature classification from Electroencephalography(EEG) signal in seizure prediction methods have shown great improvement these years. However, the variation issue of EEG signal (being awake, being asleep, severity of epilepsy, etc.) poses a fundamental difficulty in seizure prediction problem. The traditional off-line training method trains the model using a fixed training set, and expects the performance of the model to remain stable even after a long period of time, and thus suffers from variation issue. In this paper, we propose an on-line retraining method to leverage the recent input data by gradually enlarging the training set and retraining the model. Also, a simple post-processing scheme is incorporated to reduce false alarms. We develop our method based on the state of the art machine learning based classification of bivariate patterns method. The performance of the method is evaluated on Electrocorticogram(ECoG) recording from Freiburg database as well as long-term scalp EEG recording from CHB-MIT EEG Database and National Taiwan University Hospital. The proposed method achieves 74.2% sensitivity on ECoG database and 52.2% sensitivity on scalp EEG database, while improving the sensitivity of off-line training method by 29.0% and 17.4% in ECoG database and EEG database respectively. The experimental result suggests that on-line retraining can greatly improve the reliability and is promising for future seizure prediction method development. © 2011 IEEE. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84861678835&doi=10.1109%2fIEMBS.2011.6091865&partnerID=40&md5=fb646e670d500397f4c73dc68fa06120 http://scholars.lib.ntu.edu.tw/handle/123456789/362371 |
ISSN: | 1557170X | DOI: | 10.1109/IEMBS.2011.6091865 | SDG/關鍵字: | Bivariate; Brain disorders; Daily lives; EEG signals; Electrocorticogram; False alarms; Feature classification; Input datas; Long-term scalp EEG; National Taiwan University; Off-line training; Post-processing scheme; Seizure onset; Seizure prediction; State of the art; Synchronization patterns; Training sets; Database systems; Electrophysiology; Feature extraction; Forecasting; Electroencephalography; algorithm; article; artificial intelligence; classification; computer simulation; cortical synchronization; data base; electroencephalography; human; online system; pathophysiology; physiology; seizure; signal processing; time; Algorithms; Artificial Intelligence; Computer Simulation; Cortical Synchronization; Databases as Topic; Electroencephalography; Humans; Online Systems; Seizures; Signal Processing, Computer-Assisted; Time Factors |
顯示於: | 電機工程學系 |
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