Anomaly Detection in Electroencephalogram Signals Using Unconstrained Minimum Average Correlation Energy Filter| Science Publications

Journal of Computer Science

Nooritawat M. Tahir¹  Rosniwati Ghafar¹  Aini Hussain¹  Salina A. Samad¹ 

关键词: Unconstrained Minimum Average Correlation Energy (UMACE);    Electroencephalogram (EEG);    epilepsy;   

DOI  :  10.3844/jcssp.2009.501.506

学科分类：计算机科学（综合）

来源: Science Publications

【 摘 要 】

Problem statement: Electroencepharogram (EEG) is an extremely complex signal with very low signal to noise ratio and these attributed to difficulty in analyzing the signal. Hence for detecting abnormal segment, a distinctive method is required to train the technologist to distinguish the anomalous in EEG data. The objective of this study was to create a framework to analyze EEG signals recorded from epileptic patients by evaluating the potential of UMACE filter to detect changes in single-channel EEG data during routine epilepsy monitoring. Approach: Normally, the peak to side lobe ratio (PSR) of a UMACE filter was employed as an indicator if a test data is similar to an authentic class or vice versa, however in this study, the consistent changes of the correlation output known as Region Of Interest (ROI) was plotted and monitored. Based on this approach, a novel method to analyze and distinguish variances in scalp EEG as well as comparing both normal and abnormal regions of the patient

【 授权许可】

Unknown