Journal of Computer Science

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

Aini Hussain, Rosniwati Ghafar, Salina Abdul Samad and Nooritawat Md Tahir

DOI : 10.3844/jcssp.2009.501.506

Journal of Computer Science

Volume 5, Issue 7

Pages 501-506

Abstract

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’s EEG was assessed. The performance of the novelty detection was examined based on the onset and end time of each seizure in the ROI plot. Results: Results showed that using ROI plot of variances one can distinguish irregularities in the EEG data. The advantage of the proposed technique was that it did not require large amount of data for training. Conclusion: As such, it was feasible to perform seizure analysis as well as localizing seizure onsets. In short, the technique can be used as a guideline for faster diagnosis in a lengthy EEG recording.

Copyright

© 2009 Aini Hussain, Rosniwati Ghafar, Salina Abdul Samad and Nooritawat Md Tahir. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.