Anomaly detection in electroencephalogram signals using unconstrained minimum average correlation energy filter

Aini Hussain, Rosniwati Ghafar, Salina Abdul Samad, Nooritawati Md Tahir

Research output: Contribution to journalArticle

4 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)501-506
Number of pages6
JournalJournal of Computer Science
Volume5
Issue number7
DOIs
Publication statusPublished - 2009

Fingerprint

Electroencephalography
Signal to noise ratio
Monitoring

Keywords

  • Electroencephalogram (EEG)
  • Epilepsy
  • Unconstrained Minimum Average Correlation Energy (UMACE)

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications
  • Artificial Intelligence

Cite this

Anomaly detection in electroencephalogram signals using unconstrained minimum average correlation energy filter. / Hussain, Aini; Ghafar, Rosniwati; Abdul Samad, Salina; Tahir, Nooritawati Md.

In: Journal of Computer Science, Vol. 5, No. 7, 2009, p. 501-506.

Research output: Contribution to journalArticle

@article{42d2bd9df81440658ca513ddf9d94d74,
title = "Anomaly detection in electroencephalogram signals using unconstrained minimum average correlation energy filter",
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.",
keywords = "Electroencephalogram (EEG), Epilepsy, Unconstrained Minimum Average Correlation Energy (UMACE)",
author = "Aini Hussain and Rosniwati Ghafar and {Abdul Samad}, Salina and Tahir, {Nooritawati Md}",
year = "2009",
doi = "10.3844/jcssp.2009.501.506",
language = "English",
volume = "5",
pages = "501--506",
journal = "Journal of Computer Science",
issn = "1549-3636",
publisher = "Science Publications",
number = "7",

}

TY - JOUR

T1 - Anomaly detection in electroencephalogram signals using unconstrained minimum average correlation energy filter

AU - Hussain, Aini

AU - Ghafar, Rosniwati

AU - Abdul Samad, Salina

AU - Tahir, Nooritawati Md

PY - 2009

Y1 - 2009

N2 - 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.

AB - 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.

KW - Electroencephalogram (EEG)

KW - Epilepsy

KW - Unconstrained Minimum Average Correlation Energy (UMACE)

UR - http://www.scopus.com/inward/record.url?scp=68149125068&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=68149125068&partnerID=8YFLogxK

U2 - 10.3844/jcssp.2009.501.506

DO - 10.3844/jcssp.2009.501.506

M3 - Article

AN - SCOPUS:68149125068

VL - 5

SP - 501

EP - 506

JO - Journal of Computer Science

JF - Journal of Computer Science

SN - 1549-3636

IS - 7

ER -