A Wavelet-Based Artifact Reduction from Scalp EEG for Epileptic Seizure Detection

Md Kafiul Islam, Amir Rastegarnia, Zhi Yang

Research output: Contribution to journalArticlepeer-review

37 Scopus citations

Abstract

This paper presents a method to reduce artifacts from scalp EEG recordings to facilitate seizure diagnosis/detection for epilepsy patients. The proposed method is primarily based on stationary wavelet transform and takes the spectral band of seizure activities (i.e., 0.5-29 Hz) into account to separate artifacts from seizures. Different artifact templates have been simulated to mimic the most commonly appeared artifacts in real EEG recordings. The algorithm is applied on three sets of synthesized data including fully simulated, semi-simulated, and real data to evaluate both the artifact removal performance and seizure detection performance. The EEG features responsible for the detection of seizures from nonseizure epochs have been found to be easily distinguishable after artifacts are removed, and consequently, the false alarms in seizure detection are reduced. Results from an extensive experiment with these datasets prove the efficacy of the proposed algorithm, which makes it possible to use it for artifact removal in epilepsy diagnosis as well as other applications regarding neuroscience studies.

Original languageEnglish (US)
Article number7158988
Pages (from-to)1321-1332
Number of pages12
JournalIEEE Journal of Biomedical and Health Informatics
Volume20
Issue number5
DOIs
StatePublished - Sep 2016

Bibliographical note

Funding Information:
This work was supported by A?STAR PSF Grant R-263-000-699-305, NUS YIA Grant R-263-000-A29-133, and MOE R-263-000-A47-112.

Keywords

  • Artifact
  • epilepsy
  • scalp EEG
  • seizure detection
  • stationary wavelet transform (SWT)

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