title: An automatic analysis method for detecting and eliminating ECG artifacts in EEG.
abstract: An automated method for detecting and eliminating electrocardiograph (ECG) artifacts from electroencephalography (EEG) without an additional synchronous ECG channel is proposed in this paper. Considering the properties of wavelet filters and the relationship between wavelet basis and characteristics of ECG artifacts, the concepts for selecting a suitable wavelet basis and scales used in the process are developed. The analysis via the selected basis is without suffering time shift for decomposition and detection/elimination procedures after wavelet transformation. The detection rates, above 97.5% for MIT/BIH and NTUH recordings, show a pretty good performance in ECG artifact detection and elimination.
authors.affiliation: Department of Bio-Industrial Mechatronics Engineering, National Taiwan University, Taipei, Taiwan.
authors.forename: Joe-Air
authors.initials: JA
authors.lastname: Jiang
authors.forename: Chih-Feng
authors.initials: CF
authors.lastname: Chao
authors.forename: Ming-Jang
authors.initials: MJ
authors.lastname: Chiu
authors.forename: Ren-Guey
authors.initials: RG
authors.lastname: Lee
authors.forename: Chwan-Lu
authors.initials: CL
authors.lastname: Tseng
authors.forename: Robert
authors.initials: R
authors.lastname: Lin
mesh_terms: D000293:Adolescent; D000328:Adult; D000465:Algorithms; D002648:Child; D003627:Data Interpretation, Statistical; D004562:Electrocardiography / Q000706:statistics & numerical data*; D004569:Electroencephalography / Q000706:statistics & numerical data*; D004827:Epilepsy / Q000175:diagnosis / Q000503:physiopathology; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D020181:Sleep Apnea, Obstructive / Q000175:diagnosis / Q000503:physiopathology; D012894:Sleep Stages / Q000502:physiology