Automated detection and elimination of periodic ECG artifacts in EEG using the energy interval histogram method

한글 요약

뇌파(EEG) 기록에 섞여 들어오는 심전도(ECG) 잡음을 별도의 심전도 채널 없이 단일 채널 EEG만으로 자동 검출하고 제거하는 방법을 제안한 연구입니다. 심전도 잡음이 뾰족한 스파이크 형태로 주기적으로 나타나며 뇌파와는 상관이 없다는 세 가지 특성을 이용해, 에너지 구간 히스토그램(EIH) 방법으로 잡음을 찾아내고 앙상블 평균 차감을 변형한 방식으로 제거하는 두 단계 절차를 구성했습니다. 중증 수면무호흡 환자들의 밤샘 수면 EEG 기록에 적용한 결과 낮은 오검출률과 미검출률로 심전도 잡음을 성공적으로 제거할 수 있었습니다. 이 방법은 수면 뇌파처럼 장시간 기록되는 EEG의 자동 전처리에 유용하게 활용될 수 있습니다.

An automated method for electrocardiogram (ECG)-artifact detection and elimination is proposed for application to a single-channel electroencephalogram (EEG) without a separate ECG channel for reference. The method is based on three characteristics of ECG artifacts: the spike-like property, the periodicity and the lack of correlation with the EEG. The method involves a two-step process: ECG artifact detection using the energy interval histogram (EIH) method and ECG artifact elimination using a modification of ensemble average subtraction. We applied a smoothed nonlinear energy operator to the contaminated EEG, which significantly emphasized the ECG artifacts compared with the background EEG. The EIH method was initially proposed to estimate the rate of false positives (FPs) and false negatives (FNs) that were necessary to determine the optimal threshold for the detection of the ECG artifact. As a postprocessing step, we used two types of threshold adjusting algorithms that were based on the periodicity of the ECG R-peaks. The technique was applied to four whole-night sleep EEG recordings from four subjects with severe obstructive sleep apnea syndrome, from which a total of 132878 heartbeats were monitored over 31.8 h. We found that ECG artifacts were successfully detected and eliminated with FP = 0.017 and FN = 0.074 for the epochs where the elimination process is necessarily required.