Application of independent component analysis in removing artefacts from the electrocardiogram

Abstract

Routinely recorded electrocardiograms (ECGs) are often corrupted by different types of artefacts and many efforts have been made to enhance their quality by reducing the noise or artefacts. This paper addresses the problem of removing noise and artefacts from ECGs using independent component analysis (ICA). An ICA algorithm is tested on three-channel ECG recordings taken from human subjects, mostly in the coronary care unit. Results are presented that show that ICA can detect and remove a variety of noise and artefact sources in these ECGs. One difficulty with the application of ICA is the determination of the order of the independent components. A new technique based on simple statistical parameters is proposed to solve this problem in this application. The developed technique is successfully applied to the ECG data and offers potential for online processing of ECG using ICA.

Sparse Coding of Cardiac Signals for Automated Component Selection after Blind Source Separation

“…In order to compare the traditional component selection based on higher-order statistics [1,4,7], we also used skewness for an automated component selection. Since higherorder statistics are prone to outliers, an outlier-removal using Walsh's non-parametric outlier test [14] was performed on each component prior to selecting the component with the highest skewness (SKEW).…”

“…artifacts) thus indirectly obtaining the ECG component. A combination of second-order and higherorder statistics was used for that purpose in [4] whereas auto-correlative periodicity manifestation was exploited in [5]. Feature decision trees were used to classify artifacts in [6].…”

Independent Component Analysis in ECG Signal Processing

“…This may be achieved, e.g., by different statistical or waveform classification methods in time domain or in frequency domain, or by more advanced methods as described, e.g., by He et al (2006) who also give several illustrative examples. Note that in the examples in (He et al, 2006) the artifacts and noise to be removed are contained in ICs which seemingly do not carry ECG contributions, thus yielding correct ECG reconstruction which does not alter the actual ECG waveforms. In this Chapter, recognition of the ICs carrying atrial fibrillation is considered in the example shown in Fig.…”

A cardiac electro-physiological model based approach for filtering high frequency ECG noise

“…He's approach used 12 ECG leads as inputs to ICA to extract the noise from the 12 lead signals [5]. The drawback of this approach is that all 12 signals are required, which might not be available in ambulatory recordings.…”