Key result
An automated algorithm combining independent component analysis and continuous wavelet transformation outperformed algorithms using general statistical features for EKG artifact removal in EEG data.
An automated algorithm combining independent component analysis and continuous wavelet transformation effectively removes EKG artifacts from EEG data, outperforming standard statistical methods.
May aid EEG interpretation in cardiac monitoring; leaves open clinical utility pending prospective validation.
The electrical potential produced by the cardiac activity sometimes contaminates electroencephalogram (EEG) recordings, resulting in spiky activities that are referred to as electrocardiographic (EKG) artifact. For a variety of reasons it is often desirable to automatically detect and remove these artifacts. Especially, for accurate source localization of epileptic spikes in an EEG recording from a patient with epilepsy, it is of great importance to remove any concurrent artifact. Due to similarities in morphology between the EKG artifacts and epileptic spikes, any automated artifact removal algorithm must have an extremely low false-positive rate in addition to a high detection rate. In this paper, an automated algorithm for removal of EKG artifact is proposed that satisfies such criteria. The proposed method, which uses combines independent component analysis and continuous wavelet transformation, uses both temporal and spatial characteristics of EKG related potentials to identify and remove the artifacts. The method outperforms algorithms that use general statistical features such as entropy and kurtosis for artifact rejection.
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Hamaneh et al. (2014) studied Epilepsy. Automated algorithm combining independent component analysis and continuous wavelet transformation vs. Algorithms using general statistical features such as entropy and kurtosis was evaluated on EKG artifact removal (false-positive rate and detection rate). An automated algorithm combining independent component analysis and continuous wavelet transformation outperformed algorithms using general statistical features for EKG artifact removal in EEG data.
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