Design of a Time-Frequency Algorithm for Automatic Eeg Artifact Removal

Abstract

The injuries suffered by newborns during birth are a major health issue. To improve the health outcomes of sick newborns using EEG measurements, a number of recent studies focused on the use of high-resolution Time-Frequency Distributions to extract critical information from the collected signals [1]. Several algorithms have been proposed. A major problem in the implementation of such algorithms for fully automated EEG signal classification systems is caused by artifacts. In particular, previous studies have shown that a respiratory artifact looks like a seizure signal and can be misinterpreted by the automatic abnormality detection system thus resulting in false alarms. Hence, the successful removal of the artifacts is important, as shown in several previous studies [2]; and, there are two basic approaches for this: (1) use machine learning technique to detect and reject EEG segments corrupted by artifact; but this would result in the loss of EEG data [2]. (2) Correct EEG segments corrupted by artifacts; some artifacts can be corrected by a simple filter in a frequency domain, e.g. notch filter can be used to remove 50 Hz noise. This approach does not require any reference signals. For more complicated cases, when the spectrum of artifacts overlaps with the spectrum of EEG signals, blind source separation (BSS) algorithms can be used. Typically a multi-component EEG signal is transformed into a linear combination of independent components (that can be interpreted as channels (ICs)) by blind source separation techniques such as the independent component analysis (ICA) or canonical correlation analysis. The independent channels that are corrupted by artifacts are identified either manually or automatically using correlation information from a reference signal. The artifact free signal is then constructed by combining only artifact-free ICs.