Quantification of Motion Artifact Rejection Due to Active Electrodes and Driven-Right-Leg Circuit in Spike Detection Algorithms

Identification of spikes in the EEG plays an important role during the diagnosis of neurological disorders, such as epilepsy. Automatic spike detection (ASD) is attractive because it reduces the diagnostic time and improves objectivity of the scoring. Unfortunately, automatic detection is sometimes confounded by artifacts, particularly motion artifacts, which can be frequent in ambulatory recording, in the ICU, when recording from restless patients or children, etc. EEG systems have recently been improved by using active electrodes and driven-right-leg circuits (DRL) to reduce motion artifacts. However, the performances of ASD algorithms, both with unimproved and improved EEG systems, are difficult to quantify in patients because of poor reproducibility of the results. In this paper, a test setup was used to evaluate the performance of active electrodes and DRL, and assess if they can be complemented or substituted by a spike detection algorithm in avoiding motion artifact. Results show that motion artifacts can largely degrade spike detection when a traditional EEG system is used, whereas an EEG fitted with active electrodes and a DRL allows high-quality detection. When using a traditional EEG, the choice of a spike detection algorithm has a large influence on detection quality.

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