PureEEG: Automatic EEG artifact removal for epilepsy monitoring

• Published in: Neurophysiologie clinique 2014-11, Vol.44 (5), p.479-490
• Main Authors: Hartmann, M.M, Schindler, K, Gebbink, T.A, Gritsch, G, Kluge, T
• Format: Article
• Language: English
• Subjects: Algorithms; Analyse automatique; Artefacts; Artifact removal; Automatic; Automatic Data Processing; Bayes Theorem; Biological and medical sciences; Crise; EEG; Electrodiagnosis. Electric activity recording; Electroencephalography - instrumentation; Electroencephalography - methods; Epilepsy; Epilepsy - physiopathology; Female; Headache. Facial pains. Syncopes. Epilepsia. Intracranial hypertension. Brain oedema. Cerebral palsy; Humans; Investigative techniques, diagnostic techniques (general aspects); Male; Medical sciences; Models, Neurological; Monitoring, Physiologic - instrumentation; Monitoring, Physiologic - methods; Nervous system; Nervous system (semeiology, syndromes); Neurology; PureEEG; Reproducibility of Results; Seizure; Signal Processing, Computer-Assisted; Suppression d’artefacts; Épilepsie
• ISSN: 0987-7053; 1769-7131
• DOI: 10.1016/j.neucli.2014.09.001

Summary Aim of the study A novel method for removal of artifacts from long-term EEGs was developed and evaluated. The method targets most types of artifacts and works without user interaction. Materials and methods The method is based on a neurophysiological model and utilizes an iterative Bayesian estimation scheme. The performance was evaluated by two independent reviewers. From 48 consecutive epilepsy patients, 102 twenty-second seizure onset EEGs were used to evaluate artifacts before and after artifact removal and regarding the erroneous attenuation of true EEG patterns. Results The two reviewers found “major improvements” in 59% and 49% of the EEG epochs respectively, and “minor improvements” in 38% and 47% of the epochs, respectively. The answer “similar or worse” was chosen only in 0% and 4%, respectively. Neither of the reviewers found “major attenuations”, i.e., a significant attenuation of significant EEG patterns. Most EEG epochs were found to be either “mostly preserved” or “all preserved”. A “minor attenuation” was found only in 0% and 17%, respectively. Conclusions The proposed artifact removal algorithm effectively removes artifacts from EEGs and improves the readability of EEGs impaired by artifacts. Only in rare cases did the algorithm slightly attenuate EEG patterns, but the clear visibility of significant patterns was preserved in all cases of this study. Current artifact removal methods work either semi-automatically or with insufficient reliability for clinical use, whereas the “PureEEG” method works fully automatically and leaves true EEG patterns unchanged with a high reliability.