BACKGROUND: Extracellular microelectrode recording (MER) is a prominent technique for studies of extracellular single-unit neuronal activity. In order to achieve robust results in more complex analysis pipelines, it is necessary to have high quality input data with a low amount of artifacts. We show that noise (mainly electromagnetic interference and motion artifacts) may affect more than 25% of the recording length in a clinical MER database. NEW METHOD: We present several methods for automatic detection of noise in MER signals, based on (i) unsupervised detection of stationary segments, (ii) large peaks in the power spectral density, and (iii) a classifier based on multiple time- and frequency-domain features. We evaluate the proposed methods on a manually annotated database of 5735 ten-second MER signals from 58 Parkinson's disease patients. COMPARISON WITH EXISTING METHODS: The existing methods for artifact detection in single-channel MER that have been rigorously tested, are based on unsupervised change-point detection. We show on an extensive real MER database that the presented techniques are better suited for the task of artifact identification and achieve much better results. RESULTS: The best-performing classifiers (bagging and decision tree) achieved artifact classification accuracy of up to 89% on an unseen test set and outperformed the unsupervised techniques by 5-10%. This was close to the level of agreement among raters using manual annotation (93.5%). CONCLUSION: We conclude that the proposed methods are suitable for automatic MER denoising and may help in the efficient elimination of undesirable signal artifacts.

Department of Cybernetics Faculty of Electrical Engineering Czech Technical University Prague Czech Republic

Department of Cybernetics Faculty of Electrical Engineering Czech Technical University Prague Czech Republic; Department of Neurology and Center of Clinical Neuroscience 1st Faculty of Medicine and General University Hospital Charles University Prague Czech Republic

Department of Cybernetics Faculty of Electrical Engineering Czech Technical University Prague Czech Republic; National Institute of Mental Health Klecany Czech Republic

Department of Neurology and Center of Clinical Neuroscience 1st Faculty of Medicine and General University Hospital Charles University Prague Czech Republic

Department of Stereotactic Neurosurgery and Radiosurgery Na Homolce Hospital Prague Czech Republic

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Statistical segmentation model for accurate electrode positioning in Parkinson's deep brain stimulation based on clinical low-resolution image data and electrophysiology