Methods for Automatic Detection of Artifacts in Microelectrode Recordings

Publication date: Available online 20 July 2017
Source:Journal of Neuroscience Methods
Author(s): Eduard Bakštein, Tomáš Sieger, Jiří Wild, Daniel Nova'k, Jakub Schneider, Pavel Vostatek, Dušan Urgošík, Robert Jech
BackgroundExtracellular 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 MethodWe 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 MethodsThe 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.ResultsThe 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%).ConclusionWe conclude that the proposed methods are suitable for automatic MER denoising and may help in the efficient elimination of undesirable signal artifacts.

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