title: Supervised segmentation of microelectrode recording artifacts using power spectral density.
abstract: Appropriate detection of clean signal segments in extracellular microelectrode recordings (MER) is vital for maintaining high signal-to-noise ratio in MER studies. Existing alternatives to manual signal inspection are based on unsupervised change-point detection. We present a method of supervised MER artifact classification, based on power spectral density (PSD) and evaluate its performance on a database of 95 labelled MER signals. The proposed method yielded test-set accuracy of 90%, which was close to the accuracy of annotation (94%). The unsupervised methods achieved accuracy of about 77% on both training and testing data.
authors.forename: Eduard
authors.initials: E
authors.lastname: Bakstein
authors.forename: Jakub
authors.initials: J
authors.lastname: Schneider
authors.forename: Tomas
authors.initials: T
authors.lastname: Sieger
authors.forename: Daniel
authors.initials: D
authors.lastname: Novak
authors.forename: Jiri
authors.initials: J
authors.lastname: Wild
authors.forename: Robert
authors.initials: R
authors.lastname: Jech
mesh_terms: D000465:Algorithms; D016477:Artifacts; D008839:Microelectrodes; D012815:Signal Processing, Computer-Assisted; D059629:Signal-To-Noise Ratio