Machine learning for automatic prediction of the quality of electrophysiological recordings

Nowotny, Thomas, Rospars, Jean-Pierre, Martinez, Dominique, Elbanna, Shereen and Anton, Sylvia (2013) Machine learning for automatic prediction of the quality of electrophysiological recordings. PLoS ONE, 8 (12). e80838. ISSN 1932-6203

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Abstract

The quality of electrophysiological recordings varies a lot due to technical and biological variability and neuroscientists inevitably have to select “good” recordings for further analyses. This procedure is time-consuming and prone to selection biases. Here, we investigate replacing human decisions by a machine learning approach. We define 16 features, such as spike height and width, select the most informative ones using a wrapper method and train a classifier to reproduce the judgement of one of our expert electrophysiologists. Generalisation performance is then assessed on unseen data, classified by the same or by another expert. We observe that the learning machine can be equally, if not more, consistent in its judgements as individual experts amongst each other. Best performance is achieved for a limited number of informative features; the optimal feature set being different from one data set to another. With 80–90% of correct judgements, the performance of the system is very promising within the data sets of each expert but judgments are less reliable when it is used across sets of recordings from different experts. We conclude that the proposed approach is relevant to the selection of electrophysiological recordings, provided parameters are adjusted to different types of experiments and to individual experimenters.

Item Type: Article
Schools and Departments: School of Engineering and Informatics > Informatics
Subjects: Q Science > QA Mathematics > QA0075 Electronic computers. Computer science
Q Science > QP Physiology > QP0351 Neurophysiology and neuropsychology
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Depositing User: Thomas Nowotny
Date Deposited: 03 Mar 2014 15:29
Last Modified: 07 Mar 2017 10:32
URI: http://sro.sussex.ac.uk/id/eprint/47657

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Project NameSussex Project NumberFunderFunder Ref
Green brainG0924EPSRC-ENGINEERING & PHYSICAL SCIENCES RESEARCH COUNCILEP/J019690/1