Machine Learning for Automatic Prediction of the Quality of Electrophysiological Recordings
Identifieur interne : 001366 ( Main/Exploration ); précédent : 001365; suivant : 001367Machine Learning for Automatic Prediction of the Quality of Electrophysiological Recordings
Auteurs : Thomas Nowotny [Royaume-Uni] ; Jean-Pierre Rospars [France] ; Dominique Martinez [France] ; Shereen Elbanna [France] ; Sylvia Anton [France]Source :
- PLoS ONE [ 1932-6203 ] ; 2013.
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.
Url:
DOI: 10.1371/journal.pone.0080838
Affiliations:
- France, Royaume-Uni
- Angleterre, Grand Est, Lorraine (région), Pays de la Loire, Sussex de l'Est
- Angers, Brighton, Falmer, Metz, Nancy
- Université d'Angers, Université de Lorraine, Université du Sussex
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Angers</orgName></org></tutelle></tutelles></hal:affiliation><country>France</country><placeName><settlement type="city">Angers</settlement><region type="region" nuts="2">Pays de la Loire</region></placeName><orgName type="university">Université d'Angers</orgName></affiliation></author></analytic><idno type="DOI">10.1371/journal.pone.0080838</idno><series><title level="j">PLoS ONE</title><idno type="ISSN">1932-6203</idno><imprint><date type="datePub">2013</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">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.</div></front></TEI><affiliations><list><country><li>France</li><li>Royaume-Uni</li></country><region><li>Angleterre</li><li>Grand Est</li><li>Lorraine (région)</li><li>Pays de la Loire</li><li>Sussex de l'Est</li></region><settlement><li>Angers</li><li>Brighton</li><li>Falmer</li><li>Metz</li><li>Nancy</li></settlement><orgName><li>Université d'Angers</li><li>Université de Lorraine</li><li>Université du Sussex</li></orgName></list><tree><country name="Royaume-Uni"><region name="Angleterre"><name sortKey="Nowotny, Thomas" sort="Nowotny, Thomas" uniqKey="Nowotny T" first="Thomas" last="Nowotny">Thomas Nowotny</name></region></country><country name="France"><noRegion><name sortKey="Rospars, Jean Pierre" sort="Rospars, Jean Pierre" uniqKey="Rospars J" first="Jean-Pierre" last="Rospars">Jean-Pierre Rospars</name></noRegion><name sortKey="Anton, Sylvia" sort="Anton, Sylvia" uniqKey="Anton S" first="Sylvia" last="Anton">Sylvia Anton</name><name sortKey="Elbanna, Shereen" sort="Elbanna, Shereen" uniqKey="Elbanna S" first="Shereen" last="Elbanna">Shereen Elbanna</name><name sortKey="Martinez, Dominique" sort="Martinez, Dominique" uniqKey="Martinez D" first="Dominique" last="Martinez">Dominique Martinez</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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|texte= Machine Learning for Automatic Prediction of the Quality of Electrophysiological Recordings
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