An artificial neural network for automated behavioral state classification in rats
Accurate behavioral state classification is critical for many research applications. Researchers typically rely upon manual identification of behavioral state through visual inspection of electrophysiological signals, but this approach is time intensive and subject to low inter-rater reliability. To...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2021-09-01
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| Series: | PeerJ |
| Subjects: | |
| Online Access: | https://peerj.com/articles/12127.pdf |
| _version_ | 1797418472263647232 |
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| author | Jacob G. Ellen Michael B. Dash |
| author_facet | Jacob G. Ellen Michael B. Dash |
| author_sort | Jacob G. Ellen |
| collection | DOAJ |
| description | Accurate behavioral state classification is critical for many research applications. Researchers typically rely upon manual identification of behavioral state through visual inspection of electrophysiological signals, but this approach is time intensive and subject to low inter-rater reliability. To overcome these limitations, a diverse set of algorithmic approaches have been put forth to automate the classification process. Recently, novel machine learning approaches have been detailed that produce rapid and highly accurate classifications. These approaches however, are often computationally expensive, require significant expertise to implement, and/or require proprietary software that limits broader adoption. Here we detail a novel artificial neural network that uses electrophysiological features to automatically classify behavioral state in rats with high accuracy, sensitivity, and specificity. Common parameters of interest to sleep scientists, including state-dependent power spectra and homeostatic non-REM slow wave activity, did not significantly differ when using this automated classifier as compared to manual scoring. Flexible options enable researchers to further increase classification accuracy through manual rescoring of a small subset of time intervals with low model prediction certainty or further decrease researcher time by generalizing trained networks across multiple recording days. The algorithm is fully open-source and coded within a popular, and freely available, software platform to increase access to this research tool and provide additional flexibility for future researchers. In sum, we have developed a readily implementable, efficient, and effective approach for automated behavioral state classification in rats. |
| first_indexed | 2024-03-09T06:33:08Z |
| format | Article |
| id | doaj.art-8f666d4c44a449c79961903a745420a8 |
| institution | Directory Open Access Journal |
| issn | 2167-8359 |
| language | English |
| last_indexed | 2024-03-09T06:33:08Z |
| publishDate | 2021-09-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ |
| spelling | doaj.art-8f666d4c44a449c79961903a745420a82023-12-03T11:02:18ZengPeerJ Inc.PeerJ2167-83592021-09-019e1212710.7717/peerj.12127An artificial neural network for automated behavioral state classification in ratsJacob G. Ellen0Michael B. Dash1Neuroscience Program, Middlebury College, Middlebury, VT, United StatesNeuroscience Program, Middlebury College, Middlebury, VT, United StatesAccurate behavioral state classification is critical for many research applications. Researchers typically rely upon manual identification of behavioral state through visual inspection of electrophysiological signals, but this approach is time intensive and subject to low inter-rater reliability. To overcome these limitations, a diverse set of algorithmic approaches have been put forth to automate the classification process. Recently, novel machine learning approaches have been detailed that produce rapid and highly accurate classifications. These approaches however, are often computationally expensive, require significant expertise to implement, and/or require proprietary software that limits broader adoption. Here we detail a novel artificial neural network that uses electrophysiological features to automatically classify behavioral state in rats with high accuracy, sensitivity, and specificity. Common parameters of interest to sleep scientists, including state-dependent power spectra and homeostatic non-REM slow wave activity, did not significantly differ when using this automated classifier as compared to manual scoring. Flexible options enable researchers to further increase classification accuracy through manual rescoring of a small subset of time intervals with low model prediction certainty or further decrease researcher time by generalizing trained networks across multiple recording days. The algorithm is fully open-source and coded within a popular, and freely available, software platform to increase access to this research tool and provide additional flexibility for future researchers. In sum, we have developed a readily implementable, efficient, and effective approach for automated behavioral state classification in rats.https://peerj.com/articles/12127.pdfSleep scoringMachine learningRodentElectrophysiologyOpen source |
| spellingShingle | Jacob G. Ellen Michael B. Dash An artificial neural network for automated behavioral state classification in rats PeerJ Sleep scoring Machine learning Rodent Electrophysiology Open source |
| title | An artificial neural network for automated behavioral state classification in rats |
| title_full | An artificial neural network for automated behavioral state classification in rats |
| title_fullStr | An artificial neural network for automated behavioral state classification in rats |
| title_full_unstemmed | An artificial neural network for automated behavioral state classification in rats |
| title_short | An artificial neural network for automated behavioral state classification in rats |
| title_sort | artificial neural network for automated behavioral state classification in rats |
| topic | Sleep scoring Machine learning Rodent Electrophysiology Open source |
| url | https://peerj.com/articles/12127.pdf |
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