An artificial neural network for automated behavioral state classification in rats

• Published in: PeerJ (San Francisco, CA) Vol. 9; p. e12127
• Main Authors: Ellen, Jacob G., Dash, Michael B.
• Format: Journal Article
• Language: English
• Published: San Diego PeerJ. Ltd 09.09.2021; PeerJ, Inc; PeerJ Inc
• Subjects: Algorithms; Analgesics; Animal Behavior; Automation; Bioinformatics; Classification; Computer programs; Data Mining and Machine Learning; Datasets; Discriminant analysis; Electroencephalography; Electrophysiology; Experiments; Laboratory animals; Learning algorithms; Machine learning; Mechanization; Neural networks; Neuroscience; Open source; Rodent; Sleep; Sleep scoring; Software; Surgery; Technology application; United States > US
• ISSN: 2167-8359; 2167-8359
• DOI: 10.7717/peerj.12127

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.