Sleep Identification Enabled by Supervised Training Algorithms (SIESTA): An open-source platform for automatic sleep staging of rodent polysomnographic data

Caldart, Carlos S.; Sánchez, Raymond E A; Ben-Hamo, Miriam; Beck, Audrey N.; Weil, Tenley; Perez, Jazmine G.; Kalume, Franck; Brunton, Bingni W.; Iglesia, Horacio O. de la

doi:10.1101/2020.07.06.186940

Cited by 2 publications

(1 citation statement)

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“…Classical approaches towards automating sleep scoring in rodents are based on engineered features 8 which are often inspired by human sleep scorers who use visual representations of EEG signals for their scoring decisions. Power spectral densities (e.g., magnitudes and ratios of power in delta, theta, sigma bands 22,[25][26][27][28] ), and EEG amplitudes (e.g., moments of EEG amplitude distributions 8 ) are among the most frequently used features. While systems based on carefully engineered features have seen progress in scoring performance, deeplearning-based systems that can create and use learned features have repeatedly been demonstrated to achieve superior classification performance in related fields such as speech or image recognition 29,30 .…”

Section: Introductionmentioning

confidence: 99%

Automated scoring of pre-REM sleep in mice with deep learning

Grieger

Schwabedal²,

Wendel

et al. 2021

Sci Rep

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Reliable automation of the labor-intensive manual task of scoring animal sleep can facilitate the analysis of long-term sleep studies. In recent years, deep-learning-based systems, which learn optimal features from the data, increased scoring accuracies for the classical sleep stages of Wake, REM, and Non-REM. Meanwhile, it has been recognized that the statistics of transitional stages such as pre-REM, found between Non-REM and REM, may hold additional insight into the physiology of sleep and are now under vivid investigation. We propose a classification system based on a simple neural network architecture that scores the classical stages as well as pre-REM sleep in mice. When restricted to the classical stages, the optimized network showed state-of-the-art classification performance with an out-of-sample F1 score of 0.95 in male C57BL/6J mice. When unrestricted, the network showed lower F1 scores on pre-REM (0.5) compared to the classical stages. The result is comparable to previous attempts to score transitional stages in other species such as transition sleep in rats or N1 sleep in humans. Nevertheless, we observed that the sequence of predictions including pre-REM typically transitioned from Non-REM to REM reflecting sleep dynamics observed by human scorers. Our findings provide further evidence for the difficulty of scoring transitional sleep stages, likely because such stages of sleep are under-represented in typical data sets or show large inter-scorer variability. We further provide our source code and an online platform to run predictions with our trained network.

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