TY  - JOUR
A1  - Grieger, Niklas
A1  - Schwabedal, Justus T. C.
A1  - Wendel, Stefanie
A1  - Ritze, Yvonne
A1  - Bialonski, Stephan
T1  - Automated scoring of pre-REM sleep in mice with deep learning
JF  - Scientific Reports
N2  - 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.
Y1  - 2021
U6  - http://dx.doi.org/10.1038/s41598-021-91286-0
SN  - 2045-2322
N1  - Corresponding author: Stephan Bialonski
VL  - 11
IS  - Art. 12245
PB  - Springer Nature
CY  - London
ER  - 
TY  - JOUR
A1  - Kaulen, Lars
A1  - Schwabedal, Justus T. C.
A1  - Schneider, Jules
A1  - Ritter, Philipp
A1  - Bialonski, Stephan
T1  - Advanced sleep spindle identification with neural networks
JF  - Scientific Reports
N2  - Sleep spindles are neurophysiological phenomena that appear to be linked to memory formation and other functions of the central nervous system, and that can be observed in electroencephalographic recordings (EEG) during sleep. Manually identified spindle annotations in EEG recordings suffer from substantial intra- and inter-rater variability, even if raters have been highly trained, which reduces the reliability of spindle measures as a research and diagnostic tool. The Massive Online Data Annotation (MODA) project has recently addressed this problem by forming a consensus from multiple such rating experts, thus providing a corpus of spindle annotations of enhanced quality. Based on this dataset, we present a U-Net-type deep neural network model to automatically detect sleep spindles. Our model’s performance exceeds that of the state-of-the-art detector and of most experts in the MODA dataset. We observed improved detection accuracy in subjects of all ages, including older individuals whose spindles are particularly challenging to detect reliably. Our results underline the potential of automated methods to do repetitive cumbersome tasks with super-human performance.
Y1  - 2022
U6  - http://dx.doi.org/10.1038/s41598-022-11210-y
SN  - 2045-2322
N1  - Corresponding author: Stephan Bialonski
VL  - 12
IS  - Article number: 7686
SP  - 1
EP  - 10
PB  - Springer Nature
CY  - London
ER  -