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Sleep scoring made easy-Semi-automated sleep analysis software and manual rescoring tools for basic sleep research in mice.

Kreuzer M, Polta S, Gapp J, Schuler C, Kochs EF, Fenzl T - MethodsX (2015)

Bottom Line: Amplitude-based thresholds for EEG and EMG parameters trigger a decision tree assigning each EEG episode to a defined vigilance/sleep state automatically.High agreements between auto-scored and manual sleep scoring could be shown for experienced scorers and for beginners quickly and reliably.With small modifications to the software, it can be easily adapted for sleep analysis in other animal models.

View Article: PubMed Central - PubMed

Affiliation: Department of Anesthesiology, Klinikum rechts der Isar, Technische Universität München, Munich, Germany.

ABSTRACT
Studying sleep behavior in animal models demands clear separation of vigilance states. Pure manual scoring is time-consuming and commercial scoring software is costly. We present a LabVIEW-based, semi-automated scoring routine using recorded EEG and EMG signals. This scoring routine is •designed to reliably assign the vigilance/sleep states wakefulness (WAKE), non-rapid eye movement sleep (NREMS) and rapid eye movement sleep (REMS) to defined EEG/EMG episodes.•straightforward to use even for beginners in the field of sleep research.•freely available upon request. Chronic recordings from mice were used to design and evaluate the scoring routine consisting of an artifact-removal, a scoring- and a rescoring routine. The scoring routine processes EMG and different EEG frequency bands. Amplitude-based thresholds for EEG and EMG parameters trigger a decision tree assigning each EEG episode to a defined vigilance/sleep state automatically. Using the rescoring routine individual episodes or particular state transitions can be re-evaluated manually. High agreements between auto-scored and manual sleep scoring could be shown for experienced scorers and for beginners quickly and reliably. With small modifications to the software, it can be easily adapted for sleep analysis in other animal models.

No MeSH data available.


Related in: MedlinePlus

Screenshot of the semi-automated SLEEP SCORING GUI. The three graphs on the right side show the RMS parameters that are used for the decision algorithm. The user can manually set the (orange) thresholds. A click on “start scoring” starts the scoring algorithm. This procedure can be repeated and the scoring result is not saved until “Save Scored Data” is clicked. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
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fig0025: Screenshot of the semi-automated SLEEP SCORING GUI. The three graphs on the right side show the RMS parameters that are used for the decision algorithm. The user can manually set the (orange) thresholds. A click on “start scoring” starts the scoring algorithm. This procedure can be repeated and the scoring result is not saved until “Save Scored Data” is clicked. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Mentions: In each of these windows (DELTA, THETA and EMGRMS) the user can set a horizontal threshold cursor. These thresholds trigger a decision tree, published by Louis et al. [6], to define the sleep stage for the corresponding EEG episode. Fig. 4 shows the individual steps of EEG and EMG band pass filtering, RMS calculation and assignments of sleep stages based on the decision tree. Fig. 5 displays a screenshot of the GUI for semi-automated sleep scoring


Sleep scoring made easy-Semi-automated sleep analysis software and manual rescoring tools for basic sleep research in mice.

Kreuzer M, Polta S, Gapp J, Schuler C, Kochs EF, Fenzl T - MethodsX (2015)

Screenshot of the semi-automated SLEEP SCORING GUI. The three graphs on the right side show the RMS parameters that are used for the decision algorithm. The user can manually set the (orange) thresholds. A click on “start scoring” starts the scoring algorithm. This procedure can be repeated and the scoring result is not saved until “Save Scored Data” is clicked. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
© Copyright Policy - CC BY
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4487923&req=5

fig0025: Screenshot of the semi-automated SLEEP SCORING GUI. The three graphs on the right side show the RMS parameters that are used for the decision algorithm. The user can manually set the (orange) thresholds. A click on “start scoring” starts the scoring algorithm. This procedure can be repeated and the scoring result is not saved until “Save Scored Data” is clicked. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Mentions: In each of these windows (DELTA, THETA and EMGRMS) the user can set a horizontal threshold cursor. These thresholds trigger a decision tree, published by Louis et al. [6], to define the sleep stage for the corresponding EEG episode. Fig. 4 shows the individual steps of EEG and EMG band pass filtering, RMS calculation and assignments of sleep stages based on the decision tree. Fig. 5 displays a screenshot of the GUI for semi-automated sleep scoring

Bottom Line: Amplitude-based thresholds for EEG and EMG parameters trigger a decision tree assigning each EEG episode to a defined vigilance/sleep state automatically.High agreements between auto-scored and manual sleep scoring could be shown for experienced scorers and for beginners quickly and reliably.With small modifications to the software, it can be easily adapted for sleep analysis in other animal models.

View Article: PubMed Central - PubMed

Affiliation: Department of Anesthesiology, Klinikum rechts der Isar, Technische Universität München, Munich, Germany.

ABSTRACT
Studying sleep behavior in animal models demands clear separation of vigilance states. Pure manual scoring is time-consuming and commercial scoring software is costly. We present a LabVIEW-based, semi-automated scoring routine using recorded EEG and EMG signals. This scoring routine is •designed to reliably assign the vigilance/sleep states wakefulness (WAKE), non-rapid eye movement sleep (NREMS) and rapid eye movement sleep (REMS) to defined EEG/EMG episodes.•straightforward to use even for beginners in the field of sleep research.•freely available upon request. Chronic recordings from mice were used to design and evaluate the scoring routine consisting of an artifact-removal, a scoring- and a rescoring routine. The scoring routine processes EMG and different EEG frequency bands. Amplitude-based thresholds for EEG and EMG parameters trigger a decision tree assigning each EEG episode to a defined vigilance/sleep state automatically. Using the rescoring routine individual episodes or particular state transitions can be re-evaluated manually. High agreements between auto-scored and manual sleep scoring could be shown for experienced scorers and for beginners quickly and reliably. With small modifications to the software, it can be easily adapted for sleep analysis in other animal models.

No MeSH data available.


Related in: MedlinePlus