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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

The user can define short (top) and long-term artifacts (bottom) with the ARTIFACT DETECTION routine. (1) For the removal of spike-like (short) artifacts, the user can set upper and lower amplitude threshold cursors. All data points outside these cursors are set to NaN and hence excluded from the sleep scoring analysis. (2) In order to remove long term artifacts, the user defines the onset and end of such an artifact by zooming into the EEG or EMG trace. In line with step a, all visible data points are set to NaN.
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fig0010: The user can define short (top) and long-term artifacts (bottom) with the ARTIFACT DETECTION routine. (1) For the removal of spike-like (short) artifacts, the user can set upper and lower amplitude threshold cursors. All data points outside these cursors are set to NaN and hence excluded from the sleep scoring analysis. (2) In order to remove long term artifacts, the user defines the onset and end of such an artifact by zooming into the EEG or EMG trace. In line with step a, all visible data points are set to NaN.

Mentions: For the elimination of extended artifacts the user can select the affected segment of the EEG data vector. By clicking the “Long-term” artifact button the entire EEG segment displayed is defined as artifact. All “artifact” episodes are excluded from the data vectors for further analysis, labeled with NaN. A visualized scheme of the artifact detection process is shown in Fig. 2. Fig. 3 shows a screenshot of the artifact detection GUI.


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)

The user can define short (top) and long-term artifacts (bottom) with the ARTIFACT DETECTION routine. (1) For the removal of spike-like (short) artifacts, the user can set upper and lower amplitude threshold cursors. All data points outside these cursors are set to NaN and hence excluded from the sleep scoring analysis. (2) In order to remove long term artifacts, the user defines the onset and end of such an artifact by zooming into the EEG or EMG trace. In line with step a, all visible data points are set to NaN.
© Copyright Policy - CC BY
Related In: Results  -  Collection

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

fig0010: The user can define short (top) and long-term artifacts (bottom) with the ARTIFACT DETECTION routine. (1) For the removal of spike-like (short) artifacts, the user can set upper and lower amplitude threshold cursors. All data points outside these cursors are set to NaN and hence excluded from the sleep scoring analysis. (2) In order to remove long term artifacts, the user defines the onset and end of such an artifact by zooming into the EEG or EMG trace. In line with step a, all visible data points are set to NaN.
Mentions: For the elimination of extended artifacts the user can select the affected segment of the EEG data vector. By clicking the “Long-term” artifact button the entire EEG segment displayed is defined as artifact. All “artifact” episodes are excluded from the data vectors for further analysis, labeled with NaN. A visualized scheme of the artifact detection process is shown in Fig. 2. Fig. 3 shows a screenshot of the artifact detection GUI.

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