<meta charset="utf8"></head><body>Herramienta automatizada de puntuación de la etapa del sueño para roedores mediante LightGBM

<ol>
	<li>Wang, L. A., Kern, R., Yu, E., Choi, S., Pan, J. Q. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Intellisleepscorer,+a+software+package+with+a+graphic+user+interface+for+automated+sleep+stage+scoring+in+mice+based+on+a+light+gradient+boosting+machine+algorithm.">Intellisleepscorer, a software package with a graphic user interface for automated sleep stage scoring in mice based on a light gradient boosting machine algorithm.</a> Sci Rep. 13 (1), 4275 (2023).</li><li>Astori, S., Wimmer, R. D., Luthi, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Manipulating+sleep+spindles--expanding+views+on+sleep,+memory,+and+disease.">Manipulating sleep spindles--expanding views on sleep, memory, and disease.</a> Trends Neurosci. 36 (12), 738-748 (2013).</li><li>Fraigne, J. J., Torontali, Z. A., Snow, M. B., Peever, J. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Rem+sleep+at+its+core-circuits,+neurotransmitters,+and+pathophysiology.">Rem sleep at its core-circuits, neurotransmitters, and pathophysiology.</a> Front Neurol. 6, 123 (2015).</li><li>Huber, R., Deboer, T., Tobler, I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Effects+of+sleep+deprivation+on+sleep+and+sleep+eeg+in+three+mouse+strains:+Empirical+data+and+simulations.">Effects of sleep deprivation on sleep and sleep eeg in three mouse strains: Empirical data and simulations.</a> Brain Res. 857 (1-2), 8-19 (2000).</li><li>Brown, R. E., Basheer, R., Mckenna, J. T., Strecker, R. E., Mccarley, R. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Control+of+sleep+and+wakefulness.">Control of sleep and wakefulness.</a> Physiol Rev. 92 (3), 1087-1187 (2012).</li><li>Lacroix, M. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Improved+sleep+scoring+in+mice+reveals+human-like+stages.">Improved sleep scoring in mice reveals human-like stages.</a> BioRxiv. 489005, (2018).</li><li>Rayan, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Sleep+scoring+in+rodents:+Criteria,+automatic+approaches+and+outstanding+issues.">Sleep scoring in rodents: Criteria, automatic approaches and outstanding issues.</a> Eur J Neurosci. 59 (4), 526-553 (2024).</li><li>Yamabe, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mc-sleepnet:+Large-scale+sleep+stage+scoring+in+mice+by+deep+neural+networks.">Mc-sleepnet: Large-scale sleep stage scoring in mice by deep neural networks.</a> Sci Rep. 9 (1), 15793 (2019).</li><li>Katsuki, F., Spratt, T. J., Brown, R. E., Basheer, R., Uygun, D. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Sleep-deep-learner+is+taught+sleep-wake+scoring+by+the+end-user+to+complete+each+record+in+their+style.">Sleep-deep-learner is taught sleep-wake scoring by the end-user to complete each record in their style.</a> Sleep Adv. 5 (1), zpae022 (2024).</li><li>Allocca, G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Validation+of+'somnivore',+a+machine+learning+algorithm+for+automated+scoring+and+analysis+of+polysomnography+data.">Validation of 'somnivore', a machine learning algorithm for automated scoring and analysis of polysomnography data.</a> Front Neurosci. 13, 207 (2019).</li><li>Jha, P. K., Valekunja, U. K., Reddy, A. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Slumbernet:+Deep+learning+classification+of+sleep+stages+using+residual+neural+networks.">Slumbernet: Deep learning classification of sleep stages using residual neural networks.</a> Sci Rep. 14 (1), 4797 (2024).</li><li>Barger, Z., Frye, C. G., Liu, D., Dan, Y., Bouchard, K. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Robust,+automated+sleep+scoring+by+a+compact+neural+network+with+distributional+shift+correction.">Robust, automated sleep scoring by a compact neural network with distributional shift correction.</a> PLoS One. 14 (12), e0224642 (2019).</li><li>Miladinovic, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Spindle:+End-to-end+learning+from+eeg/emg+to+extrapolate+animal+sleep+scoring+across+experimental+settings,+labs+and+species.">Spindle: End-to-end learning from eeg/emg to extrapolate animal sleep scoring across experimental settings, labs and species.</a> PLoS Comput Biol. 15 (4), e1006968 (2019).</li><li>Brodersen, P. J. N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Somnotate:+A+probabilistic+sleep+stage+classifier+for+studying+vigilance+state+transitions.">Somnotate: A probabilistic sleep stage classifier for studying vigilance state transitions.</a> PLoS Comput Biol. 20 (1), e1011793 (2024).</li><li>Akada, K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+deep+learning+algorithm+for+sleep+stage+scoring+in+mice+based+on+a+multimodal+network+with+fine-tuning+technique.">A deep learning algorithm for sleep stage scoring in mice based on a multimodal network with fine-tuning technique.</a> Neurosci Res. 173, 99-105 (2021).</li><li>Rytkonen, K. M., Zitting, J., Porkka-Heiskanen, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automated+sleep+scoring+in+rats+and+mice+using+the+naive+Bayes+classifier.">Automated sleep scoring in rats and mice using the naive Bayes classifier.</a> J Neurosci Methods. 202 (1), 60-64 (2011).</li><li>Kam, K., Rapoport, D. M., Parekh, A., Ayappa, I., Varga, A. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Wavesleepnet:+An+interpretable+deep+convolutional+neural+network+for+the+continuous+classification+of+mouse+sleep+and+wake.">Wavesleepnet: An interpretable deep convolutional neural network for the continuous classification of mouse sleep and wake.</a> J Neurosci Methods. 360, 109224 (2021).</li><li>Crisler, S., Morrissey, M. J., Anch, A. M., Barnett, D. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Sleep-stage+scoring+in+the+rat+using+a+support+vector+machine.">Sleep-stage scoring in the rat using a support vector machine.</a> J Neurosci Methods. 168 (2), 524-534 (2008).</li><li>Ke, G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Lightgbm:+A+highly+efficient+gradient+boosting+decision+tree.">Lightgbm: A highly efficient gradient boosting decision tree.</a> Neural Information Processing Systems. , (2017).</li><li>Lundberg, S. M., Lee, S. I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+unified+approach+to+interpreting+model+predictions.">A unified approach to interpreting model predictions.</a> , 4768-4777 (2017).</li></ol>

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Canonical Unbuntu 18.04</td>
			<td>Canonical</td>
			<td>https://releases.ubuntu.com/18.04/</td>
			<td>Supporting Operating System for the software IntelliSleep Scorer: Windows, Mac, or Linux</td>
		</tr>
		<tr>
			<td>Intel Core i7-8550U CPU @ 1.80 GHz 1.99 GHz; RAM: 24 GB&#160;</td>
			<td>Intel Corp</td>
			<td>https://www.intel.com/content/www/us/en/products/details/processors/core-ultra.html</td>
			<td>Hardware requirment for the software: Both Inte Core listed here have been used to process the data. It takes around 10 min to process 12 h of recording sampled at 1000 Hz for both hardwares. Any similar or superior hardware would yield comparable or better performance.&#160;&#160;</td>
		</tr>
		<tr>
			<td>Intel Core i7-10610U CPU @1.80 GHz 2.30 GHz; RAM: 16 GB</td>
			<td>Intel Corp</td>
			<td>https://www.intel.com/content/www/us/en/products/details/processors/core-ultra.html</td>
			<td>Hardware requirment for the software: Both Inte Core listed here have been used to process the data. It takes around 10 min to process 12 h of recording sampled at 1000 Hz for both hardwares. Any similar or superior hardware would yield comparable or better performance.&#160;&#160;</td>
		</tr>
		<tr>
			<td>LightGBM</td>
			<td>Microsoft</td>
			<td>https://lightgbm.readthedocs.io/en/latest/index.html</td>
			<td>Machine learning-based algorithm that was used to train the software.&#160;</td>
		</tr>
		<tr>
			<td>MacBook Pro</td>
			<td>Apple</td>
			<td>https://www.apple.com/in/macbook-pro/</td>
			<td>Supporting Operating System for the software IntelliSleep Scorer: Windows, Mac, or Linux</td>
		</tr>
		<tr>
			<td>Windows</td>
			<td>Microsoft</td>
			<td>https://www.microsoft.com/en-in/windows/?r=1</td>
			<td>Supporting Operating System for the software IntelliSleep Scorer: Windows, Mac, or Linux</td>
		</tr>
	</tbody>
</table>

intellisleepscorer, a software package with a graphic user interface for mice automated sleep stage scoring

Sleep stage scoring in rodents is the procedure to identify the three stages: non-rapid eye movement sleep (NREM), rapid eye movement sleep (REM), and wake2. In rodents, NREM is characterized by reduced muscle activity, slow and regular breathing, decreased heart rate, and low-frequency oscillations of the brain waves. REM in rodents, similar to humans, shows muscle atonia, EEG activation, and rapid eye movements, although the occurrence of vivid dreaming is less clear in rodents compared to humans2,3. The &#34;wake&#34; state in rodents is marked by desynchronized brain activity with high-frequency, low-amplitude waves, increased muscle tone, and active behavior, such as grooming and exploration4. These three stages can be identified by inspecting electroencephalogram (EEG) and electromyogram (EMG) signals5.
The automatic sleep stage scoring models in rodents are in great need. First, manual sleep stage scoring by human experts is labor-intensive and time-consuming. Secondly, sleep patterns in rodents differ from those in humans and have more fragmented episodes of NREM and REM interspaced by waking, around 10 min, in contrast to 60-120 min in humans6. Therefore, identifying these brief periods during manual scoring is challenging. There have been many attempts since the 60s to develop an automatic scoring system of rodent sleep data7. Although many automated rodent sleep scoring methods exist, their performances vary8,9,10,11,12,13,14,15,16,17,18. Importantly, most high-performing models with high generalizability are not publicly available (some need special requests from developers) or are not cost-free for sleep researchers.
Therefore, to fill the current technology gap, we developed a machine learning-based model using a large dataset of 5776 h of EEG and EMG signals from 519 recordings across 124 mice with the LightGBM algorithm1. The lightGBM uses a gradient-boosting approach to construct decision trees19. In Wang et al., 2023, the LightGBM model (consisting of over 8000 decision trees) achieved an overall accuracy of 95.2% and a Cohen&#39;s kappa of 0.91, which outperformed two widely used baseline models such as the logistic regression model (accuracy = 93.3%) and the random forest model (accuracy = 94.3%, kappa = 0.89). The overall performance of the model also displayed a similar performance to that of human experts. Most importantly, the model has been proved to have generalizability and not overfitted to the original training data1: 1) It performed well (accuracy &#62; 89%) on two other publicly available independent datasets, from Miladinovic and colleagues11, with different sampling frequencies and epoch lengths; 2) The performance of the model is not impacted by the light/dark cycle of mice; 3) A modified LightGBM model performed well on data containing only one EEG and one EMG electrode with kappa &#8805; 0.89; 4) Both wildtype and mutant mice were used for the testing and the performances of the model were both accurate. This suggests the model can score sleep stages for mice with different genetic backgrounds.
In order to make this model accessible to sleep researchers who may not have coding expertise, we developed IntelliSleepScorer, a user-friendly software tool with a visually intuitive interface. The software can fully automate the sleep-scoring procedure in mice. It produces interactive visualizations of the signals, hypnogram, and Shapley Additive exPlanations (SHAP) values from an European data format (EDF)/EDF+ file input. The SHAP value approach, based on cooperative game theory, enhances the interpretability of machine learning models20. The model offers both global and epoch-level SHAP values, revealing how different feature values contribute to the scoring decision of the model overall and for each epoch. This advanced program significantly reduces the time and effort required for sleep stage scoring in mice while ensuring that downstream analysis can rely on highly accurate results. In this manuscript, we present step-by-step usage of IntelliSleepScorer (v1.2) with several updates upon version 1.0, including an option to run SHAP analysis separately from sleep pattern prediction, an user adjustable epoch length for sleep stage scoring, and a sleep stage manual correction feature integrated within the GUI.

<meta charset="utf8"></head><body>Autor destacado: IntelliSleepScorer — Un software de GUI accesible y de alta precisión para la puntuación automatizada de las etapas del sueño en ratones y su apli

IntelliSleepScorer, un paquete de software con una interfaz gráfica de usuario para la puntuación automatizada de la etapa del sueño de ratones

We developed an easy-to-use GUI software in tele sleep score to automatically score sleep stages in mice. We aim to address the challenge of labor-intensive manual scoring and improve accessibility for researchers without much coding experience. In tele sleep score achieves 95.2%accuracy in automatically scoring sleep stages in mice and proved generalizability on two other publicly-available independent data sets.The GUI includes a manual correction feature, allowing users to modify the scored sleep stage for any epoch. We also provide ASAP value visualizations to explain the model scoring decisions. Experienced users can also fine tune our pre-tree model with their own data as all necessary files and extraction scripts are available on GitHub.Our research focuses on translating emerging genetics into biology and enabling next-generation therapeutics to treat psychiatric illnesses. We have been working on genes whose dysfunction has been implicated on these illnesses using molecular, cellular, and electrophysiological approaches, both in vitro and in animals.

Sleep stage scoring in rodents is the process of identifying the three stages: nonrapid eye movement sleep (NREM), rapid eye movement sleep (REM), and ...

intellisleepscorer-software-package-with-graphic-user-interface-for

Research

JoVE Journal

Neuroscience

IntelliSleepScorer, a Software Package with a Graphic User Interface for Mice Automated Sleep Stage Scoring

192 vistas.  Broad Institute of MIT and Harvard. Desarrollamos un software de interfaz gráfica de usuario fácil de usar en tele puntuación del sueño para puntuar automáticamente las etapas del sueño en ratones. Nuestro objetivo es abordar el desafío de la puntuación manual que requiere mucha mano de obra y mejorar la accesibilidad para los investigadores sin mucha experiencia en codificación. En telesueño, la puntuación alcanza una precisión del 95,2% en la puntuación automática de las etapas ...

Video: Autor destacado: IntelliSleepScorer — Un software de GUI accesible y de alta precisión para la puntuación automatizada de las etapas del sueño en ratones y su apli

Sleep stage scoring in rodents is the process of identifying the three stages: nonrapid eye movement sleep (NREM), rapid eye movement sleep (REM), and wake. Sleep stage scoring is crucial for studying sleep stage-specific measures and effects. 
Sleep patterns in rodents differ from those in humans, characterized by shorter episodes of NREM and REM interspaced by waking, and traditional manual sleep stage scoring by human experts is time-consuming. To address this issue, previous studies have used machine learning-based approaches to develop algorithms to automatically categorize sleep stages, but high-performing models with great generalizability are often not publicly available/cost-free nor user-friendly for non-trained sleep researchers. 
Therefore, we developed a machine learning-based LightGBM algorithm trained with a large dataset. To make the model available to sleep researchers without coding experience, a software tool named IntelliSleepScorer (v1.2- newest version) was developed based on the model, which features an easy-to-use graphic user interface. In this manuscript, we present step-by-step instructions for using the software to demonstrate a convenient and effective automatic sleep stage scoring tool in mice for sleep researchers.

We present a software package with a graphic user interface for researchers without coding experience to score sleep stages in mice with a simple download and operation.

Investigación

Neurociencias

Scoring Sleep Stages in Mice with Graphic User Interface for Researchers Without Coding Experience

To begin, convert the file format into EDF and organize them into LightGBM 2EEG and LightGBM 1EEG models. Then download the Intelli Sleep Scorer software as per your operating system. For Windows, download the executable file through the Pan Group research page.For macOS, or Linux, download the source code from the GitHub repository. Download two example datasets saved as EDF files through the GitHub repository. Next, download the models dot zip file, unzip it, and copy the extracted models folder inside the Intelli Sleep Scorer repository.To launch the Intelli Sleep Scorer software in Windows, double click the Intelli Sleep Scorer dot EXE in the root folder. For macOS or Linux, open a terminal emulator, change the directory to the root folder and enter the command. In the software interface, click select EDF or EDF positive files to choose the files for scoring.Select the desired epoch length using the dropdown menu. Choose from four, 10, or 20 seconds for sleep stage scoring. Then select the model for sleep scoring based on the file structure.Choose LightGBM 2EEG for data files with 2EEG channels and 1 EMG channel. Check the run or plot SHAP check box to include the SHAP calculation before running the prediction. Next, click score all files"to score the selected EDF, or EDF positive files.The model will automatically score the files and calculate global and epoch SHAP values to interpret the scoring decisions. After completing the sleep scoring process, click visualize the selected file"to display the EEG and EMG signals and a hypnogram aligned with the signals. To change the number of epochs displayed, click the dropdown menu next to select number of epochs to display and select the desired value.Use the navigation buttons to move forward and backward through different epoch data. To view epoch-level SHAP values, right click on a specific epoch to plot the SHAP data. Use the pink transparent bar on the left side of the hypnogram to indicate the current location of the epoch.To switch to another epoch, left click on the hypnogram, or click go to epoch"and enter the epoch number. If SHAP calculation is enabled, right click the selected epoch to generate its epoch SHAP plot. To manually correct a predicted sleep stage, left click on an epoch in the sleep stage prediction plot to select it.The model's predicted stage for the selected epoch will appear on the right. Click on the widget and select a new stage from the wake and REM, and REM options in the dropdown menu. Finally, to reopen a previously saved scored file, ensure the epoch length and selected model match the settings used during the initial processing.