We thank Kerena Yan and Jingwen Hu for manually scoring sleep stages and Eunah and Soonwiik for the recordings.

This study used data collected from in vivo experiments in mice. No human experiments were involved in the study. All the experiments with animals were approved by the Institutional Animal Care and Use Committee at the Broad Institute. All experiments were performed in accordance with relevant guidelines and regulations. The ARRIVE guidelines are not applicable to this study because the focus of this study is to develop machine learning models rather than comparing different treatment groups.
<p class="jove_...

Automated Sleep Stage Scoring Tool for Rodents Using LightGBM

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This paper presents how to use the IntelliSleepScorer (v1.2) graphic user interface to automatically score the sleep stages of mice and how to leverage SHAP values/plots to better understand the sleep stage scores generated by the model.
An important consideration when using the software is data compatibility. The in-house data used in this study was limited to electrodes placed in the frontal and parietal regions. In the independent dataset from Miladinovic and colleagues11<...

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.

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	<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 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.

Author Spotlight: IntelliSleepScorer &#8212; A High-Accuracy, Accessible GUI Software for Automated Sleep Stage Scoring in Mice and its Application in Psychiatric Research

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

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.

There are three plots (only the top plot if SHAP values were not run) generated in the GUI after sleep stage scoring: the top plot presents EEG and EMG channels with a hypnogram of sleep stage prediction. The middle plot presents epoch SHAP values. The bottom plot presents Global SHAP values (Figure 1).
There are 4 types of data presented in the sleep stage prediction hypnogram plot (Figure 2). The top row is the predicted results. Th...

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.

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

134 次观看.  Broad Institute of MIT and Harvard. 我们在远程睡眠评分中开发了一种易于使用的 GUI 软件，以自动对小鼠的睡眠阶段进行评分。我们的目标是解决劳动密集型手动评分的挑战，并提高没有太多编码经验的研究人员的可访问性。在远程睡眠评分中，小鼠睡眠阶段的自动评分准确率达到 95.2%，并在另外两个公开可用的独立数据集上证明了泛化性。GUI 包括手动校正功能，允许用户修改...