<meta charset="utf8"></head><body>LightGBM을 사용하는 설치류를 위한 자동화된 수면 단계 점수 매기기 도구

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	<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>저자 스포트라이트: IntelliSleepScorer — 마우스의 자동화된 수면 단계 채점 및 정신과 연구에서의 적용을 위한 고정밀하고 액세스 가능한 GUI 소프트웨어

IntelliSleepScorer, 마우스용 그래픽 사용자 인터페이스가 포함된 소프트웨어 패키지 자동 수면 단계 점수 매기기

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

176 조회수.  Broad Institute of MIT and Harvard. 우리는 쥐의 수면 단계를 자동으로 점수화하기 위해 원격 수면 점수에서 사용하기 쉬운 GUI 소프트웨어를 개발했습니다. 우리는 노동 집약적인 수동 채점 문제를 해결하고 코딩 경험이 많지 않은 연구원의 접근성을 개선하는 것을 목표로 합니다. 원격 수면 점수는 마우스의 수면 단계를 자동으로 채점하는 데 95.2%의 정확도를 달성했으며 공개적으로 ...