Field Of Sleep Research Research Articles

Content validation of a test battery for assessing sleep quality in adolescents in Slovenia

Abstract Background Sleep constitutes a fundamental biological necessity, occupying approximately one-third of our lifespan. Consequently, the field of sleep research has experienced an increase in interest, prompted by the recognition of insufficient sleep as a significant public health challenge. As the majority of existing questionnaires in Slovenia have yet to undergo official translation and validation processes, the aim of our study was to conduct content validation of a questionnaire designed to assess the sleep quality among adolescents. Methods We developed a test battery for assessing the sleep quality of adolescents by combining a diverse array of questionnaires. We employed the Adolescent Sleep-Wake Scale, Pediatric Daytime Sleepiness Scale, Epworth Sleepiness Scale for Children and Adolescents, and Children’s Morningness-Eveningness Scale. Following the development of the entire test battery, a process of back-translation from English to Slovene occurred, followed by cultural adaptation. Results The designed questionnaire underwent content validation by a panel of 10 experts drawn from diverse disciplines. Evaluation of questionnaire items followed a planned methodology tailored for the content validation of measurement instruments, focusing on content relevance, clarity, and classification into subscales or factors. Our analysis revealed that the individual questionnaires demonstrate content validity, supported by notably high indices of content validity. Conclusions Translated questionnaires demonstrate decent content validity, thus rendering them valid clinical and research tools for assessing sleep quality in adolescents in Slovenia. Key messages • Our research indicates the availability of content-valid tools for sleep research. • Further investigation is needed to determine the reliability, specificity, sensitivity, and appropriateness of the factorial structure of measures for the quality of sleep.

Digital Measures Development: Lessons Learned from an Expert Workshop Addressing Cross-Therapeutic Area Measures of Sleep

Introduction: The Digital Measures Development: Core Measures of Sleep project, led by the Digital Medicine Society (DiMe), emphasizes the importance of sleep as a cornerstone of health and the need for standardized measurements of sleep and its disturbances outside the laboratory. This initiative recognizes the complex relationship between sleep and overall health, addressing it as both a symptom of underlying conditions and a consequence of therapeutic interventions. It aims to fill a crucial gap in healthcare by promoting the development of accessible, nonintrusive, and cost-effective digital tools for sleep assessment, focusing on factors important to patients, caregivers, and clinicians. Methods: A central feature of this project was an expert workshop conducted on April 19th, 2023. The workshop convened stakeholders from diverse backgrounds, including regulatory, payer, industry, academic, and patient groups, to deliberate on the project’s direction. This gathering focused on discussing the challenges and necessities of measuring sleep across various therapeutic areas, aiming to identify broad areas for initial focus while considering the feasibility of generalizing these measures where applicable. The methodological emphasis was on leveraging expert consensus to guide the project’s approach to digital sleep measurement. Results: The workshop resulted in the identification of seven key themes that will direct the DiMe Core Digital Measures of Sleep project and the broader field of sleep research moving forward. These themes underscore the project’s innovative approach to sleep health, highlighting the complexity of omni-therapeutic sleep measurement and identifying potential areas for targeted research and development. The discussions and outcomes of the workshop serve as a roadmap for enhancing digital sleep measurement tools, ensuring they are relevant, accurate, and capable of addressing the nuanced needs of diverse patient populations. Conclusion: The Digital Medicine Society’s Core Measures of Sleep project represents a pivotal effort to advance sleep health through digital innovation. By focusing on the development of standardized, patient-centric, and clinically relevant digital sleep assessment tools, the project addresses a significant need in healthcare. The expert workshop’s outcomes underscore the importance of collaborative, multi-stakeholder engagement in identifying and overcoming the challenges of sleep measurement. This initiative sets a new precedent for the integration of digital tools into sleep health research and practice, promising to improve outcomes for patients worldwide by enhancing our understanding and measurement of sleep.

“Counting sheep PSG”: EEGLAB-compatible open-source matlab software for signal processing, visualization, event marking and staging of polysomnographic data

BackgroundProgress in advancing sleep research employing polysomnography (PSG) has been negatively impacted by the limited availability of widely available, open-source sleep-specific analysis tools. New methodHere, we introduce Counting Sheep PSG, an EEGLAB-compatible software for signal processing, visualization, event marking and manual sleep stage scoring of PSG data for MATLAB. ResultsKey features include: (1) signal processing tools including bad channel interpolation, down-sampling, re-referencing, filtering, independent component analysis, artifact subspace reconstruction, and power spectral analysis, (2) customizable display of polysomnographic data and hypnogram, (3) event marking mode including manual sleep stage scoring, (4) automatic event detections including movement artifact, sleep spindles, slow waves and eye movements, and (5) export of main descriptive sleep architecture statistics, event statistics and publication-ready hypnogram. Comparison with existing methodsCounting Sheep PSG was built on the foundation created by sleepSMG (https://sleepsmg.sourceforge.net/). The scope and functionalities of the current software have made significant advancements in terms of EEGLAB integration/compatibility, preprocessing, artifact correction, event detection, functionality and ease of use. By comparison, commercial software can be costly and utilize proprietary data formats and algorithms, thereby restricting the ability to distribute and share data and analysis results. ConclusionsThe field of sleep research remains shackled by an industry that resists standardization, prevents interoperability, builds-in planned obsolescence, maintains proprietary black-box data formats and analysis approaches. This presents a major challenge for the field of sleep research. The need for free, open-source software that can read open-format data is essential for scientific advancement to be made in the field.

Sleep Research in the Era of AI

The field of sleep research is both broad and rapidly evolving. It spans from the diagnosis of sleep-related disorders to investigations of how sleep supports memory consolidation. The study of sleep includes a variety of approaches, starting with the sole focus on the visual interpretation of polysomnography characteristics and extending to the emergent use of advanced signal processing tools. Insights gained using artificial intelligence (AI) are rapidly reshaping the understanding of sleep-related disorders, enabling new approaches to basic neuroscientific studies. In this opinion article, we explore the emergent role of AI in sleep research, along two different axes: one clinical and one fundamental. In clinical research, we emphasize the use of AI for automated sleep scoring, diagnosing sleep-wake disorders and assessing measurements from wearable devices. In fundamental research, we highlight the use of AI to better understand the functional role of sleep in consolidating memories. While AI is likely to facilitate new advances in the field of sleep research, we also address challenges, such as bridging the gap between AI innovation and the clinic and mitigating inherent biases in AI models. AI has already contributed to major advances in the field of sleep research, and mindful deployment has the potential to enable further progress in the understanding of the neuropsychological benefits and functions of sleep.

A study on feature selection using multi-domain feature extraction for automated k-complex detection.

K-complex detection plays a significant role in the field of sleep research. However, manual annotation for electroencephalography (EEG) recordings by visual inspection from experts is time-consuming and subjective. Therefore, there is a necessity to implement automatic detection methods based on classical machine learning algorithms. However, due to the complexity of EEG signal, current feature extraction methods always produce low relevance to k-complex detection, which leads to a great performance loss for the detection. Hence, finding compact yet effective integrated feature vectors becomes a crucially core task in k-complex detection. In this paper, we first extract multi-domain features based on time, spectral analysis, and chaotic theory. Those features are extracted from a 0.5-s EEG segment, which is obtained using the sliding window technique. As a result, a vector containing twenty-two features is obtained to represent each segment. Next, we explore several feature selection methods and compare their performance in detecting k-complex. Based on the analysis of the selected features, we identify compact features which are fewer than twenty-two features and deemed as relevant and proceeded to the next step. Additionally, three classical classifiers are employed to evaluate the performance of the feature selection models. The results demonstrate that combining different features significantly improved the k-complex detection performance. The best performance is achieved by applying the feature selection method, which results in an accuracy of 93.03%7.34, sensitivity of 93.81%5.62%, and specificity 94.135.81, respectively, using a smaller number of the combined feature sets. The proposed method in this study can serve as an efficient tool for the automatic detection of k-complex, which is useful for neurologists or doctors in the diagnosis of sleep research.

‘High-Density-SleepCleaner’: An open-source, semi-automatic artifact removal routine tailored to high-density sleep EEG

Background:With up to 256 channels, high-density electroencephalography (hd-EEG) has become essential to the sleep research field. The vast amount of data resulting from this magnitude of channels in overnight EEG recordings complicates the removal of artifacts. New Method:We present a new, semi-automatic artifact removal routine specifically designed for sleep hd-EEG recordings. By employing a graphical user interface (GUI), the user assesses epochs in regard to four sleep quality markers (SQMs). Based on their topography and underlying EEG signal, the user eventually removes artifactual values. To identify artifacts, the user is required to have basic knowledge of the typical (patho-)physiological EEG they are interested in, as well as artifactual EEG. The final output consists of a binary matrix (channels x epochs). Channels affected by artifacts can be restored in afflicted epochs using epoch-wise interpolation, a function included in the online repository. Results:The routine was applied in 54 overnight sleep hd-EEG recordings. The proportion of bad epochs highly depends on the number of channels required to be artifact-free. Between 95% and 100% of bad epochs could be restored using epoch-wise interpolation. We furthermore present a detailed examination of two extreme cases (with few and many artifacts). For both nights, the topography and cyclic pattern of delta power look as expected after artifact removal. Comparison with Existing Methods:Numerous artifact removal methods exist, yet their scope of application usually targets short wake EEG recordings. The proposed routine provides a transparent, practical, and efficient approach to identify artifacts in overnight sleep hd-EEG recordings. Conclusion:This method reliably identifies artifacts simultaneously in all channels and epochs.

Functional Connectivity of White Matter and Its Association with Sleep Quality.

Functional magnetic resonance imaging (fMRI) has been widely adopted to investigate the neural activity in gray matter (GM) in the field of sleep research, but the neural activity in white matter (WM) has received much less attention. The current study set out to test our hypothesis that WM functional abnormality is associated with poor sleep quality. K-means clustering analysis was performed on 78 healthy adults drawn from the Human Connectome Project dataset to extract stable WM functional networks (WM-FNs) and GM-FNs. The differences in functional connectivity within WM-FNs and between WM- and GM-FNs, as well as the power spectrum between good sleep quality group (Pittsburgh Sleep Quality Index (PSQI) <6, daytime dysfunction = 0) and poor sleep quality group (PSQI >6, daytime dysfunction >0) were examined between groups with good and poor sleep quality. Additionally, linear relationships between sleep quality and altered functional characteristics of WM-FNs were evaluated. Functional connectivity between middle and superficial WM-FNs, short- and long-range functional connectivity between WM- and GM-FNs were decreased in poor sleepers and negatively correlated with PSQI score. The mean amplitudes of right sensorimotor WM networks at whole, high and low frequency bands were higher in poor sleepers and were positively correlated with PSQI score. WM functional abnormality is associated with poor sleep quality. The neurobiological mechanisms that underlie the functional alterations of WM-FNs in poor sleepers need to be investigated in future studies.

Howard P. Roffwarg: sleep pioneer, legend, and ontogenetic hypothesis author.

Narrated in this article are accounts of the many contributions Howard P. Roffwarg, MD, made to the field of sleep research and sleep medicine across his entire professional career as a student, a mentor, a leader in the Sleep Research Society, a sleep medicine clinician, and a scientist who performed experimental investigations in humans and animals. Dr Roffwarg was the originator of what is known as the "Ontogenetic Hypothesis" of sleep. His research over many years on physiology has contributed greatly to much of the experimental support substantiating a role for rapid eye-movement sleep (REMS) in the early development of the brain. Though much is still unknown, the Ontogenetic Hypothesis, still to this day, inspires many neuroscientists in their investigations. These studies have demonstrated roles for both REMS and NREMS in development as well as on brain function throughout his life span. Dr Howard P. Roffwarg, is one of the legends in the field of sleep research.

0151 Subjective sleep onset latency is influenced by the sleep structure and body heat loss in human subjects

Abstract Introduction Humans can estimate the time that has elapsed during sleep (time estimation ability; TEA). Although research on the TEA during sleep has advanced in the field of sleep research, few studies have focused on the relationship between the subjective sleep onset latency (SOL), which is an indicator of TEA, and objective sleep structures, body heat loss, and body temperature. This paper investigates the association of the subjective SOL with sleep structures such as the objective SOL, duration of each sleep stage, subjective sleep parameters, and body heat loss in healthy young participants. Methods Twenty six participants (7 men and 19 women, mean age of 21.5 ± 0.5 years) having no sleep problems participated in a 1-hour polysomnographic recording that obtained objective sleep parameters during the daytime while temperatures of the skin (i.e., dorsum of the hand and foot, forehead, and subclavian) and eardrum were recorded at intervals of 1 min. The distal–proximal skin temperature gradient (DPG), which is a good predictor of body heat loss and sleepiness, was calculated. Subjective parameters, such as the subjective SOL, sleep time, sleep depth, sleepiness, and mood, were evaluated before and after sleep. We examined the association of the subjective SOL with objective sleep parameters, DPG, and other subjective parameters. Results Most participants estimated their sleep latency to be longer than their actual SOL (mean objective SOL of 7.6 min vs. subjective SOL of 13.7 min). The objective SOL was significantly correlated with each sleep stage parameter whereas the subjective SOL was negatively correlated with the stage N2 sleep duration (Rho = −0.454, p = 0.020) and correlated with the stage N2 sleep latency (Rho = 0.402, p = 0.051). Participants who estimated a shorter subjective SOL had a higher DPG before sleep periods than that after sleep onset (Rho = −0.692, p &amp;lt; 0.001). Additionally, the subjective SOL was correlated with the subjective sleep depth, subjective wake after sleep onset, and restorative sleep. Conclusion The subjective sleep onset latency in the healthy young participants was affected by the degree of body heat loss before sleep onset and stable shallow nonrapid-eye-movement sleep. Support (If Any)

Altered sleep intensity upon DBS to hypothalamic sleep-wake centers in rats.

Deep brain stimulation (DBS) has been scarcely investigated in the field of sleep research. We hypothesize that DBS onto hypothalamic sleep- and wake-promoting centers will produce significant neuromodulatory effects and potentially become a therapeutic strategy for patients suffering severe, drug-refractory sleep–wake disturbances. We aimed to investigate whether continuous electrical high-frequency DBS, such as that often implemented in clinical practice, in the ventrolateral preoptic nucleus (VLPO) or the perifornical area of the posterior lateral hypothalamus (PeFLH), significantly modulates sleep–wake characteristics and behavior. We implanted healthy rats with electroencephalographic/electromyographic electrodes and recorded vigilance states in parallel to bilateral bipolar stimulation of VLPO and PeFLH at 125 Hz and 90 µA over 24 h to test the modulating effects of DBS on sleep–wake proportions, stability and spectral power in relation to the baseline. We unexpectedly found that VLPO DBS at 125 Hz deepens slow-wave sleep (SWS) as measured by increased delta power, while sleep proportions and fragmentation remain unaffected. Thus, the intensity, but not the amount of sleep or its stability, is modulated. Similarly, the proportion and stability of vigilance states remained altogether unaltered upon PeFLH DBS but, in contrast to VLPO, 125 Hz stimulation unexpectedly weakened SWS, as evidenced by reduced delta power. This study provides novel insights into non-acute functional outputs of major sleep–wake centers in the rat brain in response to electrical high-frequency stimulation, a paradigm frequently used in human DBS. In the conditions assayed, while exerting no major effects on the sleep–wake architecture, hypothalamic high-frequency stimulation arises as a provocative sleep intensity-modulating approach.

Mental fatigue measurement using eye metrics: A systematic literature review

Mental fatigue measurement techniques utilize one or a combination of the cognitive, affective, and behavioral responses of the body. Eye-tracking and electrooculography, which are used to compute eye-based features, have gained momentum with increases in accuracy and robustness of the lightweight equipment emerging in the markets and can be used for objective and continuous assessment of mental fatigue. The main goal of this systematic review was to summarize the various eye-based features that have been used to measure mental fatigue and explore the relation of eye-based features to mental fatigue. The review process, following the preferred reporting items for systematic reviews and meta-analyses, used the electronic databases Web of Science, Scopus, ACM digital library, IEEE Xplore, and PubMed. Of the 1,385 retrieved documents, 34 studies met the inclusion criteria, resulting in 21 useful eye-based features. Categorizing these into eight groups revealed saccades as the most promising category, with saccade mean and peak velocity providing quick access to the cognitive states within 30min of fatiguing activity. Complex brain networks involving sympathetic and parasympathetic nervous systems control the relation of mental fatigue to tonic pupil size and have the potential to indicate mental fatigue in controlled experimental conditions. Other categories, like blinks, are derived from the field of sleep research and should be used with caution. Several limitations emerged in the analysis, including varied experimental methods, use of dim lighting during the experiment (that could possibly also induce sleepiness), and use of unclear data analysis techniques, thereby complicating comparisons between studies.

Analysis and visualization of sleep stages based on deep neural networks

Automatic sleep stage scoring based on deep neural networks has come into focus of sleep researchers and physicians, as a reliable method able to objectively classify sleep stages would save human resources and simplify clinical routines. Due to novel open-source software libraries for machine learning, in combination with enormous recent progress in hardware development, a paradigm shift in the field of sleep research towards automatic diagnostics might be imminent. We argue that modern machine learning techniques are not just a tool to perform automatic sleep stage classification, but are also a creative approach to find hidden properties of sleep physiology. We have already developed and established algorithms to visualize and cluster EEG data, facilitating first assessments on sleep health in terms of sleep-apnea and consequently reduced daytime vigilance. In the following study, we further analyze cortical activity during sleep by determining the probabilities of momentary sleep stages, represented as hypnodensity graphs and then computing vectorial cross-correlations of different EEG channels. We can show that this measure serves to estimate the period length of sleep cycles and thus can help to find disturbances due to pathological conditions.

Principles of open, transparent and reproducible science in author guidelines of sleep research and chronobiology journals.

Background: "Open science" is an umbrella term describing various aspects of transparent and open science practices. The adoption of practices at different levels of the scientific process (e.g., individual researchers, laboratories, institutions) has been rapidly changing the scientific research landscape in the past years, but their uptake differs from discipline to discipline. Here, we asked to what extent journals in the field of sleep research and chronobiology encourage or even require following transparent and open science principles in their author guidelines. Methods: We scored the author guidelines of a comprehensive set of 27 sleep and chronobiology journals, including the major outlets in the field, using the standardised Transparency and Openness (TOP) Factor. The TOP Factor is a quantitative summary of the extent to which journals encourage or require following various aspects of open science, including data citation, data transparency, analysis code transparency, materials transparency, design and analysis guidelines, study pre-registration, analysis plan pre-registration, replication, registered reports, and the use of open science badges. Results: Across the 27 journals, we find low values on the TOP Factor (median [25 th, 75 th percentile] 3 [1, 3], min. 0, max. 9, out of a total possible score of 29) in sleep research and chronobiology journals. Conclusions: Our findings suggest an opportunity for sleep research and chronobiology journals to further support recent developments in transparent and open science by implementing transparency and openness principles in their author guidelines.

Principles of open, transparent and reproducible science in author guidelines of sleep research and chronobiology journals

Background: "Open science" is an umbrella term describing various aspects of transparent and open science practices. The adoption of practices at different levels of the scientific process (e.g., individual researchers, laboratories, institutions) has been rapidly changing the scientific research landscape in the past years, but their uptake differs from discipline to discipline. Here, we asked to what extent journals in the field of sleep research and chronobiology encourage or even require following transparent and open science principles in their author guidelines. Methods: We scored the author guidelines of a comprehensive set of 27 sleep and chronobiology journals, including the major outlets in the field, using the standardised Transparency and Openness (TOP) Factor. The TOP Factor is a quantitative summary of the extent to which journals encourage or require following various aspects of open science, including data citation, data transparency, analysis code transparency, materials transparency, design and analysis guidelines, study pre-registration, analysis plan pre-registration, replication, registered reports, and the use of open science badges. Results: Across the 27 journals, we find low values on the TOP Factor (median [25 th, 75 th percentile] 3 [1, 3], min. 0, max. 9, out of a total possible score of 29) in sleep research and chronobiology journals. Conclusions: Our findings suggest an opportunity for sleep research and chronobiology journals to further support recent developments in transparent and open science by implementing transparency and openness principles in their author guidelines.

Machine and Deep Learning in Molecular and Genetic Aspects of Sleep Research.

Epidemiological sleep research strives to identify the interactions and causal mechanisms by which sleep affects human health, and to design intervention strategies for improving sleep throughout the lifespan. These goals can be advanced by further focusing on the environmental and genetic etiology of sleep disorders, and by development of risk stratification algorithms, to identify people who are at risk or are affected by, sleep disorders. These studies rely on comprehensive sleep-related data which often contains complex multi-dimensional physiological and molecular measurements across multiple timepoints. Thus, sleep research is well-suited for the application of computational approaches that canhandle high-dimensional data. Here, we survey recent advances in machine and deep learning together with the availability of large human cohort studies with sleep data that can jointly drive the next breakthroughs in the sleep-research field. We describe sleep-related data types and datasets, and present some of the tasks in the field that can be targets for algorithmic approaches, as well as the challenges and opportunities in pursuing them.

Transparency and open science principles in reporting guidelines in sleep research and chronobiology journals

Background: "Open science" is an umbrella term describing various aspects of transparent and open science practices. The adoption of practices at different levels of the scientific process (e.g., individual researchers, laboratories, institutions) has been rapidly changing the scientific research landscape in the past years, but their uptake differs from discipline to discipline. Here, we asked to what extent journals in the field of sleep research and chronobiology encourage or even require following transparent and open science principles in their author guidelines. Methods: We scored the author guidelines of a comprehensive set of 28 sleep and chronobiology journals, including the major outlets in the field, using the standardised Transparency and Openness (TOP) Factor. This instrument rates the extent to which journals encourage or require following various aspects of open science, including data citation, data transparency, analysis code transparency, materials transparency, design and analysis guidelines, study pre-registration, analysis plan pre-registration, replication, registered reports, and the use of open science badges. Results: Across the 28 journals, we find low values on the TOP Factor (median [25th, 75th percentile] 2.5 [1, 3], min. 0, max. 9, out of a total possible score of 28) in sleep research and chronobiology journals. Conclusions: Our findings suggest an opportunity for sleep research and chronobiology journals to further support the recent developments in transparent and open science by implementing transparency and openness principles in their guidelines and making adherence to them mandatory.

Impact factor rankings for sleep research journals between 2005 and 2018.

Impact factors and ranking lists of research journals are very often used to judge our career achievements and progression by employers and granting bodies. However, a comprehensive list for the interdisciplinary field of sleep research does not currently exist because our journals tend to be placed into discipline-specific lists that do not cope well with our historic interdisciplinarity, which embraces many core disciplines. We aimed to build a ranking list specifically for sleep research journals based on the journal impact factor and the google scholar H5 indices. We then searched for all sleep journals given an impact factor by Thomson Reuters from 2005 to 2018 and all journals given a current H5 index by Google Scholar. We provide a ranking list specifically for sleep journals that might be useful for researchers to cite when providing context in their applications for employment, promotion and funding.

Sleep Stage Classification Using Time-Frequency Spectra From Consecutive Multi-Time Points.

Sleep stage classification is an open challenge in the field of sleep research. Considering the relatively small size of datasets used by previous studies, in this paper we used the Sleep Heart Health Study dataset from the National Sleep Research Resource database. A long short-term memory (LSTM) network using a time-frequency spectra of several consecutive 30 s time points as an input was used to perform the sleep stage classification. Four classical convolutional neural networks (CNNs) using a time-frequency spectra of a single 30 s time point as an input were used for comparison. Results showed that, when considering the temporal information within the time-frequency spectrum of a single 30 s time point, the LSTM network had a better classification performance than the CNNs. Moreover, when additional temporal information was taken into consideration, the classification performance of the LSTM network gradually increased. It reached its peak when temporal information from three consecutive 30 s time points was considered, with a classification accuracy of 87.4% and a Cohen’s Kappa coefficient of 0.8216. Compared with CNNs, our results indicate that for sleep stage classification, the temporal information within the data or the features extracted from the data should be considered. LSTM networks take this temporal information into account, and thus, may be more suitable for sleep stage classification.

SleepOMICS: How Big Data Can Revolutionize Sleep Science.

Sleep disorders have reached epidemic proportions worldwide, affecting the youth as well as the elderly, crossing the entire lifespan in both developed and developing countries. “Real-life” behavioral (sensor-based), molecular, digital, and epidemiological big data represent a source of an impressive wealth of information that can be exploited in order to advance the field of sleep research. It can be anticipated that big data will have a profound impact, potentially enabling the dissection of differences and oscillations in sleep dynamics and architecture at the individual level (“sleepOMICS”), thus paving the way for a targeted, “one-size-does-not-fit-all” management of sleep disorders (“precision sleep medicine”).

Noninvasive, High-throughput Determination of Sleep Duration in Rodents.

Traditionally, sleep is monitored by an electroencephalogram (EEG). EEG studies in rodents require surgical implantation of the electrodes followed by a long recovery period. To perform an EEG recording, the animal is connected to a receiver, creating an unnatural tether to the head-mount. EEG monitoring is time consuming, carries risk to the animal, and is not a completely natural setting for the measurement of sleep. Alternative methods to detect sleep, particularly in a high-throughput fashion, would greatly advance the field of sleep research. Here, we describe a validated method for detecting sleep via activity-based home-cage monitoring. Previous studies have shown that sleep assessed via this method has a high degree of agreement with sleep defined by traditional EEG-based measures. Whereas this method is validated for total sleep time, it is important to note that sleep bout duration should be assessed by an EEG which has better temporal resolution. The EEG can also differentiate rapid eye movement (REM) and non-REM sleep, giving more detail about the exact nature of sleep. Nevertheless, activity-based sleep determination can be used to analyze multiple days of undisturbed sleep and to assess sleep as a response to an acute event (like stress). Here, we show the power of this system to detect the response of mice to daily intraperitoneal injections.