Validation of ‘Somnivore’, a Machine Learning Algorithm for Automated Scoring and Analysis of Polysomnography Data

Manual scoring of polysomnography data is labor-intensive and time-consuming, and most existing software does not account for subjective differences and user variability. Therefore, we evaluated a supervised machine learning algorithm, SomnivoreTM, for automated wake–sleep stage classification. We designed an algorithm that extracts features from various input channels, following a brief session of manual scoring, and provides automated wake-sleep stage classification for each recording. For algorithm validation, polysomnography data was obtained from independent laboratories, and include normal, cognitively-impaired, and alcohol-treated human subjects (total n = 52), narcoleptic mice and drug-treated rats (total n = 56), and pigeons (n = 5). Training and testing sets for validation were previously scored manually by 1–2 trained sleep technologists from each laboratory. F-measure was used to assess precision and sensitivity for statistical analysis of classifier output and human scorer agreement. The algorithm gave high concordance with manual visual scoring across all human data (wake 0.91 ± 0.01; N1 0.57 ± 0.01; N2 0.81 ± 0.01; N3 0.86 ± 0.01; REM 0.87 ± 0.01), which was comparable to manual inter-scorer agreement on all stages. Similarly, high concordance was observed across all rodent (wake 0.95 ± 0.01; NREM 0.94 ± 0.01; REM 0.91 ± 0.01) and pigeon (wake 0.96 ± 0.006; NREM 0.97 ± 0.01; REM 0.86 ± 0.02) data. Effects of classifier learning from single signal inputs, simple stage reclassification, automated removal of transition epochs, and training set size were also examined. In summary, we have developed a polysomnography analysis program for automated sleep-stage classification of data from diverse species. Somnivore enables flexible, accurate, and high-throughput analysis of experimental and clinical sleep studies

Streetlights Disrupt Night-Time Sleep in Urban Black Swans

Artificial light at night could have widespread and detrimental impacts on sleep. To reduce disruptive effects of artificial light on sleep in humans, most smartphones and computers now have software that reduces blue light emissions at night. Little is known about whether reducing blue light emissions from city lights could also benefit urban wildlife. We investigated the effects of blue-rich (white) and blue-reduced (amber) LED streetlights on accelerometry-defined rest, electrophysiologically-identified sleep, and plasma melatonin in a diurnal bird, the black swan (Cygnus atratus). Urban swans were exposed to 20 full nights of each lighting type in an outdoor, naturalistic environment. Contrary to our predictions, we found that night-time rest was similar during exposure to amber and white lights but decreased under amber lights compared with dark conditions. By recording brain activity in a subset of swans, we also demonstrated that resting birds were almost always asleep, so amber light also reduced sleep at night. We found no effect of light treatment on total (24 h) daily rest or plasma melatonin. Our study provides the first electrophysiologically-verified evidence for effects of streetlights on sleep in an urban animal, and furthermore suggests that reducing blue wavelengths of light might not mitigate these effects.ISSN:2296-701

Urban noise restricts, fragments, and lightens sleep in Australian magpies

Urban areas are inherently noisy, and this noise can disrupt biological processes as diverse as communication, migration, and reproduction. We investigated how exposure to urban noise affects sleep, a process critical to optimal biological functioning, in Australian magpies (Cracticus tibicen). Eight magpies experimentally exposed to noise in captivity for 24-h spent more time awake, and less time in non-rapid eye movement (non-REM) and REM sleep at night than under quiet conditions. Sleep was also fragmented, with more frequent interruptions by wakefulness, shorter sleep episode durations, and less intense non-REM sleep. REM sleep was particularly sensitive to urban noise. Following exposure to noise, magpies recovered lost sleep by engaging in more, and more intense, non-REM sleep. In contrast, REM sleep showed no rebound. This might indicate a long-term cost to REM sleep loss mediated by noise, or contest hypotheses regarding the functional value of this state. Overall, urban noise has extensive, disruptive impacts on sleep composition, architecture, and intensity in magpies. Future work should consider whether noise-induced sleep restriction and fragmentation have long-term consequences

Nocturnal lighting in animal research should be replicable and reflect relevant ecological conditions.

In nature, light is a key driver of animal behaviour and physiology. When studying captive or laboratory animals, researchers usually expose animals to a period of darkness, to mimic night. However, 'darkness' is often poorly quantified and its importance is generally underappreciated in animal research. Even small differences in nocturnal light conditions can influence biology. When light levels during the dark phase are not reported accurately, experiments can be impossible to replicate and compare. Furthermore, when nocturnal light levels are unrealistically dark or bright, the research is less ecologically relevant. Such issues are exacerbated by huge differences in the sensitivity of different light meters, which are not always described in study methods. We argue that nocturnal light levels need to be reported clearly and precisely, particularly in studies of animals housed indoors (e.g. '<0.03 lux' rather than '0 lux' or 'dark'), and that these light levels should reflect conditions that the animal would experience in a natural context

White and Amber Light at Night Disrupt Sleep Physiology in Birds

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Urban noise restricts, fragments, and lightens sleep in Australian magpies

Urban areas are inherently noisy, and this noise can disrupt biological processes as diverse as communication, migration, and reproduction. We investigated how exposure to urban noise affects sleep, a process critical to optimal biological functioning, in Australian magpies (Cracticus tibicen). Eight magpies experimentally exposed to noise in captivity for 24-h spent more time awake, and less time in non-rapid eye movement (non-REM) and REM sleep at night than under quiet conditions. Sleep was also fragmented, with more frequent interruptions by wakefulness, shorter sleep episode durations, and less intense non-REM sleep. REM sleep was particularly sensitive to urban noise. Following exposure to noise, magpies recovered lost sleep by engaging in more, and more intense, non-REM sleep. In contrast, REM sleep showed no rebound. This might indicate a long-term cost to REM sleep loss mediated by noise, or contest hypotheses regarding the functional value of this state. Overall, urban noise has extensive, disruptive impacts on sleep composition, architecture, and intensity in magpies. Future work should consider whether noise-induced sleep restriction and fragmentation have long-term consequences

Light pollution: a landscape-scale issue requiring cross-realm consideration

Terrestrial, marine and freshwater realms are inherently linked through ecological, biogeochemical and/or physical processes. An understanding of these connections is critical to optimise management strategies and ensure the ongoing resilience of ecosystems. Artificial light at night (ALAN) is a global stressor that can profoundly affect a wide range of organisms and habitats and impact multiple realms. Despite this, current management practices for light pollution rarely consider connectivity between realms. Here we discuss the ways in which ALAN can have cross-realm impacts and provide case studies for each example discussed. We identified three main ways in which ALAN can affect two or more realms: 1) impacts on species that have life cycles and/or stages in two or more realms, such as diadromous fish that cross realms during ontogenetic migrations and many terrestrial insects that have juvenile phases of the life cycle in aquatic realms; 2) impacts on species interactions that occur across realm boundaries, and 3) impacts on transition zones or ecosystems such as mangroves and estuaries. We then propose a framework for cross-realm management of light pollution and discuss current challenges and potential solutions to increase the uptake of a cross-realm approach for ALAN management. We argue that the strengthening and formalisation of professional networks that involve academics, lighting practitioners, environmental managers and regulators that work in multiple realms is essential to provide an integrated approach to light pollution. Networks that have a strong multi-realm and multi-disciplinary focus are important as they enable a holistic understanding of issues related to ALAN

Validation of \u2018Somnivore\u2019, a machine learning algorithm for automated scoring and analysis of polysomnography data

Manual scoring of polysomnography data is labor-intensive and time-consuming, and most existing software does not account for subjective differences and user variability. Therefore, we evaluated a supervised machine learning algorithm, Somnivore\u2122, for automated wake-sleep stage classification. We designed an algorithm that extracts features from various input channels, following a brief session of manual scoring, and provides automated wake-sleep stage classification for each recording. For algorithm validation, polysomnography data was obtained from independent laboratories, and include normal, cognitive-impairment, and alcohol-treated human subjects (total n=52), narcoleptic mice and drug-treated rats (total n=56), and pigeons (n=5). Training and testing sets for validation were previously scored manually by 1-2 trained sleep technologists from each laboratory. F-measure was used to assess precision and sensitivity for statistical analysis of classifier output and human scorer agreement. The algorithm gave high concordance with manual visual scoring across all human data (wake 0.91\ub10.01; N1 0.57\ub10.01; N2 0.81\ub10.01; N3 0.86\ub10.01; REM 0.87\ub10.01), which was comparable to manual inter-scorer agreement on all stages. Similarly, high concordance was observed across all rodent (wake 0.95\ub10.01; NREM 0.94\ub10.01; REM 0.91\ub10.01) and pigeon (wake 0.96\ub10.006; NREM 0.97\ub10.01; REM 0.86\ub10.02) data. Effects of classifier learning from single signal inputs, simple stage reclassification, automated removal of transition epochs, and training set size were also examined. In summary, we have developed a polysomnography analysis program for automated sleep-stage classification of data from diverse species. Somnivore enables flexible, accurate, and high-throughput analysis of experimental and clinical sleep studies