Noninvasive three-state sleep-wake staging in mice using electric field sensors (original) (raw)
Related papers
Real-time sleep-wake scoring in the rat using a single EEG channel
Sleep, 1994
A new method of analysis of sleep in the rat based on the electrocorticogram (ECoG) is described. Three states, awake (W), nonrapid eye movement (NREM) and rapid eye movement (REM) sleep are automatically classified by the system. After amplification of the ECoG over a restricted bandwidth (3.18-25 Hz) and sampling at 512 Hz, the data are processed in 8-second epochs by a microcomputer, which generates three statistical and two harmonic variables. Each 8-second epoch is thus compressed into five numerical values occupying 10 bytes of memory. Epochs for each state of vigilance are identified by an expert observer to produce three reference models. The program classifies each epoch into the appropriate state by the least quadratic distance. The system was validated by comparing the results with a visual analysis of polygraph recordings. The agreement between the program and the two independent scorers for 24-hour tracings of six rats was 83% for REM sleep, 96% for W and 97% for NREM s...
Journal of Neuroscience Methods, 2009
Manual state scoring of physiological recordings in sleep studies is time-consuming, resulting in a data backlog, research delays and increased personnel costs. We developed MATLAB-based software to automate scoring of sleep/waking states in rats, potentially extendable to other animals, from a variety of recording systems. The software contains two programs, Sleep Scorer and Auto-Scorer, for manual and automated scoring. Auto-Scorer is a logic-based program that displays power spectral densities of an electromyographic signal and σ, δ, and θ frequency bands of an electroencephalographic signal, along with the δ/θ ratio and σ ×θ, for every epoch. The user defines thresholds from the training file state definitions which the Auto-Scorer uses with logic to discriminate the state of every epoch in the file. Auto-Scorer was evaluated by comparing its output to manually scored files from 6 rats under 2 experimental conditions by 3 users. Each user generated a training file, set thresholds, and autoscored the 12 files into 4 states (waking, non-REM, transitionto-REM, and REM sleep) in ¼ the time required to manually score the file. Overall performance comparisons between Auto-Scorer and manual scoring resulted in a mean agreement of 80.24 +/− 7.87%, comparable to the average agreement among 3 manual scorers (83.03 +/− 4.00%). There was no significant difference between user-user and user-Auto-Scorer agreement ratios. These results support the use of our open-source Auto-Scorer, coupled with user review, to rapidly and accurately score sleep/waking states from rat recordings.
Sleep-Stage Scoring in Mice: The Influence of Data Pre-Processing on a System's Performance.
Sleep-stage analysis in mice and rats has received growing attention in recent years, due to the fact that mice display electrical activity during sleep which has underlying similarities with that of human sleep. Both conventional manual and automatic sleep-wakefulness scoring are rule based tasks which use brain waves measured by Electroencephalogram (EEG) and activity detected by Electromyography (EMG) of skeletal muscles. Several works have been conducted trying to provide an automatic sleep-scoring system on the basis of machine learning methods. In this study we try to understand the reasons behind the complexity of this problem and we emphasize the importance of normalization procedure that leads to a better stage discrimination comparing different classification methods.
Sleep Medicine Reviews, 2021
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Rapid Assessment of Sleep-Wake Behavior in Mice
Journal of Biological Rhythms, 2012
Sleep is a fundamental biological rhythm involving the interaction of numerous brain structures and diverse neurotransmitter systems. The primary measures used to define sleep are the electroencephalogram (EEG) and electromyogram (EMG). However, EEG-based methods are often unsuitable for use in high-throughput screens as they are time-intensive and involve invasive surgery. As such, the dissection of sleep mechanisms and the discovery of novel drugs that modulate sleep would benefit greatly from further development of rapid behavioral assays to assess sleep in animal models. Here is described an automated noninvasive approach to evaluate sleep duration, latency, and fragmentation using video tracking of mice in their home cage. This approach provides a high correlation with EEG/EMG measures under both baseline conditions and following administration of pharmacological agents. Moreover, the dose-dependent effects of sedatives, stimulants, and light can be readily detected. This approach is robust yet relatively inexpensive to implement and can be easily incorporated into ongoing screening programs to provide a powerful first-pass screen for assessing sleep and allied behaviors.
Light-weight sleep monitoring: electrode distance matters more than placement for automatic scoring
2021
Modern sleep monitoring development is shifting towards the use of unobtrusive sensors combined with algorithms for automatic sleep scoring. Many different combinations of wet and dry electrodes, earcentered, forehead-mounted or headband-inspired designs have been proposed, alongside an ever growing variety of machine learning algorithms for automatic sleep scoring. In this paper, we compare 13 different, realistic sensor setups derived from the same data set and analysed with the same pipeline. We find that all setups which include both a lateral and an EOG derivation show similar, state-ofthe-art performance, with average Cohen’s kappa values of at least 0.80. This indicates that electrode distance, rather than position, is important for accurate sleep scoring. Finally, based on the results presented, we argue that with the current competitive performance of automated staging approaches, there is an urgent need for establishing an improved benchmark beyond current single human rat...
Novel method for high-throughput phenotyping of sleep in mice
Physiological Genomics, 2006
Assessment of sleep in mice currently requires initial implantation of chronic electrodes for assessment of electroencephalogram (EEG) and electromyogram (EMG) followed by time to recover from surgery. Hence, it is not ideal for high-throughput screening. To address this deficiency, a method of assessment of sleep and wakefulness in mice has been developed based on assessment of activity/inactivity either by digital video analysis or by breaking infrared beams in the mouse cage. It is based on the algorithm that any episode of continuous inactivity of ≥40 s is predicted to be sleep. The method gives excellent agreement in C57BL/6J male mice with simultaneous assessment of sleep by EEG/EMG recording. The average agreement over 8,640 10-s epochs in 24 h is 92% ( n = 7 mice) with agreement in individual mice being 88–94%. Average EEG/EMG determined sleep per 2-h interval across the day was 59.4 min. The estimated mean difference (bias) per 2-h interval between inactivity-defined sleep ...
Development of a rule-based automatic five-sleep-stage scoring method for rats
BioMedical Engineering OnLine, 2019
Background Mammals display different patterns of sleep-wake stages, which comprise cyclic patterns of wakefulness (wake), non-rapid eye movement (NREM) sleep and rapid eye movement (REM) sleep. These stages have been defined based on electrophysiological measurements that include electroencephalogram (EEG), electromyogram (EMG), and electrooculogram (EOG). Many diseases and cognitive behaviors have a relationship with sleep quality and quantity. For instance, patients with chronic widespread pain syndromes often exhibit sleep disturbance [1]. These patients usually complain unrefreshing sleep, i.e., too much light sleep or irregular sleep pattern [2]. To understand the pathogenesis of pain or comorbidity of sleep disturbance, an ideal animal model may be
SLEEP, 2014
Traditionally, sleep studies in mammals are performed using electroencephalogram/electromyogram (EEG/EMG) recordings to determine sleep-wake state. In laboratory animals, this requires surgery and recovery time and causes discomfort to the animal. In this study, we evaluated the performance of an alternative, noninvasive approach utilizing piezoelectric films to determine sleep and wakefulness in mice by simultaneous EEG/EMG recordings. The piezoelectric films detect the animal's movements with high sensitivity and the regularity of the piezo output signal, related to the regular breathing movements characteristic of sleep, serves to automatically determine sleep. Although the system is commercially available (Signal Solutions LLC, Lexington, KY), this is the first statistical validation of various aspects of sleep. Design: EEG/EMG and piezo signals were recorded simultaneously during 48 h. Setting: Mouse sleep laboratory. Participants: Nine male and nine female CFW outbred mice. Interventions: EEG/EMG surgery.
Biological Rhythm Research, 2011
The two traditional terminological systems for description of sleep–wake phenomena are based on criteria for visual scoring of sleep and on measuring EEG power densities in conventional frequency bands. There are serious limitations concerning the use of these terminologies for distinguishing different sleep–wake sub-states and transitions between them. Polysomnographic recordings obtained throughout all-night sleep (n = 14) and during 24-hour multiple sleep latency test (n = 32) were analyzed to demonstrate the possibilities (1) to uncover the universal three principal component structure of wake and sleep EEG spectra, (2) to predict such wake–sleep parameters as sleepiness, sleep latency, amount of any sleep stage, etc. from the three principal component scores, and (3) to link the magnitudes and time courses of these scores with the levels and kinetics of sleep–wake regulatory processes. This demonstration could lead to the development of a theoretically based terminological system allowing meaningful, parsimonious and quantitative description of sub-states along the sleep–wake continuum.