![]() ![]() ![]() These limitations indicate the need for non-invasive techniques that can record physiological parameters and that are amenable to rapid assessment of behavioral states.Ī number of non-invasive approaches for assessment of behavioral state have been attempted. Scoring the resultant EEG and EMG recordings to determine wakefulness and sleep states can also be time-consuming. There is also the possibility that the recording technique (e.g., cable recording) can affect the parameter being measured and/or may limit the animal’s behavior ( Tang and Sanford, 2002). These include the need for labor-intensive surgery to implant electrodes and the need to provide extensive post-surgical care to recovering animals. While conventional scoring techniques yield accurate results in discriminating arousal and sleep states through the examination of electrophysiological signals obtained from animals, they also have a number of inherent limitations. Determining these three arousal states in rats and other animals typically relies on recordings of the electroencephalogram (EEG) and electromyogram (EMG) and assessments of state-related changes using well-established scoring conventions. Three basic states of arousal and sleep are typically distinguished in basic sleep research: wakefulness, non-rapid eye movement sleep (NREM) and rapid eye movement sleep (REM). Rodents are often used as models in the sleep field due to their ready availability and the similarities of their sleep to human sleep ( Bergmann et al., 1987). However, additional information or signals will be needed to improve discrimination of NREM and REM episodes within sleep.Īccurate assessment and analysis of sleep stages is a fundamental requirement in sleep research. ![]() The results indicate that automated scoring based on non-invasively acquired movement and respiratory activity will be useful for studies requiring discrimination of wakefulness and sleep. Agreement between SVM automated scoring based on selected features and visual scores based on EEG and EMG were approximately 91% for wakefulness, 84% for NREM and 70% for REM. We then assessed the utility of the automated scoring system in discriminating wakefulness and sleep by comparing the results to standard scoring of wakefulness and sleep based on concurrently recorded EEG and EMG. Based on these features, a classification method for automated scoring of wakefulness, non-rapid eye movement sleep (NREM) and REM in rats was developed using a support vector machine (SVM). A set of 23 frequency and time-domain features were derived from these signals and were calculated in 10 s epochs. With the goal of developing a non-invasive method to determine sleep and wakefulness, we constructed a non-contact monitoring system to measure movement and respiratory activity using signals acquired with pulse Doppler radar and from digitized video analysis. The current standard for monitoring sleep in rats requires labor intensive surgical procedures and the implantation of chronic electrodes which have the potential to impact behavior and sleep. ![]()
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