Automated determination of wakefulness and sleep in rats based on non-invasively acquired measures of movement and respiratory activity

Abstract

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. 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. A set of 23 frequency and time-domain features were derived from these signals and were calculated in 10 s epochs. 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). 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. 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. 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. However, additional information or signals will be needed to improve discrimination of NREM and REM episodes within sleep.

Introduction

Accurate assessment and analysis of sleep stages is a fundamental requirement in sleep research. 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). 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). 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.

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. These include the need for labor-intensive surgery to implant electrodes and the need to provide extensive post-surgical care to recovering animals. 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). Scoring the resultant EEG and EMG recordings to determine wakefulness and sleep states can also be time-consuming. These limitations indicate the need for non-invasive techniques that can record physiological parameters and that are amenable to rapid assessment of behavioral states.

A number of non-invasive approaches for assessment of behavioral state have been attempted. These include measurements of activity resulting from infrared beam breaks, frame-by-frame analysis of digital video (Pack et al., 2007) and from a pressure sensor located on the cage floor (Donohue et al., 2008, Flores et al., 2007). Each of these methods reportedly provides reasonable accuracy in distinguishing sleep from wakefulness. However, work in both humans and animals indicate that analysis of movements is not sufficient for discriminating sleep and wakefulness in all situations. In humans, actigraphy can fail during periods of low activity in wakefulness (Karlen et al., 2008) and its accuracy may decline as sleep efficiency decreases (Ancoli-Israel et al., 2003, Morgenthaler et al., 2007a, Morgenthaler et al., 2007b). In animals, our lab has shown that movement may be less discriminating of sleep for less active strains of mice (Tang et al., 2002). In addition, while detection of wakefulness and sleep is adequate for many purposes, methods need to be developed that will enable NREM and REM to be distinguished to provide broader utility and improved data across situations.

In humans, respiratory rates show state-related differences with slower, steady rates in NREM, whereas one of the hallmark signs of REM is irregular respiratory activity, in particular, during phasic REM (Pack et al., 1988). Heart rate also slows from relaxed wakefulness to NREM. It is also low during tonic REM, but there can be wide swings in heart rate during phasic REM (Pack et al., 1988). Changes in respiratory and heart rates, by themselves, may not be sufficient to clearly distinguish sleep and wakefulness. However, in combination with a measure of movement, the validity and reliability of state determination based on these parameters may be improved considerably (Karlen et al., 2008).

Doppler radar has been used to record movement in rodents (Kjellstrand et al., 1985, Marsden and King, 1979, Rose et al., 1985) and has the potential for monitoring physiologic signals including respiratory rate (Gordon and Ali, 1984, Lin, 1975, Lin, 1992) and heart rate (Lin, 1992). Doppler radar has been applied to human respiratory and heart monitoring (Staderini, 2002) and Doppler measurements of movement and respiratory activity have been used in humans to determine sleep-wake states (de Chazal et al., 2008).

In this paper, we describe a method using pulse Doppler radar for non-invasive assessment of sleep and wakefulness in rats. We utilized a 5800 MHz pulse Doppler radar sensor to non-invasively detect movement and respiratory activity, and then based on these signals, we developed an automated computer program to classify wakefulness, NREM and REM method using support vector machines (SVMs). Parallel measures of activity using digital video analysis were also obtained. The accuracy of the of non-invasively detected changes in state was determined by comparing results to those obtained by concurrent recording and scoring of sleep states based on EEG and EMG parameters. Our goal was to devise a non-invasive sleep and arousal monitoring system suitable for high-throughput screening and for assessing sleep in experimental situations (e.g., stress paradigms) that may be susceptible to confounds produced by cabling or other recording devices. The results suggest that this approach can provide a useful complementary research method for sleep research.