Basic NeuroscienceAutomated determination of wakefulness and sleep in rats based on non-invasively acquired measures of movement and respiratory activity
Highlights
► We constructed a non-contact monitoring system to measure movement and respiration. ► We developed a method for automated scoring of wakefulness and sleep using a support vector machine. ► Agreement between automated and visual scoring was 91% for wakefulness. ► Agreement was 84% for non-rapid eye movement sleep and 70% for rapid eye movement sleep. ► Automated scoring based on movement and respiration can discriminate wakefulness and 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.
Section snippets
Subjects
The subjects were 7 male Wistar rats of approximately 10 weeks of age at the time of surgery. The rats were individually housed in polycarbonate cages and given ad libtitum access to food and water. The colony rooms were kept on a 12/12 light/dark cycle with lights on 07:00–19:00, EST. Ambient room temperature was maintained at 24.5 ± 0.5 °C.
Surgery
The rats were implanted with two screw electrodes in the skull for recording the electroencephalogram (EEG). An additional screw electrode was placed in the
Detection of activity and respiration
Fig. 2 presents sample waveforms of signals obtained by the radar sensor in wakefulness (A), NREM (D) and REM (G). Output of the Doppler radar sensor during wakefulness was characterized by large movements and corresponding greater power in the FFT at low frequencies (Fig. 2B). Output of the sensor during sleep showed cyclic oscillations that corresponded to rat chest wall motion due to respiration during NREM (Fig. 2D) and REM (Fig. 2G). The waveform of the respiratory signal during NREM was
Discussion
The aim of this study was to investigate the possibility of establishing a novel, non-invasive automated sleep scoring system in rats based on gross motor activity and respiration. The results suggest that measures of activity and respiration obtained by pulse Doppler radar and analyzed using SVM procedures can provide useful estimates of the three main behavioral states of interest in sleep research.
The bio-motion radar sensor used in this study was a quadrature pulse Doppler radar which has
Acknowledgments
This work was supported by NIH research grants RR20816, MH64827 and MH61716.
References (54)
- et al.
Protein function classification via support vector machine approach
Math Biosci
(2003) - et al.
Sleep-stage scoring in the rat using a support vector machine
J Neurosci Methods
(2008) - et al.
Measurement of ventilatory frequency in unrestrained rodents using microwave radiation
Respir Physiol
(1984) - et al.
Effects of organic solvents on motor activity in mice
Toxicology
(1985) - et al.
Design and validation of a computer-based sleep-scoring algorithm
J Neurosci Methods
(2004) - et al.
The use of Doppler shift radar to monitor physiological and drug induced activity patterns in the rat
Pharmacol Biochem Behav
(1979) - et al.
Doppler shift radar monitoring of activity of rats in a behavioural test situation
Physiol Behav
(1985) - et al.
Rat strain differences in sleep after acute mild stressors and short-term sleep loss
Behav Brain Res
(2005) - et al.
Home cage activity and behavioral performance in inbred and hybrid mice
Behav Brain Res
(2002) Support vector machines for pattern classification
(2010)
The role of actigraphy in the study of sleep and circadian rhythms
Sleep
Markov processes for the prediction of aircraft noise effects on sleep
Med Decis Making
Genetic influences on cardiovascular responses to an acoustic startle stimulus in rats
Clin Exp Pharmacol Physiol
Scoring transitions to REM sleep in rats based on the EEG phenomena of pre-REM sleep: an improved analysis of sleep structure
Sleep
NREM sleep with low-voltage EEG in the rat
Sleep
Cardiac activity in the mouse: strain differences
J Comp Physiol Psychol
A tutorial on support vector machines for pattern recognition
Data Min Knowl Disc
A coefficient of agreement for nominal scales
Educ Psychol Meas
Assessment of sleep/wake patterns using a non-contact biomotion sensor
Conf Proc IEEE Eng Med Biol Soc
Assessment of a non-invasive high-throughput classifier for behaviours associated with sleep and wake in mice
Biomed Eng Online
Range correlation and I/Q performance benefits in single-chip silicon Doppler radars for noncontact cardiopulmonary monitoring
IEEE Trans Microw Theory Tech
Clip-on wireless wearable microwave sensor for ambulatory cardiac monitoring
Pattern recognition of sleep in rodents using piezoelectric signals generated by gross body movements
IEEE Trans Biomed Eng
Ventilatory behavior during sleep among A/J and C57BL/6J mouse strains
J Appl Physiol
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