Improved sleep–wake and behavior discrimination using MEMS accelerometers
Introduction
There is a long history of determining the state of vigilance for humans (Broughton, 1999) or animals (Robert et al., 1999) using EEG criteria. Nevertheless, there is considerable uncertainty when determining the state of vigilance using EEG alone. Therefore, EEG is typically augmented with simultaneous visual behavioral monitoring, and/or the incorporation of electromyogram (EMG) or electrooculogram (EOG) recordings.
We are developing the technical capability for discriminating state of vigilance in real time. Our objective is to provide contextual input for seizure prediction and control. It is readily accepted that the dynamics of EEG change remarkably in the different stages of wakefulness and sleep (Niedermeyer, 1999). Yet much of the current work in seizure detection and prediction (Mormann et al., 2005) focuses on statistical or dynamical changes of the EEG with respect to a baseline defined without regard to state. Our end goal is to implement state-dependent seizure detection and control in medical devices suitable for human implantation during the activities of daily living, where continuous video monitoring would not be feasible and EMG or EOG electrodes might be invasive or cumbersome.
In this study, we explored combining head acceleration measurements with EEG in order to improve our ability to discriminate state of vigilance in rodent experiments. We incorporated biaxial dc-sensitive microelectromechanical system (MEMS) accelerometers into the head-mounted preamplifier circuit used for EEG recording. We used combined EEG and behavioral video to establish training and validation data sets, and then used EEG features with and without accelerometer features in a multivariate linear classifier. We tested a broad range of EEG feature sets based on those used in the recent literature for state discrimination in rodents (Robert et al., 1999). Our approach offers a novel methodology for determining the behavioral context of EEG in real time. Preliminary results of this investigation were previously reported (Peixoto et al., 2004, Sunderam et al., 2005).
Section snippets
Surgical implantation and data acquisition
Video (3 fps including visual and infrared sensitivity), EEG and head acceleration were recorded continuously from adult male Sprague-Dawley rats 200–300 g in weight (Harlan Ltd., NY) using a custom-made electronics and acquisition system (Labview, National Instruments Ltd.).
Head-mounted preamplifiers were constructed with integrated dc-sensitive biaxial MEMS accelerometers (ADXL 311, Analog Devices Ltd.; sensitivity of 312 mV per g, where g = acceleration due to gravity, at the reference voltage of
Manual scoring statistics
For each animal, the fraction of epochs in the training set manually scored as each individual state is recorded in Table 1. Not counting Indeterminate epochs, the rats spent about 60% of the time in sleep, and over 80% of sleep in SWS. These values compare well with the literature (Gervasoni et al., 2004, Antle and Mistlberger, 2005) which indicates that this data set is representative of typical circadian behavior for rats.
Motion signatures of states
The animal's overall activity level was reflected in the total power
Discussion
We have demonstrated that the addition of head acceleration measurements to EEG spectral measurements significantly improves LDA use to automatically classify sleep–wake and behavioral state. With EEG alone, we observed wide variability in discrimination performance based on the set of EEG variables used (Costa-Miserachs et al., 2003, Gervasoni et al., 2004, Louis et al., 2004, Sunderam et al., 2005). The addition of acceleration improved and equalized their performance, and rendered the choice
Conclusion
We have shown significant improvement in state discrimination accuracy afforded by the addition of acceleration measurements to EEG. MEMS accelerometers are lightweight (0.15 g) and inexpensive, less invasive than EMG or EOG, and the modifications in circuitry over that required for EEG acquisition are minimal. We intend to use this improved behavioral state detection capability to formulate and inform detection and feedback control algorithms for treating seizures.
Acknowledgements
This work was funded by National Institutes of Health grants R01EB001507, K02MH01493 and R01MH50006.
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