Automated NREM sleep staging using the Electro-oculogram: A pilot study

Automatic sleep staging from convenient and unobtrusive sensors has received considerable attention lately because this can enable a large range of potential applications in the clinical and consumer fields. In this paper the focus is on achieving non-REM (NREM) sleep staging from ocular electrodes. From these signals, specific patterns related to sleep such as slow eye movements, K-complexes, eye blinks, and spectral features are estimated. Although such patterns are characteristic of the Electroencephalogram, they can also be visible to a lesser extent on signals from ocular electrodes. Automatic sleep staging was implemented using two approaches: (i) based on a state-machine and (ii) using a neural network. The first one relied on the recommendations of the American Academy of Sleep Medicine, and the second one used a multilayer perceptron which was trained on manually sleep-staged data. Results were obtained on the data of five volunteers who participated in a nap experiment. Manual sleep staging of this data, performed by an expert, was used as reference. Five stages were considered, namely wake with eyes open, wake with eyes closed, and sleep stages N1, N2, and N3. The results were characterized in terms of confusion matrices from which the Cohen's κ coefficients were estimated. The values of κ for both the state-machine and neural-network based automatic slAutomatic sleep staging from convenient and unobtrusive sensors has received considerable attention lately because this can enable a large range of potential applications in the clinical and consumer fields. In this paper the focus is on achieving non-REM (NREM) sleep staging from ocular electrodes. From these signals, specific patterns related to sleep such as slow eye movements, K-complexes, eye blinks, and spectral features are estimated. Although such patterns are characteristic of the Electroencephalogram, they can also be visible to a lesser extent on signals from ocular electrodes. Automatic sleep staging was implemented using two approaches: (i) based on a state-machine and (ii) using a neural network. The first one relied on the recommendations of the American Academy of Sleep Medicine, and the second one used a multilayer perceptron which was trained on manually sleep-staged data. Results were obtained on the data of five volunteers who participated in a nap experiment. Manual sleep staging of this data, performed by an expert, was used as reference. Five stages were considered, namely wake with eyes open, wake with eyes closed, and sleep stages N1, N2, and N3. The results were characterized in terms of confusion matrices from which the Cohen's κ coefficients were estimated. The values of κ for both the state-machine and neural-network based automatic sleep staging approaches were 0.79 and 0.59 respectively. Thus, the state-machine based approach shows a very good agreement with manual staging of sleep-data.eep staging approaches were 0.79 and 0.59 respectively. Thus, the state-machine based approach shows a very good agreement with manual staging of sleep-data.