Automatic sleep stage identification: difficulties and possible solutions

Sukhorukova, N., Stranieri, A., Ofoghi, B., Vamplew, P., Saleem, M., Ma, L., Ugon, A., Ugon, J., Muecke, N., Amiel, H., Philippe, C., Bani­Mustafa, A., Huda, S., Bertoli, M., Lévy, P., Ganascia, J.-G.

The diagnosis of many sleep disorders is a labor intensive task that involves the specialised interpretation of numerous signals including brain wave, breath and heart rate captured in overnight polysomnogram sessions. The automation of diagnoses is challenging for data mining algorithms because the data sets are extremely large and noisy, the signals are complex and specialist's analyses vary. This work reports on the adaptation of approaches from four fields; neural networks, mathematical optimisation, financial forecasting and frequency domain analysis to the problem of automatically determining a patient's stage of sleep. Results, though preliminary, are promising and indicate that combined approaches may prove more fruitful than the reliance on a single approach.

Cite as: Sukhorukova, N., Stranieri, A., Ofoghi, B., Vamplew, P., Saleem, M., Ma, L., Ugon, A., Ugon, J., Muecke, N., Amiel, H., Philippe, C., Bani­Mustafa, A., Huda, S., Bertoli, M., Lévy, P., Ganascia, J.-G. (2010). Automatic sleep stage identification: difficulties and possible solutions. In Proc. Fourth Australasian Workshop on Health Informatics and Knowledge Management (HIKM 2010) Brisbane, Australia. CRPIT, 108. Maeder, A. and Hansen, D. Eds., ACS. 39-44

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