- Title
- Automatic sleep stage identification: difficulties and possible solutions
- Creator
- Sukhorukova, Nadezda; Stranieri, Andrew; Ofoghi, Bahadorreza; Vamplew, Peter; Saleem, Muhammad Saad; Ma, Liping; Ugon, Adrien; Ugon, Julien; Muecke, Nial; Amiel, Hélène; Philippe, Carole; Bani-Mustafa, Ahmed; Huda, Shamsul; Bertoli, Marcello; Levy, P; Ganascia, J.G
- Date
- 2010
- Type
- Text; Conference proceedings
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/56893
- Identifier
- vital:4367
- Abstract
- The diagnosis of many sleep disorders is a labour 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 determing 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 approach.
- Publisher
- Brisbane Australian Computer Society
- Rights
- Open Access
- Rights
- This metadata is freely available under a CCO license
- Subject
- Sleep stage identification; Data mining
- Full Text
- Hits: 13075
- Visitors: 11870
- Downloads: 249
| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details Download | SOURCE1 | Published version | 628 KB | Adobe Acrobat PDF | View Details Download |