Sleep scoring involves classification of polysomnographic data into the various sleep
stages as defined by Retschaffen and Kales. This process is time-consuming and
laborious as it involves experts visually scoring the data. During recent years, there has
been an increasing focus on automated sleep scoring systems and professional software
programs are finding increased use. However, these systems are not relied on for scoring
and are often used as a tool that facilitates easy visual scoring.
This thesis proposes a neural network based approach to automatic sleep scoring
using LabVIEW. Effort has been made to give the sleep expert more control over key
parameters such as the frequency bands, and thus come up with scores that are more in
agreement with the individual scorer than being a rigid interpretation of the R&K rules.
Though this thesis is limited to the development of an offline software program, given
the data acquisition facilites in LabVIEW, a complete system from data acquisition to
sleep hypnograms is a fair possibility.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/3187 |
| Date | 12 April 2006 |
| Creators | Deshpande, Parikshit Bapusaheb |
| Contributors | Bhattacharyya, Shankar, Lessard, Charles |
| Publisher | Texas A&M University |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Thesis, text |
| Format | 992237 bytes, electronic, application/pdf, born digital |
Page generated in 0.0017 seconds