An approach to the determination of differences between good and bad sleepers by means of an automatic sleep stage scoring

  • Authors:

  • J. L. Navarro-Mesa;A. G. Ravelo-García;F. D. Lorenzo-García;S. I. Martín-González;E. Hernández-Pérez;P. Quintana-Morales

  • Affiliations:

  • Departamento de Señales y Comunicaciones, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain;Departamento de Señales y Comunicaciones, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain;Departamento de Señales y Comunicaciones, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain;Departamento de Señales y Comunicaciones, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain;Departamento de Señales y Comunicaciones, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain;Departamento de Señales y Comunicaciones, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain

  • Venue:
  • ACS'06 Proceedings of the 6th WSEAS international conference on Applied computer science
  • Year:
  • 2006

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Abstract

This paper presents a sleep stage scoring method based on a Hidden Markov Model (HMM) with the goal of obtaining differences between good and bad sleepers according to the Self Rating Questionnaire for Sleep and Awakening Quality (SSA). For the design of the model, we study several parameterization techniques, the model topology and the training strategy for optimum performance. The system uses only one electroencephalographic channel (EEG), which represents an improvement over manual and automatic classifiers that use several channels. We adopt in our study the sleep stages W, S1/REM, S2 and S3/S4 according to the R&K standard. The experiments show that our system performs well compared with the inter scorer agreement. The experiments are performed over 24 recordings from SIESTA database.