Sleep stage diagnosis system with neural network analysis

Takamasa Shimada, Tsuyoshi Shiina, Yoichi Saito

Research output: Contribution to journalConference articlepeer-review

13 Citations (Scopus)

Abstract

Information of sleep stage is one of the most important clue for diagnosis of mental condition and disease in psychiatry. So far we have proposed a method of detection of characteristic waves in sleep EEG and diagnosing the sleep stages of the segmented short-terms by neural networks analysis. And we showed that it was able to diagnose the sleep stages to some extent by recognizing the time-varying spectral patterns of characteristic waves. There remains, however, a problem that results in stage diagnosis often become unstable, since the contextual relation between the present and adjacent segments is not considered. In this work, a method of diagnosing the sleep stage more accurately is proposed and its performance is evaluated. In the method, the additional neural networks processing is combined with the previous system for recognizing the context of stage sequences. As a result, it is proved that detection ratio is improved to a considerable extent by utilizing the contextual information on stages and the proper duration exists for obtaining high performance.

Original languageEnglish
Pages (from-to)2074-2077
Number of pages4
JournalAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume4
Publication statusPublished - 1998
Externally publishedYes
EventProceedings of the 1998 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Part 4 (of 6) - Hong Kong, China
Duration: 1998 Oct 291998 Nov 1

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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