MASC: Automatic sleep stage classification based on brain and myoelectric signals

Yuta Suzuki, Makito Sato, Hiroaki Shiokawa, Masashi Yanagisawa, Hiroyuki Kitagawa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Given brain and myoelectric signals taken from a mouse, how can we classify its sleep stages accurately? Classifying sleep stages is the fundamental problem in recent diagnoses and clinical researches. However, sleep staging suffers from a serious weakness; clinical experts visually inspect the brain and myoelectric signals to improve sleep staging accuracy. This is because recent diagnoses and clinical researches require classification accuracy at least 95% so as to enhance preciseness of their analyses. In this paper, we present an automatic classification method MASC based on the following three approaches: (1) it extracts effective features for fully representing each sleep stage property, (2) it classifies sleep stages by using temporal patterns of sleep stage transitions, and (3) it re-classifies sleep stages only for the results with low-confidence. As a result, MASC achieves more than 95% accuracy for both noisy and noiseless mice data.

Original languageEnglish (US)
Title of host publicationProceedings - 2017 IEEE 33rd International Conference on Data Engineering, ICDE 2017
PublisherIEEE Computer Society
Pages1489-1496
Number of pages8
ISBN (Electronic)9781509065431
DOIs
StatePublished - May 16 2017
Event33rd IEEE International Conference on Data Engineering, ICDE 2017 - San Diego, United States
Duration: Apr 19 2017Apr 22 2017

Other

Other33rd IEEE International Conference on Data Engineering, ICDE 2017
CountryUnited States
CitySan Diego
Period4/19/174/22/17

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Information Systems

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  • Cite this

    Suzuki, Y., Sato, M., Shiokawa, H., Yanagisawa, M., & Kitagawa, H. (2017). MASC: Automatic sleep stage classification based on brain and myoelectric signals. In Proceedings - 2017 IEEE 33rd International Conference on Data Engineering, ICDE 2017 (pp. 1489-1496). [7930115] IEEE Computer Society. https://doi.org/10.1109/ICDE.2017.218