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 language | English (US) |
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Title of host publication | Proceedings - 2017 IEEE 33rd International Conference on Data Engineering, ICDE 2017 |
Publisher | IEEE Computer Society |
Pages | 1489-1496 |
Number of pages | 8 |
ISBN (Electronic) | 9781509065431 |
DOIs | |
State | Published - May 16 2017 |
Event | 33rd IEEE International Conference on Data Engineering, ICDE 2017 - San Diego, United States Duration: Apr 19 2017 → Apr 22 2017 |
Other
Other | 33rd IEEE International Conference on Data Engineering, ICDE 2017 |
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Country | United States |
City | San Diego |
Period | 4/19/17 → 4/22/17 |
ASJC Scopus subject areas
- Software
- Signal Processing
- Information Systems