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) |
|---|---|
| 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 |
|---|---|
| Country | United States |
| City | San Diego |
| Period | 4/19/17 → 4/22/17 |
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ASJC Scopus subject areas
- Software
- Signal Processing
- Information Systems
Cite this
MASC : Automatic sleep stage classification based on brain and myoelectric signals. / Suzuki, Yuta; Sato, Makito; Shiokawa, Hiroaki; Yanagisawa, Masashi; Kitagawa, Hiroyuki.
Proceedings - 2017 IEEE 33rd International Conference on Data Engineering, ICDE 2017. IEEE Computer Society, 2017. p. 1489-1496 7930115.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - MASC
T2 - Automatic sleep stage classification based on brain and myoelectric signals
AU - Suzuki, Yuta
AU - Sato, Makito
AU - Shiokawa, Hiroaki
AU - Yanagisawa, Masashi
AU - Kitagawa, Hiroyuki
PY - 2017/5/16
Y1 - 2017/5/16
N2 - 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.
AB - 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.
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UR - http://www.scopus.com/inward/citedby.url?scp=85021253948&partnerID=8YFLogxK
U2 - 10.1109/ICDE.2017.218
DO - 10.1109/ICDE.2017.218
M3 - Conference contribution
AN - SCOPUS:85021253948
SP - 1489
EP - 1496
BT - Proceedings - 2017 IEEE 33rd International Conference on Data Engineering, ICDE 2017
PB - IEEE Computer Society
ER -