TY - GEN
T1 - Sleep stage classification using convolutional neural networks and bidirectional long short-Term memory
AU - Yulita, Intan Nurma
AU - Fanany, Mohamad Ivan
AU - Arymurthy, Aniati Murni
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - Classification of sleep stage is very useful to detect the occurrence of sleep apnea. This classification requires mechanisms that automatically and efficiently process polysomnography data. However, the process requires a system to be able to extract the relevant features which are then used to classify the sleep stage. The best solution is sequence classification because it not only concerns the contents of each segment or the sequence of data. One of the best order-based identifiers today is Long Short-Term Memory (LSTM). The LSTM can only update for forwarding directions. To process the data in two directions, it implemented Bidirectional Longs Short Term Memory (Bi-STM). Also, the implementation also applies Convolutional Neural Networks (CNN) as a feature learning before using Bi-LSTM. The result shows that F-measure Bi-LSTM is better than LSTM but use CNN as a learning attribute for Bi-LSTM cause an F-measure decrease.
AB - Classification of sleep stage is very useful to detect the occurrence of sleep apnea. This classification requires mechanisms that automatically and efficiently process polysomnography data. However, the process requires a system to be able to extract the relevant features which are then used to classify the sleep stage. The best solution is sequence classification because it not only concerns the contents of each segment or the sequence of data. One of the best order-based identifiers today is Long Short-Term Memory (LSTM). The LSTM can only update for forwarding directions. To process the data in two directions, it implemented Bidirectional Longs Short Term Memory (Bi-STM). Also, the implementation also applies Convolutional Neural Networks (CNN) as a feature learning before using Bi-LSTM. The result shows that F-measure Bi-LSTM is better than LSTM but use CNN as a learning attribute for Bi-LSTM cause an F-measure decrease.
KW - Bidirectional Long Short-Term Memory
KW - Convolutional Neural Networks
KW - Feature representation
KW - Sleep stage classification
UR - http://www.scopus.com/inward/record.url?scp=85051138753&partnerID=8YFLogxK
U2 - 10.1109/ICACSIS.2017.8355050
DO - 10.1109/ICACSIS.2017.8355050
M3 - Conference contribution
AN - SCOPUS:85051138753
T3 - 2017 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2017
SP - 303
EP - 307
BT - 2017 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2017
Y2 - 28 October 2017 through 29 October 2017
ER -