TY - GEN
T1 - An integrated sleep stage classification device based on electrocardiograph signal
AU - Hermawan, Indra
AU - Alvissalim, M. Sakti
AU - Tawakal, M. Iqbal
AU - Jatmiko, Wisnu
PY - 2012
Y1 - 2012
N2 - In this paper, a portable, easy to use, and real-time sleep stage classification device is presented. A simpler approach using raw features of ECG signals for sleep stage classification has been developed. Only one lead of ECG signal is required for operation which makes the device easily operable and only requiring user to attach 3 electrodes to the body. The device is constructed with singleboard computer and electronic circuits with Surface-Mount Device (SMD) technology making it small in size and highly portable. Classification can be done real time with an average delay of 20 seconds. Two sleep stages, namely the Awake and Non-Wake Sleep, can be differentiated by Random Forest algorithm. Data from the MITRA database was used for training and testing. Data from one patient was left out from the training set and used for testing. The device's recognition has high performance with weighted average value of 0.941 for Precision and value of 0.942 for Recall.
AB - In this paper, a portable, easy to use, and real-time sleep stage classification device is presented. A simpler approach using raw features of ECG signals for sleep stage classification has been developed. Only one lead of ECG signal is required for operation which makes the device easily operable and only requiring user to attach 3 electrodes to the body. The device is constructed with singleboard computer and electronic circuits with Surface-Mount Device (SMD) technology making it small in size and highly portable. Classification can be done real time with an average delay of 20 seconds. Two sleep stages, namely the Awake and Non-Wake Sleep, can be differentiated by Random Forest algorithm. Data from the MITRA database was used for training and testing. Data from one patient was left out from the training set and used for testing. The device's recognition has high performance with weighted average value of 0.941 for Precision and value of 0.942 for Recall.
UR - http://www.scopus.com/inward/record.url?scp=84875122010&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84875122010
SN - 9789791421157
T3 - 2012 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2012 - Proceedings
SP - 37
EP - 41
BT - 2012 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2012 - Proceedings
T2 - 2012 4th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2012
Y2 - 1 December 2012 through 2 December 2012
ER -