TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Stuburić, Klara
A1  - Gaiduk, Maksym
A1  - Seepold, Ralf
T1  - A deep learning approach to detect sleep stages
T2  - 24rd International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES2020), 16 - 18 September 2020, Verona, Italy, virtual; (Procedia Computer Science)
N2  - This paper presents the implementation of deep learning methods for sleep stage detection by using three signals that can be measured in a non-invasive way: heartbeat signal, respiratory signal, and movement signal. Since signals are measurements taken during the time, the problem is seen as time-series data classification. Deep learning methods are chosen to solve the problem are convolutional neural network and long-short term memory network. Input data is structured as a time-series sequence of mentioned signals that represent 30 seconds epoch, which is a standard interval for sleep analysis. The records used belong to the overall 23 subjects, which are divided into two subsets. Records from 18 subjects were used for training the data and from 5 subjects for testing the data. For detecting four sleep stages: REM (Rapid Eye Movement), Wake, Light sleep (Stage 1 and Stage 2), and Deep sleep (Stage 3 and Stage 4), the accuracy of the model is 55%, and F1 score is 44%. For five stages: REM, Stage 1, Stage 2, Deep sleep (Stage 3 and 4), and Wake, the model gives an accuracy of 40% and F1 score of 37%.
KW  - Sleep stages
KW  - Deep learning
KW  - Biosignal processing
KW  - Convolutional neural network
Y1  - 2020
SN  - 1877-0509
SS  - 1877-0509
U6  - https://doi.org/10.1016/j.procs.2020.09.280
DO  - https://doi.org/10.1016/j.procs.2020.09.280
VL  - 176
SP  - 2764
EP  - 2772
PB  - Elsevier
CY  - Amsterdam u.a.
ER  -