A new automatic sleep stage classification model using swarm intelligence-based hybrid transfer learning architecture

Existing automatic sleep stage classification systems have mostly relied on hand-crafted features selected from polysomnographic records. To measure the quality of sleep, the automatic sleep stage classification system is very important. The sleep specialists examine the signals such as Electromyogr...

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Veröffentlicht in:	Signal, image and video processing Jg. 18; H. 2; S. 1131 - 1142
Hauptverfasser:	Raja, A. Ravi, Polasi, Phani Kumar
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Springer London 01.03.2024 Springer Nature B.V
Schlagworte:	Accuracy Classification Computer Imaging Computer Science Datasets Decomposition Deep learning Design Effectiveness Electrocardiography Electroencephalography Fourier transforms Image Processing and Computer Vision Machine learning Multimedia Information Systems Optimization Optimization algorithms Original Paper Pattern Recognition and Graphics Signal processing Signal,Image and Speech Processing Sleep disorders Swarm intelligence Vision Sleep stage classification Pre-processing using artifacts removal and band pass filter Short-time fourier transform Electroencephalograms signal Optimal hybrid transfer learning approach
ISSN:	1863-1703, 1863-1711
Online-Zugang:	Volltext
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