Sleep Apnea Detection From Variational Mode Decomposed EEG Signal Using a Hybrid CNN-BiLSTM

• Published in: IEEE access Vol. 9; pp. 102355 - 102367
• Main Authors: Mahmud, Tanvir, Khan, Ishtiaque Ahmed, Mahmud, Talha Ibn, Fattah, Shaikh Anowarul, Zhu, Wei-Ping, Ahmad, M. Omair
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial neural networks; computer-aided diagnosis; Convolution; Decomposition; EEG signal; Electroencephalography; Feature extraction; Frames; Frequency conversion; Frequency spectrum; Machine learning; neural network; Neural networks; Signal processing; Sleep apnea; Training; variational mode decomposition

Sleep apnea, a severe sleep disorder, is a clinically complicated disease that requires timely diagnosis for proper treatment. In this paper, an automated deep learning-based approach is proposed for the detection of sleep apnea frames from electroencephalogram (EEG) signals. Unlike conventional met...