Transfer Learning Convolutional Neural Network for Sleep Stage Classification Using Two-Stage Data Fusion Framework

• Published in: IEEE access Vol. 8; pp. 180618 - 180632
• Main Authors: Abdollahpour, Mehdi, Rezaii, Tohid Yousefi, Farzamnia, Ali, Saad, Ismail
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial neural networks; Brain modeling; Classification; Convolutional neural network; data fusion; Data integration; Datasets; Electroencephalography; Electrooculography; Error analysis; Feature extraction; horizontal visibility graph; Human error; Information sources; Learning; Neural networks; Quality assessment; Signal monitoring; Sleep; sleep stage classification; State-of-the-art reviews; Training; transfer learning; Visibility

The most important part of sleep quality assessment is the classification of sleep stages, which helps to diagnose sleep-related disease. In the traditional sleep staging method, subjects have to spend a night in the sleep clinic for recording polysomnogram. Sleep expert classifies the sleep stages...