NeuroNet: A Novel Hybrid Self-Supervised Learning Framework for Sleep Stage Classification Using Single-Channel EEG

arXiv:2404.17585v1 Announce Type: cross
Abstract: The classification of sleep stages is a pivotal aspect of diagnosing sleep disorders and evaluating sleep quality. However, the conventional manual scoring process, conducted by clinicians, is time-consuming and prone to human bias. Recent advancements in deep learning have substantially propelled the automation of sleep stage classification. Nevertheless, challenges persist, including the need for large datasets with labels and the inherent biases in human-generated annotations. This paper introduces NeuroNet, a self-supervised learning (SSL) framework designed …

abstract arxiv bias classification clinicians cs.ai cs.hc cs.lg deep learning eeg eess.sp framework however human hybrid novel pivotal process quality scoring self-supervised learning sleep stage supervised learning type